From 7fba00bf7dedc3524dd172e9fee8df766089cfeb Mon Sep 17 00:00:00 2001 From: nehajo88 <nehajo88@gmail.com> Date: Sat, 9 Mar 2024 10:22:06 -0800 Subject: [PATCH] Mozambique MA --- WUR_MOZ_2024/Mozambique_MA.ipynb | 3377 ++++-------------------------- 1 file changed, 437 insertions(+), 2940 deletions(-) diff --git a/WUR_MOZ_2024/Mozambique_MA.ipynb b/WUR_MOZ_2024/Mozambique_MA.ipynb index 2068670..9213f80 100644 --- a/WUR_MOZ_2024/Mozambique_MA.ipynb +++ b/WUR_MOZ_2024/Mozambique_MA.ipynb @@ -2,28 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "3db00a28-01c6-48be-8f0a-dd5ec87dde7c", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Attaching package: ‘raster’\n", - "\n", - "\n", - "The following object is masked from ‘package:dplyr’:\n", - "\n", - " select\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "###########################\n", "# title: \"Downloading and pre-processing Mozambique's NFI data\"\n", @@ -59,111 +43,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "d3eee580-c9e2-42ee-a7de-5b550be11621", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading layer `Strata_reclassify_geogra' from data source \n", - " `/projects/my-private-bucket/Data/NFI_data/Mozambique/Strata_reclassify_geogra.shp' \n", - " using driver `ESRI Shapefile'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "“Error in fread() reading object of size 49756 at offset 160422616 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 41480 at offset 160472372 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 9404 at offset 160513852 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 11932 at offset 160523256 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 12492 at offset 160535188 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 20620 at offset 160547680 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 191168 at offset 160568300 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 15660 at offset 160759468 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 26332 at offset 160775128 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 10176 at offset 160801460 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 13532 at offset 160811636 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 11360 at offset 160825168 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 20348 at offset 160836528 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 2044 at offset 160856876 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 602324 at offset 160858920 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 1528 at offset 161461244 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 2424 at offset 161462772 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 744 at offset 161465196 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 552 at offset 161465940 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 760 at offset 161466492 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 3096 at offset 161467252 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 680 at offset 161470348 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 1720 at offset 161471028 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 1512 at offset 161472748 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 312 at offset 161474260 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 520 at offset 161474572 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 456 at offset 161475092 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 584 at offset 161475548 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 1672 at offset 161476132 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 23256 at offset 161477804 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 712 at offset 161501060 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 2360 at offset 161501772 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 712 at offset 161504132 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 10584 at offset 161504844 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 24216 at offset 161515428 from .shp file (GDAL error 1)â€\n", - "Warning message:\n", - "“Error in fread() reading object of size 178316 at offset 161539644 from .shp file (GDAL error 1)â€\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simple feature collection with 29357 features and 5 fields (with 36 geometries empty)\n", - "Geometry type: MULTIPOLYGON\n", - "Dimension: XY\n", - "Bounding box: xmin: 30.21215 ymin: -26.86644 xmax: 40.84063 ymax: -10.4722\n", - "Geodetic CRS: WGS 84\n" - ] - } - ], + "outputs": [], "source": [ "## Loading data\n", "\n", @@ -173,12 +58,12 @@ "\n", "# Load the Forest stratification layers\n", "\n", - "Moz_strata_geo<-st_read(\"/projects/my-private-bucket/Data/NFI_data/Mozambique/Strata_reclassify_geogra.shp\") #WGS_84" + "Moz_strata_geo<-st_read(\"/projects/my-private-bucket/Data/NFI_data/Mozambique/Strata_reclassify_geogra.shp\",quiet = TRUE) #WGS_84" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "6786a813-8715-4bf1-b9e4-b27afdd00dd2", "metadata": {}, "outputs": [ @@ -210,78 +95,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "83b7bd50-026d-4d11-9eb7-642953c17b78", "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "<table class=\"dataframe\">\n", - "<caption>A data.frame: 8 × 2</caption>\n", - "<thead>\n", - "\t<tr><th></th><th scope=col>TestFram</th><th scope=col>Freq</th></tr>\n", - "\t<tr><th></th><th scope=col><fct></th><th scope=col><int></th></tr>\n", - "</thead>\n", - "<tbody>\n", - "\t<tr><th scope=row>725</th><td>33.4935005878 -18.8102097290999</td><td>2</td></tr>\n", - "\t<tr><th scope=row>726</th><td>33.4935032236 -18.8111134824 </td><td>2</td></tr>\n", - "\t<tr><th scope=row>727</th><td>33.4944496012999 -18.8102072167</td><td>2</td></tr>\n", - "\t<tr><th scope=row>728</th><td>33.4944522421 -18.8111109699 </td><td>2</td></tr>\n", - "\t<tr><th scope=row>777</th><td>33.5660090220999 -17.761594359 </td><td>2</td></tr>\n", - "\t<tr><th scope=row>778</th><td>33.566011865 -17.7624982007 </td><td>2</td></tr>\n", - "\t<tr><th scope=row>779</th><td>33.5669523366999 -17.7615916329</td><td>2</td></tr>\n", - "\t<tr><th scope=row>780</th><td>33.5669551842999 -17.7624954744</td><td>2</td></tr>\n", - "</tbody>\n", - "</table>\n" - ], - "text/latex": [ - "A data.frame: 8 × 2\n", - "\\begin{tabular}{r|ll}\n", - " & TestFram & Freq\\\\\n", - " & <fct> & <int>\\\\\n", - "\\hline\n", - "\t725 & 33.4935005878 -18.8102097290999 & 2\\\\\n", - "\t726 & 33.4935032236 -18.8111134824 & 2\\\\\n", - "\t727 & 33.4944496012999 -18.8102072167 & 2\\\\\n", - "\t728 & 33.4944522421 -18.8111109699 & 2\\\\\n", - "\t777 & 33.5660090220999 -17.761594359 & 2\\\\\n", - "\t778 & 33.566011865 -17.7624982007 & 2\\\\\n", - "\t779 & 33.5669523366999 -17.7615916329 & 2\\\\\n", - "\t780 & 33.5669551842999 -17.7624954744 & 2\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 8 × 2\n", - "\n", - "| <!--/--> | TestFram <fct> | Freq <int> |\n", - "|---|---|---|\n", - "| 725 | 33.4935005878 -18.8102097290999 | 2 |\n", - "| 726 | 33.4935032236 -18.8111134824 | 2 |\n", - "| 727 | 33.4944496012999 -18.8102072167 | 2 |\n", - "| 728 | 33.4944522421 -18.8111109699 | 2 |\n", - "| 777 | 33.5660090220999 -17.761594359 | 2 |\n", - "| 778 | 33.566011865 -17.7624982007 | 2 |\n", - "| 779 | 33.5669523366999 -17.7615916329 | 2 |\n", - "| 780 | 33.5669551842999 -17.7624954744 | 2 |\n", - "\n" - ], - "text/plain": [ - " TestFram Freq\n", - "725 33.4935005878 -18.8102097290999 2 \n", - "726 33.4935032236 -18.8111134824 2 \n", - "727 33.4944496012999 -18.8102072167 2 \n", - "728 33.4944522421 -18.8111109699 2 \n", - "777 33.5660090220999 -17.761594359 2 \n", - "778 33.566011865 -17.7624982007 2 \n", - "779 33.5669523366999 -17.7615916329 2 \n", - "780 33.5669551842999 -17.7624954744 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -513,24 +330,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "8" - ], - "text/latex": [ - "8" - ], - "text/markdown": [ - "8" - ], - "text/plain": [ - "[1] 8" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -548,7 +347,7 @@ "\n", "TestFram=paste((NFI_data_c@coords[,1]),as.numeric(NFI_data_c@coords[,2]))\n", "TestFrameCounts<-as.data.frame(table(TestFram))\n", - "TestFrameCounts[TestFrameCounts$Freq==max(TestFrameCounts$Freq),] #identify plots with duplicated \n", + "# TestFrameCounts[TestFrameCounts$Freq==max(TestFrameCounts$Freq),] #identify plots with duplicated \n", "\n", "# The coordinates in `TestFram` will be used to identify the plots/clusters that need to be removed\n", "\n", @@ -567,12 +366,12 @@ "# We will remove clusters 218 and 269 and save the new dataframe as `NFI_Data_c2`\n", "\n", "NFI_data_c2<-NFI_data_c[!(NFI_data_c$Cluster%in%c(218,269)),]\n", - "nrow(NFI_data_c)-nrow(NFI_data_c2)" + "# nrow(NFI_data_c)-nrow(NFI_data_c2)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "c21bf6f1-6f49-49ea-ba62-6cff1aeab3b4", "metadata": {}, "outputs": [ @@ -620,1681 +419,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Spherical geometry (s2) switched off\n", - "\n", "although coordinates are longitude/latitude, st_intersects assumes that they\n", "are planar\n", - "\n", - "Registered S3 method overwritten by 'geojsonsf':\n", - " method from \n", - " print.geojson geojson\n", "\n" ] - }, - { - "data": { - "application/geo+json": { - "features": [ - { - "geometry": { - "coordinates": [ - 32.4835, - -25.2795 - ], - "type": "Point" - }, - "properties": { - "AGB.(ton/ha)": 29.3328, - "BGB.(ton/ha)": 17.7817, - "Cluster": "4", - "Cluster_new": "4", - "Estrato.Florestal": "Floresta (semi-) decidua, incluindo Miombo", - "FOREST_STR": "Semi evergreen forest", - "Plot": 1, - "ProvÃncia": "Maputo", - "Vc.(m^3/ha)": 0, - "Vt.(m^3/ha)": 32.0304, - "id_parcela": "41", - "id_plot_new": "41" - }, - "type": "Feature" - }, - { - "geometry": { - "coordinates": [ - 32.4835, - -25.2786 - ], - "type": "Point" - }, - "properties": { - "AGB.(ton/ha)": 4.9007, - "BGB.(ton/ha)": 2.8053, - "Cluster": "4", - "Cluster_new": "4", - "Estrato.Florestal": "Floresta (semi-) decidua, incluindo Miombo", - "FOREST_STR": "NA", - "Plot": 2, - "ProvÃncia": "Maputo", - "Vc.(m^3/ha)": 0, - "Vt.(m^3/ha)": 5.1161, - "id_parcela": "42", - "id_plot_new": "42" - 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"\t<tr><th></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><POINT [°]></th></tr>\n", - "</thead>\n", - "<tbody>\n", - "\t<tr><th scope=row>9.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>41 </td><td>41 </td><td>4 </td><td>4 </td><td>1</td><td> 32.0303735</td><td> 0.0000000</td><td> 29.3328399</td><td>17.7816999</td><td>Semi evergreen forest</td><td>POINT (32.48352 -25.27953)</td></tr>\n", - "\t<tr><th scope=row>10.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>42 </td><td>42 </td><td>4 </td><td>4 </td><td>2</td><td> 5.1160801</td><td> 0.0000000</td><td> 4.9007144</td><td> 2.8053331</td><td>NA </td><td>POINT (32.48352 -25.27862)</td></tr>\n", - "\t<tr><th scope=row>11.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>43 </td><td>43 </td><td>4 </td><td>4 </td><td>3</td><td> 7.1378218</td><td> 0.0000000</td><td> 7.1910497</td><td> 4.6953413</td><td>NA </td><td>POINT (32.48451 -25.27863)</td></tr>\n", - 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"\t<tr><th scope=row>16.1</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>64 </td><td>64 </td><td>6 </td><td>6 </td><td>4</td><td> 30.9214745</td><td> 3.3977827</td><td> 53.0650505</td><td>14.8582141</td><td>Semi evergreen forest</td><td>POINT (32.40611 -25.0625)</td></tr>\n", - "\t<tr><th scope=row>17.1</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>71 </td><td>71 </td><td>7 </td><td>7 </td><td>1</td><td> 71.0924167</td><td>12.3483489</td><td>125.1163321</td><td>35.0325730</td><td>Semi deciduous forest</td><td>POINT (32.20753 -24.95321)</td></tr>\n", - "\t<tr><th scope=row>18.1</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>72 </td><td>72 </td><td>7 </td><td>7 </td><td>2</td><td> 29.1466626</td><td>13.1863455</td><td> 54.2099472</td><td>15.1787852</td><td>Semi deciduous forest</td><td>POINT (32.20753 -24.95231)</td></tr>\n", - "\t<tr><th scope=row>19.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>73 </td><td>73 </td><td>7 </td><td>7 </td><td>3</td><td> 35.1352469</td><td>15.7653957</td><td> 38.2023376</td><td>20.5795378</td><td>Semi deciduous forest</td><td>POINT (32.20852 -24.95231)</td></tr>\n", - "\t<tr><th scope=row>20.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>74 </td><td>74 </td><td>7 </td><td>7 </td><td>4</td><td> 49.0069395</td><td>19.9850277</td><td> 53.4575029</td><td>28.1497527</td><td>Semi deciduous forest</td><td>POINT (32.20852 -24.95321)</td></tr>\n", - "\t<tr><th scope=row>21.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>81 </td><td>81 </td><td>8 </td><td>8 </td><td>1</td><td> 3.4051486</td><td> 1.3465824</td><td> 2.7423855</td><td> 1.9982884</td><td>NA </td><td>POINT (32.32718 -24.8093)</td></tr>\n", - "\t<tr><th scope=row>22.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>82 </td><td>82 </td><td>8 </td><td>8 </td><td>2</td><td> 0.5374994</td><td> 0.1624643</td><td> 0.4650103</td><td> 0.3736289</td><td>NA </td><td>POINT (32.32718 -24.8084)</td></tr>\n", - "\t<tr><th scope=row>23.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>83 </td><td>83 </td><td>8 </td><td>8 </td><td>3</td><td> 0.3063053</td><td> 0.3229557</td><td> 0.2559933</td><td> 0.2309457</td><td>NA </td><td>POINT (32.32817 -24.8084)</td></tr>\n", - "\t<tr><th scope=row>24.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>84 </td><td>84 </td><td>8 </td><td>8 </td><td>4</td><td> 6.8627903</td><td> 3.7032340</td><td> 6.2709382</td><td> 4.7138889</td><td>NA </td><td>POINT (32.32816 -24.80931)</td></tr>\n", - "\t<tr><th scope=row>25.1</th><td>Maputo</td><td>Mopane </td><td>91 </td><td>91 </td><td>9 </td><td>9 </td><td>1</td><td> 12.5694527</td><td> 5.1988954</td><td> 12.7656530</td><td> 5.9461055</td><td>Semi deciduous forest</td><td>POINT (32.48608 -24.66544)</td></tr>\n", - "\t<tr><th scope=row>26.1</th><td>Maputo</td><td>Mopane </td><td>92 </td><td>92 </td><td>9 </td><td>9 </td><td>2</td><td> 21.2269279</td><td> 7.2721913</td><td> 32.0594798</td><td>10.6079559</td><td>Semi deciduous forest</td><td>POINT (32.48608 -24.66453)</td></tr>\n", - "\t<tr><th scope=row>27.1</th><td>Maputo</td><td>Mopane </td><td>93 </td><td>93 </td><td>9 </td><td>9 </td><td>3</td><td> 24.6516330</td><td> 6.3610340</td><td> 42.2941803</td><td>12.3293093</td><td>Semi deciduous forest</td><td>POINT (32.48707 -24.66454)</td></tr>\n", - "\t<tr><th scope=row>28.1</th><td>Maputo</td><td>Mopane </td><td>94 </td><td>94 </td><td>9 </td><td>9 </td><td>4</td><td> 14.3877735</td><td> 4.4894680</td><td> 21.4999696</td><td> 7.9196962</td><td>Semi deciduous forest</td><td>POINT (32.48706 -24.66544)</td></tr>\n", - "\t<tr><th scope=row>29.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>101</td><td>101</td><td>10</td><td>10</td><td>1</td><td> 20.7941060</td><td> 9.4779588</td><td> 20.4826005</td><td>12.6522886</td><td>Mopane </td><td>POINT (32.52574 -24.62944)</td></tr>\n", - "\t<tr><th scope=row>30.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>102</td><td>102</td><td>10</td><td>10</td><td>2</td><td> 11.8873354</td><td> 4.2776805</td><td> 15.4512510</td><td> 9.6149010</td><td>Mopane </td><td>POINT (32.52575 -24.62854)</td></tr>\n", - "\t<tr><th scope=row>31.1</th><td>Maputo</td><td>Mopane </td><td>103</td><td>103</td><td>10</td><td>10</td><td>3</td><td> 12.4348561</td><td> 5.6914349</td><td> 14.8770162</td><td> 5.6277887</td><td>Mopane </td><td>POINT (32.52673 -24.62854)</td></tr>\n", - "\t<tr><th scope=row>32.1</th><td>Maputo</td><td>Mopane </td><td>104</td><td>104</td><td>10</td><td>10</td><td>4</td><td> 15.3479980</td><td> 5.7744358</td><td> 15.9647434</td><td> 7.1229781</td><td>Mopane </td><td>POINT (32.52673 -24.62944)</td></tr>\n", - "\t<tr><th scope=row>33.1</th><td>Maputo</td><td>Mopane </td><td>111</td><td>111</td><td>11</td><td>11</td><td>1</td><td> 9.5508556</td><td> 4.0900201</td><td> 11.7287391</td><td> 4.8197493</td><td>Mopane </td><td>POINT (32.48666 -24.52094)</td></tr>\n", - "\t<tr><th scope=row>34.1</th><td>Maputo</td><td>Mopane </td><td>112</td><td>112</td><td>11</td><td>11</td><td>2</td><td> 19.2780871</td><td> 8.7555175</td><td> 30.0669468</td><td>10.8989397</td><td>Mopane </td><td>POINT (32.48667 -24.52003)</td></tr>\n", - "\t<tr><th scope=row>35.1</th><td>Maputo</td><td>Mopane </td><td>113</td><td>113</td><td>11</td><td>11</td><td>3</td><td> 11.2365305</td><td> 4.2976303</td><td> 18.0046617</td><td> 6.3074989</td><td>Mopane </td><td>POINT (32.48766 -24.52004)</td></tr>\n", - "\t<tr><th scope=row>36.1</th><td>Maputo</td><td>Mopane </td><td>114</td><td>114</td><td>11</td><td>11</td><td>4</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td>Mopane </td><td>POINT (32.48765 -24.52094)</td></tr>\n", - "\t<tr><th scope=row>37.1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>131</td><td>131</td><td>13</td><td>13</td><td>1</td><td> 5.4639335</td><td> 2.7454434</td><td> 5.0138724</td><td> 3.1133412</td><td>Mopane </td><td>POINT (32.52642 -24.44881)</td></tr>\n", - "\t<tr><th scope=row>38.1</th><td>Maputo</td><td>Mopane </td><td>132</td><td>132</td><td>13</td><td>13</td><td>2</td><td> 0.3227232</td><td> 0.1140021</td><td> 0.6431853</td><td> 0.1800919</td><td>Mopane </td><td>POINT (32.52643 -24.44791)</td></tr>\n", - "\t<tr><th scope=row>39.1</th><td>Maputo</td><td>Mopane </td><td>133</td><td>133</td><td>13</td><td>13</td><td>3</td><td> 8.9028401</td><td> 2.9649185</td><td> 12.1633421</td><td> 3.4057358</td><td>Mopane </td><td>POINT (32.52741 -24.44791)</td></tr>\n", - "\t<tr><th scope=row>40.1</th><td>Maputo</td><td>Mopane </td><td>134</td><td>134</td><td>13</td><td>13</td><td>4</td><td> 2.4658381</td><td> 0.9389787</td><td> 4.4643936</td><td> 1.2500302</td><td>Mopane </td><td>POINT (32.52741 -24.44882)</td></tr>\n", - "\t<tr><th scope=row>41.1</th><td>Maputo</td><td>Mopane </td><td>141</td><td>141</td><td>14</td><td>14</td><td>1</td><td> 17.1014426</td><td> 8.1876188</td><td> 15.5787786</td><td> 6.8360658</td><td>Mopane </td><td>POINT (32.56589 -24.44893)</td></tr>\n", - "\t<tr><th scope=row>42.1</th><td>Maputo</td><td>Mopane </td><td>142</td><td>142</td><td>14</td><td>14</td><td>2</td><td> 10.9177056</td><td> 5.6774862</td><td> 8.1627511</td><td> 3.8526035</td><td>Mopane </td><td>POINT (32.56589 -24.44803)</td></tr>\n", - "\t<tr><th scope=row>43.1</th><td>Maputo</td><td>Mopane </td><td>143</td><td>143</td><td>14</td><td>14</td><td>3</td><td> 12.5940378</td><td> 5.8924968</td><td> 14.8211281</td><td> 5.7991870</td><td>Mopane </td><td>POINT (32.56688 -24.44803)</td></tr>\n", - "\t<tr><th scope=row>44.1</th><td>Maputo</td><td>Mopane </td><td>144</td><td>144</td><td>14</td><td>14</td><td>4</td><td> 15.4679931</td><td> 6.3144127</td><td> 14.4328502</td><td> 6.9556992</td><td>Mopane </td><td>POINT (32.56687 -24.44893)</td></tr>\n", - "\t<tr><th scope=row>45.1</th><td>Maputo</td><td>Mopane </td><td>151</td><td>151</td><td>15</td><td>15</td><td>1</td><td> 7.0769127</td><td> 3.4713101</td><td> 7.2258973</td><td> 2.9998470</td><td>NA </td><td>POINT (32.60546 -24.41291)</td></tr>\n", - "\t<tr><th scope=row>46.1</th><td>Maputo</td><td>Mopane </td><td>152</td><td>152</td><td>15</td><td>15</td><td>2</td><td> 4.8941765</td><td> 1.5404108</td><td> 3.4394442</td><td> 1.8813563</td><td>NA </td><td>POINT (32.60546 -24.41201)</td></tr>\n", - "\t<tr><th scope=row>47.1</th><td>Maputo</td><td>Mopane </td><td>153</td><td>153</td><td>15</td><td>15</td><td>3</td><td> 5.0060935</td><td> 2.3044475</td><td> 3.2541764</td><td> 2.4016335</td><td>Mopane </td><td>POINT (32.60645 -24.41201)</td></tr>\n", - "\t<tr><th scope=row>48.1</th><td>Maputo</td><td>Mopane </td><td>154</td><td>154</td><td>15</td><td>15</td><td>4</td><td> 5.3596200</td><td> 2.4559001</td><td> 3.6354570</td><td> 2.3728780</td><td>Mopane </td><td>POINT (32.60645 -24.41292)</td></tr>\n", - "</tbody>\n", - "</table>\n" - ], - "text/latex": [ - "A sf: 40 × 13\n", - "\\begin{tabular}{r|lllllllllllll}\n", - " & ProvÃncia & Estrato.Florestal & id\\_parcela & id\\_plot\\_new & Cluster & Cluster\\_new & Plot & Vt.(m\\textasciicircum{}3/ha) & Vc.(m\\textasciicircum{}3/ha) & AGB.(ton/ha) & BGB.(ton/ha) & FOREST\\_STR & geometry\\\\\n", - " & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <POINT {[}°{]}>\\\\\n", - "\\hline\n", - "\t9.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 41 & 41 & 4 & 4 & 1 & 32.0303735 & 0.0000000 & 29.3328399 & 17.7816999 & Semi evergreen forest & POINT (32.48352 -25.27953)\\\\\n", - "\t10.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 42 & 42 & 4 & 4 & 2 & 5.1160801 & 0.0000000 & 4.9007144 & 2.8053331 & NA & POINT (32.48352 -25.27862)\\\\\n", - "\t11.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 43 & 43 & 4 & 4 & 3 & 7.1378218 & 0.0000000 & 7.1910497 & 4.6953413 & NA & POINT (32.48451 -25.27863)\\\\\n", - "\t12.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 44 & 44 & 4 & 4 & 4 & 19.9746598 & 0.9664277 & 18.3825096 & 11.2088316 & Semi evergreen forest & POINT (32.48451 -25.27953)\\\\\n", - "\t13.1 & Maputo & Floresta (semi-) sempreverde & 61 & 61 & 6 & 6 & 1 & 122.6670035 & 35.4560191 & 177.3974614 & 49.6712892 & Semi evergreen forest & POINT (32.40511 -25.0625)\\\\\n", - "\t14.1 & Maputo & Floresta (semi-) sempreverde & 62 & 62 & 6 & 6 & 2 & 68.0539354 & 11.2161518 & 91.1731365 & 25.5284782 & Semi evergreen forest & POINT (32.40512 -25.06159)\\\\\n", - "\t15.1 & Maputo & Floresta (semi-) sempreverde & 63 & 63 & 6 & 6 & 3 & 47.2328506 & 10.6966009 & 59.6806678 & 16.7105870 & Semi evergreen forest & POINT (32.40611 -25.0616)\\\\\n", - "\t16.1 & Maputo & Floresta (semi-) sempreverde & 64 & 64 & 6 & 6 & 4 & 30.9214745 & 3.3977827 & 53.0650505 & 14.8582141 & Semi evergreen forest & POINT (32.40611 -25.0625)\\\\\n", - "\t17.1 & Maputo & Floresta (semi-) sempreverde & 71 & 71 & 7 & 7 & 1 & 71.0924167 & 12.3483489 & 125.1163321 & 35.0325730 & Semi deciduous forest & POINT (32.20753 -24.95321)\\\\\n", - "\t18.1 & Maputo & Floresta (semi-) sempreverde & 72 & 72 & 7 & 7 & 2 & 29.1466626 & 13.1863455 & 54.2099472 & 15.1787852 & Semi deciduous forest & POINT (32.20753 -24.95231)\\\\\n", - "\t19.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 73 & 73 & 7 & 7 & 3 & 35.1352469 & 15.7653957 & 38.2023376 & 20.5795378 & Semi deciduous forest & POINT (32.20852 -24.95231)\\\\\n", - "\t20.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 74 & 74 & 7 & 7 & 4 & 49.0069395 & 19.9850277 & 53.4575029 & 28.1497527 & Semi deciduous forest & POINT (32.20852 -24.95321)\\\\\n", - "\t21.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 81 & 81 & 8 & 8 & 1 & 3.4051486 & 1.3465824 & 2.7423855 & 1.9982884 & NA & POINT (32.32718 -24.8093)\\\\\n", - "\t22.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 82 & 82 & 8 & 8 & 2 & 0.5374994 & 0.1624643 & 0.4650103 & 0.3736289 & NA & POINT (32.32718 -24.8084)\\\\\n", - "\t23.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 83 & 83 & 8 & 8 & 3 & 0.3063053 & 0.3229557 & 0.2559933 & 0.2309457 & NA & POINT (32.32817 -24.8084)\\\\\n", - "\t24.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 84 & 84 & 8 & 8 & 4 & 6.8627903 & 3.7032340 & 6.2709382 & 4.7138889 & NA & POINT (32.32816 -24.80931)\\\\\n", - "\t25.1 & Maputo & Mopane & 91 & 91 & 9 & 9 & 1 & 12.5694527 & 5.1988954 & 12.7656530 & 5.9461055 & Semi deciduous forest & POINT (32.48608 -24.66544)\\\\\n", - "\t26.1 & Maputo & Mopane & 92 & 92 & 9 & 9 & 2 & 21.2269279 & 7.2721913 & 32.0594798 & 10.6079559 & Semi deciduous forest & POINT (32.48608 -24.66453)\\\\\n", - "\t27.1 & Maputo & Mopane & 93 & 93 & 9 & 9 & 3 & 24.6516330 & 6.3610340 & 42.2941803 & 12.3293093 & Semi deciduous forest & POINT (32.48707 -24.66454)\\\\\n", - "\t28.1 & Maputo & Mopane & 94 & 94 & 9 & 9 & 4 & 14.3877735 & 4.4894680 & 21.4999696 & 7.9196962 & Semi deciduous forest & POINT (32.48706 -24.66544)\\\\\n", - "\t29.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 101 & 101 & 10 & 10 & 1 & 20.7941060 & 9.4779588 & 20.4826005 & 12.6522886 & Mopane & POINT (32.52574 -24.62944)\\\\\n", - "\t30.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 102 & 102 & 10 & 10 & 2 & 11.8873354 & 4.2776805 & 15.4512510 & 9.6149010 & Mopane & POINT (32.52575 -24.62854)\\\\\n", - "\t31.1 & Maputo & Mopane & 103 & 103 & 10 & 10 & 3 & 12.4348561 & 5.6914349 & 14.8770162 & 5.6277887 & Mopane & POINT (32.52673 -24.62854)\\\\\n", - "\t32.1 & Maputo & Mopane & 104 & 104 & 10 & 10 & 4 & 15.3479980 & 5.7744358 & 15.9647434 & 7.1229781 & Mopane & POINT (32.52673 -24.62944)\\\\\n", - "\t33.1 & Maputo & Mopane & 111 & 111 & 11 & 11 & 1 & 9.5508556 & 4.0900201 & 11.7287391 & 4.8197493 & Mopane & POINT (32.48666 -24.52094)\\\\\n", - "\t34.1 & Maputo & Mopane & 112 & 112 & 11 & 11 & 2 & 19.2780871 & 8.7555175 & 30.0669468 & 10.8989397 & Mopane & POINT (32.48667 -24.52003)\\\\\n", - "\t35.1 & Maputo & Mopane & 113 & 113 & 11 & 11 & 3 & 11.2365305 & 4.2976303 & 18.0046617 & 6.3074989 & Mopane & POINT (32.48766 -24.52004)\\\\\n", - "\t36.1 & Maputo & Mopane & 114 & 114 & 11 & 11 & 4 & 0.0000000 & 0.0000000 & 0.0000000 & 0.0000000 & Mopane & POINT (32.48765 -24.52094)\\\\\n", - "\t37.1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 131 & 131 & 13 & 13 & 1 & 5.4639335 & 2.7454434 & 5.0138724 & 3.1133412 & Mopane & POINT (32.52642 -24.44881)\\\\\n", - "\t38.1 & Maputo & Mopane & 132 & 132 & 13 & 13 & 2 & 0.3227232 & 0.1140021 & 0.6431853 & 0.1800919 & Mopane & POINT (32.52643 -24.44791)\\\\\n", - "\t39.1 & Maputo & Mopane & 133 & 133 & 13 & 13 & 3 & 8.9028401 & 2.9649185 & 12.1633421 & 3.4057358 & Mopane & POINT (32.52741 -24.44791)\\\\\n", - "\t40.1 & Maputo & Mopane & 134 & 134 & 13 & 13 & 4 & 2.4658381 & 0.9389787 & 4.4643936 & 1.2500302 & Mopane & POINT (32.52741 -24.44882)\\\\\n", - "\t41.1 & Maputo & Mopane & 141 & 141 & 14 & 14 & 1 & 17.1014426 & 8.1876188 & 15.5787786 & 6.8360658 & Mopane & POINT (32.56589 -24.44893)\\\\\n", - "\t42.1 & Maputo & Mopane & 142 & 142 & 14 & 14 & 2 & 10.9177056 & 5.6774862 & 8.1627511 & 3.8526035 & Mopane & POINT (32.56589 -24.44803)\\\\\n", - "\t43.1 & Maputo & Mopane & 143 & 143 & 14 & 14 & 3 & 12.5940378 & 5.8924968 & 14.8211281 & 5.7991870 & Mopane & POINT (32.56688 -24.44803)\\\\\n", - "\t44.1 & Maputo & Mopane & 144 & 144 & 14 & 14 & 4 & 15.4679931 & 6.3144127 & 14.4328502 & 6.9556992 & Mopane & POINT (32.56687 -24.44893)\\\\\n", - "\t45.1 & Maputo & Mopane & 151 & 151 & 15 & 15 & 1 & 7.0769127 & 3.4713101 & 7.2258973 & 2.9998470 & NA & POINT (32.60546 -24.41291)\\\\\n", - "\t46.1 & Maputo & Mopane & 152 & 152 & 15 & 15 & 2 & 4.8941765 & 1.5404108 & 3.4394442 & 1.8813563 & NA & POINT (32.60546 -24.41201)\\\\\n", - "\t47.1 & Maputo & Mopane & 153 & 153 & 15 & 15 & 3 & 5.0060935 & 2.3044475 & 3.2541764 & 2.4016335 & Mopane & POINT (32.60645 -24.41201)\\\\\n", - "\t48.1 & Maputo & Mopane & 154 & 154 & 15 & 15 & 4 & 5.3596200 & 2.4559001 & 3.6354570 & 2.3728780 & Mopane & POINT (32.60645 -24.41292)\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A sf: 40 × 13\n", - "\n", - "| <!--/--> | ProvÃncia <chr> | Estrato.Florestal <chr> | id_parcela <chr> | id_plot_new <chr> | Cluster <chr> | Cluster_new <chr> | Plot <dbl> | Vt.(m^3/ha) <dbl> | Vc.(m^3/ha) <dbl> | AGB.(ton/ha) <dbl> | BGB.(ton/ha) <dbl> | FOREST_STR <chr> | geometry <POINT [°]> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 9.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 41 | 41 | 4 | 4 | 1 | 32.0303735 | 0.0000000 | 29.3328399 | 17.7816999 | Semi evergreen forest | POINT (32.48352 -25.27953) |\n", - "| 10.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 42 | 42 | 4 | 4 | 2 | 5.1160801 | 0.0000000 | 4.9007144 | 2.8053331 | NA | POINT (32.48352 -25.27862) |\n", - "| 11.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 43 | 43 | 4 | 4 | 3 | 7.1378218 | 0.0000000 | 7.1910497 | 4.6953413 | NA | POINT (32.48451 -25.27863) |\n", - "| 12.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 44 | 44 | 4 | 4 | 4 | 19.9746598 | 0.9664277 | 18.3825096 | 11.2088316 | Semi evergreen forest | POINT (32.48451 -25.27953) |\n", - "| 13.1 | Maputo | Floresta (semi-) sempreverde | 61 | 61 | 6 | 6 | 1 | 122.6670035 | 35.4560191 | 177.3974614 | 49.6712892 | Semi evergreen forest | POINT (32.40511 -25.0625) |\n", - "| 14.1 | Maputo | Floresta (semi-) sempreverde | 62 | 62 | 6 | 6 | 2 | 68.0539354 | 11.2161518 | 91.1731365 | 25.5284782 | Semi evergreen forest | POINT (32.40512 -25.06159) |\n", - "| 15.1 | Maputo | Floresta (semi-) sempreverde | 63 | 63 | 6 | 6 | 3 | 47.2328506 | 10.6966009 | 59.6806678 | 16.7105870 | Semi evergreen forest | POINT (32.40611 -25.0616) |\n", - "| 16.1 | Maputo | Floresta (semi-) sempreverde | 64 | 64 | 6 | 6 | 4 | 30.9214745 | 3.3977827 | 53.0650505 | 14.8582141 | Semi evergreen forest | POINT (32.40611 -25.0625) |\n", - "| 17.1 | Maputo | Floresta (semi-) sempreverde | 71 | 71 | 7 | 7 | 1 | 71.0924167 | 12.3483489 | 125.1163321 | 35.0325730 | Semi deciduous forest | POINT (32.20753 -24.95321) |\n", - "| 18.1 | Maputo | Floresta (semi-) sempreverde | 72 | 72 | 7 | 7 | 2 | 29.1466626 | 13.1863455 | 54.2099472 | 15.1787852 | Semi deciduous forest | POINT (32.20753 -24.95231) |\n", - "| 19.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 73 | 73 | 7 | 7 | 3 | 35.1352469 | 15.7653957 | 38.2023376 | 20.5795378 | Semi deciduous forest | POINT (32.20852 -24.95231) |\n", - "| 20.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 74 | 74 | 7 | 7 | 4 | 49.0069395 | 19.9850277 | 53.4575029 | 28.1497527 | Semi deciduous forest | POINT (32.20852 -24.95321) |\n", - "| 21.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 81 | 81 | 8 | 8 | 1 | 3.4051486 | 1.3465824 | 2.7423855 | 1.9982884 | NA | POINT (32.32718 -24.8093) |\n", - "| 22.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 82 | 82 | 8 | 8 | 2 | 0.5374994 | 0.1624643 | 0.4650103 | 0.3736289 | NA | POINT (32.32718 -24.8084) |\n", - "| 23.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 83 | 83 | 8 | 8 | 3 | 0.3063053 | 0.3229557 | 0.2559933 | 0.2309457 | NA | POINT (32.32817 -24.8084) |\n", - "| 24.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 84 | 84 | 8 | 8 | 4 | 6.8627903 | 3.7032340 | 6.2709382 | 4.7138889 | NA | POINT (32.32816 -24.80931) |\n", - "| 25.1 | Maputo | Mopane | 91 | 91 | 9 | 9 | 1 | 12.5694527 | 5.1988954 | 12.7656530 | 5.9461055 | Semi deciduous forest | POINT (32.48608 -24.66544) |\n", - "| 26.1 | Maputo | Mopane | 92 | 92 | 9 | 9 | 2 | 21.2269279 | 7.2721913 | 32.0594798 | 10.6079559 | Semi deciduous forest | POINT (32.48608 -24.66453) |\n", - "| 27.1 | Maputo | Mopane | 93 | 93 | 9 | 9 | 3 | 24.6516330 | 6.3610340 | 42.2941803 | 12.3293093 | Semi deciduous forest | POINT (32.48707 -24.66454) |\n", - "| 28.1 | Maputo | Mopane | 94 | 94 | 9 | 9 | 4 | 14.3877735 | 4.4894680 | 21.4999696 | 7.9196962 | Semi deciduous forest | POINT (32.48706 -24.66544) |\n", - "| 29.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 101 | 101 | 10 | 10 | 1 | 20.7941060 | 9.4779588 | 20.4826005 | 12.6522886 | Mopane | POINT (32.52574 -24.62944) |\n", - "| 30.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 102 | 102 | 10 | 10 | 2 | 11.8873354 | 4.2776805 | 15.4512510 | 9.6149010 | Mopane | POINT (32.52575 -24.62854) |\n", - "| 31.1 | Maputo | Mopane | 103 | 103 | 10 | 10 | 3 | 12.4348561 | 5.6914349 | 14.8770162 | 5.6277887 | Mopane | POINT (32.52673 -24.62854) |\n", - "| 32.1 | Maputo | Mopane | 104 | 104 | 10 | 10 | 4 | 15.3479980 | 5.7744358 | 15.9647434 | 7.1229781 | Mopane | POINT (32.52673 -24.62944) |\n", - "| 33.1 | Maputo | Mopane | 111 | 111 | 11 | 11 | 1 | 9.5508556 | 4.0900201 | 11.7287391 | 4.8197493 | Mopane | POINT (32.48666 -24.52094) |\n", - "| 34.1 | Maputo | Mopane | 112 | 112 | 11 | 11 | 2 | 19.2780871 | 8.7555175 | 30.0669468 | 10.8989397 | Mopane | POINT (32.48667 -24.52003) |\n", - "| 35.1 | Maputo | Mopane | 113 | 113 | 11 | 11 | 3 | 11.2365305 | 4.2976303 | 18.0046617 | 6.3074989 | Mopane | POINT (32.48766 -24.52004) |\n", - "| 36.1 | Maputo | Mopane | 114 | 114 | 11 | 11 | 4 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | Mopane | POINT (32.48765 -24.52094) |\n", - "| 37.1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 131 | 131 | 13 | 13 | 1 | 5.4639335 | 2.7454434 | 5.0138724 | 3.1133412 | Mopane | POINT (32.52642 -24.44881) |\n", - "| 38.1 | Maputo | Mopane | 132 | 132 | 13 | 13 | 2 | 0.3227232 | 0.1140021 | 0.6431853 | 0.1800919 | Mopane | POINT (32.52643 -24.44791) |\n", - "| 39.1 | Maputo | Mopane | 133 | 133 | 13 | 13 | 3 | 8.9028401 | 2.9649185 | 12.1633421 | 3.4057358 | Mopane | POINT (32.52741 -24.44791) |\n", - "| 40.1 | Maputo | Mopane | 134 | 134 | 13 | 13 | 4 | 2.4658381 | 0.9389787 | 4.4643936 | 1.2500302 | Mopane | POINT (32.52741 -24.44882) |\n", - "| 41.1 | Maputo | Mopane | 141 | 141 | 14 | 14 | 1 | 17.1014426 | 8.1876188 | 15.5787786 | 6.8360658 | Mopane | POINT (32.56589 -24.44893) |\n", - "| 42.1 | Maputo | Mopane | 142 | 142 | 14 | 14 | 2 | 10.9177056 | 5.6774862 | 8.1627511 | 3.8526035 | Mopane | POINT (32.56589 -24.44803) |\n", - "| 43.1 | Maputo | Mopane | 143 | 143 | 14 | 14 | 3 | 12.5940378 | 5.8924968 | 14.8211281 | 5.7991870 | Mopane | POINT (32.56688 -24.44803) |\n", - "| 44.1 | Maputo | Mopane | 144 | 144 | 14 | 14 | 4 | 15.4679931 | 6.3144127 | 14.4328502 | 6.9556992 | Mopane | POINT (32.56687 -24.44893) |\n", - "| 45.1 | Maputo | Mopane | 151 | 151 | 15 | 15 | 1 | 7.0769127 | 3.4713101 | 7.2258973 | 2.9998470 | NA | POINT (32.60546 -24.41291) |\n", - "| 46.1 | Maputo | Mopane | 152 | 152 | 15 | 15 | 2 | 4.8941765 | 1.5404108 | 3.4394442 | 1.8813563 | NA | POINT (32.60546 -24.41201) |\n", - "| 47.1 | Maputo | Mopane | 153 | 153 | 15 | 15 | 3 | 5.0060935 | 2.3044475 | 3.2541764 | 2.4016335 | Mopane | POINT (32.60645 -24.41201) |\n", - "| 48.1 | Maputo | Mopane | 154 | 154 | 15 | 15 | 4 | 5.3596200 | 2.4559001 | 3.6354570 | 2.3728780 | Mopane | POINT (32.60645 -24.41292) |\n", - "\n" - ], - "text/plain": [ - " ProvÃncia Estrato.Florestal id_parcela\n", - "9.1 Maputo Floresta (semi-) decidua, incluindo Miombo 41 \n", - "10.1 Maputo Floresta (semi-) decidua, incluindo Miombo 42 \n", - "11.1 Maputo Floresta (semi-) decidua, incluindo Miombo 43 \n", - "12.1 Maputo Floresta (semi-) decidua, incluindo Miombo 44 \n", - "13.1 Maputo Floresta (semi-) sempreverde 61 \n", - "14.1 Maputo Floresta (semi-) sempreverde 62 \n", - "15.1 Maputo Floresta (semi-) sempreverde 63 \n", - "16.1 Maputo Floresta (semi-) sempreverde 64 \n", - "17.1 Maputo Floresta (semi-) sempreverde 71 \n", - "18.1 Maputo Floresta (semi-) sempreverde 72 \n", - "19.1 Maputo Floresta (semi-) decidua, incluindo Miombo 73 \n", - "20.1 Maputo Floresta (semi-) decidua, incluindo Miombo 74 \n", - "21.1 Maputo Floresta (semi-) decidua, incluindo Miombo 81 \n", - "22.1 Maputo Floresta (semi-) decidua, incluindo Miombo 82 \n", - "23.1 Maputo Floresta (semi-) decidua, incluindo Miombo 83 \n", - "24.1 Maputo Floresta (semi-) decidua, incluindo Miombo 84 \n", - "25.1 Maputo Mopane 91 \n", - "26.1 Maputo Mopane 92 \n", - "27.1 Maputo Mopane 93 \n", - "28.1 Maputo Mopane 94 \n", - "29.1 Maputo Floresta (semi-) decidua, incluindo Miombo 101 \n", - "30.1 Maputo Floresta (semi-) decidua, incluindo Miombo 102 \n", - "31.1 Maputo Mopane 103 \n", - "32.1 Maputo Mopane 104 \n", - "33.1 Maputo Mopane 111 \n", - "34.1 Maputo Mopane 112 \n", - "35.1 Maputo Mopane 113 \n", - "36.1 Maputo Mopane 114 \n", - "37.1 Maputo Floresta (semi-) decidua, incluindo Miombo 131 \n", - "38.1 Maputo Mopane 132 \n", - "39.1 Maputo Mopane 133 \n", - "40.1 Maputo Mopane 134 \n", - "41.1 Maputo Mopane 141 \n", - "42.1 Maputo Mopane 142 \n", - "43.1 Maputo Mopane 143 \n", - "44.1 Maputo Mopane 144 \n", - "45.1 Maputo Mopane 151 \n", - "46.1 Maputo Mopane 152 \n", - "47.1 Maputo Mopane 153 \n", - "48.1 Maputo Mopane 154 \n", - " id_plot_new Cluster Cluster_new Plot Vt.(m^3/ha) Vc.(m^3/ha) AGB.(ton/ha)\n", - "9.1 41 4 4 1 32.0303735 0.0000000 29.3328399 \n", - "10.1 42 4 4 2 5.1160801 0.0000000 4.9007144 \n", - "11.1 43 4 4 3 7.1378218 0.0000000 7.1910497 \n", - "12.1 44 4 4 4 19.9746598 0.9664277 18.3825096 \n", - "13.1 61 6 6 1 122.6670035 35.4560191 177.3974614 \n", - "14.1 62 6 6 2 68.0539354 11.2161518 91.1731365 \n", - "15.1 63 6 6 3 47.2328506 10.6966009 59.6806678 \n", - "16.1 64 6 6 4 30.9214745 3.3977827 53.0650505 \n", - "17.1 71 7 7 1 71.0924167 12.3483489 125.1163321 \n", - "18.1 72 7 7 2 29.1466626 13.1863455 54.2099472 \n", - "19.1 73 7 7 3 35.1352469 15.7653957 38.2023376 \n", - "20.1 74 7 7 4 49.0069395 19.9850277 53.4575029 \n", - "21.1 81 8 8 1 3.4051486 1.3465824 2.7423855 \n", - "22.1 82 8 8 2 0.5374994 0.1624643 0.4650103 \n", - "23.1 83 8 8 3 0.3063053 0.3229557 0.2559933 \n", - "24.1 84 8 8 4 6.8627903 3.7032340 6.2709382 \n", - "25.1 91 9 9 1 12.5694527 5.1988954 12.7656530 \n", - "26.1 92 9 9 2 21.2269279 7.2721913 32.0594798 \n", - "27.1 93 9 9 3 24.6516330 6.3610340 42.2941803 \n", - "28.1 94 9 9 4 14.3877735 4.4894680 21.4999696 \n", - "29.1 101 10 10 1 20.7941060 9.4779588 20.4826005 \n", - "30.1 102 10 10 2 11.8873354 4.2776805 15.4512510 \n", - "31.1 103 10 10 3 12.4348561 5.6914349 14.8770162 \n", - "32.1 104 10 10 4 15.3479980 5.7744358 15.9647434 \n", - "33.1 111 11 11 1 9.5508556 4.0900201 11.7287391 \n", - "34.1 112 11 11 2 19.2780871 8.7555175 30.0669468 \n", - "35.1 113 11 11 3 11.2365305 4.2976303 18.0046617 \n", - "36.1 114 11 11 4 0.0000000 0.0000000 0.0000000 \n", - "37.1 131 13 13 1 5.4639335 2.7454434 5.0138724 \n", - "38.1 132 13 13 2 0.3227232 0.1140021 0.6431853 \n", - "39.1 133 13 13 3 8.9028401 2.9649185 12.1633421 \n", - "40.1 134 13 13 4 2.4658381 0.9389787 4.4643936 \n", - "41.1 141 14 14 1 17.1014426 8.1876188 15.5787786 \n", - "42.1 142 14 14 2 10.9177056 5.6774862 8.1627511 \n", - "43.1 143 14 14 3 12.5940378 5.8924968 14.8211281 \n", - "44.1 144 14 14 4 15.4679931 6.3144127 14.4328502 \n", - "45.1 151 15 15 1 7.0769127 3.4713101 7.2258973 \n", - "46.1 152 15 15 2 4.8941765 1.5404108 3.4394442 \n", - "47.1 153 15 15 3 5.0060935 2.3044475 3.2541764 \n", - "48.1 154 15 15 4 5.3596200 2.4559001 3.6354570 \n", - " BGB.(ton/ha) FOREST_STR geometry \n", - "9.1 17.7816999 Semi evergreen forest POINT (32.48352 -25.27953)\n", - "10.1 2.8053331 NA POINT (32.48352 -25.27862)\n", - "11.1 4.6953413 NA POINT (32.48451 -25.27863)\n", - "12.1 11.2088316 Semi evergreen forest POINT (32.48451 -25.27953)\n", - "13.1 49.6712892 Semi evergreen forest POINT (32.40511 -25.0625) \n", - "14.1 25.5284782 Semi evergreen forest POINT (32.40512 -25.06159)\n", - "15.1 16.7105870 Semi evergreen forest POINT (32.40611 -25.0616) \n", - "16.1 14.8582141 Semi evergreen forest POINT (32.40611 -25.0625) \n", - "17.1 35.0325730 Semi deciduous forest POINT (32.20753 -24.95321)\n", - "18.1 15.1787852 Semi deciduous forest POINT (32.20753 -24.95231)\n", - "19.1 20.5795378 Semi deciduous forest POINT (32.20852 -24.95231)\n", - "20.1 28.1497527 Semi deciduous forest POINT (32.20852 -24.95321)\n", - "21.1 1.9982884 NA POINT (32.32718 -24.8093) \n", - "22.1 0.3736289 NA POINT (32.32718 -24.8084) \n", - "23.1 0.2309457 NA POINT (32.32817 -24.8084) \n", - "24.1 4.7138889 NA POINT (32.32816 -24.80931)\n", - "25.1 5.9461055 Semi deciduous forest POINT (32.48608 -24.66544)\n", - "26.1 10.6079559 Semi deciduous forest POINT (32.48608 -24.66453)\n", - "27.1 12.3293093 Semi deciduous forest POINT (32.48707 -24.66454)\n", - "28.1 7.9196962 Semi deciduous forest POINT (32.48706 -24.66544)\n", - "29.1 12.6522886 Mopane POINT (32.52574 -24.62944)\n", - "30.1 9.6149010 Mopane POINT (32.52575 -24.62854)\n", - "31.1 5.6277887 Mopane POINT (32.52673 -24.62854)\n", - "32.1 7.1229781 Mopane POINT (32.52673 -24.62944)\n", - "33.1 4.8197493 Mopane POINT (32.48666 -24.52094)\n", - "34.1 10.8989397 Mopane POINT (32.48667 -24.52003)\n", - "35.1 6.3074989 Mopane POINT (32.48766 -24.52004)\n", - "36.1 0.0000000 Mopane POINT (32.48765 -24.52094)\n", - "37.1 3.1133412 Mopane POINT (32.52642 -24.44881)\n", - "38.1 0.1800919 Mopane POINT (32.52643 -24.44791)\n", - "39.1 3.4057358 Mopane POINT (32.52741 -24.44791)\n", - "40.1 1.2500302 Mopane POINT (32.52741 -24.44882)\n", - "41.1 6.8360658 Mopane POINT (32.56589 -24.44893)\n", - "42.1 3.8526035 Mopane POINT (32.56589 -24.44803)\n", - "43.1 5.7991870 Mopane POINT (32.56688 -24.44803)\n", - "44.1 6.9556992 Mopane POINT (32.56687 -24.44893)\n", - "45.1 2.9998470 NA POINT (32.60546 -24.41291)\n", - "46.1 1.8813563 NA POINT (32.60546 -24.41201)\n", - "47.1 2.4016335 Mopane POINT (32.60645 -24.41201)\n", - "48.1 2.3728780 Mopane POINT (32.60645 -24.41292)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "3264" - ], - "text/latex": [ - "3264" - ], - "text/markdown": [ - "3264" - ], - "text/plain": [ - "[1] 3264" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "<table class=\"dataframe\">\n", - "<caption>A data.frame: 3264 × 13</caption>\n", - "<thead>\n", - "\t<tr><th></th><th scope=col>ProvÃncia</th><th scope=col>Estrato.Florestal</th><th scope=col>id_parcela</th><th scope=col>id_plot_new</th><th scope=col>Cluster</th><th scope=col>Cluster_new</th><th scope=col>Plot</th><th scope=col>Vt.(m^3/ha)</th><th scope=col>Vc.(m^3/ha)</th><th scope=col>AGB.(ton/ha)</th><th scope=col>BGB.(ton/ha)</th><th scope=col>FOREST_STR</th><th scope=col>geometry</th></tr>\n", - "\t<tr><th></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><POINT [°]></th></tr>\n", - "</thead>\n", - "<tbody>\n", - "\t<tr><th scope=row>1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>11 </td><td>11 </td><td>1 </td><td>1 </td><td>1</td><td> 50.1303939</td><td> 3.3738418</td><td> 56.0750397</td><td>20.7384320</td><td>Semi deciduous forest</td><td>POINT (32.75884 -26.65277)</td></tr>\n", - "\t<tr><th scope=row>2</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>12 </td><td>12 </td><td>1 </td><td>1 </td><td>2</td><td> 80.9101679</td><td> 5.7177690</td><td> 92.6641231</td><td>35.5032627</td><td>Semi deciduous forest</td><td>POINT (32.75884 -26.65187)</td></tr>\n", - "\t<tr><th scope=row>3</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>13 </td><td>13 </td><td>1 </td><td>1 </td><td>3</td><td>112.9392918</td><td> 8.5599657</td><td>112.8679614</td><td>36.9961788</td><td>Semi deciduous forest</td><td>POINT (32.75984 -26.65187)</td></tr>\n", - "\t<tr><th scope=row>4</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>14 </td><td>14 </td><td>1 </td><td>1 </td><td>4</td><td> 65.2971202</td><td>13.8819974</td><td> 65.5911266</td><td>25.8603076</td><td>Semi deciduous forest</td><td>POINT (32.75984 -26.65277)</td></tr>\n", - "\t<tr><th scope=row>5</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>21 </td><td>21 </td><td>2 </td><td>2 </td><td>1</td><td> 40.1910456</td><td> 6.1295265</td><td> 54.1303691</td><td>15.1565034</td><td>Semi deciduous forest</td><td>POINT (32.35752 -26.54318)</td></tr>\n", - "\t<tr><th scope=row>6</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>22 </td><td>22 </td><td>2 </td><td>2 </td><td>2</td><td>124.8337453</td><td>38.3703569</td><td>118.3816026</td><td>59.7754415</td><td>Semi deciduous forest</td><td>POINT (32.35753 -26.54228)</td></tr>\n", - "\t<tr><th scope=row>7</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>23 </td><td>23 </td><td>2 </td><td>2 </td><td>3</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td>Semi deciduous forest</td><td>POINT (32.35853 -26.54228)</td></tr>\n", - "\t<tr><th scope=row>8</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>24 </td><td>24 </td><td>2 </td><td>2 </td><td>4</td><td> 12.4778648</td><td> 5.3156884</td><td> 13.9045715</td><td> 8.2079812</td><td>Semi deciduous forest</td><td>POINT (32.35853 -26.54318)</td></tr>\n", - "\t<tr><th scope=row>9</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>41 </td><td>41 </td><td>4 </td><td>4 </td><td>1</td><td> 32.0303735</td><td> 0.0000000</td><td> 29.3328399</td><td>17.7816999</td><td>Semi evergreen forest</td><td>POINT (32.48352 -25.27953)</td></tr>\n", - "\t<tr><th scope=row>10</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>42 </td><td>42 </td><td>4 </td><td>4 </td><td>2</td><td> 5.1160801</td><td> 0.0000000</td><td> 4.9007144</td><td> 2.8053331</td><td>NA </td><td>POINT (32.48352 -25.27862)</td></tr>\n", - "\t<tr><th scope=row>11</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>43 </td><td>43 </td><td>4 </td><td>4 </td><td>3</td><td> 7.1378218</td><td> 0.0000000</td><td> 7.1910497</td><td> 4.6953413</td><td>NA </td><td>POINT (32.48451 -25.27863)</td></tr>\n", - "\t<tr><th scope=row>12</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>44 </td><td>44 </td><td>4 </td><td>4 </td><td>4</td><td> 19.9746598</td><td> 0.9664277</td><td> 18.3825096</td><td>11.2088316</td><td>Semi evergreen forest</td><td>POINT (32.48451 -25.27953)</td></tr>\n", - "\t<tr><th scope=row>13</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>61 </td><td>61 </td><td>6 </td><td>6 </td><td>1</td><td>122.6670035</td><td>35.4560191</td><td>177.3974614</td><td>49.6712892</td><td>Semi evergreen forest</td><td>POINT (32.40511 -25.0625)</td></tr>\n", - "\t<tr><th scope=row>14</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>62 </td><td>62 </td><td>6 </td><td>6 </td><td>2</td><td> 68.0539354</td><td>11.2161518</td><td> 91.1731365</td><td>25.5284782</td><td>Semi evergreen forest</td><td>POINT (32.40512 -25.06159)</td></tr>\n", - "\t<tr><th scope=row>15</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>63 </td><td>63 </td><td>6 </td><td>6 </td><td>3</td><td> 47.2328506</td><td>10.6966009</td><td> 59.6806678</td><td>16.7105870</td><td>Semi evergreen forest</td><td>POINT (32.40611 -25.0616)</td></tr>\n", - "\t<tr><th scope=row>16</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>64 </td><td>64 </td><td>6 </td><td>6 </td><td>4</td><td> 30.9214745</td><td> 3.3977827</td><td> 53.0650505</td><td>14.8582141</td><td>Semi evergreen forest</td><td>POINT (32.40611 -25.0625)</td></tr>\n", - "\t<tr><th scope=row>17</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>71 </td><td>71 </td><td>7 </td><td>7 </td><td>1</td><td> 71.0924167</td><td>12.3483489</td><td>125.1163321</td><td>35.0325730</td><td>Semi deciduous forest</td><td>POINT (32.20753 -24.95321)</td></tr>\n", - "\t<tr><th scope=row>18</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>72 </td><td>72 </td><td>7 </td><td>7 </td><td>2</td><td> 29.1466626</td><td>13.1863455</td><td> 54.2099472</td><td>15.1787852</td><td>Semi deciduous forest</td><td>POINT (32.20753 -24.95231)</td></tr>\n", - "\t<tr><th scope=row>19</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>73 </td><td>73 </td><td>7 </td><td>7 </td><td>3</td><td> 35.1352469</td><td>15.7653957</td><td> 38.2023376</td><td>20.5795378</td><td>Semi deciduous forest</td><td>POINT (32.20852 -24.95231)</td></tr>\n", - "\t<tr><th scope=row>20</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>74 </td><td>74 </td><td>7 </td><td>7 </td><td>4</td><td> 49.0069395</td><td>19.9850277</td><td> 53.4575029</td><td>28.1497527</td><td>Semi deciduous forest</td><td>POINT (32.20852 -24.95321)</td></tr>\n", - "\t<tr><th scope=row>21</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>81 </td><td>81 </td><td>8 </td><td>8 </td><td>1</td><td> 3.4051486</td><td> 1.3465824</td><td> 2.7423855</td><td> 1.9982884</td><td>NA </td><td>POINT (32.32718 -24.8093)</td></tr>\n", - "\t<tr><th scope=row>22</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>82 </td><td>82 </td><td>8 </td><td>8 </td><td>2</td><td> 0.5374994</td><td> 0.1624643</td><td> 0.4650103</td><td> 0.3736289</td><td>NA </td><td>POINT (32.32718 -24.8084)</td></tr>\n", - "\t<tr><th scope=row>23</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>83 </td><td>83 </td><td>8 </td><td>8 </td><td>3</td><td> 0.3063053</td><td> 0.3229557</td><td> 0.2559933</td><td> 0.2309457</td><td>NA </td><td>POINT (32.32817 -24.8084)</td></tr>\n", - "\t<tr><th scope=row>24</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>84 </td><td>84 </td><td>8 </td><td>8 </td><td>4</td><td> 6.8627903</td><td> 3.7032340</td><td> 6.2709382</td><td> 4.7138889</td><td>NA </td><td>POINT (32.32816 -24.80931)</td></tr>\n", - "\t<tr><th scope=row>25</th><td>Maputo</td><td>Mopane </td><td>91 </td><td>91 </td><td>9 </td><td>9 </td><td>1</td><td> 12.5694527</td><td> 5.1988954</td><td> 12.7656530</td><td> 5.9461055</td><td>Semi deciduous forest</td><td>POINT (32.48608 -24.66544)</td></tr>\n", - "\t<tr><th scope=row>26</th><td>Maputo</td><td>Mopane </td><td>92 </td><td>92 </td><td>9 </td><td>9 </td><td>2</td><td> 21.2269279</td><td> 7.2721913</td><td> 32.0594798</td><td>10.6079559</td><td>Semi deciduous forest</td><td>POINT (32.48608 -24.66453)</td></tr>\n", - "\t<tr><th scope=row>27</th><td>Maputo</td><td>Mopane </td><td>93 </td><td>93 </td><td>9 </td><td>9 </td><td>3</td><td> 24.6516330</td><td> 6.3610340</td><td> 42.2941803</td><td>12.3293093</td><td>Semi deciduous forest</td><td>POINT (32.48707 -24.66454)</td></tr>\n", - "\t<tr><th scope=row>28</th><td>Maputo</td><td>Mopane </td><td>94 </td><td>94 </td><td>9 </td><td>9 </td><td>4</td><td> 14.3877735</td><td> 4.4894680</td><td> 21.4999696</td><td> 7.9196962</td><td>Semi deciduous forest</td><td>POINT (32.48706 -24.66544)</td></tr>\n", - "\t<tr><th scope=row>29</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>101</td><td>101</td><td>10</td><td>10</td><td>1</td><td> 20.7941060</td><td> 9.4779588</td><td> 20.4826005</td><td>12.6522886</td><td>Mopane </td><td>POINT (32.52574 -24.62944)</td></tr>\n", - "\t<tr><th scope=row>30</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>102</td><td>102</td><td>10</td><td>10</td><td>2</td><td> 11.8873354</td><td> 4.2776805</td><td> 15.4512510</td><td> 9.6149010</td><td>Mopane </td><td>POINT (32.52575 -24.62854)</td></tr>\n", - "\t<tr><th scope=row>â‹®</th><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td></tr>\n", - "\t<tr><th scope=row>3387</th><td>Gaza</td><td>Mecrusse </td><td>GZ0161393 </td><td>9633</td><td>GZ016139 </td><td>963</td><td>3</td><td> 71.950107</td><td>32.813756</td><td> 59.094777</td><td>14.652350</td><td>Mecrusse </td><td>POINT (32.18284 -22.7484)</td></tr>\n", - "\t<tr><th scope=row>3388</th><td>Gaza</td><td>Mecrusse </td><td>GZ0161394 </td><td>9634</td><td>GZ016139 </td><td>963</td><td>4</td><td> 83.186888</td><td>39.653112</td><td> 68.178365</td><td>17.085471</td><td>Mecrusse </td><td>POINT (32.18284 -22.74931)</td></tr>\n", - "\t<tr><th scope=row>3389</th><td>Gaza</td><td>Mecrusse </td><td>GZ0167321 </td><td>9641</td><td>GZ016732 </td><td>964</td><td>1</td><td> 62.472793</td><td>29.197800</td><td> 52.118765</td><td>12.578029</td><td>Mecrusse </td><td>POINT (32.20145 -22.73134)</td></tr>\n", - "\t<tr><th scope=row>3390</th><td>Gaza</td><td>Mecrusse </td><td>GZ0167322 </td><td>9642</td><td>GZ016732 </td><td>964</td><td>2</td><td> 89.146037</td><td>44.175837</td><td> 80.615671</td><td>19.941807</td><td>Mecrusse </td><td>POINT (32.20145 -22.73043)</td></tr>\n", - "\t<tr><th scope=row>3391</th><td>Gaza</td><td>Mecrusse </td><td>GZ0167323 </td><td>9643</td><td>GZ016732 </td><td>964</td><td>3</td><td> 62.124359</td><td>21.482990</td><td> 60.751427</td><td>15.860857</td><td>Mecrusse </td><td>POINT (32.20243 -22.73044)</td></tr>\n", - "\t<tr><th scope=row>3392</th><td>Gaza</td><td>Mecrusse </td><td>GZ0167324 </td><td>9644</td><td>GZ016732 </td><td>964</td><td>4</td><td>136.954691</td><td>45.094165</td><td>145.783008</td><td>37.433405</td><td>Mecrusse </td><td>POINT (32.20242 -22.73134)</td></tr>\n", - "\t<tr><th scope=row>3393</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0214041 </td><td>9651</td><td>GZ021404 </td><td>965</td><td>1</td><td> 17.184141</td><td> 7.436475</td><td> 15.084115</td><td> 7.367507</td><td>NA </td><td>POINT (32.34963 -22.28937)</td></tr>\n", - "\t<tr><th scope=row>3394</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0214042 </td><td>9652</td><td>GZ021404 </td><td>965</td><td>2</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>NA </td><td>POINT (32.34963 -22.28847)</td></tr>\n", - "\t<tr><th scope=row>3395</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0214043 </td><td>9653</td><td>GZ021404 </td><td>965</td><td>3</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>NA </td><td>POINT (32.3506 -22.28847)</td></tr>\n", - "\t<tr><th scope=row>3396</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0214044 </td><td>9654</td><td>GZ021404 </td><td>965</td><td>4</td><td> 32.484805</td><td>11.734260</td><td> 30.701611</td><td>11.926298</td><td>NA </td><td>POINT (32.3506 -22.28938)</td></tr>\n", - "\t<tr><th scope=row>3397</th><td>Gaza</td><td>Mopane </td><td>GZ0378521 </td><td>9661</td><td>GZ037852 </td><td>966</td><td>1</td><td> 4.806654</td><td> 2.005794</td><td> 3.306343</td><td> 2.050370</td><td>Mopane </td><td>POINT (32.83323 -23.71787)</td></tr>\n", - "\t<tr><th scope=row>3398</th><td>Gaza</td><td>Mopane </td><td>GZ0378522 </td><td>9662</td><td>GZ037852 </td><td>966</td><td>2</td><td> 4.584096</td><td> 1.763882</td><td> 4.652407</td><td> 2.230058</td><td>Mopane </td><td>POINT (32.83323 -23.71697)</td></tr>\n", - "\t<tr><th scope=row>3399</th><td>Gaza</td><td>Mopane </td><td>GZ0378523 </td><td>9663</td><td>GZ037852 </td><td>966</td><td>3</td><td> 23.669968</td><td> 6.137337</td><td> 32.598391</td><td>10.838602</td><td>Mopane </td><td>POINT (32.83421 -23.71697)</td></tr>\n", - "\t<tr><th scope=row>3400</th><td>Gaza</td><td>Mopane </td><td>GZ0378524 </td><td>9664</td><td>GZ037852 </td><td>966</td><td>4</td><td> 2.999270</td><td> 1.242073</td><td> 4.286961</td><td> 1.660647</td><td>Mopane </td><td>POINT (32.83421 -23.71787)</td></tr>\n", - "\t<tr><th scope=row>3401</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0520691 </td><td>9671</td><td>GZ052069 </td><td>967</td><td>1</td><td> 7.886993</td><td> 0.221875</td><td> 6.881707</td><td> 2.928738</td><td>Semi evergreen forest</td><td>POINT (33.18603 -23.42877)</td></tr>\n", - "\t<tr><th scope=row>3402</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0520692 </td><td>9672</td><td>GZ052069 </td><td>967</td><td>2</td><td> 47.108684</td><td>14.061800</td><td> 46.241351</td><td>23.402341</td><td>Semi evergreen forest</td><td>POINT (33.18603 -23.42787)</td></tr>\n", - "\t<tr><th scope=row>3403</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0520693 </td><td>9673</td><td>GZ052069 </td><td>967</td><td>3</td><td> 55.849702</td><td>19.109564</td><td> 54.593271</td><td>26.272914</td><td>Semi evergreen forest</td><td>POINT (33.18701 -23.42787)</td></tr>\n", - "\t<tr><th scope=row>3404</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0520694 </td><td>9674</td><td>GZ052069 </td><td>967</td><td>4</td><td> 52.685322</td><td> 5.162312</td><td> 50.841657</td><td>24.872779</td><td>Semi evergreen forest</td><td>POINT (33.18701 -23.42877)</td></tr>\n", - "\t<tr><th scope=row>3405</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0563611 </td><td>9681</td><td>GZ056361 </td><td>968</td><td>1</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>NA </td><td>POINT (33.29536 -24.17831)</td></tr>\n", - "\t<tr><th scope=row>3406</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0563612 </td><td>9682</td><td>GZ056361 </td><td>968</td><td>2</td><td> 27.186411</td><td> 6.083705</td><td> 26.806063</td><td>13.714432</td><td>NA </td><td>POINT (33.29536 -24.17741)</td></tr>\n", - "\t<tr><th scope=row>3407</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0563613 </td><td>9683</td><td>GZ056361 </td><td>968</td><td>3</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>NA </td><td>POINT (33.29634 -24.17741)</td></tr>\n", - "\t<tr><th scope=row>3408</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ0563614 </td><td>9684</td><td>GZ056361 </td><td>968</td><td>4</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>NA </td><td>POINT (33.29635 -24.17831)</td></tr>\n", - "\t<tr><th scope=row>3413</th><td>Gaza</td><td>Mecrusse </td><td>GZ-A-0446711</td><td>9701</td><td>GZ-A-044671</td><td>970</td><td>1</td><td> 88.097842</td><td>22.707252</td><td> 67.068143</td><td>16.560189</td><td>Mopane </td><td>POINT (33.00973 -21.93827)</td></tr>\n", - "\t<tr><th scope=row>3414</th><td>Gaza</td><td>Mecrusse </td><td>GZ-A-0446712</td><td>9702</td><td>GZ-A-044671</td><td>970</td><td>2</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>Mopane </td><td>POINT (33.00973 -21.93737)</td></tr>\n", - "\t<tr><th scope=row>3415</th><td>Gaza</td><td>Mecrusse </td><td>GZ-A-0446713</td><td>9703</td><td>GZ-A-044671</td><td>970</td><td>3</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>Mopane </td><td>POINT (33.0107 -21.93737)</td></tr>\n", - "\t<tr><th scope=row>3416</th><td>Gaza</td><td>Mecrusse </td><td>GZ-A-0446714</td><td>9704</td><td>GZ-A-044671</td><td>970</td><td>4</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td> 0.000000</td><td>Mopane </td><td>POINT (33.0107 -21.93827)</td></tr>\n", - "\t<tr><th scope=row>3417</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ-A-0632381</td><td>9711</td><td>GZ-A-063238</td><td>971</td><td>1</td><td> 6.961900</td><td> 2.772908</td><td> 7.028448</td><td> 3.431783</td><td>Semi deciduous forest</td><td>POINT (33.5275 -23.14799)</td></tr>\n", - "\t<tr><th scope=row>3418</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ-A-0632382</td><td>9712</td><td>GZ-A-063238</td><td>971</td><td>2</td><td>180.051911</td><td> 9.649130</td><td>171.303815</td><td>60.199839</td><td>Semi deciduous forest</td><td>POINT (33.52749 -23.14709)</td></tr>\n", - "\t<tr><th scope=row>3419</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ-A-0632383</td><td>9713</td><td>GZ-A-063238</td><td>971</td><td>3</td><td> 49.580407</td><td> 6.321964</td><td> 48.235308</td><td>17.238029</td><td>Semi deciduous forest</td><td>POINT (33.52847 -23.14708)</td></tr>\n", - "\t<tr><th scope=row>3420</th><td>Gaza</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>GZ-A-0632384</td><td>9714</td><td>GZ-A-063238</td><td>971</td><td>4</td><td> 27.958856</td><td> 1.378058</td><td> 25.743224</td><td>11.607162</td><td>Semi deciduous forest</td><td>POINT (33.52847 -23.14799)</td></tr>\n", - "</tbody>\n", - "</table>\n" - ], - "text/latex": [ - "A data.frame: 3264 × 13\n", - "\\begin{tabular}{r|lllllllllllll}\n", - " & ProvÃncia & Estrato.Florestal & id\\_parcela & id\\_plot\\_new & Cluster & Cluster\\_new & Plot & Vt.(m\\textasciicircum{}3/ha) & Vc.(m\\textasciicircum{}3/ha) & AGB.(ton/ha) & BGB.(ton/ha) & FOREST\\_STR & geometry\\\\\n", - " & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <POINT {[}°{]}>\\\\\n", - "\\hline\n", - "\t1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 11 & 11 & 1 & 1 & 1 & 50.1303939 & 3.3738418 & 56.0750397 & 20.7384320 & Semi deciduous forest & POINT (32.75884 -26.65277)\\\\\n", - "\t2 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 12 & 12 & 1 & 1 & 2 & 80.9101679 & 5.7177690 & 92.6641231 & 35.5032627 & Semi deciduous forest & POINT (32.75884 -26.65187)\\\\\n", - "\t3 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 13 & 13 & 1 & 1 & 3 & 112.9392918 & 8.5599657 & 112.8679614 & 36.9961788 & Semi deciduous forest & POINT (32.75984 -26.65187)\\\\\n", - "\t4 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 14 & 14 & 1 & 1 & 4 & 65.2971202 & 13.8819974 & 65.5911266 & 25.8603076 & Semi deciduous forest & POINT (32.75984 -26.65277)\\\\\n", - "\t5 & Maputo & Floresta (semi-) sempreverde & 21 & 21 & 2 & 2 & 1 & 40.1910456 & 6.1295265 & 54.1303691 & 15.1565034 & Semi deciduous forest & POINT (32.35752 -26.54318)\\\\\n", - "\t6 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 22 & 22 & 2 & 2 & 2 & 124.8337453 & 38.3703569 & 118.3816026 & 59.7754415 & Semi deciduous forest & POINT (32.35753 -26.54228)\\\\\n", - "\t7 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 23 & 23 & 2 & 2 & 3 & 0.0000000 & 0.0000000 & 0.0000000 & 0.0000000 & Semi deciduous forest & POINT (32.35853 -26.54228)\\\\\n", - "\t8 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 24 & 24 & 2 & 2 & 4 & 12.4778648 & 5.3156884 & 13.9045715 & 8.2079812 & Semi deciduous forest & POINT (32.35853 -26.54318)\\\\\n", - "\t9 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 41 & 41 & 4 & 4 & 1 & 32.0303735 & 0.0000000 & 29.3328399 & 17.7816999 & Semi evergreen forest & POINT (32.48352 -25.27953)\\\\\n", - "\t10 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 42 & 42 & 4 & 4 & 2 & 5.1160801 & 0.0000000 & 4.9007144 & 2.8053331 & NA & POINT (32.48352 -25.27862)\\\\\n", - "\t11 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 43 & 43 & 4 & 4 & 3 & 7.1378218 & 0.0000000 & 7.1910497 & 4.6953413 & NA & POINT (32.48451 -25.27863)\\\\\n", - "\t12 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 44 & 44 & 4 & 4 & 4 & 19.9746598 & 0.9664277 & 18.3825096 & 11.2088316 & Semi evergreen forest & POINT (32.48451 -25.27953)\\\\\n", - "\t13 & Maputo & Floresta (semi-) sempreverde & 61 & 61 & 6 & 6 & 1 & 122.6670035 & 35.4560191 & 177.3974614 & 49.6712892 & Semi evergreen forest & POINT (32.40511 -25.0625)\\\\\n", - "\t14 & Maputo & Floresta (semi-) sempreverde & 62 & 62 & 6 & 6 & 2 & 68.0539354 & 11.2161518 & 91.1731365 & 25.5284782 & Semi evergreen forest & POINT (32.40512 -25.06159)\\\\\n", - "\t15 & Maputo & Floresta (semi-) sempreverde & 63 & 63 & 6 & 6 & 3 & 47.2328506 & 10.6966009 & 59.6806678 & 16.7105870 & Semi evergreen forest & POINT (32.40611 -25.0616)\\\\\n", - "\t16 & Maputo & Floresta (semi-) sempreverde & 64 & 64 & 6 & 6 & 4 & 30.9214745 & 3.3977827 & 53.0650505 & 14.8582141 & Semi evergreen forest & POINT (32.40611 -25.0625)\\\\\n", - "\t17 & Maputo & Floresta (semi-) sempreverde & 71 & 71 & 7 & 7 & 1 & 71.0924167 & 12.3483489 & 125.1163321 & 35.0325730 & Semi deciduous forest & POINT (32.20753 -24.95321)\\\\\n", - "\t18 & Maputo & Floresta (semi-) sempreverde & 72 & 72 & 7 & 7 & 2 & 29.1466626 & 13.1863455 & 54.2099472 & 15.1787852 & Semi deciduous forest & POINT (32.20753 -24.95231)\\\\\n", - "\t19 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 73 & 73 & 7 & 7 & 3 & 35.1352469 & 15.7653957 & 38.2023376 & 20.5795378 & Semi deciduous forest & POINT (32.20852 -24.95231)\\\\\n", - "\t20 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 74 & 74 & 7 & 7 & 4 & 49.0069395 & 19.9850277 & 53.4575029 & 28.1497527 & Semi deciduous forest & POINT (32.20852 -24.95321)\\\\\n", - "\t21 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 81 & 81 & 8 & 8 & 1 & 3.4051486 & 1.3465824 & 2.7423855 & 1.9982884 & NA & POINT (32.32718 -24.8093)\\\\\n", - "\t22 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 82 & 82 & 8 & 8 & 2 & 0.5374994 & 0.1624643 & 0.4650103 & 0.3736289 & NA & POINT (32.32718 -24.8084)\\\\\n", - "\t23 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 83 & 83 & 8 & 8 & 3 & 0.3063053 & 0.3229557 & 0.2559933 & 0.2309457 & NA & POINT (32.32817 -24.8084)\\\\\n", - "\t24 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 84 & 84 & 8 & 8 & 4 & 6.8627903 & 3.7032340 & 6.2709382 & 4.7138889 & NA & POINT (32.32816 -24.80931)\\\\\n", - "\t25 & Maputo & Mopane & 91 & 91 & 9 & 9 & 1 & 12.5694527 & 5.1988954 & 12.7656530 & 5.9461055 & Semi deciduous forest & POINT (32.48608 -24.66544)\\\\\n", - "\t26 & Maputo & Mopane & 92 & 92 & 9 & 9 & 2 & 21.2269279 & 7.2721913 & 32.0594798 & 10.6079559 & Semi deciduous forest & POINT (32.48608 -24.66453)\\\\\n", - "\t27 & Maputo & Mopane & 93 & 93 & 9 & 9 & 3 & 24.6516330 & 6.3610340 & 42.2941803 & 12.3293093 & Semi deciduous forest & POINT (32.48707 -24.66454)\\\\\n", - "\t28 & Maputo & Mopane & 94 & 94 & 9 & 9 & 4 & 14.3877735 & 4.4894680 & 21.4999696 & 7.9196962 & Semi deciduous forest & POINT (32.48706 -24.66544)\\\\\n", - "\t29 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 101 & 101 & 10 & 10 & 1 & 20.7941060 & 9.4779588 & 20.4826005 & 12.6522886 & Mopane & POINT (32.52574 -24.62944)\\\\\n", - "\t30 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 102 & 102 & 10 & 10 & 2 & 11.8873354 & 4.2776805 & 15.4512510 & 9.6149010 & Mopane & POINT (32.52575 -24.62854)\\\\\n", - "\tâ‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹® & â‹®\\\\\n", - "\t3387 & Gaza & Mecrusse & GZ0161393 & 9633 & GZ016139 & 963 & 3 & 71.950107 & 32.813756 & 59.094777 & 14.652350 & Mecrusse & POINT (32.18284 -22.7484)\\\\\n", - "\t3388 & Gaza & Mecrusse & GZ0161394 & 9634 & GZ016139 & 963 & 4 & 83.186888 & 39.653112 & 68.178365 & 17.085471 & Mecrusse & POINT (32.18284 -22.74931)\\\\\n", - "\t3389 & Gaza & Mecrusse & GZ0167321 & 9641 & GZ016732 & 964 & 1 & 62.472793 & 29.197800 & 52.118765 & 12.578029 & Mecrusse & POINT (32.20145 -22.73134)\\\\\n", - "\t3390 & Gaza & Mecrusse & GZ0167322 & 9642 & GZ016732 & 964 & 2 & 89.146037 & 44.175837 & 80.615671 & 19.941807 & Mecrusse & POINT (32.20145 -22.73043)\\\\\n", - "\t3391 & Gaza & Mecrusse & GZ0167323 & 9643 & GZ016732 & 964 & 3 & 62.124359 & 21.482990 & 60.751427 & 15.860857 & Mecrusse & POINT (32.20243 -22.73044)\\\\\n", - "\t3392 & Gaza & Mecrusse & GZ0167324 & 9644 & GZ016732 & 964 & 4 & 136.954691 & 45.094165 & 145.783008 & 37.433405 & Mecrusse & POINT (32.20242 -22.73134)\\\\\n", - "\t3393 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0214041 & 9651 & GZ021404 & 965 & 1 & 17.184141 & 7.436475 & 15.084115 & 7.367507 & NA & POINT (32.34963 -22.28937)\\\\\n", - "\t3394 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0214042 & 9652 & GZ021404 & 965 & 2 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & NA & POINT (32.34963 -22.28847)\\\\\n", - "\t3395 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0214043 & 9653 & GZ021404 & 965 & 3 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & NA & POINT (32.3506 -22.28847)\\\\\n", - "\t3396 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0214044 & 9654 & GZ021404 & 965 & 4 & 32.484805 & 11.734260 & 30.701611 & 11.926298 & NA & POINT (32.3506 -22.28938)\\\\\n", - "\t3397 & Gaza & Mopane & GZ0378521 & 9661 & GZ037852 & 966 & 1 & 4.806654 & 2.005794 & 3.306343 & 2.050370 & Mopane & POINT (32.83323 -23.71787)\\\\\n", - "\t3398 & Gaza & Mopane & GZ0378522 & 9662 & GZ037852 & 966 & 2 & 4.584096 & 1.763882 & 4.652407 & 2.230058 & Mopane & POINT (32.83323 -23.71697)\\\\\n", - "\t3399 & Gaza & Mopane & GZ0378523 & 9663 & GZ037852 & 966 & 3 & 23.669968 & 6.137337 & 32.598391 & 10.838602 & Mopane & POINT (32.83421 -23.71697)\\\\\n", - "\t3400 & Gaza & Mopane & GZ0378524 & 9664 & GZ037852 & 966 & 4 & 2.999270 & 1.242073 & 4.286961 & 1.660647 & Mopane & POINT (32.83421 -23.71787)\\\\\n", - "\t3401 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0520691 & 9671 & GZ052069 & 967 & 1 & 7.886993 & 0.221875 & 6.881707 & 2.928738 & Semi evergreen forest & POINT (33.18603 -23.42877)\\\\\n", - "\t3402 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0520692 & 9672 & GZ052069 & 967 & 2 & 47.108684 & 14.061800 & 46.241351 & 23.402341 & Semi evergreen forest & POINT (33.18603 -23.42787)\\\\\n", - "\t3403 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0520693 & 9673 & GZ052069 & 967 & 3 & 55.849702 & 19.109564 & 54.593271 & 26.272914 & Semi evergreen forest & POINT (33.18701 -23.42787)\\\\\n", - "\t3404 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0520694 & 9674 & GZ052069 & 967 & 4 & 52.685322 & 5.162312 & 50.841657 & 24.872779 & Semi evergreen forest & POINT (33.18701 -23.42877)\\\\\n", - "\t3405 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0563611 & 9681 & GZ056361 & 968 & 1 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & NA & POINT (33.29536 -24.17831)\\\\\n", - "\t3406 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0563612 & 9682 & GZ056361 & 968 & 2 & 27.186411 & 6.083705 & 26.806063 & 13.714432 & NA & POINT (33.29536 -24.17741)\\\\\n", - "\t3407 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0563613 & 9683 & GZ056361 & 968 & 3 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & NA & POINT (33.29634 -24.17741)\\\\\n", - "\t3408 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ0563614 & 9684 & GZ056361 & 968 & 4 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & NA & POINT (33.29635 -24.17831)\\\\\n", - "\t3413 & Gaza & Mecrusse & GZ-A-0446711 & 9701 & GZ-A-044671 & 970 & 1 & 88.097842 & 22.707252 & 67.068143 & 16.560189 & Mopane & POINT (33.00973 -21.93827)\\\\\n", - "\t3414 & Gaza & Mecrusse & GZ-A-0446712 & 9702 & GZ-A-044671 & 970 & 2 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & Mopane & POINT (33.00973 -21.93737)\\\\\n", - "\t3415 & Gaza & Mecrusse & GZ-A-0446713 & 9703 & GZ-A-044671 & 970 & 3 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & Mopane & POINT (33.0107 -21.93737)\\\\\n", - "\t3416 & Gaza & Mecrusse & GZ-A-0446714 & 9704 & GZ-A-044671 & 970 & 4 & 0.000000 & 0.000000 & 0.000000 & 0.000000 & Mopane & POINT (33.0107 -21.93827)\\\\\n", - "\t3417 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ-A-0632381 & 9711 & GZ-A-063238 & 971 & 1 & 6.961900 & 2.772908 & 7.028448 & 3.431783 & Semi deciduous forest & POINT (33.5275 -23.14799)\\\\\n", - "\t3418 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ-A-0632382 & 9712 & GZ-A-063238 & 971 & 2 & 180.051911 & 9.649130 & 171.303815 & 60.199839 & Semi deciduous forest & POINT (33.52749 -23.14709)\\\\\n", - "\t3419 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ-A-0632383 & 9713 & GZ-A-063238 & 971 & 3 & 49.580407 & 6.321964 & 48.235308 & 17.238029 & Semi deciduous forest & POINT (33.52847 -23.14708)\\\\\n", - "\t3420 & Gaza & Floresta (semi-) decidua, incluindo Miombo & GZ-A-0632384 & 9714 & GZ-A-063238 & 971 & 4 & 27.958856 & 1.378058 & 25.743224 & 11.607162 & Semi deciduous forest & POINT (33.52847 -23.14799)\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 3264 × 13\n", - "\n", - "| <!--/--> | ProvÃncia <chr> | Estrato.Florestal <chr> | id_parcela <chr> | id_plot_new <chr> | Cluster <chr> | Cluster_new <chr> | Plot <dbl> | Vt.(m^3/ha) <dbl> | Vc.(m^3/ha) <dbl> | AGB.(ton/ha) <dbl> | BGB.(ton/ha) <dbl> | FOREST_STR <chr> | geometry <POINT [°]> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 11 | 11 | 1 | 1 | 1 | 50.1303939 | 3.3738418 | 56.0750397 | 20.7384320 | Semi deciduous forest | POINT (32.75884 -26.65277) |\n", - "| 2 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 12 | 12 | 1 | 1 | 2 | 80.9101679 | 5.7177690 | 92.6641231 | 35.5032627 | Semi deciduous forest | POINT (32.75884 -26.65187) |\n", - "| 3 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 13 | 13 | 1 | 1 | 3 | 112.9392918 | 8.5599657 | 112.8679614 | 36.9961788 | Semi deciduous forest | POINT (32.75984 -26.65187) |\n", - "| 4 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 14 | 14 | 1 | 1 | 4 | 65.2971202 | 13.8819974 | 65.5911266 | 25.8603076 | Semi deciduous forest | POINT (32.75984 -26.65277) |\n", - "| 5 | Maputo | Floresta (semi-) sempreverde | 21 | 21 | 2 | 2 | 1 | 40.1910456 | 6.1295265 | 54.1303691 | 15.1565034 | Semi deciduous forest | POINT (32.35752 -26.54318) |\n", - "| 6 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 22 | 22 | 2 | 2 | 2 | 124.8337453 | 38.3703569 | 118.3816026 | 59.7754415 | Semi deciduous forest | POINT (32.35753 -26.54228) |\n", - "| 7 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 23 | 23 | 2 | 2 | 3 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | Semi deciduous forest | POINT (32.35853 -26.54228) |\n", - "| 8 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 24 | 24 | 2 | 2 | 4 | 12.4778648 | 5.3156884 | 13.9045715 | 8.2079812 | Semi deciduous forest | POINT (32.35853 -26.54318) |\n", - "| 9 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 41 | 41 | 4 | 4 | 1 | 32.0303735 | 0.0000000 | 29.3328399 | 17.7816999 | Semi evergreen forest | POINT (32.48352 -25.27953) |\n", - "| 10 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 42 | 42 | 4 | 4 | 2 | 5.1160801 | 0.0000000 | 4.9007144 | 2.8053331 | NA | POINT (32.48352 -25.27862) |\n", - "| 11 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 43 | 43 | 4 | 4 | 3 | 7.1378218 | 0.0000000 | 7.1910497 | 4.6953413 | NA | POINT (32.48451 -25.27863) |\n", - "| 12 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 44 | 44 | 4 | 4 | 4 | 19.9746598 | 0.9664277 | 18.3825096 | 11.2088316 | Semi evergreen forest | POINT (32.48451 -25.27953) |\n", - "| 13 | Maputo | Floresta (semi-) sempreverde | 61 | 61 | 6 | 6 | 1 | 122.6670035 | 35.4560191 | 177.3974614 | 49.6712892 | Semi evergreen forest | POINT (32.40511 -25.0625) |\n", - "| 14 | Maputo | Floresta (semi-) sempreverde | 62 | 62 | 6 | 6 | 2 | 68.0539354 | 11.2161518 | 91.1731365 | 25.5284782 | Semi evergreen forest | POINT (32.40512 -25.06159) |\n", - "| 15 | Maputo | Floresta (semi-) sempreverde | 63 | 63 | 6 | 6 | 3 | 47.2328506 | 10.6966009 | 59.6806678 | 16.7105870 | Semi evergreen forest | POINT (32.40611 -25.0616) |\n", - "| 16 | Maputo | Floresta (semi-) sempreverde | 64 | 64 | 6 | 6 | 4 | 30.9214745 | 3.3977827 | 53.0650505 | 14.8582141 | Semi evergreen forest | POINT (32.40611 -25.0625) |\n", - "| 17 | Maputo | Floresta (semi-) sempreverde | 71 | 71 | 7 | 7 | 1 | 71.0924167 | 12.3483489 | 125.1163321 | 35.0325730 | Semi deciduous forest | POINT (32.20753 -24.95321) |\n", - "| 18 | Maputo | Floresta (semi-) sempreverde | 72 | 72 | 7 | 7 | 2 | 29.1466626 | 13.1863455 | 54.2099472 | 15.1787852 | Semi deciduous forest | POINT (32.20753 -24.95231) |\n", - "| 19 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 73 | 73 | 7 | 7 | 3 | 35.1352469 | 15.7653957 | 38.2023376 | 20.5795378 | Semi deciduous forest | POINT (32.20852 -24.95231) |\n", - "| 20 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 74 | 74 | 7 | 7 | 4 | 49.0069395 | 19.9850277 | 53.4575029 | 28.1497527 | Semi deciduous forest | POINT (32.20852 -24.95321) |\n", - "| 21 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 81 | 81 | 8 | 8 | 1 | 3.4051486 | 1.3465824 | 2.7423855 | 1.9982884 | NA | POINT (32.32718 -24.8093) |\n", - "| 22 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 82 | 82 | 8 | 8 | 2 | 0.5374994 | 0.1624643 | 0.4650103 | 0.3736289 | NA | POINT (32.32718 -24.8084) |\n", - "| 23 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 83 | 83 | 8 | 8 | 3 | 0.3063053 | 0.3229557 | 0.2559933 | 0.2309457 | NA | POINT (32.32817 -24.8084) |\n", - "| 24 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 84 | 84 | 8 | 8 | 4 | 6.8627903 | 3.7032340 | 6.2709382 | 4.7138889 | NA | POINT (32.32816 -24.80931) |\n", - "| 25 | Maputo | Mopane | 91 | 91 | 9 | 9 | 1 | 12.5694527 | 5.1988954 | 12.7656530 | 5.9461055 | Semi deciduous forest | POINT (32.48608 -24.66544) |\n", - "| 26 | Maputo | Mopane | 92 | 92 | 9 | 9 | 2 | 21.2269279 | 7.2721913 | 32.0594798 | 10.6079559 | Semi deciduous forest | POINT (32.48608 -24.66453) |\n", - "| 27 | Maputo | Mopane | 93 | 93 | 9 | 9 | 3 | 24.6516330 | 6.3610340 | 42.2941803 | 12.3293093 | Semi deciduous forest | POINT (32.48707 -24.66454) |\n", - "| 28 | Maputo | Mopane | 94 | 94 | 9 | 9 | 4 | 14.3877735 | 4.4894680 | 21.4999696 | 7.9196962 | Semi deciduous forest | POINT (32.48706 -24.66544) |\n", - "| 29 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 101 | 101 | 10 | 10 | 1 | 20.7941060 | 9.4779588 | 20.4826005 | 12.6522886 | Mopane | POINT (32.52574 -24.62944) |\n", - "| 30 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 102 | 102 | 10 | 10 | 2 | 11.8873354 | 4.2776805 | 15.4512510 | 9.6149010 | Mopane | POINT (32.52575 -24.62854) |\n", - "| â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® | â‹® |\n", - "| 3387 | Gaza | Mecrusse | GZ0161393 | 9633 | GZ016139 | 963 | 3 | 71.950107 | 32.813756 | 59.094777 | 14.652350 | Mecrusse | POINT (32.18284 -22.7484) |\n", - "| 3388 | Gaza | Mecrusse | GZ0161394 | 9634 | GZ016139 | 963 | 4 | 83.186888 | 39.653112 | 68.178365 | 17.085471 | Mecrusse | POINT (32.18284 -22.74931) |\n", - "| 3389 | Gaza | Mecrusse | GZ0167321 | 9641 | GZ016732 | 964 | 1 | 62.472793 | 29.197800 | 52.118765 | 12.578029 | Mecrusse | POINT (32.20145 -22.73134) |\n", - "| 3390 | Gaza | Mecrusse | GZ0167322 | 9642 | GZ016732 | 964 | 2 | 89.146037 | 44.175837 | 80.615671 | 19.941807 | Mecrusse | POINT (32.20145 -22.73043) |\n", - "| 3391 | Gaza | Mecrusse | GZ0167323 | 9643 | GZ016732 | 964 | 3 | 62.124359 | 21.482990 | 60.751427 | 15.860857 | Mecrusse | POINT (32.20243 -22.73044) |\n", - "| 3392 | Gaza | Mecrusse | GZ0167324 | 9644 | GZ016732 | 964 | 4 | 136.954691 | 45.094165 | 145.783008 | 37.433405 | Mecrusse | POINT (32.20242 -22.73134) |\n", - "| 3393 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0214041 | 9651 | GZ021404 | 965 | 1 | 17.184141 | 7.436475 | 15.084115 | 7.367507 | NA | POINT (32.34963 -22.28937) |\n", - "| 3394 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0214042 | 9652 | GZ021404 | 965 | 2 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NA | POINT (32.34963 -22.28847) |\n", - "| 3395 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0214043 | 9653 | GZ021404 | 965 | 3 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NA | POINT (32.3506 -22.28847) |\n", - "| 3396 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0214044 | 9654 | GZ021404 | 965 | 4 | 32.484805 | 11.734260 | 30.701611 | 11.926298 | NA | POINT (32.3506 -22.28938) |\n", - "| 3397 | Gaza | Mopane | GZ0378521 | 9661 | GZ037852 | 966 | 1 | 4.806654 | 2.005794 | 3.306343 | 2.050370 | Mopane | POINT (32.83323 -23.71787) |\n", - "| 3398 | Gaza | Mopane | GZ0378522 | 9662 | GZ037852 | 966 | 2 | 4.584096 | 1.763882 | 4.652407 | 2.230058 | Mopane | POINT (32.83323 -23.71697) |\n", - "| 3399 | Gaza | Mopane | GZ0378523 | 9663 | GZ037852 | 966 | 3 | 23.669968 | 6.137337 | 32.598391 | 10.838602 | Mopane | POINT (32.83421 -23.71697) |\n", - "| 3400 | Gaza | Mopane | GZ0378524 | 9664 | GZ037852 | 966 | 4 | 2.999270 | 1.242073 | 4.286961 | 1.660647 | Mopane | POINT (32.83421 -23.71787) |\n", - "| 3401 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0520691 | 9671 | GZ052069 | 967 | 1 | 7.886993 | 0.221875 | 6.881707 | 2.928738 | Semi evergreen forest | POINT (33.18603 -23.42877) |\n", - "| 3402 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0520692 | 9672 | GZ052069 | 967 | 2 | 47.108684 | 14.061800 | 46.241351 | 23.402341 | Semi evergreen forest | POINT (33.18603 -23.42787) |\n", - "| 3403 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0520693 | 9673 | GZ052069 | 967 | 3 | 55.849702 | 19.109564 | 54.593271 | 26.272914 | Semi evergreen forest | POINT (33.18701 -23.42787) |\n", - "| 3404 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0520694 | 9674 | GZ052069 | 967 | 4 | 52.685322 | 5.162312 | 50.841657 | 24.872779 | Semi evergreen forest | POINT (33.18701 -23.42877) |\n", - "| 3405 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0563611 | 9681 | GZ056361 | 968 | 1 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NA | POINT (33.29536 -24.17831) |\n", - "| 3406 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0563612 | 9682 | GZ056361 | 968 | 2 | 27.186411 | 6.083705 | 26.806063 | 13.714432 | NA | POINT (33.29536 -24.17741) |\n", - "| 3407 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0563613 | 9683 | GZ056361 | 968 | 3 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NA | POINT (33.29634 -24.17741) |\n", - "| 3408 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ0563614 | 9684 | GZ056361 | 968 | 4 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NA | POINT (33.29635 -24.17831) |\n", - "| 3413 | Gaza | Mecrusse | GZ-A-0446711 | 9701 | GZ-A-044671 | 970 | 1 | 88.097842 | 22.707252 | 67.068143 | 16.560189 | Mopane | POINT (33.00973 -21.93827) |\n", - "| 3414 | Gaza | Mecrusse | GZ-A-0446712 | 9702 | GZ-A-044671 | 970 | 2 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | Mopane | POINT (33.00973 -21.93737) |\n", - "| 3415 | Gaza | Mecrusse | GZ-A-0446713 | 9703 | GZ-A-044671 | 970 | 3 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | Mopane | POINT (33.0107 -21.93737) |\n", - "| 3416 | Gaza | Mecrusse | GZ-A-0446714 | 9704 | GZ-A-044671 | 970 | 4 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | Mopane | POINT (33.0107 -21.93827) |\n", - "| 3417 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ-A-0632381 | 9711 | GZ-A-063238 | 971 | 1 | 6.961900 | 2.772908 | 7.028448 | 3.431783 | Semi deciduous forest | POINT (33.5275 -23.14799) |\n", - "| 3418 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ-A-0632382 | 9712 | GZ-A-063238 | 971 | 2 | 180.051911 | 9.649130 | 171.303815 | 60.199839 | Semi deciduous forest | POINT (33.52749 -23.14709) |\n", - "| 3419 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ-A-0632383 | 9713 | GZ-A-063238 | 971 | 3 | 49.580407 | 6.321964 | 48.235308 | 17.238029 | Semi deciduous forest | POINT (33.52847 -23.14708) |\n", - "| 3420 | Gaza | Floresta (semi-) decidua, incluindo Miombo | GZ-A-0632384 | 9714 | GZ-A-063238 | 971 | 4 | 27.958856 | 1.378058 | 25.743224 | 11.607162 | Semi deciduous forest | POINT (33.52847 -23.14799) |\n", - "\n" - ], - "text/plain": [ - " ProvÃncia Estrato.Florestal id_parcela \n", - "1 Maputo Floresta (semi-) decidua, incluindo Miombo 11 \n", - "2 Maputo Floresta (semi-) decidua, incluindo Miombo 12 \n", - "3 Maputo Floresta (semi-) decidua, incluindo Miombo 13 \n", - "4 Maputo Floresta (semi-) decidua, incluindo Miombo 14 \n", - "5 Maputo Floresta (semi-) sempreverde 21 \n", - "6 Maputo Floresta (semi-) decidua, incluindo Miombo 22 \n", - "7 Maputo Floresta (semi-) decidua, incluindo Miombo 23 \n", - "8 Maputo Floresta (semi-) decidua, incluindo Miombo 24 \n", - "9 Maputo Floresta (semi-) decidua, incluindo Miombo 41 \n", - "10 Maputo Floresta (semi-) decidua, incluindo Miombo 42 \n", - "11 Maputo Floresta (semi-) decidua, incluindo Miombo 43 \n", - "12 Maputo Floresta (semi-) decidua, incluindo Miombo 44 \n", - "13 Maputo Floresta (semi-) sempreverde 61 \n", - "14 Maputo Floresta (semi-) sempreverde 62 \n", - "15 Maputo Floresta (semi-) sempreverde 63 \n", - "16 Maputo Floresta (semi-) sempreverde 64 \n", - "17 Maputo Floresta (semi-) sempreverde 71 \n", - "18 Maputo Floresta (semi-) sempreverde 72 \n", - "19 Maputo Floresta (semi-) decidua, incluindo Miombo 73 \n", - "20 Maputo Floresta (semi-) decidua, incluindo Miombo 74 \n", - "21 Maputo Floresta (semi-) decidua, incluindo Miombo 81 \n", - "22 Maputo Floresta (semi-) decidua, incluindo Miombo 82 \n", - "23 Maputo Floresta (semi-) decidua, incluindo Miombo 83 \n", - "24 Maputo Floresta (semi-) decidua, incluindo Miombo 84 \n", - "25 Maputo Mopane 91 \n", - "26 Maputo Mopane 92 \n", - "27 Maputo Mopane 93 \n", - "28 Maputo Mopane 94 \n", - "29 Maputo Floresta (semi-) decidua, incluindo Miombo 101 \n", - "30 Maputo Floresta (semi-) decidua, incluindo Miombo 102 \n", - "â‹® â‹® â‹® â‹® \n", - "3387 Gaza Mecrusse GZ0161393 \n", - "3388 Gaza Mecrusse GZ0161394 \n", - "3389 Gaza Mecrusse GZ0167321 \n", - "3390 Gaza Mecrusse GZ0167322 \n", - "3391 Gaza Mecrusse GZ0167323 \n", - "3392 Gaza Mecrusse GZ0167324 \n", - "3393 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0214041 \n", - "3394 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0214042 \n", - "3395 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0214043 \n", - "3396 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0214044 \n", - "3397 Gaza Mopane GZ0378521 \n", - "3398 Gaza Mopane GZ0378522 \n", - "3399 Gaza Mopane GZ0378523 \n", - "3400 Gaza Mopane GZ0378524 \n", - "3401 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0520691 \n", - "3402 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0520692 \n", - "3403 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0520693 \n", - "3404 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0520694 \n", - "3405 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0563611 \n", - "3406 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0563612 \n", - "3407 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0563613 \n", - "3408 Gaza Floresta (semi-) decidua, incluindo Miombo GZ0563614 \n", - "3413 Gaza Mecrusse GZ-A-0446711\n", - "3414 Gaza Mecrusse GZ-A-0446712\n", - "3415 Gaza Mecrusse GZ-A-0446713\n", - "3416 Gaza Mecrusse GZ-A-0446714\n", - "3417 Gaza Floresta (semi-) decidua, incluindo Miombo GZ-A-0632381\n", - "3418 Gaza Floresta (semi-) decidua, incluindo Miombo GZ-A-0632382\n", - "3419 Gaza Floresta (semi-) decidua, incluindo Miombo GZ-A-0632383\n", - "3420 Gaza Floresta (semi-) decidua, incluindo Miombo GZ-A-0632384\n", - " id_plot_new Cluster Cluster_new Plot Vt.(m^3/ha) Vc.(m^3/ha)\n", - "1 11 1 1 1 50.1303939 3.3738418 \n", - "2 12 1 1 2 80.9101679 5.7177690 \n", - "3 13 1 1 3 112.9392918 8.5599657 \n", - "4 14 1 1 4 65.2971202 13.8819974 \n", - "5 21 2 2 1 40.1910456 6.1295265 \n", - "6 22 2 2 2 124.8337453 38.3703569 \n", - "7 23 2 2 3 0.0000000 0.0000000 \n", - "8 24 2 2 4 12.4778648 5.3156884 \n", - "9 41 4 4 1 32.0303735 0.0000000 \n", - "10 42 4 4 2 5.1160801 0.0000000 \n", - "11 43 4 4 3 7.1378218 0.0000000 \n", - "12 44 4 4 4 19.9746598 0.9664277 \n", - "13 61 6 6 1 122.6670035 35.4560191 \n", - "14 62 6 6 2 68.0539354 11.2161518 \n", - "15 63 6 6 3 47.2328506 10.6966009 \n", - "16 64 6 6 4 30.9214745 3.3977827 \n", - "17 71 7 7 1 71.0924167 12.3483489 \n", - "18 72 7 7 2 29.1466626 13.1863455 \n", - "19 73 7 7 3 35.1352469 15.7653957 \n", - "20 74 7 7 4 49.0069395 19.9850277 \n", - "21 81 8 8 1 3.4051486 1.3465824 \n", - "22 82 8 8 2 0.5374994 0.1624643 \n", - "23 83 8 8 3 0.3063053 0.3229557 \n", - "24 84 8 8 4 6.8627903 3.7032340 \n", - "25 91 9 9 1 12.5694527 5.1988954 \n", - "26 92 9 9 2 21.2269279 7.2721913 \n", - "27 93 9 9 3 24.6516330 6.3610340 \n", - "28 94 9 9 4 14.3877735 4.4894680 \n", - "29 101 10 10 1 20.7941060 9.4779588 \n", - "30 102 10 10 2 11.8873354 4.2776805 \n", - "â‹® â‹® â‹® â‹® â‹® â‹® â‹® \n", - "3387 9633 GZ016139 963 3 71.950107 32.813756 \n", - "3388 9634 GZ016139 963 4 83.186888 39.653112 \n", - "3389 9641 GZ016732 964 1 62.472793 29.197800 \n", - "3390 9642 GZ016732 964 2 89.146037 44.175837 \n", - "3391 9643 GZ016732 964 3 62.124359 21.482990 \n", - "3392 9644 GZ016732 964 4 136.954691 45.094165 \n", - "3393 9651 GZ021404 965 1 17.184141 7.436475 \n", - "3394 9652 GZ021404 965 2 0.000000 0.000000 \n", - "3395 9653 GZ021404 965 3 0.000000 0.000000 \n", - "3396 9654 GZ021404 965 4 32.484805 11.734260 \n", - "3397 9661 GZ037852 966 1 4.806654 2.005794 \n", - "3398 9662 GZ037852 966 2 4.584096 1.763882 \n", - "3399 9663 GZ037852 966 3 23.669968 6.137337 \n", - "3400 9664 GZ037852 966 4 2.999270 1.242073 \n", - "3401 9671 GZ052069 967 1 7.886993 0.221875 \n", - "3402 9672 GZ052069 967 2 47.108684 14.061800 \n", - "3403 9673 GZ052069 967 3 55.849702 19.109564 \n", - "3404 9674 GZ052069 967 4 52.685322 5.162312 \n", - "3405 9681 GZ056361 968 1 0.000000 0.000000 \n", - "3406 9682 GZ056361 968 2 27.186411 6.083705 \n", - "3407 9683 GZ056361 968 3 0.000000 0.000000 \n", - "3408 9684 GZ056361 968 4 0.000000 0.000000 \n", - "3413 9701 GZ-A-044671 970 1 88.097842 22.707252 \n", - "3414 9702 GZ-A-044671 970 2 0.000000 0.000000 \n", - "3415 9703 GZ-A-044671 970 3 0.000000 0.000000 \n", - "3416 9704 GZ-A-044671 970 4 0.000000 0.000000 \n", - "3417 9711 GZ-A-063238 971 1 6.961900 2.772908 \n", - "3418 9712 GZ-A-063238 971 2 180.051911 9.649130 \n", - "3419 9713 GZ-A-063238 971 3 49.580407 6.321964 \n", - "3420 9714 GZ-A-063238 971 4 27.958856 1.378058 \n", - " AGB.(ton/ha) BGB.(ton/ha) FOREST_STR geometry \n", - "1 56.0750397 20.7384320 Semi deciduous forest POINT (32.75884 -26.65277)\n", - "2 92.6641231 35.5032627 Semi deciduous forest POINT (32.75884 -26.65187)\n", - "3 112.8679614 36.9961788 Semi deciduous forest POINT (32.75984 -26.65187)\n", - "4 65.5911266 25.8603076 Semi deciduous forest POINT (32.75984 -26.65277)\n", - "5 54.1303691 15.1565034 Semi deciduous forest POINT (32.35752 -26.54318)\n", - "6 118.3816026 59.7754415 Semi deciduous forest POINT (32.35753 -26.54228)\n", - "7 0.0000000 0.0000000 Semi deciduous forest POINT (32.35853 -26.54228)\n", - "8 13.9045715 8.2079812 Semi deciduous forest POINT (32.35853 -26.54318)\n", - "9 29.3328399 17.7816999 Semi evergreen forest POINT (32.48352 -25.27953)\n", - "10 4.9007144 2.8053331 NA POINT (32.48352 -25.27862)\n", - "11 7.1910497 4.6953413 NA POINT (32.48451 -25.27863)\n", - "12 18.3825096 11.2088316 Semi evergreen forest POINT (32.48451 -25.27953)\n", - "13 177.3974614 49.6712892 Semi evergreen forest POINT (32.40511 -25.0625) \n", - "14 91.1731365 25.5284782 Semi evergreen forest POINT (32.40512 -25.06159)\n", - "15 59.6806678 16.7105870 Semi evergreen forest POINT (32.40611 -25.0616) \n", - "16 53.0650505 14.8582141 Semi evergreen forest POINT (32.40611 -25.0625) \n", - "17 125.1163321 35.0325730 Semi deciduous forest POINT (32.20753 -24.95321)\n", - "18 54.2099472 15.1787852 Semi deciduous forest POINT (32.20753 -24.95231)\n", - "19 38.2023376 20.5795378 Semi deciduous forest POINT (32.20852 -24.95231)\n", - "20 53.4575029 28.1497527 Semi deciduous forest POINT (32.20852 -24.95321)\n", - "21 2.7423855 1.9982884 NA POINT (32.32718 -24.8093) \n", - "22 0.4650103 0.3736289 NA POINT (32.32718 -24.8084) \n", - "23 0.2559933 0.2309457 NA POINT (32.32817 -24.8084) \n", - "24 6.2709382 4.7138889 NA POINT (32.32816 -24.80931)\n", - "25 12.7656530 5.9461055 Semi deciduous forest POINT (32.48608 -24.66544)\n", - "26 32.0594798 10.6079559 Semi deciduous forest POINT (32.48608 -24.66453)\n", - "27 42.2941803 12.3293093 Semi deciduous forest POINT (32.48707 -24.66454)\n", - "28 21.4999696 7.9196962 Semi deciduous forest POINT (32.48706 -24.66544)\n", - "29 20.4826005 12.6522886 Mopane POINT (32.52574 -24.62944)\n", - "30 15.4512510 9.6149010 Mopane POINT (32.52575 -24.62854)\n", - "â‹® â‹® â‹® â‹® â‹® \n", - "3387 59.094777 14.652350 Mecrusse POINT (32.18284 -22.7484) \n", - "3388 68.178365 17.085471 Mecrusse POINT (32.18284 -22.74931)\n", - "3389 52.118765 12.578029 Mecrusse POINT (32.20145 -22.73134)\n", - "3390 80.615671 19.941807 Mecrusse POINT (32.20145 -22.73043)\n", - "3391 60.751427 15.860857 Mecrusse POINT (32.20243 -22.73044)\n", - "3392 145.783008 37.433405 Mecrusse POINT (32.20242 -22.73134)\n", - "3393 15.084115 7.367507 NA POINT (32.34963 -22.28937)\n", - "3394 0.000000 0.000000 NA POINT (32.34963 -22.28847)\n", - "3395 0.000000 0.000000 NA POINT (32.3506 -22.28847) \n", - "3396 30.701611 11.926298 NA POINT (32.3506 -22.28938) \n", - "3397 3.306343 2.050370 Mopane POINT (32.83323 -23.71787)\n", - "3398 4.652407 2.230058 Mopane POINT (32.83323 -23.71697)\n", - "3399 32.598391 10.838602 Mopane POINT (32.83421 -23.71697)\n", - "3400 4.286961 1.660647 Mopane POINT (32.83421 -23.71787)\n", - "3401 6.881707 2.928738 Semi evergreen forest POINT (33.18603 -23.42877)\n", - "3402 46.241351 23.402341 Semi evergreen forest POINT (33.18603 -23.42787)\n", - "3403 54.593271 26.272914 Semi evergreen forest POINT (33.18701 -23.42787)\n", - "3404 50.841657 24.872779 Semi evergreen forest POINT (33.18701 -23.42877)\n", - "3405 0.000000 0.000000 NA POINT (33.29536 -24.17831)\n", - "3406 26.806063 13.714432 NA POINT (33.29536 -24.17741)\n", - "3407 0.000000 0.000000 NA POINT (33.29634 -24.17741)\n", - "3408 0.000000 0.000000 NA POINT (33.29635 -24.17831)\n", - "3413 67.068143 16.560189 Mopane POINT (33.00973 -21.93827)\n", - "3414 0.000000 0.000000 Mopane POINT (33.00973 -21.93737)\n", - "3415 0.000000 0.000000 Mopane POINT (33.0107 -21.93737) \n", - "3416 0.000000 0.000000 Mopane POINT (33.0107 -21.93827) \n", - "3417 7.028448 3.431783 Semi deciduous forest POINT (33.5275 -23.14799) \n", - "3418 171.303815 60.199839 Semi deciduous forest POINT (33.52749 -23.14709)\n", - "3419 48.235308 17.238029 Semi deciduous forest POINT (33.52847 -23.14708)\n", - "3420 25.743224 11.607162 Semi deciduous forest POINT (33.52847 -23.14799)" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -2324,10 +452,10 @@ "\n", "# Remove funny duplicated rows that appear from the previous procedure: \n", "\n", - "NFI_data_strata[duplicated(NFI_data_strata),]\n", + "# NFI_data_strata[duplicated(NFI_data_strata),]\n", "NFI_data_strata = NFI_data_strata[!duplicated(NFI_data_strata),]\n", - "nrow(NFI_data_strata)\n", - "as.data.frame(NFI_data_strata)\n", + "# nrow(NFI_data_strata)\n", + "# as.data.frame(NFI_data_strata)\n", "\n", "# Convert it back to a dataframe that includes the stratification info per plot\n", "\n", @@ -2339,311 +467,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "217e2df7-f2f3-42c1-a4e1-9cf145ff208b", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<table class=\"dataframe\">\n", - "<caption>A data.frame: 412 × 4</caption>\n", - "<thead>\n", - "\t<tr><th></th><th scope=col>id_plot_new</th><th scope=col>Cluster_new</th><th scope=col>FOREST_STR</th><th scope=col>FOREST_STR_NEW</th></tr>\n", - "\t<tr><th></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th></tr>\n", - "</thead>\n", - "<tbody>\n", - "\t<tr><th scope=row>21</th><td>81 </td><td>8 </td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>22</th><td>82 </td><td>8 </td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>23</th><td>83 </td><td>8 </td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>24</th><td>84 </td><td>8 </td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>81</th><td>261</td><td>26</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>82</th><td>262</td><td>26</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>83</th><td>263</td><td>26</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>84</th><td>264</td><td>26</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>113</th><td>351</td><td>35</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>114</th><td>352</td><td>35</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>115</th><td>353</td><td>35</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>116</th><td>354</td><td>35</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>157</th><td>471</td><td>47</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>158</th><td>472</td><td>47</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>159</th><td>473</td><td>47</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>160</th><td>474</td><td>47</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>173</th><td>521</td><td>52</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>174</th><td>522</td><td>52</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>175</th><td>523</td><td>52</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>176</th><td>524</td><td>52</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>177</th><td>531</td><td>53</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>178</th><td>532</td><td>53</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>179</th><td>533</td><td>53</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>180</th><td>534</td><td>53</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>265</th><td>791</td><td>79</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>266</th><td>792</td><td>79</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>267</th><td>793</td><td>79</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>268</th><td>794</td><td>79</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>277</th><td>841</td><td>84</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>278</th><td>842</td><td>84</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>â‹®</th><td>â‹®</td><td>â‹®</td><td>â‹®</td><td>â‹®</td></tr>\n", - "\t<tr><th scope=row>3211</th><td>9193</td><td>919</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3212</th><td>9194</td><td>919</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3277</th><td>9361</td><td>936</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3278</th><td>9362</td><td>936</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3279</th><td>9363</td><td>936</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3280</th><td>9364</td><td>936</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3301</th><td>9421</td><td>942</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3302</th><td>9422</td><td>942</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3303</th><td>9423</td><td>942</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3304</th><td>9424</td><td>942</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3313</th><td>9451</td><td>945</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3314</th><td>9452</td><td>945</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3315</th><td>9453</td><td>945</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3316</th><td>9454</td><td>945</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3317</th><td>9461</td><td>946</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3318</th><td>9462</td><td>946</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3319</th><td>9463</td><td>946</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3320</th><td>9464</td><td>946</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3381</th><td>9621</td><td>962</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3382</th><td>9622</td><td>962</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3383</th><td>9623</td><td>962</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3384</th><td>9624</td><td>962</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3393</th><td>9651</td><td>965</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3394</th><td>9652</td><td>965</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3395</th><td>9653</td><td>965</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3396</th><td>9654</td><td>965</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3405</th><td>9681</td><td>968</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3406</th><td>9682</td><td>968</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3407</th><td>9683</td><td>968</td><td>NA</td><td>NA</td></tr>\n", - "\t<tr><th scope=row>3408</th><td>9684</td><td>968</td><td>NA</td><td>NA</td></tr>\n", - "</tbody>\n", - "</table>\n" - ], - "text/latex": [ - "A data.frame: 412 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & id\\_plot\\_new & Cluster\\_new & FOREST\\_STR & FOREST\\_STR\\_NEW\\\\\n", - " & <chr> & <chr> & <chr> & <chr>\\\\\n", - "\\hline\n", - "\t21 & 81 & 8 & NA & NA\\\\\n", - "\t22 & 82 & 8 & NA & NA\\\\\n", - "\t23 & 83 & 8 & NA & NA\\\\\n", - "\t24 & 84 & 8 & NA & NA\\\\\n", - "\t81 & 261 & 26 & NA & NA\\\\\n", - "\t82 & 262 & 26 & NA & NA\\\\\n", - "\t83 & 263 & 26 & NA & NA\\\\\n", - "\t84 & 264 & 26 & NA & NA\\\\\n", - "\t113 & 351 & 35 & NA & NA\\\\\n", - "\t114 & 352 & 35 & NA & NA\\\\\n", - "\t115 & 353 & 35 & NA & NA\\\\\n", - "\t116 & 354 & 35 & NA & NA\\\\\n", - "\t157 & 471 & 47 & NA & NA\\\\\n", - "\t158 & 472 & 47 & NA & NA\\\\\n", - "\t159 & 473 & 47 & NA & NA\\\\\n", - "\t160 & 474 & 47 & NA & NA\\\\\n", - "\t173 & 521 & 52 & NA & NA\\\\\n", - "\t174 & 522 & 52 & NA & NA\\\\\n", - "\t175 & 523 & 52 & NA & NA\\\\\n", - "\t176 & 524 & 52 & NA & NA\\\\\n", - "\t177 & 531 & 53 & NA & NA\\\\\n", - "\t178 & 532 & 53 & NA & NA\\\\\n", - "\t179 & 533 & 53 & NA & NA\\\\\n", - "\t180 & 534 & 53 & NA & NA\\\\\n", - "\t265 & 791 & 79 & NA & NA\\\\\n", - "\t266 & 792 & 79 & NA & NA\\\\\n", - "\t267 & 793 & 79 & NA & NA\\\\\n", - "\t268 & 794 & 79 & NA & NA\\\\\n", - "\t277 & 841 & 84 & NA & NA\\\\\n", - "\t278 & 842 & 84 & NA & NA\\\\\n", - "\tâ‹® & â‹® & â‹® & â‹® & â‹®\\\\\n", - "\t3211 & 9193 & 919 & NA & NA\\\\\n", - "\t3212 & 9194 & 919 & NA & NA\\\\\n", - "\t3277 & 9361 & 936 & NA & NA\\\\\n", - "\t3278 & 9362 & 936 & NA & NA\\\\\n", - "\t3279 & 9363 & 936 & NA & NA\\\\\n", - "\t3280 & 9364 & 936 & NA & NA\\\\\n", - "\t3301 & 9421 & 942 & NA & NA\\\\\n", - "\t3302 & 9422 & 942 & NA & NA\\\\\n", - "\t3303 & 9423 & 942 & NA & NA\\\\\n", - "\t3304 & 9424 & 942 & NA & NA\\\\\n", - "\t3313 & 9451 & 945 & NA & NA\\\\\n", - "\t3314 & 9452 & 945 & NA & NA\\\\\n", - "\t3315 & 9453 & 945 & NA & NA\\\\\n", - "\t3316 & 9454 & 945 & NA & NA\\\\\n", - "\t3317 & 9461 & 946 & NA & NA\\\\\n", - "\t3318 & 9462 & 946 & NA & NA\\\\\n", - "\t3319 & 9463 & 946 & NA & NA\\\\\n", - "\t3320 & 9464 & 946 & NA & NA\\\\\n", - "\t3381 & 9621 & 962 & NA & NA\\\\\n", - "\t3382 & 9622 & 962 & NA & NA\\\\\n", - "\t3383 & 9623 & 962 & NA & NA\\\\\n", - "\t3384 & 9624 & 962 & NA & NA\\\\\n", - "\t3393 & 9651 & 965 & NA & NA\\\\\n", - "\t3394 & 9652 & 965 & NA & NA\\\\\n", - "\t3395 & 9653 & 965 & NA & NA\\\\\n", - "\t3396 & 9654 & 965 & NA & NA\\\\\n", - "\t3405 & 9681 & 968 & NA & NA\\\\\n", - "\t3406 & 9682 & 968 & NA & NA\\\\\n", - "\t3407 & 9683 & 968 & NA & NA\\\\\n", - "\t3408 & 9684 & 968 & NA & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 412 × 4\n", - "\n", - "| <!--/--> | id_plot_new <chr> | Cluster_new <chr> | FOREST_STR <chr> | FOREST_STR_NEW <chr> |\n", - "|---|---|---|---|---|\n", - "| 21 | 81 | 8 | NA | NA |\n", - "| 22 | 82 | 8 | NA | NA |\n", - "| 23 | 83 | 8 | NA | NA |\n", - "| 24 | 84 | 8 | NA | NA |\n", - "| 81 | 261 | 26 | NA | NA |\n", - "| 82 | 262 | 26 | NA | NA |\n", - "| 83 | 263 | 26 | NA | NA |\n", - "| 84 | 264 | 26 | NA | NA |\n", - "| 113 | 351 | 35 | NA | NA |\n", - "| 114 | 352 | 35 | NA | NA |\n", - "| 115 | 353 | 35 | NA | NA |\n", - "| 116 | 354 | 35 | NA | NA |\n", - "| 157 | 471 | 47 | NA | NA |\n", - "| 158 | 472 | 47 | NA | NA |\n", - "| 159 | 473 | 47 | NA | NA |\n", - "| 160 | 474 | 47 | NA | NA |\n", - "| 173 | 521 | 52 | NA | NA |\n", - "| 174 | 522 | 52 | NA | NA |\n", - "| 175 | 523 | 52 | NA | NA |\n", - "| 176 | 524 | 52 | NA | NA |\n", - "| 177 | 531 | 53 | NA | NA |\n", - "| 178 | 532 | 53 | NA | NA |\n", - "| 179 | 533 | 53 | NA | NA |\n", - "| 180 | 534 | 53 | NA | NA |\n", - "| 265 | 791 | 79 | NA | NA |\n", - "| 266 | 792 | 79 | NA | NA |\n", - "| 267 | 793 | 79 | NA | NA |\n", - "| 268 | 794 | 79 | NA | NA |\n", - "| 277 | 841 | 84 | NA | NA |\n", - "| 278 | 842 | 84 | NA | NA |\n", - "| â‹® | â‹® | â‹® | â‹® | â‹® |\n", - "| 3211 | 9193 | 919 | NA | NA |\n", - "| 3212 | 9194 | 919 | NA | NA |\n", - "| 3277 | 9361 | 936 | NA | NA |\n", - "| 3278 | 9362 | 936 | NA | NA |\n", - "| 3279 | 9363 | 936 | NA | NA |\n", - "| 3280 | 9364 | 936 | NA | NA |\n", - "| 3301 | 9421 | 942 | NA | NA |\n", - "| 3302 | 9422 | 942 | NA | NA |\n", - "| 3303 | 9423 | 942 | NA | NA |\n", - "| 3304 | 9424 | 942 | NA | NA |\n", - "| 3313 | 9451 | 945 | NA | NA |\n", - "| 3314 | 9452 | 945 | NA | NA |\n", - "| 3315 | 9453 | 945 | NA | NA |\n", - "| 3316 | 9454 | 945 | NA | NA |\n", - "| 3317 | 9461 | 946 | NA | NA |\n", - "| 3318 | 9462 | 946 | NA | NA |\n", - "| 3319 | 9463 | 946 | NA | NA |\n", - "| 3320 | 9464 | 946 | NA | NA |\n", - "| 3381 | 9621 | 962 | NA | NA |\n", - "| 3382 | 9622 | 962 | NA | NA |\n", - "| 3383 | 9623 | 962 | NA | NA |\n", - "| 3384 | 9624 | 962 | NA | NA |\n", - "| 3393 | 9651 | 965 | NA | NA |\n", - "| 3394 | 9652 | 965 | NA | NA |\n", - "| 3395 | 9653 | 965 | NA | NA |\n", - "| 3396 | 9654 | 965 | NA | NA |\n", - "| 3405 | 9681 | 968 | NA | NA |\n", - "| 3406 | 9682 | 968 | NA | NA |\n", - "| 3407 | 9683 | 968 | NA | NA |\n", - "| 3408 | 9684 | 968 | NA | NA |\n", - "\n" - ], - "text/plain": [ - " id_plot_new Cluster_new FOREST_STR FOREST_STR_NEW\n", - "21 81 8 NA NA \n", - "22 82 8 NA NA \n", - "23 83 8 NA NA \n", - "24 84 8 NA NA \n", - "81 261 26 NA NA \n", - "82 262 26 NA NA \n", - "83 263 26 NA NA \n", - "84 264 26 NA NA \n", - "113 351 35 NA NA \n", - "114 352 35 NA NA \n", - "115 353 35 NA NA \n", - "116 354 35 NA NA \n", - "157 471 47 NA NA \n", - "158 472 47 NA NA \n", - "159 473 47 NA NA \n", - "160 474 47 NA NA \n", - "173 521 52 NA NA \n", - "174 522 52 NA NA \n", - "175 523 52 NA NA \n", - "176 524 52 NA NA \n", - "177 531 53 NA NA \n", - "178 532 53 NA NA \n", - "179 533 53 NA NA \n", - "180 534 53 NA NA \n", - "265 791 79 NA NA \n", - "266 792 79 NA NA \n", - "267 793 79 NA NA \n", - "268 794 79 NA NA \n", - "277 841 84 NA NA \n", - "278 842 84 NA NA \n", - "â‹® â‹® â‹® â‹® â‹® \n", - "3211 9193 919 NA NA \n", - "3212 9194 919 NA NA \n", - "3277 9361 936 NA NA \n", - "3278 9362 936 NA NA \n", - "3279 9363 936 NA NA \n", - "3280 9364 936 NA NA \n", - "3301 9421 942 NA NA \n", - "3302 9422 942 NA NA \n", - "3303 9423 942 NA NA \n", - "3304 9424 942 NA NA \n", - "3313 9451 945 NA NA \n", - "3314 9452 945 NA NA \n", - "3315 9453 945 NA NA \n", - "3316 9454 945 NA NA \n", - "3317 9461 946 NA NA \n", - "3318 9462 946 NA NA \n", - "3319 9463 946 NA NA \n", - "3320 9464 946 NA NA \n", - "3381 9621 962 NA NA \n", - "3382 9622 962 NA NA \n", - "3383 9623 962 NA NA \n", - "3384 9624 962 NA NA \n", - "3393 9651 965 NA NA \n", - "3394 9652 965 NA NA \n", - "3395 9653 965 NA NA \n", - "3396 9654 965 NA NA \n", - "3405 9681 968 NA NA \n", - "3406 9682 968 NA NA \n", - "3407 9683 968 NA NA \n", - "3408 9684 968 NA NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "412" - ], - "text/latex": [ - "412" - ], - "text/markdown": [ - "412" - ], - "text/plain": [ - "[1] 412" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# 4. Re-define strata so all clusters fall within the same strata.\n", "\n", @@ -2666,12 +495,12 @@ "# Additional step: remove clusters with Strata=NA\n", "# \"Clusters that did not fall within the four forest strata as defined by the re-classified Agro-ecological map (our area of interest) were disregarded, assuming they had been moved and departed from the original design.\"\n", "\n", - "NFI_data_df[(is.na(NFI_data_df$FOREST_STR_NEW)),c(4,6,12,15)] # daniela: change columns if these are not the ones of interest\n", + "# NFI_data_df[(is.na(NFI_data_df$FOREST_STR_NEW)),c(4,6,12,15)]\n", "\n", "# Remove rows with NA for strata\n", "\n", "NFI_data_n=NFI_data_df[!is.na(NFI_data_df$FOREST_STR_NEW),]\n", - "length(NFI_data_df$FOREST_STR_NEW)-length(NFI_data_n$FOREST_STR_NEW)\n", + "# length(NFI_data_df$FOREST_STR_NEW)-length(NFI_data_n$FOREST_STR_NEW)\n", "\n", "# Save the cleaned database `NFI_data_n`\n", "\n", @@ -2680,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "id": "3b1be31e-f763-4dcc-a166-cf1c97127574", "metadata": {}, "outputs": [ @@ -2782,19 +611,6 @@ "Geodetic CRS: WGS 84\n" ] }, - { - "data": { - "text/plain": [ - "class : Extent \n", - "xmin : 30.21215 \n", - "xmax : 40.84063 \n", - "ymin : -26.86644 \n", - "ymax : -10.4722 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "image/png": 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lrTJI/sVWPHaKhajQ1Dr1bmUdP6W3C05sOQnqDZZkhabNEm4vYAgJ5Kzvzw6kTs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eSk+rM93uoSfYkk6wC/pSD1ZU68EeMD3g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\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dZm8VjR3tKmjfrAF6n1PZZFp0SvX4//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\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rao8wt0MIf5viIjIVhdxv5NH3h/xoza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@@ -2973,7 +789,7 @@ "# Load the stratification layer and check its extent and coordinate reference system\n", "\n", "Moz_strata_geo<-st_read(\"/projects/my-private-bucket/Data/NFI_data/Mozambique/Strata_reclassify_geogra.shp\") #WGS_84\n", - "extent(Moz_strata_geo)\n", + "# extent(Moz_strata_geo)\n", "plot(Moz_strata_geo[\"FOREST_STR\"])\n", "\n", "# Crop `CCI_2017`, and then force again the extent in order for the raster to overlay \n", @@ -3050,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "id": "a7d3cc5f-9f9b-47f7-8740-6372ba8b9122", "metadata": {}, "outputs": [ @@ -3072,31 +888,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "data": { - "text/html": [ - "446" - ], - "text/latex": [ - "446" - ], - "text/markdown": [ - "446" - ], - "text/plain": [ - "[1] 446" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " num [1:446] 33.7 25.7 22.1 23 19.6 ...\n" 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scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th></tr>\n", - "</thead>\n", - "<tbody>\n", - "\t<tr><th scope=row>9</th><td>Maputo </td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>41 </td><td>41 </td><td>4 </td><td>4 </td><td>1</td><td> 32.030373</td><td> 0.000000</td><td> 29.332840</td><td>17.78170</td><td>Semi evergreen forest</td><td>32.48352</td><td>-25.27953</td><td>Semi evergreen forest</td><td>35.822230</td></tr>\n", - "\t<tr><th scope=row>13</th><td>Maputo </td><td>Floresta (semi-) sempreverde </td><td>61 </td><td>61 </td><td>6 </td><td>6 </td><td>1</td><td>122.667004</td><td>35.456019</td><td>177.397461</td><td>49.67129</td><td>Semi evergreen forest</td><td>32.40511</td><td>-25.06250</td><td>Semi evergreen forest</td><td>22.646070</td></tr>\n", - "\t<tr><th scope=row>81</th><td>Inhambane</td><td>Mecrusse </td><td>291</td><td>291</td><td>29</td><td>29</td><td>1</td><td>151.706978</td><td>50.557943</td><td>218.266392</td><td>57.60258</td><td>Semi evergreen forest</td><td>34.49358</td><td>-23.93650</td><td>Semi evergreen forest</td><td>36.067244</td></tr>\n", - "\t<tr><th scope=row>89</th><td>Inhambane</td><td>Floresta (semi-) sempreverde </td><td>311</td><td>311</td><td>31</td><td>31</td><td>1</td><td> 6.041614</td><td> 2.078271</td><td> 6.725606</td><td> 3.77981</td><td>Semi evergreen forest</td><td>34.61146</td><td>-23.93531</td><td>Semi evergreen forest</td><td> 6.212954</td></tr>\n", - "\t<tr><th scope=row>93</th><td>Inhambane</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>321</td><td>321</td><td>32</td><td>32</td><td>1</td><td> 54.853805</td><td>20.512851</td><td> 53.216208</td><td>27.52943</td><td>Semi evergreen forest</td><td>34.45388</td><td>-23.90076</td><td>Semi evergreen forest</td><td>39.787049</td></tr>\n", - "\t<tr><th scope=row>101</th><td>Inhambane</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>341</td><td>341</td><td>34</td><td>34</td><td>1</td><td> 65.269865</td><td>33.140545</td><td> 61.315836</td><td>26.75058</td><td>Semi evergreen forest</td><td>34.49317</td><td>-23.90038</td><td>Semi evergreen forest</td><td>12.385105</td></tr>\n", - "</tbody>\n", - "</table>\n" - ], - "text/latex": [ - "A data.frame: 6 × 16\n", - "\\begin{tabular}{r|llllllllllllllll}\n", - " & ProvÃncia & Estrato.Florestal & id\\_parcela & id\\_plot\\_new & Cluster & Cluster\\_new & Plot & Vt..m.3.ha. & Vc..m.3.ha. & AGB..ton.ha. & BGB..ton.ha. & FOREST\\_STR & x & y & FOREST\\_STR\\_NEW & MapBiom\\_Pol\\\\\n", - " & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <chr> & <dbl>\\\\\n", - "\\hline\n", - "\t9 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 41 & 41 & 4 & 4 & 1 & 32.030373 & 0.000000 & 29.332840 & 17.78170 & Semi evergreen forest & 32.48352 & -25.27953 & Semi evergreen forest & 35.822230\\\\\n", - "\t13 & Maputo & Floresta (semi-) sempreverde & 61 & 61 & 6 & 6 & 1 & 122.667004 & 35.456019 & 177.397461 & 49.67129 & Semi evergreen forest & 32.40511 & -25.06250 & Semi evergreen forest & 22.646070\\\\\n", - "\t81 & Inhambane & Mecrusse & 291 & 291 & 29 & 29 & 1 & 151.706978 & 50.557943 & 218.266392 & 57.60258 & Semi evergreen forest & 34.49358 & -23.93650 & Semi evergreen forest & 36.067244\\\\\n", - "\t89 & Inhambane & Floresta (semi-) sempreverde & 311 & 311 & 31 & 31 & 1 & 6.041614 & 2.078271 & 6.725606 & 3.77981 & Semi evergreen forest & 34.61146 & -23.93531 & Semi evergreen forest & 6.212954\\\\\n", - "\t93 & Inhambane & Floresta (semi-) decidua, incluindo Miombo & 321 & 321 & 32 & 32 & 1 & 54.853805 & 20.512851 & 53.216208 & 27.52943 & Semi evergreen forest & 34.45388 & -23.90076 & Semi evergreen forest & 39.787049\\\\\n", - "\t101 & Inhambane & Floresta (semi-) decidua, incluindo Miombo & 341 & 341 & 34 & 34 & 1 & 65.269865 & 33.140545 & 61.315836 & 26.75058 & Semi evergreen forest & 34.49317 & -23.90038 & Semi evergreen forest & 12.385105\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 16\n", - "\n", - "| <!--/--> | ProvÃncia <chr> | Estrato.Florestal <chr> | id_parcela <chr> | id_plot_new <chr> | Cluster <chr> | Cluster_new <chr> | Plot <dbl> | Vt..m.3.ha. <dbl> | Vc..m.3.ha. <dbl> | AGB..ton.ha. <dbl> | BGB..ton.ha. <dbl> | FOREST_STR <chr> | x <dbl> | y <dbl> | FOREST_STR_NEW <chr> | MapBiom_Pol <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 9 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 41 | 41 | 4 | 4 | 1 | 32.030373 | 0.000000 | 29.332840 | 17.78170 | Semi evergreen forest | 32.48352 | -25.27953 | Semi evergreen forest | 35.822230 |\n", - "| 13 | Maputo | Floresta (semi-) sempreverde | 61 | 61 | 6 | 6 | 1 | 122.667004 | 35.456019 | 177.397461 | 49.67129 | Semi evergreen forest | 32.40511 | -25.06250 | Semi evergreen forest | 22.646070 |\n", - "| 81 | Inhambane | Mecrusse | 291 | 291 | 29 | 29 | 1 | 151.706978 | 50.557943 | 218.266392 | 57.60258 | Semi evergreen forest | 34.49358 | -23.93650 | Semi evergreen forest | 36.067244 |\n", - "| 89 | Inhambane | Floresta (semi-) sempreverde | 311 | 311 | 31 | 31 | 1 | 6.041614 | 2.078271 | 6.725606 | 3.77981 | Semi evergreen forest | 34.61146 | -23.93531 | Semi evergreen forest | 6.212954 |\n", - "| 93 | Inhambane | Floresta (semi-) decidua, incluindo Miombo | 321 | 321 | 32 | 32 | 1 | 54.853805 | 20.512851 | 53.216208 | 27.52943 | Semi evergreen forest | 34.45388 | -23.90076 | Semi evergreen forest | 39.787049 |\n", - "| 101 | Inhambane | Floresta (semi-) decidua, incluindo Miombo | 341 | 341 | 34 | 34 | 1 | 65.269865 | 33.140545 | 61.315836 | 26.75058 | Semi evergreen forest | 34.49317 | -23.90038 | Semi evergreen forest | 12.385105 |\n", - "\n" - ], - "text/plain": [ - " ProvÃncia Estrato.Florestal id_parcela id_plot_new\n", - "9 Maputo Floresta (semi-) decidua, incluindo Miombo 41 41 \n", - "13 Maputo Floresta (semi-) sempreverde 61 61 \n", - "81 Inhambane Mecrusse 291 291 \n", - "89 Inhambane Floresta (semi-) sempreverde 311 311 \n", - "93 Inhambane Floresta (semi-) decidua, incluindo Miombo 321 321 \n", - "101 Inhambane Floresta (semi-) decidua, incluindo Miombo 341 341 \n", - " Cluster Cluster_new Plot Vt..m.3.ha. Vc..m.3.ha. AGB..ton.ha. BGB..ton.ha.\n", - "9 4 4 1 32.030373 0.000000 29.332840 17.78170 \n", - "13 6 6 1 122.667004 35.456019 177.397461 49.67129 \n", - "81 29 29 1 151.706978 50.557943 218.266392 57.60258 \n", - "89 31 31 1 6.041614 2.078271 6.725606 3.77981 \n", - "93 32 32 1 54.853805 20.512851 53.216208 27.52943 \n", - "101 34 34 1 65.269865 33.140545 61.315836 26.75058 \n", - " FOREST_STR x y FOREST_STR_NEW MapBiom_Pol\n", - "9 Semi evergreen forest 32.48352 -25.27953 Semi evergreen forest 35.822230 \n", - "13 Semi evergreen forest 32.40511 -25.06250 Semi evergreen forest 22.646070 \n", - "81 Semi evergreen forest 34.49358 -23.93650 Semi evergreen forest 36.067244 \n", - "89 Semi evergreen forest 34.61146 -23.93531 Semi evergreen forest 6.212954 \n", - "93 Semi evergreen forest 34.45388 -23.90076 Semi evergreen forest 39.787049 \n", - "101 Semi evergreen forest 34.49317 -23.90038 Semi evergreen forest 12.385105 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "\n", - "FALSE \n", - " 713 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "<table class=\"dataframe\">\n", - "<caption>A data.frame: 6 × 15</caption>\n", - "<thead>\n", - "\t<tr><th></th><th scope=col>ProvÃncia</th><th scope=col>Estrato.Florestal</th><th scope=col>id_parcela</th><th scope=col>id_plot_new</th><th scope=col>Cluster</th><th scope=col>Cluster_new</th><th scope=col>Plot</th><th scope=col>Vt.(m^3/ha)</th><th scope=col>Vc.(m^3/ha)</th><th scope=col>subplot_AGB</th><th scope=col>BGB.(ton/ha)</th><th scope=col>FOREST_STR</th><th scope=col>x</th><th scope=col>y</th><th scope=col>FOREST_STR_NEW</th></tr>\n", - "\t<tr><th></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th></tr>\n", - "</thead>\n", - "<tbody>\n", - "\t<tr><th scope=row>1</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>11</td><td>11</td><td>1</td><td>1</td><td>1</td><td> 50.13039</td><td> 3.373842</td><td> 56.07504</td><td>20.73843</td><td>Semi deciduous forest</td><td>32.75884</td><td>-26.65277</td><td>Semi deciduous forest</td></tr>\n", - "\t<tr><th scope=row>2</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>12</td><td>12</td><td>1</td><td>1</td><td>2</td><td> 80.91017</td><td> 5.717769</td><td> 92.66412</td><td>35.50326</td><td>Semi deciduous forest</td><td>32.75884</td><td>-26.65187</td><td>Semi deciduous forest</td></tr>\n", - "\t<tr><th scope=row>3</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>13</td><td>13</td><td>1</td><td>1</td><td>3</td><td>112.93929</td><td> 8.559966</td><td>112.86796</td><td>36.99618</td><td>Semi deciduous forest</td><td>32.75984</td><td>-26.65187</td><td>Semi deciduous forest</td></tr>\n", - "\t<tr><th scope=row>4</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>14</td><td>14</td><td>1</td><td>1</td><td>4</td><td> 65.29712</td><td>13.881997</td><td> 65.59113</td><td>25.86031</td><td>Semi deciduous forest</td><td>32.75984</td><td>-26.65277</td><td>Semi deciduous forest</td></tr>\n", - "\t<tr><th scope=row>5</th><td>Maputo</td><td>Floresta (semi-) sempreverde </td><td>21</td><td>21</td><td>2</td><td>2</td><td>1</td><td> 40.19105</td><td> 6.129526</td><td> 54.13037</td><td>15.15650</td><td>Semi deciduous forest</td><td>32.35752</td><td>-26.54318</td><td>Semi deciduous forest</td></tr>\n", - "\t<tr><th scope=row>6</th><td>Maputo</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>22</td><td>22</td><td>2</td><td>2</td><td>2</td><td>124.83375</td><td>38.370357</td><td>118.38160</td><td>59.77544</td><td>Semi deciduous forest</td><td>32.35753</td><td>-26.54228</td><td>Semi deciduous forest</td></tr>\n", - "</tbody>\n", - "</table>\n" - ], - "text/latex": [ - "A data.frame: 6 × 15\n", - "\\begin{tabular}{r|lllllllllllllll}\n", - " & ProvÃncia & Estrato.Florestal & id\\_parcela & id\\_plot\\_new & Cluster & Cluster\\_new & Plot & Vt.(m\\textasciicircum{}3/ha) & Vc.(m\\textasciicircum{}3/ha) & subplot\\_AGB & BGB.(ton/ha) & FOREST\\_STR & x & y & FOREST\\_STR\\_NEW\\\\\n", - " & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <chr>\\\\\n", - "\\hline\n", - "\t1 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 11 & 11 & 1 & 1 & 1 & 50.13039 & 3.373842 & 56.07504 & 20.73843 & Semi deciduous forest & 32.75884 & -26.65277 & Semi deciduous forest\\\\\n", - "\t2 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 12 & 12 & 1 & 1 & 2 & 80.91017 & 5.717769 & 92.66412 & 35.50326 & Semi deciduous forest & 32.75884 & -26.65187 & Semi deciduous forest\\\\\n", - "\t3 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 13 & 13 & 1 & 1 & 3 & 112.93929 & 8.559966 & 112.86796 & 36.99618 & Semi deciduous forest & 32.75984 & -26.65187 & Semi deciduous forest\\\\\n", - "\t4 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 14 & 14 & 1 & 1 & 4 & 65.29712 & 13.881997 & 65.59113 & 25.86031 & Semi deciduous forest & 32.75984 & -26.65277 & Semi deciduous forest\\\\\n", - "\t5 & Maputo & Floresta (semi-) sempreverde & 21 & 21 & 2 & 2 & 1 & 40.19105 & 6.129526 & 54.13037 & 15.15650 & Semi deciduous forest & 32.35752 & -26.54318 & Semi deciduous forest\\\\\n", - "\t6 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 22 & 22 & 2 & 2 & 2 & 124.83375 & 38.370357 & 118.38160 & 59.77544 & Semi deciduous forest & 32.35753 & -26.54228 & Semi deciduous forest\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 15\n", - "\n", - "| <!--/--> | ProvÃncia <chr> | Estrato.Florestal <chr> | id_parcela <chr> | id_plot_new <chr> | Cluster <chr> | Cluster_new <chr> | Plot <dbl> | Vt.(m^3/ha) <dbl> | Vc.(m^3/ha) <dbl> | subplot_AGB <dbl> | BGB.(ton/ha) <dbl> | FOREST_STR <chr> | x <dbl> | y <dbl> | FOREST_STR_NEW <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 11 | 11 | 1 | 1 | 1 | 50.13039 | 3.373842 | 56.07504 | 20.73843 | Semi deciduous forest | 32.75884 | -26.65277 | Semi deciduous forest |\n", - "| 2 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 12 | 12 | 1 | 1 | 2 | 80.91017 | 5.717769 | 92.66412 | 35.50326 | Semi deciduous forest | 32.75884 | -26.65187 | Semi deciduous forest |\n", - "| 3 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 13 | 13 | 1 | 1 | 3 | 112.93929 | 8.559966 | 112.86796 | 36.99618 | Semi deciduous forest | 32.75984 | -26.65187 | Semi deciduous forest |\n", - "| 4 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 14 | 14 | 1 | 1 | 4 | 65.29712 | 13.881997 | 65.59113 | 25.86031 | Semi deciduous forest | 32.75984 | -26.65277 | Semi deciduous forest |\n", - "| 5 | Maputo | Floresta (semi-) sempreverde | 21 | 21 | 2 | 2 | 1 | 40.19105 | 6.129526 | 54.13037 | 15.15650 | Semi deciduous forest | 32.35752 | -26.54318 | Semi deciduous forest |\n", - "| 6 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 22 | 22 | 2 | 2 | 2 | 124.83375 | 38.370357 | 118.38160 | 59.77544 | Semi deciduous forest | 32.35753 | -26.54228 | Semi deciduous forest |\n", - "\n" - ], - "text/plain": [ - " ProvÃncia Estrato.Florestal id_parcela id_plot_new\n", - "1 Maputo Floresta (semi-) decidua, incluindo Miombo 11 11 \n", - "2 Maputo Floresta (semi-) decidua, incluindo Miombo 12 12 \n", - "3 Maputo Floresta (semi-) decidua, incluindo Miombo 13 13 \n", - "4 Maputo Floresta (semi-) decidua, incluindo Miombo 14 14 \n", - "5 Maputo Floresta (semi-) sempreverde 21 21 \n", - "6 Maputo Floresta (semi-) decidua, incluindo Miombo 22 22 \n", - " Cluster Cluster_new Plot Vt.(m^3/ha) Vc.(m^3/ha) subplot_AGB BGB.(ton/ha)\n", - "1 1 1 1 50.13039 3.373842 56.07504 20.73843 \n", - "2 1 1 2 80.91017 5.717769 92.66412 35.50326 \n", - "3 1 1 3 112.93929 8.559966 112.86796 36.99618 \n", - "4 1 1 4 65.29712 13.881997 65.59113 25.86031 \n", - "5 2 2 1 40.19105 6.129526 54.13037 15.15650 \n", - "6 2 2 2 124.83375 38.370357 118.38160 59.77544 \n", - " FOREST_STR x y FOREST_STR_NEW \n", - "1 Semi deciduous forest 32.75884 -26.65277 Semi deciduous forest\n", - "2 Semi deciduous forest 32.75884 -26.65187 Semi deciduous forest\n", - "3 Semi deciduous forest 32.75984 -26.65187 Semi deciduous forest\n", - "4 Semi deciduous forest 32.75984 -26.65277 Semi deciduous forest\n", - "5 Semi deciduous forest 32.35752 -26.54318 Semi deciduous forest\n", - "6 Semi deciduous forest 32.35753 -26.54228 Semi deciduous forest" + "[1] TRUE" ] }, "metadata": {}, @@ -3449,7 +1006,7 @@ "# Subset plots in the corresponding forest strata, select coordinates\n", "\n", "Pol_subset_sdf<-NFI_data[(NFI_data$FOREST_STR_NEW==\"Semi deciduous forest\"&NFI_data$Plot==1),c(\"x\",\"y\")] \n", - "nrow(Pol_subset_sdf)\n", + "# nrow(Pol_subset_sdf)\n", "\n", "\n", "# Using the previous functions, create the polygons (PSUs), extract the CCI biomass maps values for the pixels intersecting the PSUs and calculate mean weighted mean of those pixel values\n", @@ -3463,7 +1020,7 @@ "\n", "\n", "# Create `Mapbiomass_SDF` to store PSUs map value means \n", - "str(Mapbiomass_SDF)\n", + "# str(Mapbiomass_SDF)\n", "\n", "\n", "# Now, repeat the procedure for the other 3 remaining strata\n", @@ -3473,7 +1030,7 @@ "\n", "# Select the SW plot coordinate of every cluster\n", "Pol_subset_sef<-NFI_data[(NFI_data$FOREST_STR_NEW==\"Semi evergreen forest\"&NFI_data$Plot==1),c(\"x\",\"y\")]\n", - "nrow(Pol_subset_sef)\n", + "# nrow(Pol_subset_sef)\n", "\n", "# Create polygons and calculate mean CCI Biomass values\n", "polygon_Tmp=c() \n", @@ -3485,7 +1042,7 @@ "\n", "\n", "# Store those PSUs map value means as stored as `Mapbiomass_SEF`\n", - "str(Mapbiomass_SEF)\n", + "# str(Mapbiomass_SEF)\n", "\n", "# * Mercrusse Forest (ME): PSU size = 198m x 198 m\n", "CCIbi<-\"/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_Mecrusse.tif\" #change for each ecozone\n", @@ -3493,7 +1050,7 @@ "\n", "# Select the SW plot coordinate of every cluster\n", "Pol_subset_Me<-NFI_data[(NFI_data$FOREST_STR_NEW==\"Mecrusse\"&NFI_data$Plot==1),c(\"x\",\"y\")] \n", - "nrow(Pol_subset_Me)\n", + "# nrow(Pol_subset_Me)\n", "\n", "\n", "# Create polygons and calculate mean CCI Biomass values\n", @@ -3506,7 +1063,7 @@ "\n", "\n", "# Store those PSUs map value means as stored as `Mapbiomass_ME`\n", - "str(Mapbiomass_ME)\n", + "# str(Mapbiomass_ME)\n", "\n", "\n", "# * Mopane Forests (MO): PSU size = 198m x 198 m\n", @@ -3516,7 +1073,7 @@ "\n", "# Select the SW plot coordinate of every cluster\n", "Pol_subset_Mo<-NFI_data[(NFI_data$FOREST_STR_NEW==\"Mopane\"&NFI_data$Plot==1),c(\"x\",\"y\")]\n", - "nrow(Pol_subset_Mo)\n", + "# nrow(Pol_subset_Mo)\n", "\n", "# Create polygons and calculate mean CCI Biomass values\n", "polygon_Tmp=c() \n", @@ -3528,7 +1085,7 @@ "\n", "\n", "# Store those PSUs map value means as stored as `Mapbiomass_MO`\n", - "str(Mapbiomass_MO)\n", + "# str(Mapbiomass_MO)\n", "\n", "\n", "# 3. Bind all data frames\n", @@ -3539,18 +1096,17 @@ "Mopane_Biom<-data.frame((NFI_data[(NFI_data$FOREST_STR_NEW==\"Mopane\"&NFI_data$Plot==1),]),MapBiom_Pol=Mapbiomass_MO)\n", "Mecrusse_Biom<-data.frame((NFI_data[(NFI_data$FOREST_STR_NEW==\"Mecrusse\"&NFI_data$Plot==1),]),MapBiom_Pol=Mapbiomass_ME)\n", "All_Biom<-rbind(SEF_Biom,SDF_Biom,Mopane_Biom,Mecrusse_Biom)\n", - "head(All_Biom)\n", + "# head(All_Biom)\n", "All_Biom<-All_Biom[order(All_Biom$id_plot_new),]\n", - "table(is.na(All_Biom$MapBiom_Pol))\n", + "# table(is.na(All_Biom$MapBiom_Pol))\n", "\n", "\n", "# In this final step, we compute the mean AGB over the plots within the i-th PSU (NFI_cluster_mean) and the mean AGB over the CCI biomass map units within the i-th PSU (MapBiom_Pol). Then, we join all the information in one dataframe `All_Biom_j`\n", "names(NFI_data)<-c(names(NFI_data)[1:9],\"subplot_AGB\",names(NFI_data)[11:15])\n", - "head(NFI_data)\n", + "# head(NFI_data)\n", "AGB_cluster<-NFI_data%>%group_by(Cluster_new)%>%summarise(NFI_cluster_mean=mean(subplot_AGB))\n", "AGB_cluster<-as.data.frame(AGB_cluster)\n", - "All_Biom_j <- All_Biom %>% inner_join( AGB_cluster, \n", - " by=c('Cluster_new'))\n", + "All_Biom_j <- All_Biom %>% inner_join( AGB_cluster, by=c('Cluster_new'))\n", "\n", "# Don't forget to save ;)\n", "openxlsx::write.xlsx(All_Biom_j,\"Map_cluster_biomassv4_2.xlsx\") " @@ -3558,95 +1114,10 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "b0cb7252-f5a4-4132-8f9e-46cdc7f51309", + "execution_count": 21, + "id": "1683bf7c-dea8-4e6b-9032-897a55eef08e", "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "<table class=\"dataframe\">\n", - "<caption>A data.frame: 6 × 17</caption>\n", - "<thead>\n", - "\t<tr><th></th><th scope=col>ProvÃncia</th><th scope=col>Estrato.Florestal</th><th scope=col>id_parcela</th><th scope=col>id_plot_new</th><th scope=col>Cluster</th><th scope=col>Cluster_new</th><th scope=col>Plot</th><th scope=col>Vt..m.3.ha.</th><th scope=col>Vc..m.3.ha.</th><th scope=col>AGB..ton.ha.</th><th scope=col>BGB..ton.ha.</th><th scope=col>FOREST_STR</th><th scope=col>x</th><th scope=col>y</th><th scope=col>FOREST_STR_NEW</th><th scope=col>MapBiom_Pol</th><th scope=col>NFI_cluster_mean</th></tr>\n", - "\t<tr><th></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", - "</thead>\n", - "<tbody>\n", - "\t<tr><th scope=row>1</th><td>Inhambane</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>1001</td><td>1001</td><td>100</td><td>100</td><td>1</td><td> 31.81777</td><td> 3.722567</td><td> 32.12933</td><td>19.33363</td><td>Mopane </td><td>33.62141</td><td>-22.31658</td><td>Mopane </td><td>12.82971615</td><td> 36.82168</td></tr>\n", - "\t<tr><th scope=row>2</th><td>Maputo </td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>101 </td><td>101 </td><td>10 </td><td>10 </td><td>1</td><td> 20.79411</td><td> 9.477959</td><td> 20.48260</td><td>12.65229</td><td>Mopane </td><td>32.52574</td><td>-24.62944</td><td>Mopane </td><td>12.03455030</td><td> 16.69390</td></tr>\n", - "\t<tr><th scope=row>3</th><td>Inhambane</td><td>Mecrusse </td><td>1011</td><td>1011</td><td>101</td><td>101</td><td>1</td><td>162.57089</td><td>88.062745</td><td>129.07449</td><td>32.10173</td><td>Mecrusse</td><td>33.77635</td><td>-22.24364</td><td>Mecrusse</td><td>21.23975746</td><td>176.81289</td></tr>\n", - "\t<tr><th scope=row>4</th><td>Inhambane</td><td>Mecrusse </td><td>1021</td><td>1021</td><td>102</td><td>102</td><td>1</td><td>104.40611</td><td>43.770760</td><td> 84.00228</td><td>21.72098</td><td>Mecrusse</td><td>33.42700</td><td>-22.24493</td><td>Mecrusse</td><td>12.61468250</td><td> 39.36223</td></tr>\n", - "\t<tr><th scope=row>5</th><td>Inhambane</td><td>Mecrusse </td><td>1041</td><td>1041</td><td>104</td><td>104</td><td>1</td><td> 58.36695</td><td>19.397150</td><td> 79.96115</td><td>24.42729</td><td>Mopane </td><td>33.85376</td><td>-22.20712</td><td>Mopane </td><td> 0.06997281</td><td> 33.89095</td></tr>\n", - "\t<tr><th scope=row>6</th><td>Inhambane</td><td>Floresta (semi-) decidua, incluindo Miombo</td><td>1061</td><td>1061</td><td>106</td><td>106</td><td>1</td><td> 52.41861</td><td>14.434355</td><td> 51.46176</td><td>25.31102</td><td>Mopane </td><td>33.62109</td><td>-22.24431</td><td>Mopane </td><td>25.19654436</td><td> 45.70476</td></tr>\n", - "</tbody>\n", - "</table>\n" - ], - "text/latex": [ - "A data.frame: 6 × 17\n", - "\\begin{tabular}{r|lllllllllllllllll}\n", - " & ProvÃncia & Estrato.Florestal & id\\_parcela & id\\_plot\\_new & Cluster & Cluster\\_new & Plot & Vt..m.3.ha. & Vc..m.3.ha. & AGB..ton.ha. & BGB..ton.ha. & FOREST\\_STR & x & y & FOREST\\_STR\\_NEW & MapBiom\\_Pol & NFI\\_cluster\\_mean\\\\\n", - " & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <chr> & <dbl> & <dbl>\\\\\n", - "\\hline\n", - "\t1 & Inhambane & Floresta (semi-) decidua, incluindo Miombo & 1001 & 1001 & 100 & 100 & 1 & 31.81777 & 3.722567 & 32.12933 & 19.33363 & Mopane & 33.62141 & -22.31658 & Mopane & 12.82971615 & 36.82168\\\\\n", - "\t2 & Maputo & Floresta (semi-) decidua, incluindo Miombo & 101 & 101 & 10 & 10 & 1 & 20.79411 & 9.477959 & 20.48260 & 12.65229 & Mopane & 32.52574 & -24.62944 & Mopane & 12.03455030 & 16.69390\\\\\n", - "\t3 & Inhambane & Mecrusse & 1011 & 1011 & 101 & 101 & 1 & 162.57089 & 88.062745 & 129.07449 & 32.10173 & Mecrusse & 33.77635 & -22.24364 & Mecrusse & 21.23975746 & 176.81289\\\\\n", - "\t4 & Inhambane & Mecrusse & 1021 & 1021 & 102 & 102 & 1 & 104.40611 & 43.770760 & 84.00228 & 21.72098 & Mecrusse & 33.42700 & -22.24493 & Mecrusse & 12.61468250 & 39.36223\\\\\n", - "\t5 & Inhambane & Mecrusse & 1041 & 1041 & 104 & 104 & 1 & 58.36695 & 19.397150 & 79.96115 & 24.42729 & Mopane & 33.85376 & -22.20712 & Mopane & 0.06997281 & 33.89095\\\\\n", - "\t6 & Inhambane & Floresta (semi-) decidua, incluindo Miombo & 1061 & 1061 & 106 & 106 & 1 & 52.41861 & 14.434355 & 51.46176 & 25.31102 & Mopane & 33.62109 & -22.24431 & Mopane & 25.19654436 & 45.70476\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 17\n", - "\n", - "| <!--/--> | ProvÃncia <chr> | Estrato.Florestal <chr> | id_parcela <chr> | id_plot_new <chr> | Cluster <chr> | Cluster_new <chr> | Plot <dbl> | Vt..m.3.ha. <dbl> | Vc..m.3.ha. <dbl> | AGB..ton.ha. <dbl> | BGB..ton.ha. <dbl> | FOREST_STR <chr> | x <dbl> | y <dbl> | FOREST_STR_NEW <chr> | MapBiom_Pol <dbl> | NFI_cluster_mean <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | Inhambane | Floresta (semi-) decidua, incluindo Miombo | 1001 | 1001 | 100 | 100 | 1 | 31.81777 | 3.722567 | 32.12933 | 19.33363 | Mopane | 33.62141 | -22.31658 | Mopane | 12.82971615 | 36.82168 |\n", - "| 2 | Maputo | Floresta (semi-) decidua, incluindo Miombo | 101 | 101 | 10 | 10 | 1 | 20.79411 | 9.477959 | 20.48260 | 12.65229 | Mopane | 32.52574 | -24.62944 | Mopane | 12.03455030 | 16.69390 |\n", - "| 3 | Inhambane | Mecrusse | 1011 | 1011 | 101 | 101 | 1 | 162.57089 | 88.062745 | 129.07449 | 32.10173 | Mecrusse | 33.77635 | -22.24364 | Mecrusse | 21.23975746 | 176.81289 |\n", - "| 4 | Inhambane | Mecrusse | 1021 | 1021 | 102 | 102 | 1 | 104.40611 | 43.770760 | 84.00228 | 21.72098 | Mecrusse | 33.42700 | -22.24493 | Mecrusse | 12.61468250 | 39.36223 |\n", - "| 5 | Inhambane | Mecrusse | 1041 | 1041 | 104 | 104 | 1 | 58.36695 | 19.397150 | 79.96115 | 24.42729 | Mopane | 33.85376 | -22.20712 | Mopane | 0.06997281 | 33.89095 |\n", - "| 6 | Inhambane | Floresta (semi-) decidua, incluindo Miombo | 1061 | 1061 | 106 | 106 | 1 | 52.41861 | 14.434355 | 51.46176 | 25.31102 | Mopane | 33.62109 | -22.24431 | Mopane | 25.19654436 | 45.70476 |\n", - "\n" - ], - "text/plain": [ - " ProvÃncia Estrato.Florestal id_parcela id_plot_new\n", - "1 Inhambane Floresta (semi-) decidua, incluindo Miombo 1001 1001 \n", - "2 Maputo Floresta (semi-) decidua, incluindo Miombo 101 101 \n", - "3 Inhambane Mecrusse 1011 1011 \n", - "4 Inhambane Mecrusse 1021 1021 \n", - "5 Inhambane Mecrusse 1041 1041 \n", - "6 Inhambane Floresta (semi-) decidua, incluindo Miombo 1061 1061 \n", - " Cluster Cluster_new Plot Vt..m.3.ha. Vc..m.3.ha. AGB..ton.ha. BGB..ton.ha.\n", - "1 100 100 1 31.81777 3.722567 32.12933 19.33363 \n", - "2 10 10 1 20.79411 9.477959 20.48260 12.65229 \n", - "3 101 101 1 162.57089 88.062745 129.07449 32.10173 \n", - "4 102 102 1 104.40611 43.770760 84.00228 21.72098 \n", - "5 104 104 1 58.36695 19.397150 79.96115 24.42729 \n", - "6 106 106 1 52.41861 14.434355 51.46176 25.31102 \n", - " FOREST_STR x y FOREST_STR_NEW MapBiom_Pol NFI_cluster_mean\n", - "1 Mopane 33.62141 -22.31658 Mopane 12.82971615 36.82168 \n", - "2 Mopane 32.52574 -24.62944 Mopane 12.03455030 16.69390 \n", - "3 Mecrusse 33.77635 -22.24364 Mecrusse 21.23975746 176.81289 \n", - "4 Mecrusse 33.42700 -22.24493 Mecrusse 12.61468250 39.36223 \n", - "5 Mopane 33.85376 -22.20712 Mopane 0.06997281 33.89095 \n", - "6 Mopane 33.62109 -22.24431 Mopane 25.19654436 45.70476 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "\n", - "FALSE TRUE \n", - " 698 15 " - ] - }, - "metadata": {}, - "output_type": 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KSr0uZEPA6ULZDWkHzzrwpCMpwz1eqs/HXfwgp33RA9iTHhoFVwz/nBJRkr9xQ6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\nyaIHMSC9Q7IdSl2+LPWQzfVepg/pzuzXAGowV+gcxDLfvOeWmj0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O/ff+PGjflMdY8ePRo/fry34cup/ubu7j5v3rwLFy7kZ/8AANghJk/Ajmg0mpkz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\nip1IeXl5dezYcfTo0TIy+JMHAAAAAUO9EClNTc0ff/yRdgoAA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Model assisted estimation\"\n", + "#######################\n", + "\n", + "# This code and text has been adapted from Malaga et al. (under review), \"Global biomass maps can increase the precision of 1 (sub)national aboveground biomass estimates: a 2 comparison across tropical countries\". In the previous three notebooks we have shown how to pre-process Mozambique's NFI dataset and the CCI Biomass map (yr 2017, v.4), as well as computing the mean AGB mean values over the remote sensing sampling unit (PSU). \n", + "# In this notebook, we will perform a model-assisted estimation of AGB (also referred to a scenario B). In order to assess the gain in precision, we will also estimate AGB using exclusively the NFI data (field-based scenario; also referred to as scenario A). The gain in precision from a model-assisted scenario can only be assessed if compared to a baseline scenario, where the CCI biomass map is not used as auxiliary information.\n", + "\n", + "###################################################################################################\n", + "# **PART 1: Model-assisted estimation** \n", + "###################################################################################################\n", + "\n", + "# 1. Load the data set\n", + "# At this point, the data should already be clean and pre-processed, including mean AGB values for PSUs\n", + "\n", + "ALL_data=openxlsx::read.xlsx(\"/projects/my-private-bucket/Data/NFI_data/Mozambique/Map_cluster_biomassv4_2.xlsx\") \n", + "# head(ALL_data)\n", + "# table(ALL_data$MapBiom_Pol<=0) #There are 15 cluster with Map AGB means =0\n", + "\n", + "\n", + "# Subset the data per forest strata\n", + "Data_Me<-ALL_data[ALL_data$FOREST_STR_NEW==\"Mecrusse\",]\n", + "Data_Mo<-ALL_data[ALL_data$FOREST_STR_NEW==\"Mopane\",]\n", + "Data_sdf<-ALL_data[ALL_data$FOREST_STR_NEW==\"Semi deciduous forest\",]\n", + "Data_sef<-ALL_data[ALL_data$FOREST_STR_NEW==\"Semi evergreen forest\",]\n", + "\n", + "\n", + "# 2. Exploring the Map-to-plot regressions \n", + "## Mercrusse\n", + "Me_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Me)\n", + "plot(residuals(Me_reg)~fitted(Me_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", + "\n", + "## Mopane\n", + "Mo_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Mo)\n", + "plot(residuals(Mo_reg)~fitted(Mo_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", + "\n", + "## Semi-decidious forest\n", + "sdf_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sdf)\n", + "plot(residuals(sdf_reg)~fitted(sdf_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", + "\n", + "## Semi-evergreen forest\n", + "sef_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sef)\n", + "plot(residuals(sef_reg)~fitted(sef_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", + "\n", + "\n", + "# Note that for some strata, the residuals of the selected linear models exhibited non-negligible heteroscedasticity according to the Breusch-Pagan test (Zeileis and Hothorn, 2002), for which we corrected following the procedure described in McRoberts et al. (2016). For the sake of keepint this excercise simple, we are not following this procedure here.\n", + "\n", + "# 3. Visualise the models\n", + "\n", + "# One visualization example of the regression model\n", + "## Mercrusse forests\n", + "\n", + "summary(Me_reg)\n", + "ggplot(Data_Me, aes(x = MapBiom_Pol, y = NFI_cluster_mean)) + \n", + " geom_point() +\n", + " geom_smooth(col = \"red\", method=\"lm\", se = FALSE) +\n", + " geom_point(shape = 5)+\n", + " geom_abline(intercept = 0, slope = 1, linetype=\"dashed\")+\n", + " theme_classic()+\n", + " theme(axis.text.x=element_text(), axis.text.y=element_text(), \n", + " axis.title=element_text(color=\"black\", face=\"bold\"), plot.title = element_text(color=\"black\", face=\"bold\", hjust = 0.5))+\n", + " ggtitle(\"MOZAMBIQUE: Mecrusse\")+\n", + " labs(y=\"AGB plot data [Mg ha-1]\")+\n", + " labs(x=\"AGB map data [Mg ha-1]\")+\n", + " geom_text(x = 60, y = 180, label = \"\\U0177 = 78.1+0.5x R\\u00b2 0.01\", color=\"red\", parse = F)+\n", + " scale_x_continuous(limits=c(0,100), expand = c(0, 0))+\n", + " scale_y_continuous(limits = c(0,220), expand = c(0, 0))+\n", + " theme(plot.margin = unit(c(0.1, 1, 0.1, 0.2), \"cm\")) \n", + "\n", + "\n", + "# 4. Predicting locally calibrated AGB map mean values (y_caret_ps)\n", + "y_hut_ps_Me = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Me))\n", + "y_hut_ps_Mo = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Mo))\n", + "y_hut_ps_sdf = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sdf))\n", + "y_hut_ps_sef = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sef))\n", + "\n", + "\n", + "# 5. Estimating the model-assisted Mean and Variance\n", + "# From Máalaga et al. (in review): For the model-assisted scenario, we implemented case A two-stage estimators (Särndal et al., 1992). The estimator of the population mean consists of the sum of a prediction-based term and a residual-based adjustment term. For wall-to-wall auxiliary data, the prediction-based term is the synthetic estimator calculated as the mean of calibrated map predictions over all PSUs within the population (g) in stratum h (Särndal et al., 1992, p. 399), which in this case corresponds to AGB means over the 2x2 map units from our country-calibrated maps. The within-stratum adjustment term (e_mean) is computed as the difference between the AGB mean observations over the plots within the selected ith PSU (NFI_cluster_mean), and their corresponding mean model prediction for the ith PSU (y_hut_ps). \n", + "\n", + "## i) calculating the residuals \n", + "\n", + "# Calculating the residuals for every PSU\n", + "Me_ei= Data_Me$NFI_cluster_mean-y_hut_ps_Me\n", + "Mo_ei= Data_Mo$NFI_cluster_mean-y_hut_ps_Mo\n", + "sdf_ei= Data_sdf$NFI_cluster_mean-y_hut_ps_sdf\n", + "sef_ei= Data_sef$NFI_cluster_mean-y_hut_ps_sef\n", + "\n", + "# Calculate the stratum-wise mean residuals\n", + "e_mean_Me<- mean(Me_ei)\n", + "e_mean_Mo<- mean(Mo_ei)\n", + "e_mean_sdf<- mean(sdf_ei)\n", + "e_mean_sef<- mean(sef_ei)\n", + "\n", + "## ii) estimating the synthetic estimator \n", + "# The synthetic estimator is calculated as the mean of calibrated map predictions over all PSUs within the population in each stratum. \n", + "# Load the population - 2x2 aggregated raster of the CCI biomass maps, croped to the population of interest (4 strata)\n", + "\n", + "setwd(\"/projects/my-private-bucket/Data/NFI_data/Mozambique\") #set the directory for the CCI Biomass values\n", + "CCI_Mo_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_Mopane_agg2.tif')\n", + "CCI_Me_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_Mecrusse_agg2.tif')\n", + "CCI_SDF_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_SDF_agg2.tif')\n", + "CCI_SEF_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_SEF_agg2.tif')\n", + "\n", + "\n", + "# Turn rasters into a matrix, to facilitate prediction\n", + "CCI_Mo_m<-as.data.frame(terra::as.matrix(CCI_Mo_ag)) \n", + "colnames(CCI_Mo_m)[1]<-'MapBiom_Pol'\n", + "CCI_Me_m<-as.data.frame(terra::as.matrix(CCI_Me_ag))\n", + "colnames(CCI_Me_m)[1]<-'MapBiom_Pol'\n", + "CCI_sdf_m<-as.data.frame(terra::as.matrix(CCI_SDF_ag)) \n", + "colnames(CCI_sdf_m)[1]<-'MapBiom_Pol'\n", + "CCI_sef_m<-as.data.frame(terra::as.matrix(CCI_SEF_ag)) \n", + "colnames(CCI_sef_m)[1]<-'MapBiom_Pol'\n", + "\n", + "\n", + "# calculate the population mean (y_caret pp)\n", + "\n", + "# * Mercrusse\n", + "\n", + "global(CCI_Me_ag,mean,na.rm=TRUE) #This is the mean of the un-calibrated version of the map\n", + "Y_syn_Me = predict(Me_reg, newdata=CCI_Me_m)\n", + "mean(Y_syn_Me,na.rm=TRUE) #This is the mean of the calibrated version of the map\n", + "\n", + "# * Mopane\n", + "\n", + "global(CCI_Mo_ag,mean,na.rm=TRUE) ##This is the mean of the un-calibrated version of the map\n", + "Y_syn_Mo = predict(Mo_reg, newdata=CCI_Mo_m)\n", + "mean(Y_syn_Mo,na.rm=TRUE) ##This is the mean of the calibrated version of the map\n", + "\n", + "\n", + "# * Semi-deciduous forests\n", + "\n", + "global(CCI_SDF_ag,mean,na.rm=TRUE) # #This is the mean of the un-calibrated version of the map\n", + "Y_syn_sdf = predict(sdf_reg, newdata=CCI_sdf_m)\n", + "mean(Y_syn_sdf,na.rm=TRUE) ##This is the mean of the calibrated version of the map\n", + "\n", + "\n", + "# * Semi-evergreen forests\n", + "\n", + "global(CCI_SEF_ag,mean,na.rm=TRUE) # This is the mean of the un-calibrated version of the map\n", + "Y_syn_sef = predict(sef_reg, newdata=CCI_sdf_m)\n", + "mean(Y_syn_sef,na.rm=TRUE) #This is the mean of the calibrated version of the map\n", + "\n", + "\n", + "## iii) Estimating the locally calibrated mean and variance\n", + "\n", + "# Using the estimator of the population mean (McRoberts et al., in review) \n", + "u_reg_Me <- mean(Y_syn_Me,na.rm=TRUE)+e_mean_Me\n", + "u_reg_Mo <- mean(Y_syn_Mo,na.rm=TRUE)+e_mean_Mo\n", + "u_reg_sdf <- mean(Y_syn_sdf,na.rm=TRUE)+e_mean_sdf\n", + "u_reg_sef <- mean(Y_syn_sef,na.rm=TRUE)+e_mean_sef\n", + "\n", + "\n", + "# Calculate variance\n", + "# The model-assisted variance estimator is the two-stage variance estimator, assuming the second-stage component of the variance to be negligible, equivalent to (Málaga et al., 2022) with observations replaced by model prediction residuals.\n", + "Var_u_reg_Me=1/(length(Me_ei)*(length(Me_ei)-1))*sum((as.vector(Me_ei)-rep(e_mean_Me,length(Me_ei)))^2)\n", + "Var_u_reg_Me\n", + "\n", + "Var_u_reg_Mo=1/(length(Mo_ei)*(length(Mo_ei)-1))*sum((as.vector(Mo_ei)-rep(e_mean_Mo,length(Mo_ei)))^2)\n", + "Var_u_reg_Mo\n", + "\n", + "Var_u_reg_sdf=1/(length(sdf_ei)*(length(sdf_ei)-1))*sum((as.vector(sdf_ei)-rep(e_mean_sdf,length(sdf_ei)))^2)\n", + "Var_u_reg_sdf\n", + "\n", + "Var_u_reg_sef=1/(length(sef_ei)*(length(sef_ei)-1))*sum((as.vector(sef_ei)-rep(e_mean_sef,length(sef_ei)))^2)\n", + "Var_u_reg_sef" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "69a06435-f711-4f95-baca-f80d86aa3383", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "88.6693372037819" + ], + "text/latex": [ + "88.6693372037819" + ], + "text/markdown": [ + "88.6693372037819" + ], + "text/plain": [ + "[1] 88.66934" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "49.4433628401904" + ], + "text/latex": [ + "49.4433628401904" + ], + "text/markdown": [ + "49.4433628401904" + ], + "text/plain": [ + "[1] 49.44336" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "74.9866557416443" + ], + "text/latex": [ + "74.9866557416443" + ], + "text/markdown": [ + "74.9866557416443" + ], + "text/plain": [ + "[1] 74.98666" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "81.5957905878193" + ], + "text/latex": [ + "81.5957905878193" + ], + "text/markdown": [ + "81.5957905878193" + ], + "text/plain": [ + "[1] 81.59579" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "45.6509877282196" + ], + "text/latex": [ + "45.6509877282196" + ], + "text/markdown": [ + "45.6509877282196" + ], + "text/plain": [ + "[1] 45.65099" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "15.7978427886313" + ], + "text/latex": [ + "15.7978427886313" + ], + "text/markdown": [ + "15.7978427886313" + ], + "text/plain": [ + "[1] 15.79784" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "8.52859094634131" + ], + "text/latex": [ + "8.52859094634131" + ], + "text/markdown": [ + "8.52859094634131" + ], + "text/plain": [ + "[1] 8.528591" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "27.6073684411682" + ], + "text/latex": [ "27.6073684411682" ], "text/markdown": [ @@ -4187,7 +1849,45 @@ }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "###########################################################################################\n", + "# **Part 2: Scenario A - Simple Expansion Estimator **\n", + "###########################################################################################\n", + "\n", + "# For the field-based scenario (baseline), we implemented a simple expansion estimator. The gain in precision from a model-assisted scenario can only be assessed if compared to a baseline scenario, where the CCI biomass map is not used as auxiliary information.\n", + "\n", + "# Mean\n", + "\n", + "u_Me<-mean(Data_Me$NFI_cluster_mean)\n", + "u_Me\n", + "u_Mo<-mean(Data_Mo$NFI_cluster_mean)\n", + "u_Mo\n", + "u_sdf<-mean(Data_sdf$NFI_cluster_mean)\n", + "u_sdf\n", + "u_sef<-mean(Data_sef$NFI_cluster_mean)\n", + "u_sef\n", + "\n", + "\n", + "# Variance\n", + "\n", + "var_Me<-var(Data_Me$NFI_cluster_mean)/nrow(Data_Me)\n", + "var_Me\n", + "var_Mo<-var(Data_Mo$NFI_cluster_mean)/nrow(Data_Mo)\n", + "var_Mo\n", + "var_sdf<-var(Data_sdf$NFI_cluster_mean)/nrow(Data_sdf)\n", + "var_sdf\n", + "var_sef<-var(Data_sef$NFI_cluster_mean)/nrow(Data_sef)\n", + "var_sef" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b9b9bd44-efd4-4850-bc5e-bda9241ee489", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -4347,7 +2047,41 @@ }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "###########################################################################################\n", + "# **PART 3: Country total of the different scenarios** \n", + "###########################################################################################\n", + "\n", + "# Estimate totals for all forest strata\n", + "\n", + "# Count number of cells of per strata and sum into `N_total`\n", + "Nh_Me<-global(CCI_Me_ag,sum,na.rm=TRUE)\n", + "Nh_Mo<-global(CCI_Mo_ag,sum,na.rm=TRUE)\n", + "Nh_sdf<-global(CCI_SDF_ag,sum,na.rm=TRUE)\n", + "Nh_sef<-global(CCI_SEF_ag,sum,na.rm=TRUE)\n", + "N_total<-Nh_Me+Nh_Mo+Nh_sdf+Nh_sef\n", + "\n", + "# Country estimate of the baseline scenario (scenario A)\n", + "Mean_Mozam_scA<-Nh_Me/N_total*u_Me+Nh_Mo/N_total*u_Mo+Nh_sdf/N_total*u_sdf+Nh_sef/N_total*u_sef\n", + "Mean_Mozam_scA\n", + "Var_Mozam_scA<- ((Nh_Me/N_total)^2)*var_Me+((Nh_Mo/N_total)^2)*var_Mo+((Nh_sdf/N_total)^2)*var_sdf+((Nh_sef/N_total)^2)*var_sef\n", + "Var_Mozam_scA\n", + "\n", + "# Country estimate of the model-assisted scenario (scenario B)\n", + "Mean_Mozam_scB<-Nh_Me/N_total*u_reg_Me+Nh_Mo/N_total*u_reg_Mo+Nh_sdf/N_total*u_reg_sdf+Nh_sef/N_total*u_reg_sef\n", + "Mean_Mozam_scB\n", + "Var_Mozam_scB<- ((Nh_Me/N_total)^2)*Var_u_reg_Me+((Nh_Mo/N_total)^2)*Var_u_reg_Mo+((Nh_sdf/N_total)^2)*Var_u_reg_sdf+((Nh_sef/N_total)^2)*Var_u_reg_sef\n", + "Var_Mozam_scB" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ea64e173-0b82-49a7-9ed4-49beac5b50ef", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -4459,246 +2193,9 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "image/png": 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FBQnj592rZtW3mebMmSJSMiIgwfegIA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\nip1IeXl5dezYcfTo0TIy+JMHAAAAAUO9EClNTc0ff/yRdgoAA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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" } ], "source": [ - "#######################\n", - "# title: \"4. Model assisted estimation\"\n", - "#######################\n", - "\n", - "# This code and text has been adapted from Malaga et al. (under review), \"Global biomass maps can increase the precision of 1 (sub)national aboveground biomass estimates: a 2 comparison across tropical countries\". In the previous three notebooks we have shown how to pre-process Mozambique's NFI dataset and the CCI Biomass map (yr 2017, v.4), as well as computing the mean AGB mean values over the remote sensing sampling unit (PSU). \n", - "# In this notebook, we will perform a model-assisted estimation of AGB (also referred to a scenario B). In order to assess the gain in precision, we will also estimate AGB using exclusively the NFI data (field-based scenario; also referred to as scenario A). The gain in precision from a model-assisted scenario can only be assessed if compared to a baseline scenario, where the CCI biomass map is not used as auxiliary information.\n", - "\n", - "###################################################################################################\n", - "# **PART 1: Model-assisted estimation** \n", - "###################################################################################################\n", - "\n", - "# 1. Load the data set\n", - "# At this point, the data should already be clean and pre-processed, including mean AGB values for PSUs\n", - "\n", - "ALL_data=openxlsx::read.xlsx(\"/projects/my-private-bucket/Data/NFI_data/Mozambique/Map_cluster_biomassv4_2.xlsx\") \n", - "head(ALL_data)\n", - "table(ALL_data$MapBiom_Pol<=0) #There are 15 cluster with Map AGB means =0\n", - "\n", - "\n", - "# Subset the data per forest strata\n", - "Data_Me<-ALL_data[ALL_data$FOREST_STR_NEW==\"Mecrusse\",]\n", - "Data_Mo<-ALL_data[ALL_data$FOREST_STR_NEW==\"Mopane\",]\n", - "Data_sdf<-ALL_data[ALL_data$FOREST_STR_NEW==\"Semi deciduous forest\",]\n", - "Data_sef<-ALL_data[ALL_data$FOREST_STR_NEW==\"Semi evergreen forest\",]\n", - "\n", - "\n", - "# 2. Exploring the Map-to-plot regressions \n", - "## Mercrusse\n", - "Me_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Me)\n", - "plot(residuals(Me_reg)~fitted(Me_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", - "\n", - "## Mopane\n", - "Mo_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Mo)\n", - "plot(residuals(Mo_reg)~fitted(Mo_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", - "\n", - "## Semi-decidious forest\n", - "sdf_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sdf)\n", - "plot(residuals(sdf_reg)~fitted(sdf_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", - "\n", - "## Semi-evergreen forest\n", - "sef_reg<-lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sef)\n", - "plot(residuals(sef_reg)~fitted(sef_reg),main=\"residuals vs. fitted\",xlab=\"residuals\",ylab = \"fitted values\")\n", - "\n", - "\n", - "# Note that for some strata, the residuals of the selected linear models exhibited non-negligible heteroscedasticity according to the Breusch-Pagan test (Zeileis and Hothorn, 2002), for which we corrected following the procedure described in McRoberts et al. (2016). For the sake of keepint this excercise simple, we are not following this procedure here.\n", - "\n", - "# 3. Visualise the models\n", - "\n", - "# One visualization example of the regression model\n", - "## Mercrusse forests\n", - "\n", - "summary(Me_reg)\n", - "ggplot(Data_Me, aes(x = MapBiom_Pol, y = NFI_cluster_mean)) + \n", - " geom_point() +\n", - " geom_smooth(col = \"red\", method=\"lm\", se = FALSE) +\n", - " geom_point(shape = 5)+\n", - " geom_abline(intercept = 0, slope = 1, linetype=\"dashed\")+\n", - " theme_classic()+\n", - " theme(axis.text.x=element_text(), axis.text.y=element_text(), \n", - " axis.title=element_text(color=\"black\", face=\"bold\"), plot.title = element_text(color=\"black\", face=\"bold\", hjust = 0.5))+\n", - " ggtitle(\"MOZAMBIQUE: Mecrusse\")+\n", - " labs(y=\"AGB plot data [Mg ha-1]\")+\n", - " labs(x=\"AGB map data [Mg ha-1]\")+\n", - " geom_text(x = 60, y = 180, label = \"\\U0177 = 78.1+0.5x R\\u00b2 0.01\", color=\"red\", parse = F)+\n", - " scale_x_continuous(limits=c(0,100), expand = c(0, 0))+\n", - " scale_y_continuous(limits = c(0,220), expand = c(0, 0))+\n", - " theme(plot.margin = unit(c(0.1, 1, 0.1, 0.2), \"cm\")) \n", - "\n", - "\n", - "# 4. Predicting locally calibrated AGB map mean values (y_caret_ps)\n", - "y_hut_ps_Me = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Me))\n", - "y_hut_ps_Mo = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_Mo))\n", - "y_hut_ps_sdf = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sdf))\n", - "y_hut_ps_sef = predict(lm(NFI_cluster_mean~MapBiom_Pol,data=Data_sef))\n", - "\n", - "\n", - "# 5. Estimating the model-assisted Mean and Variance\n", - "# From Máalaga et al. (in review): For the model-assisted scenario, we implemented case A two-stage estimators (Särndal et al., 1992). The estimator of the population mean consists of the sum of a prediction-based term and a residual-based adjustment term. For wall-to-wall auxiliary data, the prediction-based term is the synthetic estimator calculated as the mean of calibrated map predictions over all PSUs within the population (g) in stratum h (Särndal et al., 1992, p. 399), which in this case corresponds to AGB means over the 2x2 map units from our country-calibrated maps. The within-stratum adjustment term (e_mean) is computed as the difference between the AGB mean observations over the plots within the selected ith PSU (NFI_cluster_mean), and their corresponding mean model prediction for the ith PSU (y_hut_ps). \n", - "\n", - "## i) calculating the residuals \n", - "\n", - "# Calculating the residuals for every PSU\n", - "Me_ei= Data_Me$NFI_cluster_mean-y_hut_ps_Me\n", - "Mo_ei= Data_Mo$NFI_cluster_mean-y_hut_ps_Mo\n", - "sdf_ei= Data_sdf$NFI_cluster_mean-y_hut_ps_sdf\n", - "sef_ei= Data_sef$NFI_cluster_mean-y_hut_ps_sef\n", - "\n", - "# Calculate the stratum-wise mean residuals\n", - "e_mean_Me<- mean(Me_ei)\n", - "e_mean_Mo<- mean(Mo_ei)\n", - "e_mean_sdf<- mean(sdf_ei)\n", - "e_mean_sef<- mean(sef_ei)\n", - "\n", - "## ii) estimating the synthetic estimator \n", - "# The synthetic estimator is calculated as the mean of calibrated map predictions over all PSUs within the population in each stratum. \n", - "# Load the population - 2x2 aggregated raster of the CCI biomass maps, croped to the population of interest (4 strata)\n", - "\n", - "setwd(\"/projects/my-private-bucket/Data/NFI_data/Mozambique\") #set the directory for the CCI Biomass values\n", - "CCI_Mo_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_Mopane_agg2.tif')\n", - "CCI_Me_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_Mecrusse_agg2.tif')\n", - "CCI_SDF_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_SDF_agg2.tif')\n", - "CCI_SEF_ag<-terra::rast('/projects/my-private-bucket/Data/NFI_data/Mozambique/CCI_SEF_agg2.tif')\n", - "\n", - "\n", - "# Turn rasters into a matrix, to facilitate prediction\n", - "CCI_Mo_m<-as.data.frame(terra::as.matrix(CCI_Mo_ag)) \n", - "colnames(CCI_Mo_m)[1]<-'MapBiom_Pol'\n", - "CCI_Me_m<-as.data.frame(terra::as.matrix(CCI_Me_ag))\n", - "colnames(CCI_Me_m)[1]<-'MapBiom_Pol'\n", - "CCI_sdf_m<-as.data.frame(terra::as.matrix(CCI_SDF_ag)) \n", - "colnames(CCI_sdf_m)[1]<-'MapBiom_Pol'\n", - "CCI_sef_m<-as.data.frame(terra::as.matrix(CCI_SEF_ag)) \n", - "colnames(CCI_sef_m)[1]<-'MapBiom_Pol'\n", - "\n", - "\n", - "# calculate the population mean (y_caret pp)\n", - "\n", - "# * Mercrusse\n", - "\n", - "global(CCI_Me_ag,mean,na.rm=TRUE) #This is the mean of the un-calibrated version of the map\n", - "Y_syn_Me = predict(Me_reg, newdata=CCI_Me_m)\n", - "mean(Y_syn_Me,na.rm=TRUE) #This is the mean of the calibrated version of the map\n", - "\n", - "# * Mopane\n", - "\n", - "global(CCI_Mo_ag,mean,na.rm=TRUE) ##This is the mean of the un-calibrated version of the map\n", - "Y_syn_Mo = predict(Mo_reg, newdata=CCI_Mo_m)\n", - "mean(Y_syn_Mo,na.rm=TRUE) ##This is the mean of the calibrated version of the map\n", - "\n", - "\n", - "# * Semi-deciduous forests\n", - "\n", - "global(CCI_SDF_ag,mean,na.rm=TRUE) # #This is the mean of the un-calibrated version of the map\n", - "Y_syn_sdf = predict(sdf_reg, newdata=CCI_sdf_m)\n", - "mean(Y_syn_sdf,na.rm=TRUE) ##This is the mean of the calibrated version of the map\n", - "\n", - "\n", - "# * Semi-evergreen forests\n", - "\n", - "global(CCI_SEF_ag,mean,na.rm=TRUE) # This is the mean of the un-calibrated version of the map\n", - "Y_syn_sef = predict(sef_reg, newdata=CCI_sdf_m)\n", - "mean(Y_syn_sef,na.rm=TRUE) #This is the mean of the calibrated version of the map\n", - "\n", - "\n", - "## iii) Estimating the locally calibrated mean and variance\n", - "\n", - "# Using the estimator of the population mean (McRoberts et al., in review) \n", - "u_reg_Me <- mean(Y_syn_Me,na.rm=TRUE)+e_mean_Me\n", - "u_reg_Mo <- mean(Y_syn_Mo,na.rm=TRUE)+e_mean_Mo\n", - "u_reg_sdf <- mean(Y_syn_sdf,na.rm=TRUE)+e_mean_sdf\n", - "u_reg_sef <- mean(Y_syn_sef,na.rm=TRUE)+e_mean_sef\n", - "\n", - "\n", - "# Calculate variance\n", - "# The model-assisted variance estimator is the two-stage variance estimator, assuming the second-stage component of the variance to be negligible, equivalent to (Málaga et al., 2022) with observations replaced by model prediction residuals.\n", - "Var_u_reg_Me=1/(length(Me_ei)*(length(Me_ei)-1))*sum((as.vector(Me_ei)-rep(e_mean_Me,length(Me_ei)))^2)\n", - "Var_u_reg_Me\n", - "\n", - "Var_u_reg_Mo=1/(length(Mo_ei)*(length(Mo_ei)-1))*sum((as.vector(Mo_ei)-rep(e_mean_Mo,length(Mo_ei)))^2)\n", - "Var_u_reg_Mo\n", - "\n", - "Var_u_reg_sdf=1/(length(sdf_ei)*(length(sdf_ei)-1))*sum((as.vector(sdf_ei)-rep(e_mean_sdf,length(sdf_ei)))^2)\n", - "Var_u_reg_sdf\n", - "\n", - "Var_u_reg_sef=1/(length(sef_ei)*(length(sef_ei)-1))*sum((as.vector(sef_ei)-rep(e_mean_sef,length(sef_ei)))^2)\n", - "Var_u_reg_sef\n", - "\n", - "\n", - "###########################################################################################\n", - "# **Part 2: Scenario A - Simple Expansion Estimator **\n", - "###########################################################################################\n", - "\n", - "# For the field-based scenario (baseline), we implemented a simple expansion estimator. The gain in precision from a model-assisted scenario can only be assessed if compared to a baseline scenario, where the CCI biomass map is not used as auxiliary information.\n", - "\n", - "# Mean\n", - "\n", - "u_Me<-mean(Data_Me$NFI_cluster_mean)\n", - "u_Me\n", - "u_Mo<-mean(Data_Mo$NFI_cluster_mean)\n", - "u_Mo\n", - "u_sdf<-mean(Data_sdf$NFI_cluster_mean)\n", - "u_sdf\n", - "u_sef<-mean(Data_sef$NFI_cluster_mean)\n", - "u_sef\n", - "\n", - "\n", - "# Variance\n", - "\n", - "var_Me<-var(Data_Me$NFI_cluster_mean)/nrow(Data_Me)\n", - "var_Me\n", - "var_Mo<-var(Data_Mo$NFI_cluster_mean)/nrow(Data_Mo)\n", - "var_Mo\n", - "var_sdf<-var(Data_sdf$NFI_cluster_mean)/nrow(Data_sdf)\n", - "var_sdf\n", - "var_sef<-var(Data_sef$NFI_cluster_mean)/nrow(Data_sef)\n", - "var_sef\n", - "\n", - "###########################################################################################\n", - "# **PART 3: Country total of the different scenarios** \n", - "###########################################################################################\n", - "\n", - "# Estimate totals for all forest strata\n", - "\n", - "# Count number of cells of per strata and sum into `N_total`\n", - "Nh_Me<-global(CCI_Me_ag,sum,na.rm=TRUE)\n", - "Nh_Mo<-global(CCI_Mo_ag,sum,na.rm=TRUE)\n", - "Nh_sdf<-global(CCI_SDF_ag,sum,na.rm=TRUE)\n", - "Nh_sef<-global(CCI_SEF_ag,sum,na.rm=TRUE)\n", - "N_total<-Nh_Me+Nh_Mo+Nh_sdf+Nh_sef\n", - "\n", - "# Country estimate of the baseline scenario (scenario A)\n", - "Mean_Mozam_scA<-Nh_Me/N_total*u_Me+Nh_Mo/N_total*u_Mo+Nh_sdf/N_total*u_sdf+Nh_sef/N_total*u_sef\n", - "Mean_Mozam_scA\n", - "Var_Mozam_scA<- ((Nh_Me/N_total)^2)*var_Me+((Nh_Mo/N_total)^2)*var_Mo+((Nh_sdf/N_total)^2)*var_sdf+((Nh_sef/N_total)^2)*var_sef\n", - "Var_Mozam_scA\n", - "\n", - "# Country estimate of the model-assisted scenario (scenario B)\n", - "Mean_Mozam_scB<-Nh_Me/N_total*u_reg_Me+Nh_Mo/N_total*u_reg_Mo+Nh_sdf/N_total*u_reg_sdf+Nh_sef/N_total*u_reg_sef\n", - "Mean_Mozam_scB\n", - "Var_Mozam_scB<- ((Nh_Me/N_total)^2)*Var_u_reg_Me+((Nh_Mo/N_total)^2)*Var_u_reg_Mo+((Nh_sdf/N_total)^2)*Var_u_reg_sdf+((Nh_sef/N_total)^2)*Var_u_reg_sef\n", - "Var_Mozam_scB\n", - "\n", "###########################################################################################\n", "# **PART 4: Assessing the gain in precision ** \n", "###########################################################################################\n", @@ -4711,21 +2208,21 @@ "var_sef/Var_u_reg_sef\n", "Var_Mozam_scA/Var_Mozam_scB\n", "\n", - "# Output values indicate a gain in precision (smaller varience) under the model-assisted scenario (scenario B). \n", - "\n", - "# References: \n", - "# - Malaga et al. (under review), Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: a comparison across tropical countries.\n", - "# - Malaga et al. (2022), Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map. International Journal of Applied Earth Observation and Geoinformation, 115, 103102. https://doi.org/10.1016/j.jag.2022.103102\n", - "# - McRoberts et al. (2016). Hybrid estimators for mean aboveground carbon per unit area. Forest Ecology and Management, 378, 44–56.\n" + "# Output values indicate a gain in precision (smaller varience) under the model-assisted scenario (scenario B). " ] }, { "cell_type": "code", - "execution_count": null, - "id": "a4284467-b524-4c04-8ec3-bc69f574bee2", + "execution_count": 25, + "id": "767618fc-d9c7-4cee-94d4-b4acd7d559a9", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# References: \n", + "# - Malaga et al. (under review), Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: a comparison across tropical countries.\n", + "# - Malaga et al. (2022), Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map. International Journal of Applied Earth Observation and Geoinformation, 115, 103102. https://doi.org/10.1016/j.jag.2022.103102\n", + "# - McRoberts et al. (2016). Hybrid estimators for mean aboveground carbon per unit area. Forest Ecology and Management, 378, 44–56." + ] } ], "metadata": { -- GitLab