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<h1 class="title toc-ignore">Crop mapping</h1>
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<div id="background" class="section level2">
<h2>Background</h2>
<p>Information on spatial distribution of crops is an important step towards yield estimation. We need to know where the crops are before we estimate the yield in a given region. Ground mapping approaches like surveying are expensive and time intensive. Remote sensing offers an effective and efficient platform for mapping thanks to improved temporal and spatial resolutions. In this case we supplement optical data with UAV images for training sites collection, and image fusion for crop mapping.</p>
<p>For crop mapping we use two different classification algorithms:</p>
<ol style="list-style-type: decimal">
<li>Random Forests (RF) by <a href="https://link.springer.com/article/10.1023/A:1010933404324">Breiman 2001</a>.</li>
<li>Maximum Likelihood Classification (MLC).</li>
</ol>
</div>
<div id="data-preparation" class="section level2">
<h2>Data preparation</h2>
<p>Load libraries, declare variables and data paths.</p>
<pre class="r"><code>rm(list=ls(all=TRUE)) #Clears R memory
unlink(".RData")
if (!require("pacman")) install.packages("pacman"); library(pacman)
p_load(raster, terra, randomForest, RStoolbox, tictoc)
options(warn=1)
cat("Set variables and start processing\n")</code></pre>
<pre><code>## Set variables and start processing</code></pre>
<pre class="r"><code>Root <- 'D:/JKUAT/RESEARCH_Projects/Eswatini/Data/'
Path_out <- paste0(Root,"Output/")</code></pre>
<p>Load Mpolonjeni UAV images for mission 1–5 and Sentinel 2. We will mosaic the UAV images to one image at 1 m spatial resolution and save to disk later. Once this is done we skip this process every other time we run the script.</p>
<pre class="r"><code>#Sentinel 2
path <- list.files(paste0(Root,'S2/interim/'),pattern = (".tif$"), recursive = TRUE, full.names = TRUE)
s <- rast(path)
names(s) <- c("b", "g","r", "nir")
s</code></pre>
<pre><code>## class : SpatRaster
## dimensions : 10980, 10980, 4 (nrow, ncol, nlyr)
## resolution : 10, 10 (x, y)
## extent : 3e+05, 409800, 6990220, 7100020 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs
## sources : RT_T36JUR_20210418T073611_B02.tif
## RT_T36JUR_20210418T073611_B03.tif
## RT_T36JUR_20210418T073611_B04.tif
## ... and 1 more source(s)
## names : b, g, r, nir</code></pre>
<pre class="r"><code>#UAV
mosaicname <- paste0(Path_out,'Mpolonjeni_W1_Mosaic.tif')
if(!file.exists(mosaicname)){
folders <- list.dirs(paste0(Root,'WingtraOne/Mpolonjeni'),recursive=TRUE)[-1]
folders
for (i in 1:length(folders)) {
path <- list.files(folders[i], pattern = (".tif$"))
# Remove all before and up to "reflectance_" in gsub
path <- path[order(gsub(".*reflectance_","",path))][-4]
#reorder the bands to match those in S2
paths <- path
paths[3] <- path[4]
paths[4] <- path[3]
temp <- rast(paste0(folders[i],"/",paths))
names(temp) <- c("b", "g", "r", "nir")
assign(paste0("v", i), temp)
}
}else{
v <- rast(mosaicname)
}</code></pre>
<p>We now have all the image missions loaded from corresponding sub-folders, stacked, and dynamically allocated variables i.e. <span class="math inline">\(\text{v1},\text{v2},\dots,\text{v5}\)</span>. Resample all the images to 1 m spatial resolution using bilinear approach and mosaic them.</p>
<pre class="r"><code>if(!file.exists(mosaicname)){
temp <- aggregate(v1[[1]], 9)
res(temp) <- c(1, 1)
v1 <- resample(v1, temp, method='bilinear')
temp <- aggregate(v2[[1]], 9)
res(temp) <- c(1, 1)
v2 <- resample(v2, temp, method='bilinear')
temp <- aggregate(v3[[1]], 9)
res(temp) <- c(1, 1)
v3 <- resample(v3, temp, method='bilinear')
temp <- aggregate(v4[[1]], 9)
res(temp) <- c(1, 1)
v4 <- resample(v4, temp, method='bilinear')
temp <- aggregate(v5[[1]], 9)
res(temp) <- c(1, 1)
v5 <- resample(v5, temp, method='bilinear')
}</code></pre>
<p>Let’s now mosaic the scenes to form one image. We will use median to average out the overlaps. Median is preferred because it has been shown to be robust to outliers compared to the mean.</p>
<pre class="r"><code>if(!file.exists(mosaicname)){
v <- mosaic(v1, v2, v3, v4, v5, fun="median")
}</code></pre>
<p>Save the mosaic to disk.</p>
<pre class="r"><code>mosaicname <- paste0(Path_out,'Mpolonjeni_W1_Mosaic.tif')
if(!file.exists(mosaicname)){
writeRaster(v, mosaicname)
}</code></pre>
<p>Crop/clip Sentinel 2 image to UAV image extents.</p>
<pre class="r"><code>s <- crop(s, ext(v), snap="near")</code></pre>
<p>Display the images side by side.</p>
<pre class="r"><code>x11()
par(mfrow = c(1, 2)) #c(bottom, left, top, right)
plotRGB(s, r="nir", g="r", b="g", stretch="lin", axes=T, mar = c(4, 5, 1.4, 0.2), main="S2", cex.axis=0.5)
box()
plotRGB(v, r="nir", g="r", b="g", stretch="lin", axes=T, mar = c(4, 5, 1.4, 0.2), main="UAV", cex.axis=0.5)
box()</code></pre>
<p><img src="mapping_files/figure-html/d7-1.png" width="672" /></p>
</div>
<div id="training-data-sampling" class="section level2">
<h2>Training data sampling</h2>
<p>Load train data.</p>
<pre class="r"><code>#ref <- vect(paste0(Root,'Vector/Training_Sites_Mpolonjeni.shp'), "polygons")
ref <- shapefile(paste0(Root,'Vector/Training_Sites_Mpolonjeni.shp'))
ref <- spTransform(ref, crs(v))</code></pre>
<p>Sample points from the polygons (stratified random sampling).</p>
<pre class="r"><code>set.seed(530)
samp <- spsample(ref, 4000, type='stratified')</code></pre>
<pre><code>## Warning in proj4string(obj): CRS object has comment, which is lost in output</code></pre>
<pre class="r"><code># add the land cover class to the points
samp$class <- over(samp, ref)$Name
samp$code <- over(samp, ref)$code
knitr::kable(table(samp$class), align = 'l')</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Var1</th>
<th align="left">Freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">built_up</td>
<td align="left">734</td>
</tr>
<tr class="even">
<td align="left">cassava</td>
<td align="left">109</td>
</tr>
<tr class="odd">
<td align="left">grass</td>
<td align="left">132</td>
</tr>
<tr class="even">
<td align="left">maize</td>
<td align="left">749</td>
</tr>
<tr class="odd">
<td align="left">sorghum</td>
<td align="left">235</td>
</tr>
<tr class="even">
<td align="left">sweet_potato</td>
<td align="left">738</td>
</tr>
<tr class="odd">
<td align="left">trees</td>
<td align="left">918</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="left">368</td>
</tr>
</tbody>
</table>
<pre class="r"><code>sum(table(samp$class))</code></pre>
<pre><code>## [1] 3983</code></pre>
<p>Declare the class names and their number.</p>
<pre class="r"><code>#samp <- spTransform(samp, crs(v))
nClasses <- 8
Classes <- data.frame(classID=c(1:8),class=c('Built_up','Cassava', 'Grass', 'Maize', 'Sorghum', 'Sweet_potato','Trees','Water'))</code></pre>
<p>Display S2 image and the training points.</p>
<pre class="r"><code>add_legend <- function(...) {
opar <- par(fig=c(0, 1, 0, 1), oma=c(0, 0, 0, 0),
mar=c(0, 0, 0, 0), new=TRUE)
on.exit(par(opar))
plot(0, 0, type='n', bty='n', xaxt='n', yaxt='n')
legend(...)
}
x11()
#par(mar = c(4, 4, 1.4, 0.1)) #c(bottom, left, top, right)
plotRGB(s, r="nir", g="r", b="g", stretch="lin", axes = TRUE, mar = c(4, 5, 1.4, 0.1))
#points(samp, col="blue", cex=.5)
lines(spTransform(ref, crs(s)), col="blue", lwd=1.5)
add_legend("bottom", legend="Reference data",
pch=c(0,15), col="blue", horiz=T, bty='n', cex=1.1)</code></pre>
<p><img src="mapping_files/figure-html/v4-1.png" width="672" /></p>
<p>Extract UAV and S2 pixels values overlaid by points and split them into training and validation points.</p>
<pre class="r"><code>#library(spatialEco)
trainData.v <- extract(brick(v), samp, cellnumbers=F, df=T, sp=T)
knitr::kable(head(trainData.v))</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">class</th>
<th align="right">code</th>
<th align="right">b</th>
<th align="right">g</th>
<th align="right">r</th>
<th align="right">nir</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0643933</td>
<td align="right">0.0971735</td>
<td align="right">0.1009569</td>
<td align="right">0.0849024</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0642581</td>
<td align="right">0.0969432</td>
<td align="right">0.1003542</td>
<td align="right">0.0865044</td>
</tr>
<tr class="odd">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0645692</td>
<td align="right">0.0981981</td>
<td align="right">0.1022022</td>
<td align="right">0.0864920</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0633345</td>
<td align="right">0.0975087</td>
<td align="right">0.0983355</td>
<td align="right">0.0855006</td>
</tr>
<tr class="odd">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0642581</td>
<td align="right">0.0969432</td>
<td align="right">0.1003542</td>
<td align="right">0.0865044</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0642581</td>
<td align="right">0.0969432</td>
<td align="right">0.1003542</td>
<td align="right">0.0865044</td>
</tr>
</tbody>
</table>
<pre class="r"><code>trainData.s <- extract(brick(s), samp, cellnumbers=F, df=T, sp=T)
knitr::kable(head(trainData.s))</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">class</th>
<th align="right">code</th>
<th align="right">b</th>
<th align="right">g</th>
<th align="right">r</th>
<th align="right">nir</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0228</td>
<td align="right">0.0253</td>
<td align="right">0.024</td>
<td align="right">0.0227</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0228</td>
<td align="right">0.0253</td>
<td align="right">0.024</td>
<td align="right">0.0227</td>
</tr>
<tr class="odd">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0228</td>
<td align="right">0.0253</td>
<td align="right">0.024</td>
<td align="right">0.0227</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0228</td>
<td align="right">0.0253</td>
<td align="right">0.024</td>
<td align="right">0.0227</td>
</tr>
<tr class="odd">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0228</td>
<td align="right">0.0253</td>
<td align="right">0.024</td>
<td align="right">0.0227</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="right">8</td>
<td align="right">0.0228</td>
<td align="right">0.0253</td>
<td align="right">0.024</td>
<td align="right">0.0227</td>
</tr>
</tbody>
</table>
<pre class="r"><code>library(caTools)
#(NB: 0.4 means 40% for training and 60% for validation)
labels <- as.data.frame(trainData.v[,1])
split <- sample.split(labels, SplitRatio = 0.4)
#UAV
valid.v <- subset(trainData.v, split == FALSE)
train.v <- subset(trainData.v, split == TRUE)
knitr::kable(table(train.v$class), align = 'l')</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Var1</th>
<th align="left">Freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">built_up</td>
<td align="left">248</td>
</tr>
<tr class="even">
<td align="left">cassava</td>
<td align="left">38</td>
</tr>
<tr class="odd">
<td align="left">grass</td>
<td align="left">43</td>
</tr>
<tr class="even">
<td align="left">maize</td>
<td align="left">246</td>
</tr>
<tr class="odd">
<td align="left">sorghum</td>
<td align="left">77</td>
</tr>
<tr class="even">
<td align="left">sweet_potato</td>
<td align="left">246</td>
</tr>
<tr class="odd">
<td align="left">trees</td>
<td align="left">307</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="left">122</td>
</tr>
</tbody>
</table>
<pre class="r"><code>knitr::kable(table(valid.v$class), align = 'l')</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Var1</th>
<th align="left">Freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">built_up</td>
<td align="left">486</td>
</tr>
<tr class="even">
<td align="left">cassava</td>
<td align="left">71</td>
</tr>
<tr class="odd">
<td align="left">grass</td>
<td align="left">89</td>
</tr>
<tr class="even">
<td align="left">maize</td>
<td align="left">503</td>
</tr>
<tr class="odd">
<td align="left">sorghum</td>
<td align="left">158</td>
</tr>
<tr class="even">
<td align="left">sweet_potato</td>
<td align="left">492</td>
</tr>
<tr class="odd">
<td align="left">trees</td>
<td align="left">611</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="left">246</td>
</tr>
</tbody>
</table>
<pre class="r"><code>#Sentinel 2
valid.s <- subset(trainData.s, split == FALSE)
train.s <- subset(trainData.s, split == TRUE)
knitr::kable(table(train.s$class), align = 'l')</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Var1</th>
<th align="left">Freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">built_up</td>
<td align="left">248</td>
</tr>
<tr class="even">
<td align="left">cassava</td>
<td align="left">38</td>
</tr>
<tr class="odd">
<td align="left">grass</td>
<td align="left">43</td>
</tr>
<tr class="even">
<td align="left">maize</td>
<td align="left">246</td>
</tr>
<tr class="odd">
<td align="left">sorghum</td>
<td align="left">77</td>
</tr>
<tr class="even">
<td align="left">sweet_potato</td>
<td align="left">246</td>
</tr>
<tr class="odd">
<td align="left">trees</td>
<td align="left">307</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="left">122</td>
</tr>
</tbody>
</table>
<pre class="r"><code>knitr::kable(table(valid.s$class), align = 'l')</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Var1</th>
<th align="left">Freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">built_up</td>
<td align="left">486</td>
</tr>
<tr class="even">
<td align="left">cassava</td>
<td align="left">71</td>
</tr>
<tr class="odd">
<td align="left">grass</td>
<td align="left">89</td>
</tr>
<tr class="even">
<td align="left">maize</td>
<td align="left">503</td>
</tr>
<tr class="odd">
<td align="left">sorghum</td>
<td align="left">158</td>
</tr>
<tr class="even">
<td align="left">sweet_potato</td>
<td align="left">492</td>
</tr>
<tr class="odd">
<td align="left">trees</td>
<td align="left">611</td>
</tr>
<tr class="even">
<td align="left">waterbody</td>
<td align="left">246</td>
</tr>
</tbody>
</table>
</div>
<div id="maximum-likelihood-classification" class="section level2">
<h2>Maximum Likelihood Classification</h2>
<p>Classify UAV and S2 image using MLC algorithm.</p>
<pre class="r"><code>#UAV classification
mlc.uav <- superClass(brick(v), train.v[-2], responseCol = "class",
model = "mlc", minDist = 1)
val.uav <- validateMap(mlc.uav$map, valid.v[-2], responseCol="class", mode='classification',
classMapping = mlc.uav$classMapping)
#Sentinel 2 classification
mlc.s <- superClass(brick(s), train.s, responseCol = "class",
model = "mlc", minDist = 1)
val.s <- validateMap(mlc.s$map, valid.s, responseCol="class", mode='classification',
classMapping = mlc.s$classMapping)</code></pre>
<p>Make a function to display classified images and use it to display the MLC map.</p>
<pre class="r"><code>display <- function(map, method, nClasses){
x11()
par(mar = c(7, 2, 1.6, 6)) #c(bottom, left, top, right)
image(map, col=c("red", "orange", "cyan" , "yellow", "brown", "magenta", "green1","blue"), axes=T, ann=F)
classes.Palette <- colorRampPalette(c("red", "orange", "cyan" , "yellow", "brown", "magenta", "green1","blue", "white"))
add_legend("bottom", legend=c('Built_up','Cassava', 'Grass',
'Maize', 'Sorghum', 'Sweet_potato','Trees','Water', "No data"), fill=classes.Palette(nClasses+1), ncol=3, bty='n', cex=1.1, pt.bg = NA)
title(paste0(method," Classification"))
}
display(mlc.uav$map, "UAV MLC", nClasses)
display(mlc.s$map, "S2 MLC", nClasses)</code></pre>
<p>Lets design a function for accuracy assessment.</p>
<pre class="r"><code>accuracy <- function(val.test){
assessment.storage <- val.test$performance
#print(assessment.storage)
list_of_datasets <- list("ConfusionMatrix" = as.matrix(assessment.storage$table),
"OverallAcc" = as.matrix(assessment.storage$overall),
"byClass" = as.matrix(assessment.storage$byClass))
return(list_of_datasets)
}</code></pre>
<p>Assess the accuracy of MLC.</p>
<pre class="r"><code>print('UAV Confusion matrixs')</code></pre>
<pre><code>## [1] "UAV Confusion matrixs"</code></pre>
<pre class="r"><code>List.v <- accuracy(val.uav)
knitr::kable(List.v$ConfusionMatrix,align='l')</code></pre>
<table>
<thead>
<tr class="header">
<th align="left"></th>
<th align="left">built_up</th>
<th align="left">cassava</th>
<th align="left">grass</th>
<th align="left">maize</th>
<th align="left">sorghum</th>
<th align="left">sweet_potato</th>
<th align="left">trees</th>
<th align="left">waterbody</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">built_up</td>
<td align="left">268</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">1</td>
<td align="left">0</td>
</tr>
<tr class="even">
<td align="left">cassava</td>
<td align="left">0</td>
<td align="left">39</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
</tr>
<tr class="odd">
<td align="left">grass</td>
<td align="left">1</td>
<td align="left">0</td>
<td align="left">56</td>
<td align="left">22</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">5</td>
<td align="left">0</td>
</tr>
<tr class="even">
<td align="left">maize</td>
<td align="left">13</td>
<td align="left">0</td>
<td align="left">2</td>
<td align="left">233</td>
<td align="left">6</td>
<td align="left">0</td>
<td align="left">6</td>
<td align="left">4</td>
</tr>
<tr class="odd">
<td align="left">sorghum</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">5</td>
<td align="left">70</td>
<td align="left">0</td>
<td align="left">3</td>
<td align="left">0</td>
</tr>
<tr class="even">
<td align="left">sweet_potato</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">239</td>
<td align="left">0</td>
<td align="left">0</td>
</tr>
<tr class="odd">
<td align="left">trees</td>
<td align="left">0</td>