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<title>P6 Solutions to exercises with raster data (parts 3-4)</title>
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<h1 class="title toc-ignore">P6 Solutions to exercises with raster data (parts 3-4)</h1>
<h4 class="author"><em>João Gonçalves</em></h4>
<h4 class="date"><em>28 de Novembro de 2017</em></h4>
</div>
<p>Below are the solutions to <a href="http://r-exercises.com/2018/02/07/exercises-with-raster-data-parts-3-4/">these</a> exercises on raster data (parts 3-4).</p>
<pre class="r"><code>####################
# #
# Exercise 1 #
# #
####################
library(raster)</code></pre>
<pre><code>## Loading required package: sp</code></pre>
<pre><code>## Warning: package 'sp' was built under R version 3.4.1</code></pre>
<pre class="r"><code>## Create a folder named data-raw inside the working directory to place downloaded data
if(!dir.exists("./data-raw")) dir.create("./data-raw")
## If you run into download problems try changing: method = "wget"
download.file("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/srtm_pnpg.zip", "./data-raw/srtm_pnpg.zip", method = "auto")
# Unzip the data to the target folder
unzip("./data-raw/srtm_pnpg.zip", exdir = "./data-raw")
# Load data into R
rst <- raster("./data-raw/srtm_pnpg.tif")
# Create the extent object
extMask <- extent(c(xmin = 560640, xmax = 577390,
ymin = 4629790, ymax = 4646770))
# Mask the values to the extent
# Notice that the extent object is converted to SpatialPolygons
# to be used in mask function
rstMasked <- mask(rst, as(extMask,"SpatialPolygons"))</code></pre>
<pre class="r"><code>####################
# #
# Exercise 2 #
# #
####################
# Perform aggregation to multiple factors
rst2 <- aggregate(rst, fact = 2)
rst5 <- aggregate(rst, fact = 5)
rst10 <- aggregate(rst, fact = 10) </code></pre>
<pre class="r"><code>####################
# #
# Exercise 3 #
# #
####################
# Cut data by quantiles
rstReclassQuantiles <- cut(rst, breaks = quantile(values(rst)))</code></pre>
<pre class="r"><code>####################
# #
# Exercise 4 #
# #
####################
# Do k-means to elevation data
rstValues <- data.frame(elev = values(rst))
km <- kmeans(rstValues, centers = 5, iter.max = 50)
rstKM5 <- rst
values(rstKM5) <- km$cluster</code></pre>
<pre class="r"><code>####################
# #
# Exercise 5 #
# #
####################
# Calculate slope
rstSlope <- terrain(rst, opt = "slope", unit = "degrees", neighbors = 8)
cellStats(rstSlope, stat = function(x,...) quantile(x,probs=c(0.05,0.5,0.95),...))</code></pre>
<pre><code>## 5% 50% 95%
## 2.641758 11.584000 27.131897</code></pre>
<pre class="r"><code>####################
# #
# Exercise 6 #
# #
####################
## If you run into download problems try changing: method = "wget"
download.file("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/CIVPARISH_PNPG.zip", "./data-raw/CIVPARISH_PNPG.zip", method = "auto")
unzip("./data-raw/CIVPARISH_PNPG.zip", exdir = "./data-raw")
rstCivPar <- raster("./data-raw/PNPG_CivilParishes.tif")
# Calculate zonal stats for elevation
zonElev <- zonal(rst, rstCivPar, fun=mean)
# Calculate zonal stats for slope
zonSlope <- zonal(rstSlope, rstCivPar, fun=mean)
# Zone with average highest elevation
zonElev[which.max(zonElev[,2]), ]</code></pre>
<pre><code>## zone value
## 11.000 1156.901</code></pre>
<pre class="r"><code># Zone with highest average topographic roughness/heterogeneity
zonSlope[which.max(zonSlope[,2]), ]</code></pre>
<pre><code>## zone value
## 5.00000 17.91057</code></pre>
<pre class="r"><code>####################
# #
# Exercise 7 #
# #
####################
set.seed(12345)
# Generate random points with uniform distribution bounded by the raster extent
xyRandPoints <- data.frame(x = runif(50, xmin(rst), xmax(rst)),
y = runif(50, ymin(rst), ymax(rst)))
# Convert the initial data frame into a SpatialPoints objects for clarity
xyRandPoints <- SpatialPoints(xyRandPoints, proj4string = crs(rst))
# Calculate the distance to points raster dataset
distPoints <- distanceFromPoints(rst, xyRandPoints)
# Calculate the mean
cellStats(distPoints, stat = mean)</code></pre>
<pre><code>## [1] 3357.174</code></pre>
<pre class="r"><code>####################
# #
# Exercise 8 #
# #
####################
## Create a folder named data-raw inside the working directory to place downloaded data
if(!dir.exists("./data-raw")) dir.create("./data-raw")
## If you run into download problems try changing: method = "wget"
download.file("https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/data/MODIS_EVI_TS_PGNP_MultiBand.zip", "./data-raw/MODIS_EVI_TS_PGNP_MultiBand.zip", method = "auto")
## Uncompress the zip file
unzip("./data-raw/MODIS_EVI_TS_PGNP_MultiBand.zip", exdir = "./data-raw")</code></pre>
<pre class="r"><code># Load the raster data into a RasterBrick object
rst <- brick("./data-raw/MOD13Q1.2012_2016.PGNP_250m_EVI_16days.tif")
sgSmooth <- function(x, filtLen=21, ...) pracma::savgol(x, fl=filtLen, ...)
# Do Savitzy-Golay smoothing
rst_SGsmooth <- calc(rst, fun = sgSmooth)
# Extract data to test point
xyPt <- data.frame(x = 570760, y = 4628265)
eviTS1 <- extract(rst, xyPt)
eviTS2 <- extract(rst_SGsmooth, xyPt)
# Make plot comparing the original and smoothed EVI series
plot(1:115, eviTS1[1,], type = "l", lwd = 2, col="light grey",
main = "EVI time-series 2012-2016", xlab="Obs #index", ylab="EVI")
points(1:115, eviTS1[1,], col="dark grey")
lines(1:115, eviTS2[1,], col = "red", lwd = 2)
legend("topright",legend = c("Original","Smooth"),lwd=2,col = c("light grey","red"))</code></pre>
<div class="figure">
<img src="https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/img/P6_solution_ex8-1.png" />
</div>
<pre class="r"><code>####################
# #
# Exercise 9 #
# #
####################
# Calculate the yearly mean with stack apply
# Recall that each year has 23 observations
rstYrMean <- stackApply(rst, fun=mean, indices = rep(1:5,each=23))</code></pre>
<pre class="r"><code>####################
# #
# Exercise 10 #
# #
####################
# Calculate the yearly mean with stack apply for the smoothed series
rstSGsmoothYrMean <- stackApply(rst_SGsmooth, fun=mean, indices = rep(1:5,each=23))
# Calculate squared differences
sqDiffs <- (rstYrMean - rstSGsmoothYrMean)^2
# Calculate the root mean of the squared differences
rstRMSE <- sqrt(calc(sqDiffs, fun = mean))
# Calculate the quantiles
cellStats(rstRMSE, quantile)</code></pre>
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