-
Notifications
You must be signed in to change notification settings - Fork 3
/
P10_rasterPackage_solutions_III-v1.html
405 lines (298 loc) · 11.4 KB
/
P10_rasterPackage_solutions_III-v1.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="author" content="João Gonçalves" />
<title>P10 Solutions to exercises with raster data (advanced)</title>
<script src="P10_rasterPackage_solutions_III-v1_files/jquery-1.11.3/jquery.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="P10_rasterPackage_solutions_III-v1_files/bootstrap-3.3.5/css/bootstrap.min.css" rel="stylesheet" />
<script src="P10_rasterPackage_solutions_III-v1_files/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="P10_rasterPackage_solutions_III-v1_files/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="P10_rasterPackage_solutions_III-v1_files/bootstrap-3.3.5/shim/respond.min.js"></script>
<script src="P10_rasterPackage_solutions_III-v1_files/navigation-1.1/tabsets.js"></script>
<link href="P10_rasterPackage_solutions_III-v1_files/highlightjs-9.12.0/default.css" rel="stylesheet" />
<script src="P10_rasterPackage_solutions_III-v1_files/highlightjs-9.12.0/highlight.js"></script>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css">
pre:not([class]) {
background-color: white;
}
</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type="text/css">
h1 {
font-size: 34px;
}
h1.title {
font-size: 38px;
}
h2 {
font-size: 30px;
}
h3 {
font-size: 24px;
}
h4 {
font-size: 18px;
}
h5 {
font-size: 16px;
}
h6 {
font-size: 12px;
}
.table th:not([align]) {
text-align: left;
}
</style>
</head>
<body>
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
code {
color: inherit;
background-color: rgba(0, 0, 0, 0.04);
}
img {
max-width:100%;
height: auto;
}
.tabbed-pane {
padding-top: 12px;
}
button.code-folding-btn:focus {
outline: none;
}
</style>
<div class="container-fluid main-container">
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
</script>
<!-- code folding -->
<div class="fluid-row" id="header">
<h1 class="title toc-ignore">P10 Solutions to exercises with raster data (advanced)</h1>
<h4 class="author"><em>João Gonçalves</em></h4>
<h4 class="date"><em>5 December 2017</em></h4>
</div>
<p>Below are the solutions to <a href="http://r-exercises.com/2018/04/04/exercises-with-raster-data-advanced/">these</a> exercises on raster data (advanced).</p>
<pre class="r"><code>####################
# #
# Exercise 1 #
# #
####################
## 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/LT8_PNPG_MultiBand.zip", "./data-raw/LT8_PNPG_MultiBand.zip", method = "auto")
# Unzip the data to the target folder
unzip("./data-raw/LT8_PNPG_MultiBand.zip", exdir = "./data-raw")</code></pre>
<pre class="r"><code>library(raster)
# Load data into R
rst <- brick("./data-raw/LC82040312015193LGN00_sr_b_1_7.tif")
ncell(rst)</code></pre>
<pre><code>## [1] 2286600</code></pre>
<pre class="r"><code>nlayers(rst)</code></pre>
<pre><code>## [1] 7</code></pre>
<pre class="r"><code>####################
# #
# Exercise 2 #
# #
####################
plotRGB(rst, 5, 1, 3, stretch = "lin")</code></pre>
<div class="figure">
<img src="https://raw.githubusercontent.com/joaofgoncalves/R_exercises_raster_tutorial/master/img/P10_solution_ex2-1.png" />
</div>
<pre class="r"><code>####################
# #
# Exercise 3 #
# #
####################
# Get a data.frame with all data
rstDF <- values(rst)
# Index for non-NA values
idx <- complete.cases(rstDF)
# Perform k-means
km <- kmeans(rstDF[idx, ], centers = 5, iter.max = 100)
# Create a temporary integer vector for holding cluster numbers
kmClust <- vector(mode = "integer", length = ncell(rst))
# Generate the temporary clustering vector for K-means (keeps track of NA's)
kmClust[!idx] <- NA
kmClust[idx] <- km$cluster
# Set cluster values
kmRst <- rst[[1]]
values(kmRst) <- kmClust</code></pre>
<pre class="r"><code>####################
# #
# Exercise 4 #
# #
####################
library(cluster)
# Perform CLARA's clustering (using euclidean distance)
cla <- clara(rstDF[idx, ], k = 5, metric = "euclidean")
# Create a temporary integer vector for holding cluster numbers
claClust <- vector(mode = "integer", length = ncell(rst))
# Generate the temporary clustering vector for K-means (keeps track of NA's)
claClust[!idx] <- NA
claClust[idx] <- km$cluster
# Set cluster values
claRst <- rst[[1]]
values(claRst) <- claClust</code></pre>
<pre class="r"><code>####################
# #
# Exercise 5 #
# #
####################
library(RStoolbox)
# For Landsat8OLI use only bands: 2, 3, 4, 5, 6, and, 7
tctL8 <- tasseledCap(rst[[2:7]], sat="Landsat8OLI")</code></pre>
<pre class="r"><code>####################
# #
# Exercise 6 #
# #
####################
library(RStoolbox)
pcaL8 <- rasterPCA(rst, spca = TRUE)
print(pcaL8)
# Explained in the 3 comps = 99.04%
summary(pcaL8$model)</code></pre>
<pre class="r"><code>####################
# #
# Exercise 7 #
# #
####################
## (a)
##
## 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/CLIM_DATA_PT.zip", "./data-raw/CLIM_DATA_PT.zip", method = "auto")
## Uncompress the zip file
unzip("./data-raw/CLIM_DATA_PT.zip", exdir = "./data-raw")</code></pre>
<pre class="r"><code>library(gstat)</code></pre>
<pre><code>## Warning: package 'gstat' was built under R version 3.4.2</code></pre>
<pre class="r"><code>climDataPT <- read.csv("./data-raw/ClimData/clim_data_pt.csv")
proj4Str <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
statPoints <- SpatialPointsDataFrame(coords = climDataPT[,c("Lon","Lat")],
data = climDataPT,
proj4string = CRS(proj4Str))
## (b)
##
## ORDINARY KRIGING --------------------------------
set.seed(12345)
formMod <- AvgTemp ~ 1
modSph <- vgm(model = "Sph", psill = 3, range = 150, nugget = 0.5)
variog <- variogram(formMod, statPoints)
# Variogram fitting by Ordinary Least Squares (OLS)
variogFitOLS_Sph<-fit.variogram(variog, model = modSph, fit.method = 6)
# Kriging CV
OK.sph.cv <- krige.cv(formMod, statPoints, model=variogFitOLS_Sph, nfold=5)
# RMSE Spherical model
sqrt(mean((OK.sph.cv$residual)^2))</code></pre>
<pre><code>## [1] 1.337588</code></pre>
<pre class="r"><code>####################
# #
# Exercise 8 #
# #
####################
library(gstat)
## ORDINARY KRIGING --------------------------------
set.seed(12345)
formMod <- AvgTemp ~ 1
modExp <- vgm(model = "Exp", psill = 3, range = 150, nugget = 0.5)
variog <- variogram(formMod, statPoints)
# Variogram fitting by Ordinary Least Squares (OLS)
variogFitOLS_Exp<-fit.variogram(variog, model = modExp, fit.method = 6)
# Kriging CV
OK.Exp.cv <- krige.cv(formMod, statPoints, model=variogFitOLS_Exp, nfold=5)
# RMSE Exponential model
sqrt(mean((OK.Exp.cv$residual)^2))</code></pre>
<pre><code>## [1] 1.223307</code></pre>
<p>The Exponential model provided better results with lower RMSE.</p>
<pre class="r"><code>####################
# #
# Exercise 9 #
# #
####################
library(Cubist)</code></pre>
<pre><code>## Warning: package 'Cubist' was built under R version 3.4.3</code></pre>
<pre><code>## Loading required package: lattice</code></pre>
<pre class="r"><code>set.seed(12345)
idx <- sample(1:nrow(climDataPT), size = 15)
cub <- cubist(x = climDataPT[-idx, c("Lat","Elev","distCoast")],
y = climDataPT[-idx, "AvgTemp"])
obs <- climDataPT[idx, "AvgTemp"]
pred <- predict(cub, newdata = climDataPT[idx, ], type="response")
# RMSE Exponential model
sqrt(mean((obs - pred)^2))</code></pre>
<pre><code>## [1] 0.624294</code></pre>
<pre class="r"><code>####################
# #
# Exercise 10 #
# #
####################
library(gstat)
resid.cub <- climDataPT[-idx, "AvgTemp"] - predict(cub, newdata = climDataPT[-idx, ], type="response")
idxbool <- 1:nrow(climDataPT) %in% idx
statPointsTrain <- statPoints[!idxbool, ]
statPointsTrain@data <- cbind(statPointsTrain@data, residCubist = resid.cub)
statPointsTest <- statPoints[idxbool, ]
formMod <- residCubist ~ 1
modExp <- vgm(model = "Exp", psill = 0.35, range = 5, nugget = 0.01)
variog <- variogram(formMod, statPointsTrain)
# Variogram fitting by Ordinary Least Squares (OLS)
variogFitOLS_Exp<-fit.variogram(variog, model = modExp, fit.method = 6)</code></pre>
<pre><code>## Warning in fit.variogram(variog, model = modExp, fit.method = 6): No
## convergence after 200 iterations: try different initial values?</code></pre>
<pre class="r"><code>#plot(variog, variogFitOLS_Exp, main="OLS Model")
# kriging predictions
OK <- krige(formula = residCubist ~ 1 ,
locations = statPointsTrain,
model = variogFitOLS_Exp,
newdata = statPointsTest,
debug.level = 0)
pred.resid <- OK@data$var1.pred
pred.cubist <- predict(cub, newdata = climDataPT[idx, ], type="response")
pred.RK <- pred.cubist + pred.resid
obs <- climDataPT[idx, "AvgTemp"]
sqrt(mean((pred.RK - obs)^2))</code></pre>
<pre><code>## [1] 0.6110832</code></pre>
<p>Yes, regression-kriging does improve slightly the average temperature predictions for the test set.</p>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.header').parent('thead').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>