-
Notifications
You must be signed in to change notification settings - Fork 1
/
deforestmap.R
1166 lines (1044 loc) · 47 KB
/
deforestmap.R
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
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#!/usr/bin/Rscript
# ==============================================================================
# author :Ghislain Vieilledent
# email :ghislain.vieilledent@cirad.fr, ghislainv@gmail.com
# web :https://ghislainv.github.io
# license :GPLv3
# ==============================================================================
##=========================================================================
## Six decades of deforestation and forest fragmentation in Madagascar
##=========================================================================
## gdal library is needed to run this script
## http://www.gdal.org/
## GRASS GIS 7.2.x is also needed to run this script
## https://grass.osgeo.org/
Sys.unsetenv("PROJ_LIB")
##= Libraries
pkg <- c("broom","sp","rgdal","raster","ggplot2","gridExtra","RColorBrewer",
"dataverse",
"rasterVis","knitr","rmarkdown","kableExtra","rgeos","rgrass7")
## broom: to convert map into data-frame with tiny()
## gridExtra: to combine several ggplots
## rasterVis: for gplot()
## rgeos: for crop()
load.pkg <- function(x) {
if(!require(x, character.only = T)) {
install.packages(x)
require(x, character.only = T)
}
}
loaded <- lapply(pkg,load.pkg)
## Remove useless objects
rm(pkg,load.pkg,loaded)
##=====================================
## Name of objects to save
SavedObjects <- c()
##=====================================
## Create new grass location in UTM 38S
#dir.create("grassdata")
#system("grass72 -c epsg:32738 grassdata/deforestmap") # Ignore errors
## Connect R to grass location
## Make sure that /usr/lib/grass72/lib is in your PATH in RStudio
## On Linux, find the path to GRASS GIS with: $ grass72 --config path
## It should be somethin like: "/usr/lib/grass72"
## On Windows, find the path to GRASS GIS with: C:\>grass72.bat --config path
## If you use OSGeo4W, it should be: "C:\OSGeo4W\apps\grass\grass-7.2"
Sys.setenv(LD_LIBRARY_PATH=paste("/usr/lib/grass76/lib", Sys.getenv("LD_LIBRARY_PATH"),sep=":"))
## Initialize GRASS
initGRASS(gisBase="/usr/lib/grass76",home=tempdir(),
gisDbase="grassdata",
location="deforestmap",mapset="PERMANENT",
override=TRUE)
##==================================================================
## Download data from Cirad Dataverse: http://dx.doi.org/10.18167/DVN1/2FP7LR
down <- FALSE
if (down) {
library(dataverse)
Sys.setenv("DATAVERSE_SERVER"="dataverse.cirad.fr")
dataverse::get_dataset("doi:10.18167/DVN1/2FP7LR")
f <- dataverse::get_file(file="gisdata.zip",
dataset="doi:10.18167/DVN1/2FP7LR")
writeBin(f, "gisdata.zip")
unzip("gisdata.zip", exdir="gisdata")
}
##===========================================================
## Create new directories to save outputs
dir.create("outputs")
#= Projection UTM38S (EPSG:32738)
#= Reproject Harper 1990-2000-2005 map
# Region
Extent <- "298440 7155900 1100820 8682420"
Res <- "30"
nodat <- "0"
proj.s <- "EPSG:32638"
proj.t <- "EPSG:32738"
Input <- "gisdata/rasters/harper/905_dsp_nosea.img"
Output <- "outputs/harper.tif"
# gdalwarp
system(paste0("gdalwarp -overwrite -dstnodata ",nodat," -s_srs ",proj.s," -t_srs ",proj.t," -te ",Extent,
" -tr ",Res," ",Res," -r near -co 'COMPRESS=LZW' -co 'PREDICTOR=2' ",Input," ",Output))
#= Import harper into grass location
system("r.in.gdal --o input=outputs/harper.tif output=harper")
# Set region
system("g.region rast=harper -ap")
#= Typology of harper map 1990-2000-2005
system("r.info harper")
system("r.describe harper")
# * 111 112 115 122 152 155 222 444 555 755 775 777
# 1=forest, 2=nonforest, 5=cloud, 4=water, 7=mangrove
system("r.report --o harper units=h output='outputs/report_harper.txt'")
## +-----------------------------------------------------------------------------+
## | RASTER MAP CATEGORY REPORT |
## |LOCATION: deforestmap Wed Mar 1 09:22:04 2017|
## |-----------------------------------------------------------------------------|
## | north: 8682420 east: 1100820 |
## |REGION south: 7155900 west: 298440 |
## | res: 30 res: 30 |
## |-----------------------------------------------------------------------------|
## |MASK: none |
## |-----------------------------------------------------------------------------|
## |MAP: (untitled) (harper in PERMANENT) |
## |-----------------------------------------------------------------------------|
## | Category Information | |
## | #|description | hectares|
## |-----------------------------------------------------------------------------|
## |111| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 9,059,766|
## |112| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 241,399|
## |115| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 214,753|
## |122| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 791,318|
## |152| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 180,540|
## |155| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 17,862|
## |222| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 47,922,638|
## |444| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 470,196|
## |555| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 9603|
## |755| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 187|
## |775| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 22|
## |777| . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 254,977|
## | *|no data. . . . . . . . . . . . . . . . . . . . . . . . . . . | 63,321,651|
## |-----------------------------------------------------------------------------|
## |TOTAL |122,484,912|
## +-----------------------------------------------------------------------------+
#====================================================================================
# Report on clouds
#= Whole forest
# Import the GRASS report into R
r <- readLines("outputs/report_harper.txt")
r <- r[-c(1:15,28:31)]
writeLines(r, "outputs/report_harper.txt")
report.harper <- read.table(file="outputs/report_harper.txt", sep="|", header=FALSE)
report.harper <- report.harper[,c(2,4)]
names(report.harper) <- c("code", "area")
report.harper$area <- as.numeric(gsub(pattern=",", replacement="", report.harper$area))
# Hectares of cloud in 2000 Harper map
ha.cloud.2000 <- sum(report.harper$area[report.harper$code %in% c(152, 155, 555, 755)]) # 208192 ha
SavedObjects <- c(SavedObjects, "ha.cloud.2000")
#= By ecoregion
# 1: spiny, 2: mangroves, 3: moist, 4: dry
# Rasterize ecoregions
Extent <- "298440 7155900 1100820 8682420"
Res <- "30"
nodat <- "255"
Input <- "gisdata/vectors/madagascar_ecoregion_tenaizy_38s.shp"
Output <- "outputs/ecoregion.tif"
# gdalwarp
system(paste0("gdal_rasterize -a 'ecoregion' -ot Byte -a_nodata 255 -te ",Extent,
" -tr ",Res," ",Res," -co 'COMPRESS=LZW' -co 'PREDICTOR=2' ",Input," ",Output))
# Import into grass
system("r.in.gdal --o input=outputs/ecoregion.tif output=ecoregion")
# Cross-tabulation table
system("r.report --o map=ecoregion,harper units=h output='outputs/report_harper_ecoregion.txt'")
# Moist ecoregions
# Import the GRASS report into R
r <- readLines("outputs/report_harper_ecoregion.txt")
r <- r[c(47:57)] # select outputs for the moist ecoregion
writeLines(r, "outputs/report_harper_ecoregion.txt")
report.harper.moist <- read.table(file="outputs/report_harper_ecoregion.txt", sep="|", header=FALSE)
report.harper.moist <- report.harper.moist[,c(3,5)]
names(report.harper.moist) <- c("code", "area")
report.harper.moist$area <- as.numeric(gsub(pattern=",", replacement="", report.harper.moist$area))
# Hectares of cloud in 2000 Harper map fr moist ecoregion
ha.cloud.2000.moist <- sum(report.harper.moist$area[report.harper.moist$code %in% c(152, 155, 555, 755)]) # 182650 ha
perc.cloud.moist <- 100*ha.cloud.2000.moist/ha.cloud.2000 # 88%
SavedObjects <- c(SavedObjects, "ha.cloud.2000.moist", "perc.cloud.moist")
##========================
## Tree cover 2000
##========================
#= Mosaic with gdalbuildvrt
Input <- "gisdata/rasters/hansen/treecover2000/*.tif"
Output <- "outputs/treecover2000.vrt"
system(paste0("gdalbuildvrt -overwrite ",Output," ",Input))
#= Resample with gdalwrap
# Region
Extent <- "298440 7155900 1100820 8682420"
Res <- "30"
nodat <- "None"
proj.s <- "EPSG:4326"
proj.t <- "EPSG:32738"
otype <- "Byte"
Input <- "outputs/treecover2000.vrt"
Output <- "outputs/treecover2000.tif"
# gdalwarp
system(paste0("gdalwarp -overwrite -ot ",otype," -dstnodata ",nodat," -s_srs ",proj.s," -t_srs ",proj.t," -te ",Extent,
" -tr ",Res," ",Res," -r bilinear -co 'COMPRESS=LZW' -co 'PREDICTOR=2' ",Input," ",Output))
#= Import treecover2000.tif into grass location
system("r.in.gdal --o input=outputs/treecover2000.tif output=tc2000")
##========================
## Histogram of tree cover
##========================
# Moist forest 2000 in Harper
system("r.mapcalc --o 'for2000_Harper_moist = if((harper==111 || harper==112 || harper==115 || \\
harper==775 || harper==777) && ecoregion==3, 1, null())'")
system("r.mask --o raster=for2000_Harper_moist")
system("r.mapcalc --o 'tc2000_moist = tc2000'")
system("r.mask -r")
hist_tc2000_moist <- system("r.stats -c input=tc2000_moist nsteps=100", intern=TRUE)
df_tc2000_moist <- data.frame(matrix(unlist(strsplit(hist_tc2000_moist, " ")), ncol=2, byrow=TRUE))
df_tc2000_moist <- df_tc2000_moist[-c(1,102),]
names(df_tc2000_moist) <- c("tc","npix")
df_tc2000_moist$npix <- as.numeric(as.character(df_tc2000_moist$npix))
df_tc2000_moist$tc <- as.numeric(as.character(df_tc2000_moist$tc))
df_tc2000_moist$nha <- df_tc2000_moist$npix*30*30/10000
df_tc2000_moist$freq <- 100*df_tc2000_moist$npix/sum(df_tc2000_moist$npix)
# Cumulative proportion
for (i in 100:1) {
df_tc2000_moist$cum_prop[i] <- 100*sum(df_tc2000_moist$npix[100:i])/sum(df_tc2000_moist$npix)
}
write.table(df_tc2000_moist, file="outputs/df_tc2000_moist.txt", sep="\t", row.names=FALSE)
# How much moist forest has a TC over 75%
prop_tc75 <- round(df_tc2000_moist$cum_prop[df_tc2000_moist$tc==75]) # 90%
prop_tc70 <- round(df_tc2000_moist$cum_prop[df_tc2000_moist$tc==70]) # 92%
# Plot pdf
pdf("outputs/tc_threshold_moist.pdf", width=8, height=8)
par(mar=c(5,4,2,1),cex=1.4)
plot(df_tc2000_moist$tc,df_tc2000_moist$cum_prop, type="l",
lwd=2,
xlab="Tree cover (%)",
ylab="Percentage of moist forest",
ylim=c(0,100), axes=FALSE)
axis(1, at=seq(0,100,25), labels=seq(0,100,25))
axis(2, at=seq(0,100,10), labels=seq(0,100,10))
abline(v=75, lty=2)
abline(h=90, lty=2)
dev.off()
#======================================================================================================
# The objective is to remove clouds in the year 2000 (which are mainly located above the moist forest).
# To do so, we will use the tree cover map by Hansen et al. using a threshold of 75\%
#= Forest in 2000
# Integrating classes of forest in 2000 (111, 112, 115, 775 and 777) and uncertainties (152, 155, 555 and 755)
system("r.mapcalc --o 'for2000_temp = if(harper==111 || harper==112 || harper==115 || \\
harper==775 || harper==777 || harper==152 || harper==155 || \\
harper==555 || harper==755, 1, null())'")
# Replace uncertainties using Hansen tree cover and a threshold of 75\%
system("r.mapcalc --o 'for2000 = if((harper==152 || harper==155 || \\
harper==555 || harper==755) && tc2000<75 , null(), for2000_temp)'")
#system("r.stats -c for2000_temp")
#1 110878983
#* 1250064481
#system("r.stats -c for2000")
#1 109763775
#* 1251179689
system("g.remove -f type=raster name=for2000_temp")
#= lossyear Hansen
#= Mosaic with gdalbuildvrt
Input <- "gisdata/rasters/hansen/lossyear/*.tif"
Output <-"outputs/lossyear.vrt"
system(paste("gdalbuildvrt -overwrite ",Output," ",Input,sep=""))
#= Resample with gdalwrap
# Region
Extent <- "298440 7155900 1100820 8682420"
Res <- "30"
nodat <- "0"
proj.s <- "EPSG:4326"
proj.t <- "EPSG:32738"
otype <- "Byte"
Input <- "outputs/lossyear.vrt"
Output <- "outputs/lossyear.tif"
# gdalwarp
system(paste0("gdalwarp -overwrite -ot ",otype," -dstnodata ",nodat," -s_srs ",proj.s," -t_srs ",proj.t," -te ",Extent,
" -tr ",Res," ",Res," -r near -co 'COMPRESS=LZW' -co 'PREDICTOR=2' ",Input," ",Output))
#= Import lossyear.tif into grass location
system("r.in.gdal --o input=outputs/lossyear.tif output=lossyear")
#system("r.describe lossyear")
# * 1-17
#= Forest in year 2001-2017 ("at the end of the year", since 1 in lossyear is deforestation in 2001)
for (i in 1:17) {
# Message
cat(paste("Year: ",i,"\n",sep=""))
# Computation
system(paste0("r.mapcalc --o 'defor = if(lossyear>0 && lossyear<=",i,",1,null())'"))
if (i <= 9) {
system(paste0("r.mapcalc --o 'for200",i," = if(!isnull(defor),null(),for2000)'"))
}
else {
system(paste0("r.mapcalc --o 'for20",i," = if(!isnull(defor),null(),for2000)'"))
}
}
#====================================================================================
# Forest in 1990
#= Integrating classes of forest in 1990 (111, 112, 122, 115, 152, 155, 755, 775 and 777) and uncertainties (555)
system("r.mapcalc --o 'for1990_temp = if(harper==111 || harper==112 || harper==122 || \\
harper==115 || harper==152 || harper==155 || harper==755 || \\
harper==775 || harper==777, 1, if(harper==555, 5, null()))'")
system("r.stats -c for1990_temp")
#= If forest in 2000, then forest in 1990
system("r.mapcalc --o 'for1990_temp = if(!isnull(for2000) &&& for1990_temp==5, 1, for1990_temp)'")
#= How many ha of remaining clouds in harper for the year 1990
stats.residual <- system("r.stats -c for1990_temp", intern=TRUE) # "1 119582689" "5 88716" "* 1241272059"
df.stats.residual <- data.frame(matrix(as.numeric(unlist(strsplit(stats.residual," "))), ncol=2, byrow=TRUE))
names(df.stats.residual) <- c("class","ncells")
df.stats.residual$area <- round(df.stats.residual$ncells*30*30/10000)
residual.clouds.1990 <- df.stats.residual$area[df.stats.residual$class==5 & !is.na(df.stats.residual$class)]
SavedObjects <- c(SavedObjects, "residual.clouds.1990")
#= Removing remaining uncertainties (5) using forest-cover in 2000
system("r.mapcalc --o 'for1990 = if(for1990_temp==5, for2000, for1990_temp)'")
system("r.stats -c for1990") # 1 119582689 * 1241360775, all clouds were reclassified as non-forest
# system("g.remove -f type=raster name=for1990_temp")
#====================================================================================
# Forest c.1973
#= Reproject
Extent <- "298440 7155900 1100820 8682420"
Res <- "30"
nodat <- "0"
proj.s <- "EPSG:32638"
proj.t <- "EPSG:32738"
Input <- "gisdata/rasters/harper/for1973.tif"
Output <- "outputs/harper1973.tif"
# gdalwarp
system(paste0("gdalwarp -overwrite -dstnodata ",nodat," -s_srs ",proj.s," -t_srs ",proj.t," -te ",Extent,
" -tr ",Res," ",Res," -r near -co 'COMPRESS=LZW' -co 'PREDICTOR=2' ",Input," ",Output))
#= Import forest map for the year 1973
system("r.in.gdal --o input=outputs/harper1973.tif output=harper1973")
#= Corrections from for1990
system("r.mapcalc --o 'for1973 = if(!isnull(for1990), for1990, harper1973)'")
#= Combine mangrove (7) and forest (1)
system("r.mapcalc --o 'for1973 = if(for1973==7, 1, for1973)'")
#= Cloud cover on for1973
stats.1973 <- system("r.stats -c for1973", intern=TRUE)
df.stats.1973 <- data.frame(matrix(as.numeric(unlist(strsplit(stats.1973," "))), ncol=2, byrow=TRUE))
names(df.stats.1973) <- c("class","ncells")
df.stats.1973$area <- round(df.stats.1973$ncells*30*30/10000)
cloud.1973 <- df.stats.1973$area[df.stats.1973$class==5 & !is.na(df.stats.1973$class)]
SavedObjects <- c(SavedObjects, "cloud.1973")
#= Export
system("r.out.gdal --o input=for1973 createopt='compress=lzw,predictor=2' type=Byte output=outputs/for1973.tif")
#= We rename for1973 (with cloud class 5) as for1973_5
#= for1973 will be without clouds
system("g.rename raster=for1973,for1973_5")
system("r.mapcalc --o 'for1973 = if(!isnull(for1973_5) &&& for1973_5==1, 1, null())'")
#====================================================================================
# Reproject c.1953
#= Reproject
Extent <- "298440 7155900 1100820 8682420"
Res <- "30"
nodat <- "0"
proj.s <- "EPSG:32638"
proj.t <- "EPSG:32738"
Input <- "gisdata/rasters/harper/madagascar_1950_4bit.img"
Output <- "outputs/harper1953.tif"
# gdalwarp
system(paste0("gdalwarp -overwrite -dstnodata ",nodat," -s_srs ",proj.s," -t_srs ",proj.t," -te ",Extent,
" -tr ",Res," ",Res," -r near -co 'COMPRESS=LZW' -co 'PREDICTOR=2' ",Input," ",Output))
#= Import harper1953.tif into grass location
system("r.in.gdal --o input=outputs/harper1953.tif output=harper1953")
system("r.info harper1953")
system("r.stats -c harper1953")
#= Compute for1953
system("r.mapcalc --o 'for1953 = if(harper1953!=1,null(),1)'")
system("r.stats -c for1953")
## 1 225792265
## * 1135151199
# =======================================
# Remove salt and pepper
# =======================================
Year <- c(1953,1973,1990,2000,2005,2010,2013,2015,2017)
system("g.region rast=harper -ap")
system("r.mask --o raster=harper")
for (i in 1:length(Year)) {
# Message
cat(paste0("Year: ",Year[i],"\n"))
# Computation
system(paste0("r.neighbors --o input=for",Year[i]," output=for",Year[i],"_sum method=sum size=3"))
system(paste0("r.mapcalc --o 'for",Year[i]," = if(isnull(for",Year[i],") &&& for",Year[i],"_sum==8, 1, for",Year[i],")'"))
# Export raster as .tif
if (Year[i]!=1973) {
system(paste0("r.out.gdal --o input=for",Year[i]," createopt='compress=lzw,predictor=2' \\
type=Byte output=outputs/for",Year[i],".tif"))
} else {
system("r.out.gdal --o input=for1973_5 createopt='compress=lzw,predictor=2' \\
type=Byte output=outputs/for1973.tif")
}
}
system("r.mask -r")
#= Counting how many pixels were set as forest
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
system("g.region rast=harper -ap")
system("r.mask --o raster=harper")
for (i in 1:length(Year)) {
# Message
cat(paste0("Year: ",Year[i],"\n"))
# Computation
system(paste0("r.report --o map=for",Year[i],"_sum unit=h output=outputs/for",Year[i],"_sum.txt"))
}
system("r.mask -r")
# Summary data.frame
df.saltpepper <- data.frame(Year=Year,ha.salt=NA)
df.saltpepper$ha.salt <- c(95209,452572,481855,455098,538907,605073,685860,666164)
write.table(df.saltpepper, file="outputs/saltpepper.txt", sep="\t", row.names=FALSE)
# ===========================================
# Annual fcc from 2000 to 2017, GV 24.11.2020
# ===========================================
# Remove salt and pepper
Year <- 2000:2017
system("g.region rast=harper -ap")
system("r.mask --o raster=harper")
for (i in 1:length(Year)) {
# Message
cat(paste0("Year: ",Year[i],"\n"))
# Computation
system(paste0("r.neighbors --o input=for",Year[i]," output=for",Year[i],"_sum method=sum size=3"))
system(paste0("r.mapcalc --o 'for",Year[i]," = if(isnull(for",Year[i],") &&& for",Year[i],"_sum==8, 1, for",Year[i],")'"))
}
system("r.mask -r")
# Combine fcc in one raster
system("r.mapcalc --o 'fcc_2000_2017 = 118*for2017'")
for (i in (length(Year)-1):1) {
system(paste0("r.mapcalc --o 'fcc_2000_2017 = if(isnull(fcc_2000_2017) && !isnull(for",Year[i],"), ", i,", fcc_2000_2017)'"))
}
#Colors
system("r.colors map=fcc_2000_2017 rules=- << EOF
1 255:165:0
17 227:26:28
118 34:139:34
nv 255:255:255
EOF")
# Export as GeoTIFF
system("r.out.gdal --o input=fcc_2000_2017 nodata=0 createopt='compress=lzw,predictor=2' \\
type=Byte output=outputs/fcc_2000_2017.tif")
# Deforestation by year
system("r.report -n map=fcc_2000_2017 units=h output=outputs/fcc_2000_2017.txt")
fcc_data <- read.table("outputs/fcc_2000_2017.txt", skip=15, nrows=17, sep="|", header=FALSE)
df <- data.frame(Year=c(2001:2018), d=as.numeric(c(sub(",", "", fcc_data$V4), NA)))
# Forest cover at 1sr January 2001
fc2001_in <- read.table("outputs/fcc_2000_2017.txt", skip=34, nrows=1, sep="|", header=FALSE)$V3
fc2001 <- as.numeric(gsub(",", "", (sub(" ", "", as.character(fc2001_in)))))
fc <- fc2001
for (i in 1:(length(Year)-1)) {fc[i+1] <- fc[i]-df$d[i]}
df$fc <- fc
write.table(df, "outputs/fcc_2000_2017.csv", sep=",", row.names=FALSE)
# Plot
p <- ggplot(data=df, aes(x=Year, y=d, group=1)) +
geom_smooth(method=lm) +
geom_line() +
geom_point() +
ylab("Annual deforested area (ha/yr)") +
theme_bw()
ggsave("outputs/fcc_2000_2017.png", plot=p)
#====================================================================================
# Export images as .png and .gif
#= Colors
system("echo '1 26:152:80' | r.colors for1953 rules=-")
system("echo '1 26:152:80 \n 5 grey' | r.colors for1973_5 rules=-")
system("echo '1 26:152:80' | r.colors for1990 rules=-")
system("echo '1 26:152:80' | r.colors for2000 rules=-")
system("echo '1 26:152:80' | r.colors for2005 rules=-")
system("echo '1 26:152:80' | r.colors for2010 rules=-")
system("echo '1 26:152:80' | r.colors for2015 rules=-")
system("echo '1 26:152:80' | r.colors for2017 rules=-")
#= PNG
system("g.region res=1000 -p")
system("r.out.png --o input=for1953 output=outputs/for1953.png")
system("r.out.png --o input=for1973_5 output=outputs/for1973.png")
system("r.out.png --o input=for1990 output=outputs/for1990.png")
system("r.out.png --o input=for2000 output=outputs/for2000.png")
system("r.out.png --o input=for2005 output=outputs/for2005.png")
system("r.out.png --o input=for2010 output=outputs/for2010.png")
system("r.out.png --o input=for2015 output=outputs/for2015.png")
system("r.out.png --o input=for2017 output=outputs/for2017.png")
#= Animated GIF
# Image Magick library should be installed: https://www.imagemagick.org
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '1953'\" outputs/for1953.png outputs/for1953.gif")
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '1973'\" outputs/for1973.png outputs/for1973.gif")
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '1990'\" outputs/for1990.png outputs/for1990.gif")
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '2000'\" outputs/for2000.png outputs/for2000.gif")
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '2005'\" outputs/for2005.png outputs/for2005.gif")
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '2010'\" outputs/for2010.png outputs/for2010.gif")
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '2015'\" outputs/for2015.png outputs/for2015.gif")
system("convert -pointsize 72 -gravity North -draw \"text 0,0 '2017'\" outputs/for2017.png outputs/for2017.gif")
system("convert -delay 100 -loop 0 outputs/*.gif outputs/defor_Mada.gif")
## system("rm outputs/*.png")
##==========================
## Forest density
##==========================
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
system("g.region rast=harper -ap")
# Density rules
txt <- c("0 thru 20 = 1", "21 thru 40 = 2", "41 thru 60 = 3", "61 thru 80 = 4", "81 thru 100 = 5")
writeLines(txt, "outputs/densrules.txt")
# Loop on years
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Mask on forest
system(paste0("r.mask --o raster=for",Year[i]))
# Forest density (takes several hours to run)
system(paste0("r.neighbors --o input=for",Year[i]," output=forcount",Year[i]," method=count size=51"))
system(paste0("r.mapcalc --o 'fordens",Year[i]," = round(100*forcount",Year[i],"/2601)'"))
# Reclassify
system(paste0("r.reclass --o input=fordens",Year[i]," output=fordens",Year[i],"_class rules=outputs/densrules.txt"))
# Export
system(paste0("r.out.gdal --o input=fordens",Year[i]," createopt='compress=lzw,predictor=2' type=Byte output=outputs/fordens",Year[i],".tif"))
system(paste0("r.out.gdal --o input=fordens",Year[i],"_class createopt='compress=lzw,predictor=2' type=Byte output=outputs/fordens",Year[i],"_class.tif"))
}
# Prepare fordens2017_class for figures
system("r.mask --o raster=harper")
system("r.mapcalc --o 'fordens2017_class_mask = if(isnull(fordens2017_class), 0, fordens2017_class)'")
system("r.out.gdal --o input=fordens2017_class_mask nodata=255 type=Byte createopt='compress=lzw,predictor=2' \\
output=outputs/fordens2017_class_mask.tif")
system("r.mask -r")
##==========================
## Forest fragmentation
##==========================
## The GRASS Add-on r.forestfrag must be installed
## https://grass.osgeo.org/grass72/manuals/addons/r.forestfrag.html
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
system("g.region rast=harper -ap")
system("r.mask --o raster=harper")
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
system(paste0("r.mapcalc --o 'for",Year[i],"_0 = if(!isnull(for",Year[i],"), 1, 0)'"))
system(paste0("r.forestfrag input=for",Year[i],"_0 output=frag",Year[i]," size=7 --overwrite"))
system(paste0("r.out.gdal --o input=frag",Year[i]," createopt='compress=lzw,predictor=2' type=Byte output=outputs/frag",Year[i],".tif"))
}
system("r.mask -r")
##==========================
## Distance to forest edge
##==========================
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
system("g.region rast=harper -ap")
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
system(paste0("gdal_proximity.py outputs/for",Year[i],".tif outputs/_dist_edge_",Year[i],".tif \\
-co 'COMPRESS=LZW' -co 'PREDICTOR=2' -values 255 -ot UInt32 -distunits GEO"))
system(paste0("gdal_translate -a_nodata 0 -co 'COMPRESS=LZW' -co 'PREDICTOR=2' \\
outputs/_dist_edge_",Year[i],".tif outputs/dist_edge_",Year[i],".tif"))
file.remove(paste0("outputs/_dist_edge_",Year[i],".tif"))
system(paste0("r.in.gdal --o input=outputs/dist_edge_",Year[i],".tif output=dist_edge_",Year[i]))
}
# Prepare dist_edge_2017 for figures
system("r.mask --o raster=harper")
system("r.mapcalc --o 'dist_edge_2017_mask = if(isnull(dist_edge_2017), 0, dist_edge_2017)'")
system("r.out.gdal --o input=dist_edge_2017_mask nodata=9999 createopt='compress=lzw,predictor=2' \\
output=outputs/dist_edge_2017_mask.tif")
system("r.mask -r")
##==========================
## Histograms forest edge
##==========================
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
year <- length(Year)
# Bins of distance
bins <- seq(0,5000,by=100)
nbins <- length(bins)-1
# Data-frame to store results
r <- data.frame()
# Loop on years
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Histogram
his <- system(paste0("r.stats -nc input=dist_edge_",Year[i]), intern=TRUE)
d <- data.frame(matrix(as.numeric(unlist(strsplit(his," "))), ncol=2, byrow=TRUE))
names(d) <- c("bin","ncell")
# Number of cells per bin of distance
hist.df <- data.frame(bins=1:nbins,ncell=NA)
for (i in 1:nbins) {
w <- which(d$bin>=bins[i] & d$bin<bins[i+1])
hist.df$ncell[i] <- sum(d$ncell[w])
}
# Proportion
tot.cell <- sum(hist.df$ncell)
hist.df$prop <- 100*hist.df$ncell/tot.cell
# Store results
r <- rbind(r,hist.df)
}
# Year as factor
r$Year <- as.factor(rep(Year,each=nbins))
# Reformat bins
r$bins <- r$bins*100-50
# Save results
write.table(r,file="outputs/histdist.txt",row.names=FALSE,sep="\t")
# Plot with ggplot
pp <- ggplot(data=r, aes(x=bins, y=prop, colour=Year)) +
xlab("Distance to forest edge (m)") +
ylab("Proportion of forest (%)") +
xlim(c(0,5000)) +
geom_line()
ggsave("outputs/histdist.png",pp)
##========================
## Statistics whole forest
##========================
#= Deforestation statistics
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
defor.df <- data.frame(Year=Year,area=NA,clouds=0,ann.defor=NA,theta=NA)
# Areas
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
if (Year[i]=="1973") {
statcell <- system("r.stats -c for1973_5", intern=TRUE)
} else {
statcell <- system(paste0("r.stats -c for",Year[i]), intern=TRUE)
}
ncells <- as.numeric(strsplit(statcell[1],split=" ")[[1]][2])
defor.df$area[i] <- round(ncells*(as.numeric(Res)^2)/10000)
}
# Annual rates
for (i in 2:length(Year)) {
theta.prim <- (defor.df$area[i-1]-defor.df$area[i])/defor.df$area[i-1]
Y <- defor.df$Year[i]-defor.df$Year[i-1]
defor.df$ann.defor[i] <- round((defor.df$area[i-1]-defor.df$area[i])/Y)
defor.df$theta[i] <- round(100*(1-(1-theta.prim)^(1/Y)),2)
}
# Clouds in 1973
defor.df$clouds[defor.df$Year==1973] <- cloud.1973
# Export
write.table(defor.df,"outputs/defor.txt",row.names=FALSE,sep="\t")
#= Deforestation statistics for comparison
Year <- c(1953,1973,1990,2000,2005,2010,2013) # Here we add 2013
defor.df <- data.frame(Year=Year,area=NA,ann.defor=NA,theta=NA)
# Areas
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
statcell <- system(paste0("r.stats -c for",Year[i]), intern=TRUE)
ncells <- as.numeric(strsplit(statcell[1],split=" ")[[1]][2])
defor.df$area[i] <- round(ncells*(as.numeric(Res)^2)/10000)
}
# Annual rates
for (i in 2:length(Year)) {
theta.prim <- (defor.df$area[i-1]-defor.df$area[i])/defor.df$area[i-1]
Y <- defor.df$Year[i]-defor.df$Year[i-1]
defor.df$ann.defor[i] <- round((defor.df$area[i-1]-defor.df$area[i])/Y)
defor.df$theta[i] <- round(100*(1-(1-theta.prim)^(1/Y)),2)
}
# Export
write.table(defor.df,"outputs/defor_for_comp.txt",row.names=FALSE,sep="\t")
#= Forest density statistics
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
fordens.df <- data.frame(Year=Year,forest=NA,cat1=NA,cat2=NA,
cat3=NA,cat4=NA,cat5=NA)
# Areas
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
statcell <- system(paste0("r.stats -c fordens",Year[i],"_class"), intern=TRUE)
ncells <- as.numeric(matrix(unlist(strsplit(statcell,split=" ")),ncol=2,byrow=TRUE)[-c(6),2])
fordens.df$forest[i] <- round(sum(ncells)*(as.numeric(Res)^2)/10000)
fordens.df[i,c(3:7)] <- round(100*ncells/sum(ncells),2)
}
write.table(fordens.df,"outputs/fordens.txt",row.names=FALSE,sep="\t")
#= Fragmentation statistics
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
frag.df <- data.frame(Year=Year,forest=NA,patch=NA,transitional=NA,
edge=NA,perforated=NA,interior=NA,undetermined=NA)
Res <- "30"
# Areas
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
statcell <- system(paste0("r.stats -c frag",Year[i]), intern=TRUE)
ncells <- as.numeric(matrix(unlist(strsplit(statcell,split=" ")),ncol=2,byrow=TRUE)[-c(1,8),2])
frag.df$forest[i] <- round(sum(ncells)*(as.numeric(Res)^2)/10000)
frag.df[i,c(3:8)] <- round(100*ncells/sum(ncells),2)
}
write.table(frag.df,"outputs/frag.txt",row.names=FALSE,sep="\t")
#= Distance to forest edge statistics ## Quantiles with r.quantile
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
dist.quant.df <- data.frame(Year=Year,median=NA,q1=NA,q2=NA)
# Loop on year
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
statcell <- system(paste0("r.quantile percentiles=5,50,95 input=dist_edge_",Year[i]), intern=TRUE)
mat.statcell <- matrix(as.numeric(unlist(strsplit(statcell,split=":"))), byrow=TRUE, nrow=3)
dist.quant.df$median[i] <- mat.statcell[2,3]
dist.quant.df$q1[i] <- mat.statcell[1,3]
dist.quant.df$q2[i] <- mat.statcell[3,3]
}
write.table(dist.quant.df,"outputs/dist.quant.txt",row.names=FALSE,sep="\t")
#= Distance to forest edge statistics
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
dist.df <- data.frame(Year=Year,min=NA,max=NA,mean=NA,sd=NA,q1=NA,q2=NA)
# Areas
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
statcell <- system(paste0("r.univar -e percentile=5,95 map=dist_edge_",Year[i]), intern=TRUE)
dist.df$min[i] <- unlist(strsplit(statcell[7],split=" "))[2]
dist.df$max[i] <- unlist(strsplit(statcell[8],split=" "))[2]
dist.df$mean[i] <- unlist(strsplit(statcell[10],split=" "))[2]
dist.df$sd[i] <- unlist(strsplit(statcell[12],split=" "))[3]
dist.df$q1[i] <- unlist(strsplit(statcell[19],split=" "))[3]
dist.df$q2[i] <- unlist(strsplit(statcell[20],split=" "))[3]
}
# Replacing approximated quantiles with exact values
dist.df$q1 <- dist.quant.df$q1
dist.df$q2 <- dist.quant.df$q2
write.table(dist.df,"outputs/dist.txt",row.names=FALSE,sep="\t")
#= Percentage of forest at less than 500m from the forest edge
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
perc.500m.dist.df <- data.frame(Year=Year,perc=NA)
# Intermediate rast
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
system(paste0("r.mapcalc 'dist_edge_500m_",Year[i]," = if(dist_edge_",Year[i],">=500,0,1)'"))
}
# Stats
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
stat500m <- system(paste0("r.stats -nacp input=dist_edge_500m_",Year[i]), intern=TRUE)
mat.stat500m <- matrix(unlist(strsplit(stat500m,split=" ")), byrow=TRUE, nrow=2)
perc.500m.dist.df$perc[i] <- as.numeric(sub("%","",mat.stat500m[2,4]))
}
write.table(perc.500m.dist.df,"outputs/perc_500m_dist.txt",row.names=FALSE,sep="\t")
#= Percentage of forest at less than 1km from the forest edge
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
perc.1km.dist.df <- data.frame(Year=Year,perc=NA)
# Intermediate rast
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
system(paste0("r.mapcalc --o 'dist_edge_1km_",Year[i]," = if(dist_edge_",Year[i],">=1000,0,1)'"))
}
# Stats
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
stat1km <- system(paste0("r.stats -nacp input=dist_edge_1km_",Year[i]), intern=TRUE)
mat.stat1km <- matrix(unlist(strsplit(stat1km,split=" ")), byrow=TRUE, nrow=2)
perc.1km.dist.df$perc[i] <- as.numeric(sub("%","",mat.stat1km[2,4]))
}
write.table(perc.1km.dist.df,"outputs/perc_1km_dist.txt",row.names=FALSE,sep="\t")
#= Percentage of forest at less than 100m from the forest edge
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
perc.100m.dist.df <- data.frame(Year=Year,perc=NA)
# Intermediate rast
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
system(paste0("r.mapcalc --o 'dist_edge_100m_",Year[i]," = if(dist_edge_",Year[i],">=100,0,1)'"))
}
# Stats
for (i in 1:length(Year)) {
# Message
cat(paste("Year: ",Year[i],"\n",sep=""))
# Computation
stat100m <- system(paste0("r.stats -nacp input=dist_edge_100m_",Year[i]), intern=TRUE)
mat.stat100m <- matrix(unlist(strsplit(stat100m,split=" ")), byrow=TRUE, nrow=2)
perc.100m.dist.df$perc[i] <- as.numeric(sub("%","",mat.stat100m[2,4]))
}
write.table(perc.100m.dist.df,"outputs/perc_100m_dist.txt",row.names=FALSE,sep="\t")
##========================
## Statistics by ecoregion
##========================
# Ecoregions
# 1: spiny, 2: mangroves, 3: moist, 4: dry
ecoregion <- c("spiny","mangroves","moist","dry")
Res <- 30
# Loop on ecoregions
for (j in 1:length(ecoregion)) {
# Message
cat(paste0("Ecoregion: ",ecoregion[j]))
# Mask
system(paste0("r.mask --o raster=ecoregion maskcats='",j,"'"))
# ===========================
# A. Deforestation statistics
Year <- c(1953,1973,1990,2000,2005,2010,2015,2017)
defor.df <- data.frame(Year=Year,area=NA,ann.defor=NA,theta=NA)
# Areas
for (i in 1:length(Year)) {
# Message
cat(paste0("Year: ",Year[i]))
# Computation
statcell <- system(paste0("r.stats -c for",Year[i]), intern=TRUE)
ncells <- as.numeric(strsplit(statcell[1],split=" ")[[1]][2])
defor.df$area[i] <- round(ncells*(as.numeric(Res)^2)/10000)
}
# Annual rates
for (i in 2:length(Year)) {
theta.prim <- (defor.df$area[i-1]-defor.df$area[i])/defor.df$area[i-1]
Y <- defor.df$Year[i]-defor.df$Year[i-1]
defor.df$ann.defor[i] <- round((defor.df$area[i-1]-defor.df$area[i])/Y)
defor.df$theta[i] <- round(100*(1-(1-theta.prim)^(1/Y)),2)
}
# Export
write.table(defor.df,paste0("outputs/defor_",ecoregion[j],".txt"),row.names=FALSE,sep="\t")
# ==========================================
# B. Deforestation statistics for comparison
Year <- c(1953,1973,1990,2000,2005,2010,2013) # Here we add 2013 for comparison
defor.df <- data.frame(Year=Year,area=NA,ann.defor=NA,theta=NA)
# Areas
for (i in 1:length(Year)) {
# Message
cat(paste0("Year: ",Year[i]))
# Computation
statcell <- system(paste0("r.stats -c for",Year[i]), intern=TRUE)
ncells <- as.numeric(strsplit(statcell[1],split=" ")[[1]][2])
defor.df$area[i] <- round(ncells*(as.numeric(Res)^2)/10000)
}
# Annual rates
for (i in 2:length(Year)) {
theta.prim <- (defor.df$area[i-1]-defor.df$area[i])/defor.df$area[i-1]
Y <- defor.df$Year[i]-defor.df$Year[i-1]
defor.df$ann.defor[i] <- round((defor.df$area[i-1]-defor.df$area[i])/Y)
defor.df$theta[i] <- round(100*(1-(1-theta.prim)^(1/Y)),2)
}
# Export
write.table(defor.df,paste0("outputs/defor_",ecoregion[j],"_for_comp.txt"),row.names=FALSE,sep="\t")
# Remove mask
system("r.mask -r")
}
##========================================================
## Comparison with previous studies on forest-cover change
##========================================================
## Forest-cover
# Historical data
fcc.comp <- read.csv("data/fcc_comp.csv",header=TRUE)
ha.forest.2000.moist <- fcc.comp$y2000[fcc.comp$Source=="Harper2007" & fcc.comp$ForestType=="Moist"]
SavedObjects <- c(SavedObjects,"ha.forest.2000.moist")
# This study
defor_for_comp <- read.table("outputs/defor_for_comp.txt",header=TRUE)
defor_moist_for_comp <- read.table("outputs/defor_moist_for_comp.txt",header=TRUE)
defor_dry_for_comp <- read.table("outputs/defor_dry_for_comp.txt",header=TRUE)
defor_spiny_for_comp <- read.table("outputs/defor_spiny_for_comp.txt",header=TRUE)
defor_mangroves_for_comp <- read.table("outputs/defor_mangroves_for_comp.txt",header=TRUE)
# Complete data
fcc.comp[fcc.comp$ForestType=="Total" & fcc.comp$Source=="this study", 3:9] <- defor_for_comp$area
fcc.comp[fcc.comp$ForestType=="Moist" & fcc.comp$Source=="this study", 3:9] <- defor_moist_for_comp$area
fcc.comp[fcc.comp$ForestType=="Dry" & fcc.comp$Source=="this study", 3:9] <- defor_dry_for_comp$area
fcc.comp[fcc.comp$ForestType=="Spiny" & fcc.comp$Source=="this study", 3:9] <- defor_spiny_for_comp$area
fcc.comp[fcc.comp$ForestType=="Mangroves" & fcc.comp$Source=="this study", 3:9] <- defor_mangroves_for_comp$area
# This study for year 2017
defor <- read.table("outputs/defor.txt",header=TRUE)
defor_moist <- read.table("outputs/defor_moist.txt",header=TRUE)
defor_dry <- read.table("outputs/defor_dry.txt",header=TRUE)
defor_spiny <- read.table("outputs/defor_spiny.txt",header=TRUE)
defor_mangroves <- read.table("outputs/defor_mangroves.txt",header=TRUE)
# Complete data for year 2017
fcc.comp$y2017[fcc.comp$ForestType=="Total" & fcc.comp$Source=="this study"] <- defor$area[defor$Year==2017]
fcc.comp$y2017[fcc.comp$ForestType=="Moist" & fcc.comp$Source=="this study"] <- defor_moist$area[defor_moist$Year==2017]
fcc.comp$y2017[fcc.comp$ForestType=="Dry" & fcc.comp$Source=="this study"] <- defor_dry$area[defor_dry$Year==2017]
fcc.comp$y2017[fcc.comp$ForestType=="Spiny" & fcc.comp$Source=="this study"] <- defor_spiny$area[defor_spiny$Year==2017]
fcc.comp$y2017[fcc.comp$ForestType=="Mangroves" & fcc.comp$Source=="this study"] <- defor_mangroves$area[defor_mangroves$Year==2017]
# Save results
write.table(fcc.comp,file="outputs/fcc_comp.txt",row.names=FALSE)
SavedObjects <- c(SavedObjects,"fcc.comp")
## RMSE between studies forest-cover
this_study <- c(t(matrix(rep(as.numeric(unlist(fcc.comp[c(4,8,12,16,20),c(-1,-2)])),each=3),nrow=15)))
previous_studies <- c(t(fcc.comp[c(-4,-8,-12,-16,-20),c(-1,-2)]))
rmse.studies <- as.data.frame(cbind(this_study,previous_studies))
cor.fc <- cor(rmse.studies$this_study,rmse.studies$previous_studies, use="complete.obs") # 0.99
errors.fc <- rmse.studies$this_study-rmse.studies$previous_studies
Mean.fc <- mean(rmse.studies$this_study[!is.na(rmse.studies$previous_studies)]) # 4.8 Mha
RMSE.fc <- sqrt(mean(errors.fc^2, na.rm=TRUE)) # 299750 ha
PRMSE.fc <- 100*RMSE.fc/Mean.fc # 6%
SavedObjects <- c(SavedObjects,"cor.fc","Mean.fc","RMSE.fc","PRMSE.fc")
## Deforestation
# Historical data
defor.comp <- read.csv("data/defor_comp.csv",header=TRUE,stringsAsFactors=FALSE)
defor.comp.thistudy <- defor.comp # a copy of defor.comp
# Deforestation ha/yr
defor.ha.comp <- fcc.comp[,c(2,1,3:8)]
periods <- c("p1953-1973","p1973-1990","p1990-2000","p2000-2005","p2005-2010","p2010-2013")
lp <- c(20,17,10,5,5,3)
names(defor.ha.comp)[3:8] <- periods
for (i in 1:length(periods)) {
defor.ha.comp[,2+i] <- round((fcc.comp[,2+i]-fcc.comp[,2+1+i])/lp[i])
}
# Deforestation %/yr
defor.perc.comp <- defor.ha.comp
for (i in 1:length(periods)) {
defor.perc.comp[,2+i] <- round(100*(1-(1-(fcc.comp[,2+i]-fcc.comp[,2+1+i])/fcc.comp[,2+i])^(1/lp[i])),1)
}
# Combining ha and percentages
ha <- as.numeric(unlist(defor.ha.comp[,3:8]))
ha <- format(round(ha/1000), big.mark=",")
perc <- sprintf("%.1f", as.numeric(unlist(defor.perc.comp[,3:8])))
defor.comp.thistudy[,3:8] <- data.frame(matrix(paste(ha," (",perc,")", sep=""),ncol=6),stringsAsFactors=FALSE)
defor.comp.thistudy[defor.comp.thistudy=="NA (NA)"] <- NA
# We can check how defor.comp (original deforestation rate estimates) and defor.comp.study (deforestation rates estimated here) differ
#defor.comp
#defor.comp.thistudy
# We keep only the results of our study
defor.comp[defor.comp$Source=="this study",3:8] <- defor.comp.thistudy[defor.comp.thistudy$Source=="this study",3:8]
# Save results
write.table(defor.comp,file="outputs/defor_comp.txt",row.names=FALSE)
SavedObjects <- c(SavedObjects,"defor.comp")
##========================
## Forest-cover change map
##========================
## Raster of water-bodies over Madagascar
# Region
Extent <- "298440 7155900 1100820 8682420"
Res <- "30"