Practicas 4 - Percepción Remota
Elkin Alberto Aponte Gómez
library(raster)
landsat5 <- stack('data/rs/centralvalley-2011LT5.tif')
names(landsat5) <- c('blue','green','red','NIR','SWIR1','SWIR2')
landsat5class : RasterStack
dimensions : 1230, 1877, 2308710, 6 (nrow, ncol, ncell, nlayers)
resolution : 0.0002694946, 0.0002694946 (x, y)
extent : -121.9258, -121.42, 37.85402, 38.1855 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
names : blue, green, red, NIR, SWIR1, SWIR2 plotRGB (landsat5[[c('red','green','blue')]], axes =TRUE, stretch = "lin", main = "Landsat True Color Composite-croped")
plotRGB (landsat5[[c('NIR','red','green')]], axes =TRUE, stretch = "lin", main = "Landsat True Color Composite-croped")
ndvi <- (landsat5[['NIR']] - landsat5[['red']]) / (landsat5[['NIR']] + landsat5[['red']])e <- extent(-121.807,-121.725,38.004,38.072)
ndvi <- crop(ndvi,e)
ndviclass : RasterLayer
dimensions : 252, 304, 76608 (nrow, ncol, ncell)
resolution : 0.0002694946, 0.0002694946 (x, y)
extent : -121.807, -121.725, 38.00413, 38.07204 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : memory
names : layer
values : -0.3360085, 0.7756007 (min, max)#Convirtiendo el raster a un vector
nr <- getValues(ndvi)
str(nr) num [1:76608] 0.245 0.236 0.272 0.277 0.277 ...#La importancia de que los valores iniciales sean configurados radica en que de forma automática *kmeans* los centra aleatoreamente
set.seed(99)
#seran creados 10 grupos con 500 iteraciones máximas, iniciando con 5 conjuntos aleatorios, empleando el algoritmo 'Lloyd'
kmncluster <- kmeans(na.omit(nr), centers = 10, iter.max = 500, nstart = 5, algorithm = "Lloyd")
#la función 'kmeans' retornará un objeto de tipo "kmeans"
str(kmncluster)List of 9
$ cluster : int [1:76608] 4 4 3 3 3 3 3 4 4 4 ...
$ centers : num [1:10, 1] 0.55425 0.00498 0.29997 0.20892 -0.20902 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:10] "1" "2" "3" "4" ...
.. ..$ : NULL
$ totss : num 6459
$ withinss : num [1:10] 5.69 6.13 4.91 4.9 5.75 ...
$ tot.withinss: num 55.8
$ betweenss : num 6403
$ size : int [1:10] 8932 4550 7156 6807 11672 8624 8736 5040 9893 5198
$ iter : int 108
$ ifault : NULL
- attr(*, "class")= chr "kmeans"kmnclusterK-means clustering with 10 clusters of sizes 8932, 4550, 7156, 6807, 11672, 8624, 8736, 5040, 9893, 5198
Cluster means:
[,1]
1 0.55424794
2 0.00498429
3 0.29997047
4 0.20892299
5 -0.20901839
6 -0.14263451
7 0.38985922
8 0.64951387
9 0.47100012
10 0.11189216
Clustering vector:
[1] 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 10 10 4 10 10 10 10 4 10 10 4 4 4 4 4 4 4 4 4 4 4 4
[49] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 6
[97] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[145] 5 5 5 5 5 5 5 6 5 5 5 5 2 3 9 9 9 9 9 9 9 9 7 3 4 4 3 3 3 3 3 3 4 3 3 7 7 7 9 9 1 1 1 1 1 9 9 7
[193] 7 7 7 9 9 9 1 1 1 1 1 9 7 9 1 1 8 1 8 8 8 8 1 8 8 1 1 9 7 7 3 3 3 3 7 7 7 7 7 7 7 7 3 3 3 3 7 7
[241] 7 7 7 3 3 4 4 4 4 4 4 4 4 3 3 7 9 9 9 7 3 3 3 3 3 3 3 3 3 3 4 3 3 3 7 7 7 7 7 7 3 3 3 3 3 4 3 3
[289] 7 3 3 3 3 3 3 3 3 3 7 3 3 3 4 7 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 10 10 10 10 10 10 10 10 10
[337] 10 10 10 10 10 10 10 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[385] 4 4 4 4 4 4 4 4 4 4 4 3 3 3 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[433] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 5 5 6 10 10 7 9 9 7 7 7 9 7 7 7 7 3 3 3 3 3 7 9 9 9
[481] 3 3 3 7 7 9 1 1 1 1 1 1 1 7 7 7 7 7 9 1 8 8 8 1 1 1 1 9 9 9 1 1 8 8 8 8 1 1 1 1 1 9 9 9 3 3 4 3
[529] 3 3 3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3 3 3 3 4 4 4 4 4 4 4 3 3 7 9 9 1 1 9 9 7 7 3 3 3 3 3 3 3 3
[577] 3 3 7 7 7 7 7 7 3 3 3 3 3 3 3 3 7 3 3 7 7 7 3 7 7 3 3 3 3 3 3 9 4 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4
[625] 4 4 4 10 10 10 10 10 10 10 4 4 4 10 10 4 4 10 10 10 10 10 10 10 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 4 4 4 4 4 4 4 4 4
[673] 4 4 4 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 7 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[721] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 5 5 5 5 6 4 7 7 1 7 7 1
[769] 1 1 7 4 4 10 4 3 9 9 9 9 1 9 9 9 7 3 3 9 1 1 1 1 1 1 1 9 9 3 3 7 9 9 9 1 1 1 1 1 1 1 1 9 9 1 8 8
[817] 8 1 1 1 1 1 1 1 1 1 1 9 3 3 4 3 3 3 3 3 3 3 3 7 7 3 7 7 7 7 7 7 7 7 7 3 3 3 4 4 4 4 4 4 4 3 3 7
[865] 9 1 1 1 9 9 7 7 3 3 3 3 3 3 4 3 3 3 3 3 7 7 7 7 3 3 3 3 7 3 3 3 7 3 3 7 7 3 7 7 7 3 3 7 9 9 1 1
[913] 3 3 3 3 3 3 3 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 10 10 4 4 4 10 10 4 10 10 10 10 10 4 4 4 4 4 4 4
[961] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[ reached getOption("max.print") -- omitted 75608 entries ]
Within cluster sum of squares by cluster:
[1] 5.693691 6.126246 4.914637 4.904445 5.754583 6.106546 5.180387 6.942609 5.528454 4.664211
(between_SS / total_SS = 99.1 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" "size" "iter" "ifault" #Mediante el objeto ndvi, se establecen los valores del clúster en el ráster nuevo
knr <- setValues(ndvi, kmncluster$cluster)#Forma alternativa:
knr <- raster(ndvi)
values(knr) <-kmncluster$clusterknrclass : RasterLayer
dimensions : 252, 304, 76608 (nrow, ncol, ncell)
resolution : 0.0002694946, 0.0002694946 (x, y)
extent : -121.807, -121.725, 38.00413, 38.07204 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : memory
names : layer
values : 1, 10 (min, max)head(knr) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4
2 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4
3 4 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 10
4 3 3 3 3 3 3 3 4 4 4 4 3 4 4 4 4 4 4 4 4
5 3 3 4 4 4 3 3 4 4 4 4 3 4 4 4 4 4 4 4 4
6 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 4 4 4 4
7 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 4
8 4 4 4 4 4 4 4 4 4 4 3 3 3 7 7 7 7 7 7 7
9 3 3 3 4 4 4 4 4 3 3 3 3 7 9 9 9 9 7 7 7
10 3 3 3 3 3 3 3 4 3 3 3 7 7 9 9 9 9 9 7 7#Definiendoi un vector de color para los cluster
mycolor <- c("#fef65b","#ff0000", "#daa520","#0000ff","#0000ff","#00ff00","#cbbeb5",
"#c3ff5b", "#ff7373", "#00ff00", "#808080")
#Se representa el NDVI y la clasificación supervisada para comprobar los grupos que han sido creados.
par(mfrow=c(1,2))
plot(ndvi, col = rev(terrain.colors(10)), main='Landsat-NDVI')
plot(knr, main = 'Unsupervised classification', col =mycolor)
landsatcrop <- crop(landsat5, e)
landsatcropclass : RasterBrick
dimensions : 252, 304, 76608, 6 (nrow, ncol, ncell, nlayers)
resolution : 0.0002694946, 0.0002694946 (x, y)
extent : -121.807, -121.725, 38.00413, 38.07204 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : memory
names : blue, green, red, NIR, SWIR1, SWIR2
min values : 0.084590562, 0.055765331, 0.040395390, 0.034050025, 0.010290771, 0.006807715
max values : 0.3684652, 0.5407562, 0.5847761, 0.5988820, 0.5135725, 0.5523322 kmnclusterK-means clustering with 10 clusters of sizes 8932, 4550, 7156, 6807, 11672, 8624, 8736, 5040, 9893, 5198
Cluster means:
[,1]
1 0.55424794
2 0.00498429
3 0.29997047
4 0.20892299
5 -0.20901839
6 -0.14263451
7 0.38985922
8 0.64951387
9 0.47100012
10 0.11189216
Clustering vector:
[1] 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 10 10 4 10 10 10 10 4 10 10 4 4 4 4 4 4 4 4 4 4 4 4
[49] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 6
[97] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[145] 5 5 5 5 5 5 5 6 5 5 5 5 2 3 9 9 9 9 9 9 9 9 7 3 4 4 3 3 3 3 3 3 4 3 3 7 7 7 9 9 1 1 1 1 1 9 9 7
[193] 7 7 7 9 9 9 1 1 1 1 1 9 7 9 1 1 8 1 8 8 8 8 1 8 8 1 1 9 7 7 3 3 3 3 7 7 7 7 7 7 7 7 3 3 3 3 7 7
[241] 7 7 7 3 3 4 4 4 4 4 4 4 4 3 3 7 9 9 9 7 3 3 3 3 3 3 3 3 3 3 4 3 3 3 7 7 7 7 7 7 3 3 3 3 3 4 3 3
[289] 7 3 3 3 3 3 3 3 3 3 7 3 3 3 4 7 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 10 10 10 10 10 10 10 10 10
[337] 10 10 10 10 10 10 10 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[385] 4 4 4 4 4 4 4 4 4 4 4 3 3 3 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[433] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 5 5 6 10 10 7 9 9 7 7 7 9 7 7 7 7 3 3 3 3 3 7 9 9 9
[481] 3 3 3 7 7 9 1 1 1 1 1 1 1 7 7 7 7 7 9 1 8 8 8 1 1 1 1 9 9 9 1 1 8 8 8 8 1 1 1 1 1 9 9 9 3 3 4 3
[529] 3 3 3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3 3 3 3 4 4 4 4 4 4 4 3 3 7 9 9 1 1 9 9 7 7 3 3 3 3 3 3 3 3
[577] 3 3 7 7 7 7 7 7 3 3 3 3 3 3 3 3 7 3 3 7 7 7 3 7 7 3 3 3 3 3 3 9 4 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4
[625] 4 4 4 10 10 10 10 10 10 10 4 4 4 10 10 4 4 10 10 10 10 10 10 10 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 4 4 4 4 4 4 4 4 4
[673] 4 4 4 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 7 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[721] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 5 5 5 5 6 4 7 7 1 7 7 1
[769] 1 1 7 4 4 10 4 3 9 9 9 9 1 9 9 9 7 3 3 9 1 1 1 1 1 1 1 9 9 3 3 7 9 9 9 1 1 1 1 1 1 1 1 9 9 1 8 8
[817] 8 1 1 1 1 1 1 1 1 1 1 9 3 3 4 3 3 3 3 3 3 3 3 7 7 3 7 7 7 7 7 7 7 7 7 3 3 3 4 4 4 4 4 4 4 3 3 7
[865] 9 1 1 1 9 9 7 7 3 3 3 3 3 3 4 3 3 3 3 3 7 7 7 7 3 3 3 3 7 3 3 3 7 3 3 7 7 3 7 7 7 3 3 7 9 9 1 1
[913] 3 3 3 3 3 3 3 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 10 10 4 4 4 10 10 4 10 10 10 10 10 4 4 4 4 4 4 4
[961] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[ reached getOption("max.print") -- omitted 75608 entries ]
Within cluster sum of squares by cluster:
[1] 5.693691 6.126246 4.914637 4.904445 5.754583 6.106546 5.180387 6.942609 5.528454 4.664211
(between_SS / total_SS = 99.1 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" "size" "iter" "ifault" #Definiendoi un vector de color para los cluster
mycolor <- c("magenta","black", "red","steelblue","blue","yellow","deepskyblue",
"orange", "green", "pink", "grey")
plot(knr, main = 'Unsupervised classification', col =mycolor)
| Number of Cluster | Cluster means | Class |
|---|---|---|
| 1 | 0.55424794 | Agrícola |
| 2 | 0.00498429 | Urbano y bancos de arena |
| 3 | 0.29997047 | Calles, Urbano |
| 4 | 0.20892299 | Suelo descubierto |
| 5 | -0.20901839 | Agua |
| 6 | -0.14263451 | Agua |
| 7 | 0.38985922 | Suelo descubierto |
| 8 | 0.64951387 | Vegetación turgente |
| 9 | 0.47100012 | Barbecho (baja cobertura vegetal) |
| 10 | 0.11189216 | Urbano |
par(mfrow=c(1,2))
plotRGB(landsatcrop[[c('red','green','blue')]], axes =TRUE, stretch = "lin", main = "Landsat True Color Composite")
plot(knr, main = 'Unsupervised classification', col =mycolor)
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