Glaciares en Chile

I Parte: Random Forest con imágenes satelitales

DataIntelligence

Abstract

Aplicar Random Forest a imágenes satelitales consiste en: 1 declarar un diccionario de categorías asociadas a geometrías y contrastarlas con una imagen, lo que constituye la fase de elaboración de la muestra de entrenamiento, 2 el proceso de construcción del modelo y por último, 3 la imagen original reclasificada, esto es, el output del Random Forest. Ésta entrega hace el desarrollo inicial de los requerimientos.

Índice

1 Elaboración del NDGI

2 Construcción de muestras

3 Aplicación de RF

1 Elaboración del NDGI

(volver al índice)

1. Gráfica del shape de la región de los lagos

Tenemos una función que grafica inmediatamente la forma de un shape:

# leemos el shp:
mask <- st_read("region_los_lagos.shp", 
        quiet = TRUE) %>% 
        sf_as_ee()

# convertimos el shp en geometria:
region <- mask$geometry()

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(region)

Cruzamos con data Sentinel filtrada:

start <- ee$Date("2014-06-01")
finish <- ee$Date("2014-10-01")
cc <- 20

sentinel1 = ee$ImageCollection('COPERNICUS/S2')$filterDate("2019-03-01", "2020-03-01")$filterBounds(region)$
  filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cc))

first <- sentinel1$median()

# definimos los parametros de visualizacion:
vizParams <- list(
  bands = c("B8","B5" , "B3"),
  #  bands = c("B2", "B3"),
  min = 100,
  max = 1000,
  gamma = 2
)
Map$setCenter(lon = -73.079, lat = -42.611, zoom = 6)
Map$addLayer(first, vizParams, "Landsat 8 image")

2. Matemáticas de mapas

Cálculo del Normalized Difference Glacier Index (NDGI):

\[ NDGI = {B_3-B_4 \over B_3+B_4} \]

normalizedDifference calcula la diferencia normalizada entre dos bandas. Es:

\[{B1 - B2 } \over { B1 + B2}\]

Los valores de entrada negativos se fuerzan a 0 para que el resultado se limite al rango (-1, 1).

3. Obtengamos los píxeles del NDGI para tres rangos:

El Índice Glaciar Diferencial Normalizado (NDGI) se utiliza para ayudar a detectar y monitorear glaciares utilizando las bandas espectrales verde y roja. Esta ecuación se utiliza comúnmente en la detección de glaciares y en aplicaciones de monitoreo de glaciares.

1. NDGI

getNDGI <- function(image) 
{
  #image$normalizedDifference(c("B3", "B4")) 
   image$normalizedDifference(c("B3", "B4")) 
}

ndgi <- getNDGI(first)

ndgiParams <- list(palette = c(
  "#d73027", "#f46d43", "#fdae61",
  "#fee08b", "#d9ef8b", "#a6d96a",
  "#66bd63", "#1a9850"
))

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 6)
Map$addLayer(ndgi, ndgiParams, "NDGI")

2. NDGI < 0

getNDGI <- function(image) 
{
  #image$normalizedDifference(c("B3", "B4")) 
   image$normalizedDifference(c("B3", "B4")) < 0
}

ndgi_04 <- getNDGI(first)

ndgiParams <- list(palette = c(
  "#d73027", "#f46d43", "#fdae61",
  "#fee08b", "#d9ef8b", "#a6d96a",
  "#66bd63", "#1a9850"
))

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 6)
Map$addLayer(ndgi_04, ndgiParams, "NDGI")

3. NDGI > 0

Aca hay que agregar un paso. Debemos pintar solo los pixeles rojos

getNDGI <- function(image) 
{
  #image$normalizedDifference(c("B3", "B4")) 
   image$normalizedDifference(c("B3", "B4")) > 0
}

ndgi_01 <- getNDGI(first)

ndgiParams <- list(palette = c(
  "#d73027", "#f46d43", "#fdae61",
  "#fee08b", "#d9ef8b", "#a6d96a",
  "#66bd63", "#1a9850"
))

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 6)
Map$addLayer(ndgi_01, ndgiParams, "NDGI")

4. Cortamos con geometrías geográficas de glaciares

mask_0 <- st_read("Lim_glaciares_v2_Simplify.shp",
        quiet = TRUE) %>%
        sf_as_ee()

# convertimos el shp en geometria:
region_0 <- mask_0$geometry()

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(region_0)

# mask_0 <- st_read("cl_lagos_geo/cl_lagos_geoPolygon.shp",
#         quiet = TRUE) %>%
#         sf_as_ee()
# 
# # convertimos el shp en geometria:
# region_0 <- mask_0$geometry()
# 
# Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
# Map$addLayer(region_0)

Tomemos el raster generado con NDGI > 0: ngdi_01 en una caja coords = c(-73,-43,-72,-42.5)

box <- ee$Geometry$Rectangle(coords = c(-73,-43,-72,-42.5),
                             ## WGS 84
                             proj = "EPSG:4326",
                             geodesic = FALSE)

sale <- ndgi_01$clip(box)

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(sale, ndgiParams, "NDGI")

el raster sale contiene el corte de ndgi_01 con la caja.

tomemos una caja mas amplia:

box_1 <- ee$Geometry$Rectangle(coords = c(-74.83774,-44.06706,-71.58093,-40.23878),
                             proj = "EPSG:4326",
                             geodesic = FALSE)

sale_2 <- ndgi_01$clip(box_1)

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(sale_2, ndgiParams, "NDGI")

y ahora intersectamos con el shp de los glaciares de Chile:

figura n°1

sale_4 <- sale_2$clip(region_0)
Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(sale_4, ndgiParams, "NDGI")

---

Segunda parte: construcción de muestras

(volver al índice)

Se deben construir dos mapas que sirvan de alimento a dos poblaciones muestrales. El primero ya lo tenemos y lo acabamos de desplegar: se llama sale_4. Las muestras aquí contienen el valor 0: rojo.

Para el segundo necesitamos construir una región que contenga las áreas glaciares como espacios vacíos. Es lo que vamos a hacer a continuación. Las muestras aquí contienen el valor 1: verde.

Construyamos una geometría en forma de rectángulo box_2:

box_2 <- ee$Geometry$Rectangle(coords = c(-73,-43,-72.2,-42),
                             proj = "EPSG:4326",
                             geodesic = FALSE)
Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(box_2)

Restémosela al shape de la región y coloreémosla de amarillo:

diff1 <- region$difference(box_2, ee$ErrorMargin(1))
Map$addLayer(diff1, list(color = "FFFF00"), "diff1")

La misma idea vamos a aplicar para seleccionar una región que excluya las áreas glaciares. Vamos a considerar el mismo cuadrado y eliminaremos las áreas de no interés. El proceso es muy largo, así que consideramos por el momento sólo la región acotada por el rectángulo.

diff1 <- box_2$difference(region_0, ee$ErrorMargin(1))
Map$addLayer(diff1, list(color = "FFFF00"), "diff1")

Obtenemos el negativo de figura n°1 en el rectángulo en cuestión y la llamamos figura n°2:

sale_3 <- sale_2$clip(diff1)
Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(sale_3, ndgiParams, "NDGI")

Seleccion de muestras:

Ahora tenemos listos los dos rasters a partir de los cuales podemos extraer las muestras.

I Intersectamos nuestro raster resultado con la region de glaciares:

extraer aleatoriamente las muestras de 10 mts. encontrar funcion

primero resolver en R.

 mySamples <- sale_3$sampleRegions(
    collection = ee$Feature(region_0),
    scale = 1000,
    tileScale = 16,
    geometries = TRUE
  )

Desplegamos la data y la cantidad de registros:

  mySamples_sf_batch <-  ee_as_sf(mySamples) 
  mySamples_sf_batch <- as.data.frame((mySamples_sf_batch))
  
  mySamples_sf_batch  %>%  kbl() %>%
  kable_material(c("striped", "hover"), font_size = 12)%>%
  scroll_box(width = "100%", height = "500px")

nd	geometry
0	POINT (-72.23802 -42.99786)
0	POINT (-72.22904 -42.99786)
1	POINT (-72.25599 -42.98888)
0	POINT (-72.23802 -42.98888)
0	POINT (-72.22904 -42.98888)
0	POINT (-72.22006 -42.98888)
1	POINT (-72.31887 -42.97989)
0	POINT (-72.25599 -42.97989)
0	POINT (-72.21107 -42.97989)
0	POINT (-72.27396 -42.97091)
0	POINT (-72.22006 -42.97091)
0	POINT (-72.22904 -42.90803)
0	POINT (-72.24701 -42.89905)
0	POINT (-72.23802 -42.89905)
0	POINT (-72.52548 -42.88108)
1	POINT (-72.45362 -42.88108)
0	POINT (-72.53447 -42.8721)
1	POINT (-72.50752 -42.8721)
1	POINT (-72.48955 -42.8721)
1	POINT (-72.45362 -42.8721)
1	POINT (-72.44464 -42.8721)
0	POINT (-72.53447 -42.86311)
1	POINT (-72.50752 -42.86311)
1	POINT (-72.49854 -42.86311)
1	POINT (-72.48955 -42.86311)
1	POINT (-72.39972 -42.86311)
0	POINT (-72.4626 -42.85413)
1	POINT (-72.45362 -42.85413)
1	POINT (-72.43565 -42.85413)
1	POINT (-72.39972 -42.85413)
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1	POINT (-72.39972 -42.84515)
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0	POINT (-72.30989 -42.83616)
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1	POINT (-72.39074 -42.82718)
0	POINT (-72.26497 -42.82718)
1	POINT (-72.5165 -42.8182)
1	POINT (-72.39074 -42.8182)
1	POINT (-72.38175 -42.80921)
1	POINT (-72.37277 -42.80921)
0	POINT (-72.50752 -42.80023)
1	POINT (-72.37277 -42.80023)
1	POINT (-72.49854 -42.79125)
1	POINT (-72.49854 -42.78227)
1	POINT (-72.48955 -42.78227)
1	POINT (-72.33684 -42.78227)
1	POINT (-72.48057 -42.77328)
1	POINT (-72.43565 -42.77328)
1	POINT (-72.42667 -42.77328)
0	POINT (-72.37277 -42.77328)
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0	POINT (-72.3548 -42.77328)
0	POINT (-72.47159 -42.7643)
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0	POINT (-72.45362 -42.7643)
1	POINT (-72.42667 -42.7643)
1	POINT (-72.41769 -42.7643)
1	POINT (-72.39972 -42.7643)
0	POINT (-72.39074 -42.7643)
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1	POINT (-72.42667 -42.75532)
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1	POINT (-72.23802 -42.63853)
0	POINT (-72.28294 -42.62955)
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0	POINT (-72.26497 -42.61159)
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0	POINT (-72.37277 -42.44989)
1	POINT (-72.31887 -42.44989)
0	POINT (-72.38175 -42.44091)
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1	POINT (-72.27396 -42.10853)
1	POINT (-72.22904 -42.10853)
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1	POINT (-72.23802 -42.09955)
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1	POINT (-72.3548 -42.09056)
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1	POINT (-72.26497 -42.09056)
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1	POINT (-72.27396 -42.06361)
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0	POINT (-72.34582 -42.0187)
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1	POINT (-72.23802 -42.0187)
0	POINT (-72.22904 -42.0187)
0	POINT (-72.22006 -42.0187)
0	POINT (-72.21107 -42.0187)
0	POINT (-72.20209 -42.0187)
0	POINT (-72.3548 -42.00971)
0	POINT (-72.34582 -42.00971)
1	POINT (-72.23802 -42.00971)
0	POINT (-72.22904 -42.00971)
0	POINT (-72.22006 -42.00971)
0	POINT (-72.21107 -42.00971)
0	POINT (-72.3548 -42.00073)
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1	POINT (-72.32786 -42.00073)
0	POINT (-72.25599 -42.00073)
1	POINT (-72.21107 -42.00073)

nrow(mySamples_sf_batch)

## [1] 171

la minoría son verdes: 1

filtros_de_1 <- subset(mySamples_sf_batch, mySamples_sf_batch$nd == "1")
saveRDS(filtros_de_1,"muestra_1.rds")
filtros_de_1  %>%  kbl() %>%
  kable_material(c("striped", "hover"), font_size = 12)%>%
  scroll_box(width = "100%", height = "500px")

	nd	geometry
3	1	POINT (-72.25599 -42.98888)
7	1	POINT (-72.31887 -42.97989)
16	1	POINT (-72.45362 -42.88108)
18	1	POINT (-72.50752 -42.8721)
19	1	POINT (-72.48955 -42.8721)
20	1	POINT (-72.45362 -42.8721)
21	1	POINT (-72.44464 -42.8721)
23	1	POINT (-72.50752 -42.86311)
24	1	POINT (-72.49854 -42.86311)
25	1	POINT (-72.48955 -42.86311)
26	1	POINT (-72.39972 -42.86311)
28	1	POINT (-72.45362 -42.85413)
29	1	POINT (-72.43565 -42.85413)
30	1	POINT (-72.39972 -42.85413)
31	1	POINT (-72.47159 -42.84515)
33	1	POINT (-72.42667 -42.84515)
34	1	POINT (-72.41769 -42.84515)
35	1	POINT (-72.4087 -42.84515)
36	1	POINT (-72.39972 -42.84515)
43	1	POINT (-72.39074 -42.82718)
45	1	POINT (-72.5165 -42.8182)
46	1	POINT (-72.39074 -42.8182)
47	1	POINT (-72.38175 -42.80921)
48	1	POINT (-72.37277 -42.80921)
50	1	POINT (-72.37277 -42.80023)
51	1	POINT (-72.49854 -42.79125)
52	1	POINT (-72.49854 -42.78227)
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66	1	POINT (-72.39972 -42.7643)
69	1	POINT (-72.42667 -42.75532)
72	1	POINT (-72.23802 -42.63853)
88	1	POINT (-72.31887 -42.44989)
94	1	POINT (-72.36379 -42.40497)
105	1	POINT (-72.21107 -42.27921)
116	1	POINT (-72.24701 -42.13548)
120	1	POINT (-72.27396 -42.10853)
121	1	POINT (-72.22904 -42.10853)
122	1	POINT (-72.24701 -42.09955)
123	1	POINT (-72.23802 -42.09955)
125	1	POINT (-72.3548 -42.09056)
127	1	POINT (-72.26497 -42.09056)
136	1	POINT (-72.27396 -42.06361)
137	1	POINT (-72.26497 -42.06361)
144	1	POINT (-72.32786 -42.04565)
148	1	POINT (-72.32786 -42.03666)
156	1	POINT (-72.23802 -42.0187)
163	1	POINT (-72.23802 -42.00971)
169	1	POINT (-72.32786 -42.00073)
171	1	POINT (-72.21107 -42.00073)

nrow(filtros_de_1)

## [1] 55

la mayoría son rojos: 0

filtros_de_0 <- subset(mySamples_sf_batch, mySamples_sf_batch$nd == "0")
filtros_de_0 %>%  kbl() %>%
  kable_material(c("striped", "hover"), font_size = 12)%>%
  scroll_box(width = "100%", height = "500px")

	nd	geometry
1	0	POINT (-72.23802 -42.99786)
2	0	POINT (-72.22904 -42.99786)
4	0	POINT (-72.23802 -42.98888)
5	0	POINT (-72.22904 -42.98888)
6	0	POINT (-72.22006 -42.98888)
8	0	POINT (-72.25599 -42.97989)
9	0	POINT (-72.21107 -42.97989)
10	0	POINT (-72.27396 -42.97091)
11	0	POINT (-72.22006 -42.97091)
12	0	POINT (-72.22904 -42.90803)
13	0	POINT (-72.24701 -42.89905)
14	0	POINT (-72.23802 -42.89905)
15	0	POINT (-72.52548 -42.88108)
17	0	POINT (-72.53447 -42.8721)
22	0	POINT (-72.53447 -42.86311)
27	0	POINT (-72.4626 -42.85413)
32	0	POINT (-72.45362 -42.84515)
37	0	POINT (-72.33684 -42.83616)
38	0	POINT (-72.32786 -42.83616)
39	0	POINT (-72.31887 -42.83616)
40	0	POINT (-72.30989 -42.83616)
41	0	POINT (-72.52548 -42.82718)
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108	0	POINT (-72.22006 -42.25226)
109	0	POINT (-72.21107 -42.25226)
110	0	POINT (-72.20209 -42.25226)
111	0	POINT (-72.20209 -42.16243)
112	0	POINT (-72.21107 -42.15344)
113	0	POINT (-72.20209 -42.15344)
114	0	POINT (-72.23802 -42.14446)
115	0	POINT (-72.25599 -42.13548)
117	0	POINT (-72.22904 -42.13548)
118	0	POINT (-72.25599 -42.1265)
119	0	POINT (-72.24701 -42.1265)
124	0	POINT (-72.22006 -42.09955)
126	0	POINT (-72.34582 -42.09056)
128	0	POINT (-72.25599 -42.09056)
129	0	POINT (-72.23802 -42.09056)
130	0	POINT (-72.22006 -42.09056)
131	0	POINT (-72.21107 -42.09056)
132	0	POINT (-72.23802 -42.08158)
133	0	POINT (-72.21107 -42.0726)
134	0	POINT (-72.33684 -42.06361)
135	0	POINT (-72.29192 -42.06361)
138	0	POINT (-72.25599 -42.06361)
139	0	POINT (-72.33684 -42.05463)
140	0	POINT (-72.32786 -42.05463)
141	0	POINT (-72.30989 -42.05463)
142	0	POINT (-72.3548 -42.04565)
143	0	POINT (-72.33684 -42.04565)
145	0	POINT (-72.30989 -42.04565)
146	0	POINT (-72.36379 -42.03666)
147	0	POINT (-72.3548 -42.03666)
149	0	POINT (-72.30091 -42.03666)
150	0	POINT (-72.29192 -42.03666)
151	0	POINT (-72.31887 -42.02768)
152	0	POINT (-72.30091 -42.02768)
153	0	POINT (-72.3548 -42.0187)
154	0	POINT (-72.34582 -42.0187)
155	0	POINT (-72.29192 -42.0187)
157	0	POINT (-72.22904 -42.0187)
158	0	POINT (-72.22006 -42.0187)
159	0	POINT (-72.21107 -42.0187)
160	0	POINT (-72.20209 -42.0187)
161	0	POINT (-72.3548 -42.00971)
162	0	POINT (-72.34582 -42.00971)
164	0	POINT (-72.22904 -42.00971)
165	0	POINT (-72.22006 -42.00971)
166	0	POINT (-72.21107 -42.00971)
167	0	POINT (-72.3548 -42.00073)
168	0	POINT (-72.33684 -42.00073)
170	0	POINT (-72.25599 -42.00073)

nrow(filtros_de_0)

## [1] 116

II Ahora intersectamos nuestro raster resultado sale_2, con la región que excluye los límites glaciares: diff1. Recordemos que acá seleccionamos los verdes: 1

Obtenemos las muestras:

 mySamples <- sale_2$sampleRegions(
    collection = ee$Feature(diff1),
    scale= 10000,
    tileScale=16,
    geometries=TRUE
  )

Desplegamos la data y el numero de columnas:

mySamples_sf_batch <-  ee_as_sf(mySamples) 
  mySamples_sf_batch <- as.data.frame((mySamples_sf_batch))

mySamples_sf_batch  %>%  kbl() %>%
  kable_material(c("striped", "hover"), font_size = 12)%>%
  scroll_box(width = "100%", height = "500px")

nd	geometry
1	POINT (-72.98812 -42.98439)
1	POINT (-72.89829 -42.98439)
1	POINT (-72.80845 -42.98439)
1	POINT (-72.71862 -42.98439)
1	POINT (-72.62879 -42.98439)
1	POINT (-72.53896 -42.98439)
1	POINT (-72.44913 -42.98439)
1	POINT (-72.3593 -42.98439)
1	POINT (-72.26946 -42.98439)
1	POINT (-72.98812 -42.89455)
1	POINT (-72.89829 -42.89455)
1	POINT (-72.80845 -42.89455)
1	POINT (-72.71862 -42.89455)
0	POINT (-72.62879 -42.89455)
1	POINT (-72.53896 -42.89455)
1	POINT (-72.44913 -42.89455)
1	POINT (-72.3593 -42.89455)
0	POINT (-72.26946 -42.89455)
1	POINT (-72.98812 -42.80472)
1	POINT (-72.89829 -42.80472)
1	POINT (-72.80845 -42.80472)
1	POINT (-72.71862 -42.80472)
1	POINT (-72.62879 -42.80472)
1	POINT (-72.53896 -42.80472)
1	POINT (-72.3593 -42.80472)
1	POINT (-72.26946 -42.80472)
1	POINT (-72.98812 -42.71489)
1	POINT (-72.89829 -42.71489)
1	POINT (-72.80845 -42.71489)
1	POINT (-72.71862 -42.71489)
1	POINT (-72.62879 -42.71489)
1	POINT (-72.53896 -42.71489)
1	POINT (-72.44913 -42.71489)
1	POINT (-72.3593 -42.71489)
1	POINT (-72.26946 -42.71489)
1	POINT (-72.98812 -42.62506)
1	POINT (-72.89829 -42.62506)
1	POINT (-72.80845 -42.62506)
1	POINT (-72.71862 -42.62506)
1	POINT (-72.62879 -42.62506)
1	POINT (-72.53896 -42.62506)
1	POINT (-72.44913 -42.62506)
1	POINT (-72.3593 -42.62506)
1	POINT (-72.26946 -42.62506)
1	POINT (-72.98812 -42.53523)
1	POINT (-72.89829 -42.53523)
1	POINT (-72.80845 -42.53523)
1	POINT (-72.71862 -42.53523)
1	POINT (-72.62879 -42.53523)
1	POINT (-72.53896 -42.53523)
1	POINT (-72.44913 -42.53523)
1	POINT (-72.3593 -42.53523)
1	POINT (-72.26946 -42.53523)
1	POINT (-72.98812 -42.4454)
1	POINT (-72.89829 -42.4454)
1	POINT (-72.80845 -42.4454)
1	POINT (-72.71862 -42.4454)
1	POINT (-72.62879 -42.4454)
1	POINT (-72.53896 -42.4454)
1	POINT (-72.44913 -42.4454)
1	POINT (-72.3593 -42.4454)
1	POINT (-72.26946 -42.4454)
1	POINT (-72.98812 -42.35557)
1	POINT (-72.89829 -42.35557)
1	POINT (-72.80845 -42.35557)
1	POINT (-72.71862 -42.35557)
1	POINT (-72.62879 -42.35557)
1	POINT (-72.53896 -42.35557)
1	POINT (-72.44913 -42.35557)
1	POINT (-72.3593 -42.35557)
1	POINT (-72.26946 -42.35557)
1	POINT (-72.98812 -42.26573)
1	POINT (-72.89829 -42.26573)
1	POINT (-72.80845 -42.26573)
1	POINT (-72.71862 -42.26573)
1	POINT (-72.62879 -42.26573)
1	POINT (-72.53896 -42.26573)
1	POINT (-72.44913 -42.26573)
1	POINT (-72.3593 -42.26573)
1	POINT (-72.26946 -42.26573)
1	POINT (-72.98812 -42.1759)
1	POINT (-72.89829 -42.1759)
1	POINT (-72.80845 -42.1759)
1	POINT (-72.71862 -42.1759)
1	POINT (-72.62879 -42.1759)
1	POINT (-72.53896 -42.1759)
1	POINT (-72.44913 -42.1759)
1	POINT (-72.3593 -42.1759)
1	POINT (-72.26946 -42.1759)
1	POINT (-72.98812 -42.08607)
1	POINT (-72.89829 -42.08607)
1	POINT (-72.80845 -42.08607)
1	POINT (-72.71862 -42.08607)
1	POINT (-72.62879 -42.08607)
1	POINT (-72.53896 -42.08607)
1	POINT (-72.44913 -42.08607)
1	POINT (-72.3593 -42.08607)
0	POINT (-72.26946 -42.08607)

  nrow(mySamples_sf_batch)

## [1] 98

Obtenemos las muestras para el color rojo: 0

filtros_de_0_r <- subset(mySamples_sf_batch, mySamples_sf_batch$nd == "0")
saveRDS(filtros_de_0_r,"muestra_2.rds")
filtros_de_0_r %>%  kbl() %>%
  kable_material(c("striped", "hover"), font_size = 12)%>%
  scroll_box(width = "100%", height = "500px")

	nd	geometry
14	0	POINT (-72.62879 -42.89455)
18	0	POINT (-72.26946 -42.89455)
98	0	POINT (-72.26946 -42.08607)

nrow(filtros_de_0_r)

## [1] 3

Obtenemos las muestras para el color verde: 1

filtros_de_1_r <- subset(mySamples_sf_batch, mySamples_sf_batch$nd == "1")
filtros_de_1_r %>%  kbl() %>%
  kable_material(c("striped", "hover"), font_size = 12)%>%
  scroll_box(width = "100%", height = "500px")

	nd	geometry
1	1	POINT (-72.98812 -42.98439)
2	1	POINT (-72.89829 -42.98439)
3	1	POINT (-72.80845 -42.98439)
4	1	POINT (-72.71862 -42.98439)
5	1	POINT (-72.62879 -42.98439)
6	1	POINT (-72.53896 -42.98439)
7	1	POINT (-72.44913 -42.98439)
8	1	POINT (-72.3593 -42.98439)
9	1	POINT (-72.26946 -42.98439)
10	1	POINT (-72.98812 -42.89455)
11	1	POINT (-72.89829 -42.89455)
12	1	POINT (-72.80845 -42.89455)
13	1	POINT (-72.71862 -42.89455)
15	1	POINT (-72.53896 -42.89455)
16	1	POINT (-72.44913 -42.89455)
17	1	POINT (-72.3593 -42.89455)
19	1	POINT (-72.98812 -42.80472)
20	1	POINT (-72.89829 -42.80472)
21	1	POINT (-72.80845 -42.80472)
22	1	POINT (-72.71862 -42.80472)
23	1	POINT (-72.62879 -42.80472)
24	1	POINT (-72.53896 -42.80472)
25	1	POINT (-72.3593 -42.80472)
26	1	POINT (-72.26946 -42.80472)
27	1	POINT (-72.98812 -42.71489)
28	1	POINT (-72.89829 -42.71489)
29	1	POINT (-72.80845 -42.71489)
30	1	POINT (-72.71862 -42.71489)
31	1	POINT (-72.62879 -42.71489)
32	1	POINT (-72.53896 -42.71489)
33	1	POINT (-72.44913 -42.71489)
34	1	POINT (-72.3593 -42.71489)
35	1	POINT (-72.26946 -42.71489)
36	1	POINT (-72.98812 -42.62506)
37	1	POINT (-72.89829 -42.62506)
38	1	POINT (-72.80845 -42.62506)
39	1	POINT (-72.71862 -42.62506)
40	1	POINT (-72.62879 -42.62506)
41	1	POINT (-72.53896 -42.62506)
42	1	POINT (-72.44913 -42.62506)
43	1	POINT (-72.3593 -42.62506)
44	1	POINT (-72.26946 -42.62506)
45	1	POINT (-72.98812 -42.53523)
46	1	POINT (-72.89829 -42.53523)
47	1	POINT (-72.80845 -42.53523)
48	1	POINT (-72.71862 -42.53523)
49	1	POINT (-72.62879 -42.53523)
50	1	POINT (-72.53896 -42.53523)
51	1	POINT (-72.44913 -42.53523)
52	1	POINT (-72.3593 -42.53523)
53	1	POINT (-72.26946 -42.53523)
54	1	POINT (-72.98812 -42.4454)
55	1	POINT (-72.89829 -42.4454)
56	1	POINT (-72.80845 -42.4454)
57	1	POINT (-72.71862 -42.4454)
58	1	POINT (-72.62879 -42.4454)
59	1	POINT (-72.53896 -42.4454)
60	1	POINT (-72.44913 -42.4454)
61	1	POINT (-72.3593 -42.4454)
62	1	POINT (-72.26946 -42.4454)
63	1	POINT (-72.98812 -42.35557)
64	1	POINT (-72.89829 -42.35557)
65	1	POINT (-72.80845 -42.35557)
66	1	POINT (-72.71862 -42.35557)
67	1	POINT (-72.62879 -42.35557)
68	1	POINT (-72.53896 -42.35557)
69	1	POINT (-72.44913 -42.35557)
70	1	POINT (-72.3593 -42.35557)
71	1	POINT (-72.26946 -42.35557)
72	1	POINT (-72.98812 -42.26573)
73	1	POINT (-72.89829 -42.26573)
74	1	POINT (-72.80845 -42.26573)
75	1	POINT (-72.71862 -42.26573)
76	1	POINT (-72.62879 -42.26573)
77	1	POINT (-72.53896 -42.26573)
78	1	POINT (-72.44913 -42.26573)
79	1	POINT (-72.3593 -42.26573)
80	1	POINT (-72.26946 -42.26573)
81	1	POINT (-72.98812 -42.1759)
82	1	POINT (-72.89829 -42.1759)
83	1	POINT (-72.80845 -42.1759)
84	1	POINT (-72.71862 -42.1759)
85	1	POINT (-72.62879 -42.1759)
86	1	POINT (-72.53896 -42.1759)
87	1	POINT (-72.44913 -42.1759)
88	1	POINT (-72.3593 -42.1759)
89	1	POINT (-72.26946 -42.1759)
90	1	POINT (-72.98812 -42.08607)
91	1	POINT (-72.89829 -42.08607)
92	1	POINT (-72.80845 -42.08607)
93	1	POINT (-72.71862 -42.08607)
94	1	POINT (-72.62879 -42.08607)
95	1	POINT (-72.53896 -42.08607)
96	1	POINT (-72.44913 -42.08607)
97	1	POINT (-72.3593 -42.08607)

nrow(filtros_de_1_r)

## [1] 95

como es de esperar, en ésta zona la abrumadora mayoría de los pixeles son verdes.

Suma de las muestras:

filtros_de_0 y filtros_de_1_r

Ahora tenemos un dataframe que debemos convertir a FeatureCollection para poder construir la muestra de entrenamiento.

union_de_muestras <- rbind(filtros_de_0,filtros_de_1_r)
union_de_muestras %>%  kbl() %>%
  kable_material(c("striped", "hover"), font_size = 12)%>%
  scroll_box(width = "100%", height = "500px")

	nd	geometry
1	0	POINT (-72.23802 -42.99786)
2	0	POINT (-72.22904 -42.99786)
4	0	POINT (-72.23802 -42.98888)
5	0	POINT (-72.22904 -42.98888)
6	0	POINT (-72.22006 -42.98888)
8	0	POINT (-72.25599 -42.97989)
9	0	POINT (-72.21107 -42.97989)
10	0	POINT (-72.27396 -42.97091)
11	0	POINT (-72.22006 -42.97091)
12	0	POINT (-72.22904 -42.90803)
13	0	POINT (-72.24701 -42.89905)
14	0	POINT (-72.23802 -42.89905)
15	0	POINT (-72.52548 -42.88108)
17	0	POINT (-72.53447 -42.8721)
22	0	POINT (-72.53447 -42.86311)
27	0	POINT (-72.4626 -42.85413)
32	0	POINT (-72.45362 -42.84515)
37	0	POINT (-72.33684 -42.83616)
38	0	POINT (-72.32786 -42.83616)
39	0	POINT (-72.31887 -42.83616)
40	0	POINT (-72.30989 -42.83616)
41	0	POINT (-72.52548 -42.82718)
42	0	POINT (-72.5165 -42.82718)
44	0	POINT (-72.26497 -42.82718)
49	0	POINT (-72.50752 -42.80023)
58	0	POINT (-72.37277 -42.77328)
59	0	POINT (-72.36379 -42.77328)
60	0	POINT (-72.3548 -42.77328)
61	0	POINT (-72.47159 -42.7643)
62	0	POINT (-72.4626 -42.7643)
63	0	POINT (-72.45362 -42.7643)
67	0	POINT (-72.39074 -42.7643)
68	0	POINT (-72.38175 -42.7643)
70	0	POINT (-72.33684 -42.6565)
71	0	POINT (-72.30091 -42.63853)
73	0	POINT (-72.28294 -42.62955)
74	0	POINT (-72.33684 -42.61159)
75	0	POINT (-72.26497 -42.61159)
76	0	POINT (-72.34582 -42.6026)
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78	0	POINT (-72.34582 -42.59362)
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84	0	POINT (-72.21107 -42.53972)
85	0	POINT (-72.20209 -42.50379)
86	0	POINT (-72.38175 -42.44989)
87	0	POINT (-72.37277 -42.44989)
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91	0	POINT (-72.38175 -42.43192)
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100	0	POINT (-72.23802 -42.33311)
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102	0	POINT (-72.22006 -42.31514)
103	0	POINT (-72.22904 -42.30616)
104	0	POINT (-72.22006 -42.29718)
106	0	POINT (-72.25599 -42.26124)
107	0	POINT (-72.27396 -42.25226)
108	0	POINT (-72.22006 -42.25226)
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110	0	POINT (-72.20209 -42.25226)
111	0	POINT (-72.20209 -42.16243)
112	0	POINT (-72.21107 -42.15344)
113	0	POINT (-72.20209 -42.15344)
114	0	POINT (-72.23802 -42.14446)
115	0	POINT (-72.25599 -42.13548)
117	0	POINT (-72.22904 -42.13548)
118	0	POINT (-72.25599 -42.1265)
119	0	POINT (-72.24701 -42.1265)
124	0	POINT (-72.22006 -42.09955)
126	0	POINT (-72.34582 -42.09056)
128	0	POINT (-72.25599 -42.09056)
129	0	POINT (-72.23802 -42.09056)
130	0	POINT (-72.22006 -42.09056)
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133	0	POINT (-72.21107 -42.0726)
134	0	POINT (-72.33684 -42.06361)
135	0	POINT (-72.29192 -42.06361)
138	0	POINT (-72.25599 -42.06361)
139	0	POINT (-72.33684 -42.05463)
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141	0	POINT (-72.30989 -42.05463)
142	0	POINT (-72.3548 -42.04565)
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145	0	POINT (-72.30989 -42.04565)
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161	0	POINT (-72.3548 -42.00971)
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166	0	POINT (-72.21107 -42.00971)
167	0	POINT (-72.3548 -42.00073)
168	0	POINT (-72.33684 -42.00073)
170	0	POINT (-72.25599 -42.00073)
18	1	POINT (-72.98812 -42.98439)
210	1	POINT (-72.89829 -42.98439)
3	1	POINT (-72.80845 -42.98439)
410	1	POINT (-72.71862 -42.98439)
510	1	POINT (-72.62879 -42.98439)
610	1	POINT (-72.53896 -42.98439)
7	1	POINT (-72.44913 -42.98439)
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105	1	POINT (-72.98812 -42.89455)
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121	1	POINT (-72.80845 -42.89455)
136	1	POINT (-72.71862 -42.89455)
156	1	POINT (-72.53896 -42.89455)
16	1	POINT (-72.44913 -42.89455)
171	1	POINT (-72.3593 -42.89455)
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221	1	POINT (-72.71862 -42.80472)
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25	1	POINT (-72.3593 -42.80472)
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271	1	POINT (-72.98812 -42.71489)
28	1	POINT (-72.89829 -42.71489)
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30	1	POINT (-72.71862 -42.71489)
31	1	POINT (-72.62879 -42.71489)
321	1	POINT (-72.53896 -42.71489)
33	1	POINT (-72.44913 -42.71489)
34	1	POINT (-72.3593 -42.71489)
35	1	POINT (-72.26946 -42.71489)
36	1	POINT (-72.98812 -42.62506)
371	1	POINT (-72.89829 -42.62506)
381	1	POINT (-72.80845 -42.62506)
391	1	POINT (-72.71862 -42.62506)
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411	1	POINT (-72.53896 -42.62506)
421	1	POINT (-72.44913 -42.62506)
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601	1	POINT (-72.44913 -42.4454)
611	1	POINT (-72.3593 -42.4454)
621	1	POINT (-72.26946 -42.4454)
631	1	POINT (-72.98812 -42.35557)
64	1	POINT (-72.89829 -42.35557)
65	1	POINT (-72.80845 -42.35557)
66	1	POINT (-72.71862 -42.35557)
671	1	POINT (-72.62879 -42.35557)
681	1	POINT (-72.53896 -42.35557)
69	1	POINT (-72.44913 -42.35557)
701	1	POINT (-72.3593 -42.35557)
711	1	POINT (-72.26946 -42.35557)
72	1	POINT (-72.98812 -42.26573)
731	1	POINT (-72.89829 -42.26573)
741	1	POINT (-72.80845 -42.26573)
751	1	POINT (-72.71862 -42.26573)
761	1	POINT (-72.62879 -42.26573)
771	1	POINT (-72.53896 -42.26573)
781	1	POINT (-72.44913 -42.26573)
791	1	POINT (-72.3593 -42.26573)
801	1	POINT (-72.26946 -42.26573)
811	1	POINT (-72.98812 -42.1759)
821	1	POINT (-72.89829 -42.1759)
831	1	POINT (-72.80845 -42.1759)
841	1	POINT (-72.71862 -42.1759)
851	1	POINT (-72.62879 -42.1759)
861	1	POINT (-72.53896 -42.1759)
871	1	POINT (-72.44913 -42.1759)
88	1	POINT (-72.3593 -42.1759)
891	1	POINT (-72.26946 -42.1759)
901	1	POINT (-72.98812 -42.08607)
911	1	POINT (-72.89829 -42.08607)
921	1	POINT (-72.80845 -42.08607)
931	1	POINT (-72.71862 -42.08607)
94	1	POINT (-72.62879 -42.08607)
951	1	POINT (-72.53896 -42.08607)
961	1	POINT (-72.44913 -42.08607)
971	1	POINT (-72.3593 -42.08607)

nrow(union_de_muestras)

## [1] 211

saveRDS(union_de_muestras, "union_de_muestras.rds")

Algebra de conjuntos:

# Create two circular geometries.
poly1 <- ee$Geometry$Point(c(-50, 30))$buffer(1e6)
poly2 <- ee$Geometry$Point(c(-40, 30))$buffer(1e6)

# Display polygon 1 in red and polygon 2 in blue.
Map$setCenter(-45, 30,  zoom = 5)
Map$addLayer(poly1, list(color = "FF0000"), "poly1") +
Map$addLayer(poly2, list(color = "0000FF"), "poly2")

# Compute the intersection, display it in blue.
Map$setCenter(-45, 30,  zoom = 5)
intersection <- poly1$intersection(poly2, ee$ErrorMargin(1))
Map$addLayer(intersection, list(color = "00FF00"), "intersection")

# Compute the union, display it in magenta.
Map$setCenter(-45, 30,  zoom = 5)
union <- poly1$union(poly2, ee$ErrorMargin(1))
Map$addLayer(union, list(color = "FF00FF"), "union")

# Compute the difference, display in yellow.
Map$setCenter(-45, 30,  zoom = 5)
diff1 <- poly1$difference(poly2, ee$ErrorMargin(1))
Map$addLayer(diff1, list(color = "FFFF00"), "diff1")

https://www.rdocumentation.org/packages/BiocGenerics/versions/0.18.0/topics/sets

# selectMethod("intersect",c(region, region_0))

# ggg

Ejemplo 1: grafica de una caja

box <- ee$Geometry$Rectangle(coords = c(-73,-43,-72.462,-42.811),
                             ## WGS 84
                             proj = "EPSG:4326",
                             geodesic = FALSE)

Map$setCenter(lon = -73.079, lat = -42.611, zoom = 7)
Map$addLayer(box)

Ejemplo 2: estadisticas de un shp:

vias<-readOGR(".","region_los_lagos")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\usuario\Desktop\ds\ds_rgee\cart", layer: "region_los_lagos"
## with 1 features
## It has 17 fields
## Integer64 fields read as strings:  FID_1 TOTAL_VIVI PARTICULAR COLECTIVAS TOTAL_PERS HOMBRES MUJERES

summary(vias)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##         min       max
## x -74.83774 -71.58093
## y -44.06706 -40.23878
## Is projected: FALSE 
## proj4string : [+proj=longlat +datum=WGS84 +no_defs]
## Data attributes:
##     FID_1              REGION           NOM_REGION         TOTAL_VIVI       
##  Length:1           Length:1           Length:1           Length:1          
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##   PARTICULAR         COLECTIVAS         TOTAL_PERS          HOMBRES         
##  Length:1           Length:1           Length:1           Length:1          
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##    MUJERES             DENSIDAD       INDICE_MAS      INDICE_DEP   
##  Length:1           Min.   :17.11   Min.   :97.64   Min.   :47.03  
##  Class :character   1st Qu.:17.11   1st Qu.:97.64   1st Qu.:47.03  
##  Mode  :character   Median :17.11   Median :97.64   Median :47.03  
##                     Mean   :17.11   Mean   :97.64   Mean   :47.03  
##                     3rd Qu.:17.11   3rd Qu.:97.64   3rd Qu.:47.03  
##                     Max.   :17.11   Max.   :97.64   Max.   :47.03  
##    IND_DEP_JU      IND_DEP_VE      SHAPE_Leng        Shape_Le_1   
##  Min.   :30.55   Min.   :16.48   Min.   :7760164   Min.   :60.65  
##  1st Qu.:30.55   1st Qu.:16.48   1st Qu.:7760164   1st Qu.:60.65  
##  Median :30.55   Median :16.48   Median :7760164   Median :60.65  
##  Mean   :30.55   Mean   :16.48   Mean   :7760164   Mean   :60.65  
##  3rd Qu.:30.55   3rd Qu.:16.48   3rd Qu.:7760164   3rd Qu.:60.65  
##  Max.   :30.55   Max.   :16.48   Max.   :7760164   Max.   :60.65  
##    Shape_Area   
##  Min.   :5.259  
##  1st Qu.:5.259  
##  Median :5.259  
##  Mean   :5.259  
##  3rd Qu.:5.259  
##  Max.   :5.259

https://ecodata.nrel.colostate.edu/gdpe-gee-remote-sensing-lessons/module7.html

Referencias:

https://drive.google.com/drive/folders/1yDB9oZBS6ZSZ-U1Rf7WsNlLdaHdHl-3z

https://rstudio-pubs-static.s3.amazonaws.com/643255_63554e7e1f9f466dad4f9e62ff977a88.html

https://drive.google.com/drive/folders/1yDB9oZBS6ZSZ-U1Rf7WsNlLdaHdHl-3z?usp=sharing

https://rstudio-pubs-static.s3.amazonaws.com/639598_f8b124e23b7949a49250693dc3c5a6a7.html

https://github.com/csaybar/rgee/blob/examples//GetStarted/03_finding_images.R

https://rpubs.com/daniballari/raster

https://stackoverflow.com/questions/63543942/raster-does-not-align-with-shapefile-after-processing-with-rgee

https://rpubs.com/ohfrancom/618462

https://www.ide.cl/index.php/limites-y-fronteras/item/1528-division-politica-administrativa-2020

Catastro de Lagos https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos

Lagos de Chile
http://datos.cedeus.cl/layers/geonode:cl_lagos_geo