Glaciares en Chile

Tarapacá: Automatización del proceso de extracción de data por glaciar (análisis espacial) y en el tiempo.

VE-CC-CF

DataIntelligence

28-05-2021

Abstract

Éste trabajo contiene elas líneas para extraer el área considerada glaciar en cada límite administrativo entregado en un shp. Se propone una lógica para calcular el porcentaje glaciar. Se añade el código para automatizar la extracción en el tiempo. También avanzamos en torno a la determinación de las matrices de confusión para cada una de las técnicas de ML utilizadas, para mejorar las imágenes: RF, CART y SVM.

library(rgee)
ee_Initialize("tarredwall@gmail.com", drive = TRUE)

## -- rgee 1.0.9 --------------------------------------- earthengine-api 0.1.259 -- 
##  v email: tarredwall@gmail.com 
##  v Google Drive credentials:
 v Google Drive credentials:  FOUND
##  v Initializing Google Earth Engine:
 v Initializing Google Earth Engine:  DONE!
## 
 v Earth Engine user: users/tarredwall 
## --------------------------------------------------------------------------------

R

A partir del 15 de febrero de 2018, se debe utilizar la siguiente denominación:

I Región de Arica y Parinacota. Lunes 24 II Región de Tarapacá. Martes 25 III Región de Antofagasta. Miercoles 26 IV Región de Atacama. Jueves 27 V Región de Coquimbo. Viernes 28 VI Región de Valparaíso. Lunes 31

Victor

VII Región Metropolitana de Santiago. Lunes 24 VIII Región del Libertador General Bernardo O’Higgins. Martes 25 IX Región del Maule. Miercoles 26 X Región del Ñuble. Jueves 27 XI Región del Biobío. Viernes 28 XII Región de La Araucanía. Lunes 31

Abner

XIII Región de Los Ríos. Miercoles 26 XIV Región de Los Lagos. Jueves 27 XV Región de Aysén del General Carlos Ibáñez del Campo. Viernes 28 XVI Región de Magallanes y la Antártica Chilena. Lunes 31

1. Cargamos el shp de la primera region y lo filterboundiamos con la imagen satelital sentinel. Cargamos el shp de glaciares, y obtenemos las muestras interiores de los limites administrativos de color rojo que significaran glaciares que tendren numero 0. Obtenemos muestras para los colores verdes fuera de los limites administrativos. Unimos ambas muestras y categorizamos nuevamente la imagen satelital sentinel.

I Región de Tarapacá.

1 Lo primero es dibujar el shp de la Región de Tarapacá

region_tara <- st_read("rrss/Region_01.shp", 
        quiet = TRUE) %>% 
        sf_as_ee()

## Registered S3 method overwritten by 'geojsonsf':
##   method        from   
##   print.geojson geojson

tara <- region_tara$geometry()
Map$setCenter(lon = -69.7522, lat = -20.18, zoom = 7)
Map$addLayer(tara)

2 lo segundo es asociar una imagen satelital Sentinel filtrada por fecha, nubosidad y vinculada a la region de estudio. Y establecemos que el parametro sea la media.

start <- ee$Date("2019-01-01")
finish <- ee$Date("2019-04-01")
cc <- 20

sentinel1 = ee$ImageCollection('COPERNICUS/S2')$filterDate(start, finish)$filterBounds(tara)$
  filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cc))

first <- sentinel1$median()

vizParams <- list(
  bands = c("B8","B5" , "B3"),
  #  bands = c("B2", "B3"),
  min = 100,
  max = 1000,
  gamma = 2
)
Map$setCenter(lon = -69.7522, lat = -20.18, zoom = 7)
Map$addLayer(first, vizParams, "Landsat 8 image")

3 Lo tercero es construir un raster con NDGI mayor a 0 siempre vinculada a la primera imagen satelital que hemos extraído.

Construimos una imagen raster con una columna llamada nd con dos categorías para cada pixel: 0 si es rojo, 1 si es verde

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

ndgi <- getNDGI(first)

## Warning: Ops.ee.image.Image will be deprecated in rgee v.1.1.0. Please install
## rgeeExtra (https://github.com/r-earthengine/rgeeExtra). Deeply sorry for the
## inconveniences.

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

Map$setCenter(lon = -69.7522, lat = -20.18, zoom = 7)
Map$addLayer(ndgi, ndgiParams, "NDGI")

4 obtenemos los shp que nos indican la presencia de glaciares en la Region de Tarapaca.

glaciar_tara <- st_read("GlaciaresxRegion/Simple_TARAPACA.shp",
        quiet = TRUE) %>%
        sf_as_ee()
glaciar_tara <- glaciar_tara$geometry()
Map$setCenter(lon = -69.7522, lat = -20.18, zoom = 7)
Map$addLayer(glaciar_tara, ndgiParams, "NDGI")

5 Intersectamos las regiones glaciares de la region de Tarapaca con nuestro ndvi, y de esta forma obtenemos un universo muestral del cual poder extraer nuestro primer set de muestras (pixeles rojos interiores).

ESTA ES LA IMAGEN SOBRE LA QUE EXTRAEREMOS EL PRIMER SET DE MUESTRAS

glaciares_adm_con_ngvi <- ndgi$clip(glaciar_tara)
Map$setCenter(lon = -69.7522, lat = -20.18, zoom = 7)
Map$addLayer(glaciares_adm_con_ngvi, ndgiParams, "NDGI")

Con esto concluimos la primera parte de nuestra generacion de raster para, como siguiente paso, extraer las muestras.

6 Hay que hacer una diferencia (se le resta al shp de la región de Tarapaca los shps de los glaciares) para intersectarla con nuestra imagen ngdi y obtener el segundo universo muestral.

6.1 Primero obtenemos nuestros shapes sin zonas administrativas glaciares:

tara_sin_glacia <- tara$difference(glaciar_tara, ee$ErrorMargin(1))
Map$setCenter(lon = -69.7522, lat = -20.18, zoom = 7)
Map$addLayer(tara_sin_glacia, ndgiParams, "NDGI")

6.2 Necesitamos intersectar nuestro ndvi con la zona de Tarapaca que excluye los glaciares.

ndvi_tarapaca_sin_glaciares <- ndgi$clip(tara_sin_glacia)
Map$setCenter(lon = -69.7522, lat = -20.18, zoom = 7)
Map$addLayer(ndvi_tarapaca_sin_glaciares, ndgiParams, "NDGI")

7 Ahora tenemos dos regiones preparadas sobre las cuales podemos extraer nuestras muestras. A efectos de orden nombrémoslas:

7.1. pixeles rojos interiores

7.2. pixeles verdes exteriores

7.1. pixeles rojos interiores

Necesitamos intersectar las regiones administrativas glaciares con nuestro raster ndvi y de ahi extraer las muestras de color rojo que simbolizarán los glaciares.

limites_glaciares_con_rasters <- ndgi$clip(glaciar_tara)
Map$setCenter(lon = -68.69644, lat = -19.7581, zoom = 15)
Map$addLayer(limites_glaciares_con_rasters , ndgiParams, "NDGI")

1 Muestreo

# region_arica <- st_read("Regiones_separadas/Region_15.shp", quiet = TRUE)
# region_arica

Los rojos interiores son glaciares, 0 = rojo y son llamados muestras_in_gee_rojas.

muestras_in <- ndgi$sampleRegions(
    collection = ee$Feature(glaciar_tara),
    scale = 100,
    tileScale = 16,
    geometries = TRUE
  )

muestras_in_gee_rojas = muestras_in$filter(ee$Filter$eq('nd', 0))

Map$setCenter(lon = -68.69644, lat = -19.7581, zoom = 15)
Map$addLayer(
  eeObject =  muestras_in_gee_rojas,
  visParams = {},
  name = "puntos glaciares"
)

7.2. Pixeles verdes exteriores

ndvi_tara_sin_glaciares <- ndgi$clip(tara_sin_glacia)
Map$setCenter(lon = -68.69644, lat = -19.7581, zoom = 8)
Map$addLayer(ndvi_tara_sin_glaciares, ndgiParams, "NDGI")

Los verdes exteriores no son glaciares 1 = verde

muestras_in <- ndgi$sampleRegions(
    collection = ee$Feature(ndvi_tara_sin_glaciares),
    scale = 10000,
    tileScale = 16,
    geometries = TRUE
  )

muestras_out_gee_verdes = muestras_in$filter(ee$Filter$eq('nd', 1))

Map$setCenter(lon = -69.80584, lat = -17.67837, zoom = 7)
Map$addLayer(
  eeObject =  muestras_out_gee_verdes,
  visParams = {},
  name = "puntos no glaciares"
)

III debemos unir muestras y generar el random forest

union_total <- muestras_out_gee_verdes$merge(muestras_in_gee_rojas)

3 Aplicamos Random Forest

(volver al índice)

region_arica <- st_read("rrss/Region_01.shp", 
        quiet = TRUE) %>% 
        sf_as_ee()
arica <- region_arica$geometry()

start <- ee$Date("2019-01-01")
finish <- ee$Date("2019-04-01")
cc <- 20

sentinel1 = ee$ImageCollection('COPERNICUS/S2')$filterDate(start, finish)$filterBounds(arica)$
  filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cc))

first <- sentinel1$median()

# Use these bands for prediction.
bands <- c("B2", "B3", "B4", "B8")

# This property of the table stores the land cover labels.
label <- "nd"

# Overlay the points on the imagery to get training.
training <- first$select(bands)$sampleRegions(
  collection = union_total,
  properties = list(label),
  scale = 10
)

# Train a CART classifier with default parameters.
trained <- ee$Classifier$smileRandomForest(10)$train(training, label, bands)

# Classify the image with the same bands used for training.
classified_rf <- first$select(bands)$classify(trained)

# Viz parameters.
# viz_img <- list(bands = c("B4", "B3", "B2"), max = 0.4)
vizParams <- list(
  bands = c("B8","B5" , "B3"),
  #  bands = c("B2", "B3"),
  min = 100,
  max = 1000,
  gamma = 2
)

viz_class <- list(palette = c("red", "green", "blue"), min = 0, max = 2)

Map$addLayer(first, vizParams, name = "image") +
Map$addLayer(classified_rf, viz_class, name = "classification")

18:29 18:30

Le aplicamos la matriz de confusion:

trainAccuracy = trained$confusionMatrix()
trainAccuracy$getInfo()

## [[1]]
## [1] 11  1
## 
## [[2]]
## [1]   0 451

18:40 18:43

4 Aplicamos CART

(volver al índice)

region_tara <- st_read("rrss/Region_01.shp", 
        quiet = TRUE) %>% 
        sf_as_ee()
tara <- region_tara$geometry()

start <- ee$Date("2019-01-01")
finish <- ee$Date("2019-04-01")
cc <- 20

sentinel1 = ee$ImageCollection('COPERNICUS/S2')$filterDate(start, finish)$filterBounds(tara)$
  filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cc))

first <- sentinel1$median()

# Use these bands for prediction.
bands <- c("B2", "B3", "B4", "B8")

# This property of the table stores the land cover labels.
label <- "nd"

# Overlay the points on the imagery to get training.
training <- first$select(bands)$sampleRegions(
  collection = union_total,
  properties = list(label),
  scale = 10
)

# Train a CART classifier with default parameters.
trained <- ee$Classifier$smileCart(10)$train(training, label, bands)

# Classify the image with the same bands used for training.
classified <- first$select(bands)$classify(trained)

# Viz parameters.
# viz_img <- list(bands = c("B4", "B3", "B2"), max = 0.4)
vizParams <- list(
  bands = c("B8","B5" , "B3"),
  #  bands = c("B2", "B3"),
  min = 100,
  max = 1000,
  gamma = 2
)

viz_class <- list(palette = c("red", "green", "blue"), min = 0, max = 2)

Map$addLayer(first, vizParams, name = "image") +
Map$addLayer(classified, viz_class, name = "classification")

18:47 18:50

trainAccuracy = trained$confusionMatrix()
trainAccuracy$getInfo()

## [[1]]
## [1] 12  0
## 
## [[2]]
## [1]   0 451

18:51 18 53

4 Aplicamos SVM

(volver al índice)

region_arica <- st_read("Regiones_separadas/Region_15.shp", 
        quiet = TRUE) %>% 
        sf_as_ee()
arica <- region_arica$geometry()

start <- ee$Date("2019-01-01")
finish <- ee$Date("2019-04-01")
cc <- 20

sentinel1 = ee$ImageCollection('COPERNICUS/S2')$filterDate(start, finish)$filterBounds(arica)$
  filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cc))

first <- sentinel1$median()

# Use these bands for prediction.
bands <- c("B2", "B3", "B4", "B8")

# This property of the table stores the land cover labels.
label <- "nd"

# Overlay the points on the imagery to get training.
training <- first$select(bands)$sampleRegions(
  collection = union_total,
  properties = list(label),
  scale = 10
)
classifier = ee$Classifier$libsvm(
  kernelType = "RBF",
  gamma = 0.5,
  cost = 10
)
# Train a CART classifier with default parameters.
trained = classifier$train(training, label, bands)

# Classify the image with the same bands used for training.
classified <- first$select(bands)$classify(trained)

# Viz parameters.
# viz_img <- list(bands = c("B4", "B3", "B2"), max = 0.4)
vizParams <- list(
  bands = c("B8","B5" , "B3"),
  #  bands = c("B2", "B3"),
  min = 100,
  max = 1000,
  gamma = 2
)

viz_class <- list(palette = c("red", "green", "blue"), min = 0, max = 2)

Map$addLayer(first, vizParams, name = "image") +
Map$addLayer(classified, viz_class, name = "classification")

No se puede obtener la matriz de confusion para libsvm pues excede limites de memoria

trainAccuracy = trained$confusionMatrix()
trainAccuracy$getInfo()

## [[1]]
## [1] 1 0
## 
## [[2]]
## [1]   0 142

Analisis espacial

2. Una vez obtenido nuestro raster random forest interceptamos por limites glaciares, Y EN CADA UNO DE ELLOS determinamos la cantidad de pixeles rojos

mask_0 <- read_sf("GlaciaresxRegion/Simple_TARAPACA.shp")
#mask_0

reg_15_2 <- mask_0 %>% dplyr::select(COD_GLA,AREA_Km2)
varia <- reg_15_2 %>%
sf_as_ee()

region_0 <- varia$geometry()
sale_4 <- classified_rf$clip(region_0)

puntos_r <- sale_4$select('classification')$eq(0)


# Reducer$sum() returns a Reducer that computes the (weighted) sum of its inputs.

areas_rojas = puntos_r$reduceRegions(
reducer= ee$Reducer$sum(),
collection= varia,
scale= 30
)

rojas <- areas_rojas$getInfo()

rojas1 <- as.data.frame(rojas)
# rojas1


puntos_v <- sale_4$select('classification')$eq(1)

areas_verdes = puntos_v$reduceRegions(
reducer= ee$Reducer$sum(),
collection= varia,
scale= 30
)

verdes <- areas_verdes$getInfo()

verdes1 <- as.data.frame(verdes)
# verdes1



r_v <- rbind(colnames(rojas1),rojas1,verdes1)
r_v <- r_v[1:4,]
r_v <- r_v[-3,]
r_v <- t(r_v)
r_v <- as.data.frame(r_v)
names(r_v)[1] <- "col1"
names(r_v)[2] <- "col2"
names(r_v)[3] <- "col3"
r_v$col4 <- r_v$col3
r_v$col5 <- r_v$col3
# r_v


data1 <- filter(r_v, grepl("features.properties.COD_GLA",col1))
data1 <- as.data.frame(data1[,2])
# data1

data2 <- filter(r_v, grepl("features.properties.sum",col1))
data2 <- as.data.frame(data2[,c(2,3)])
# data2

data3 <- filter(r_v, grepl("features.properties.AREA_Km2",col1))
data3 <- as.data.frame(data3[,c(2,3)])
# data3

data4 <- cbind(data1,data2,data3)
colnames(data4) <- c("codigo_gla", "rojo", "verde", "area_Km2(shp)")
data4

##                             codigo_gla              rojo              verde
## features.properties.sum    CL101044025  49.2274509803922                  0
## features.properties.sum.1  CL101044018  10.2509803921569   102.247058823529
## features.properties.sum.2  CL101044017                 2   124.862745098039
## features.properties.sum.3  CL101044019                 0   97.7882352941177
## features.properties.sum.4  CL101044016  1.05098039215686   74.0392156862745
## features.properties.sum.5  CL101044015                 0   34.8666666666667
## features.properties.sum.6  CL101044014  1.02352941176471   29.9019607843137
## features.properties.sum.7  CL101044013                 0   40.1607843137255
## features.properties.sum.8  CL101044012                 0   13.8980392156863
## features.properties.sum.9  CL101044011                 0   265.913725490196
## features.properties.sum.10 CL101044010                 0   5.82352941176471
## features.properties.sum.11 CL101044007                 0   57.3803921568627
## features.properties.sum.12 CL101044008                 0   201.643137254902
## features.properties.sum.13 CL101044006                 0   221.235294117647
## features.properties.sum.14 CL101042002                 0   81.0078431372549
## features.properties.sum.15 CL101042001  2.75294117647059   77.1843137254902
## features.properties.sum.16 CL101042003                 0   63.2117647058824
## features.properties.sum.17 CL101042005  1.00392156862745    32.878431372549
## features.properties.sum.18 CL101042004                 0    20.121568627451
## features.properties.sum.19 CL101610012                 0   154.098039215686
## features.properties.sum.20 CL101610013                 0   110.176470588235
## features.properties.sum.21 CL101610011                 0   6.61960784313725
## features.properties.sum.22 CL101610010                 0   94.2666666666667
## features.properties.sum.23 CL101610009                 0   72.7764705882353
## features.properties.sum.24 CL101610008                 0   41.6274509803922
## features.properties.sum.25 CL101610005                 0   78.9098039215686
## features.properties.sum.26 CL101610004                 0   63.0549019607843
## features.properties.sum.27 CL101610002                 0   113.752941176471
## features.properties.sum.28 CL101610003                 0   97.6431372549019
## features.properties.sum.29 CL101610001                 0    114.43137254902
## features.properties.sum.30 CL101611002                 0   159.976470588235
## features.properties.sum.31 CL101720004                 0   30.7843137254902
## features.properties.sum.32 CL101720003                 0                129
## features.properties.sum.33 CL101720001                 0   9.56470588235294
## features.properties.sum.34 CL101720002                 0   73.6862745098039
## features.properties.sum.35 CL101720008                 0   188.541176470588
## features.properties.sum.36 CL101720005                 0   22.3333333333333
## features.properties.sum.37 CL101720006                 0   15.2666666666667
## features.properties.sum.38 CL101720009                 0   13.2156862745098
## features.properties.sum.39 CL101720007                 0   9.65490196078431
## features.properties.sum.40 CL101730001                 0   129.050980392157
## features.properties.sum.41 CL101730002                 0   35.5254901960784
## features.properties.sum.42 CL101730009                 0    21.278431372549
## features.properties.sum.43 CL101730008                 0   19.0705882352941
## features.properties.sum.44 CL101730011                 0   53.4156862745098
## features.properties.sum.45 CL101730012 0.203921568627451   72.8862745098039
## features.properties.sum.46 CL101730013                 0   63.9176470588235
## features.properties.sum.47 CL101730010                 0   7.74117647058823
## features.properties.sum.48 CL101730014                 0   144.137254901961
## features.properties.sum.49 CL101730016                 0   25.1607843137255
## features.properties.sum.50 CL101730017                 0   9.94901960784314
## features.properties.sum.51 CL101730015                 0   9.06666666666666
## features.properties.sum.52 CL101730018                 0   11.5607843137255
## features.properties.sum.53 CL101730004                 0   33.2745098039216
## features.properties.sum.54 CL101730003                 0   79.1529411764706
## features.properties.sum.55 CL101730006                 0   27.6901960784314
## features.properties.sum.56 CL101730007                 0   24.5843137254902
## features.properties.sum.57 CL101730005                 0   13.2509803921569
## features.properties.sum.58 CL101044001                 2   47.4078431372549
## features.properties.sum.59 CL101044002                 0   27.0235294117647
## features.properties.sum.60 CL101044003 0.156862745098039   97.0078431372549
## features.properties.sum.61 CL101044004                 0   31.4117647058824
## features.properties.sum.62 CL101044009                 0   13.6901960784314
## features.properties.sum.63 CL101044005  3.29411764705882   18.8235294117647
## features.properties.sum.64 CL101040008                 0   51.1137254901961
## features.properties.sum.65 CL101611001                 0   128.905882352941
## features.properties.sum.66 CL101610024                 0   53.6862745098039
## features.properties.sum.67 CL101610023                 0    256.78431372549
## features.properties.sum.68 CL101610022                 0   229.074509803922
## features.properties.sum.69 CL101610021                 0   180.870588235294
## features.properties.sum.70 CL101610020                 0   106.007843137255
## features.properties.sum.71 CL101610018                 0    145.83137254902
## features.properties.sum.72 CL101610017                 0   119.317647058824
## features.properties.sum.73 CL101610016                 0   63.3176470588235
## features.properties.sum.74 CL101040006                 0   53.2901960784314
## features.properties.sum.75 CL101040005                 0   55.3372549019608
## features.properties.sum.76 CL101040004   3.6078431372549   9.25882352941177
## features.properties.sum.77 CL101040003                 0   7.43529411764706
## features.properties.sum.78 CL101610015                 0   9.92156862745098
## features.properties.sum.79 CL101610014                 0   59.9921568627451
## features.properties.sum.80 CL101610019                 0   253.152941176471
## features.properties.sum.81 CL101040002                 0   50.8196078431372
## features.properties.sum.82 CL101040007  3.75686274509804   45.8470588235294
## features.properties.sum.83 CL101040001  23.7176470588235    24.756862745098
## features.properties.sum.84 CL101040009  45.1254901960784               54.4
## features.properties.sum.85 CL101044024  410.019607843137 0.0549019607843137
## features.properties.sum.86 CL101070001  18.7686274509804   10.3019607843137
## features.properties.sum.87 CL101044020  30.8039215686274                  0
## features.properties.sum.88 CL101044022  24.9058823529412                  0
## features.properties.sum.89 CL101044021  15.3411764705882                  0
## features.properties.sum.90 CL101044023   11.956862745098                  0
##                              area_Km2(shp)              NA
## features.properties.sum    0.0397270010711 0.0397270010711
## features.properties.sum.1   0.100876482862  0.100876482862
## features.properties.sum.2   0.109852809052  0.109852809052
## features.properties.sum.3  0.0873709651722 0.0873709651722
## features.properties.sum.4  0.0639482581884 0.0639482581884
## features.properties.sum.5  0.0390825631323 0.0390825631323
## features.properties.sum.6  0.0327605622869 0.0327605622869
## features.properties.sum.7  0.0382199791074 0.0382199791074
## features.properties.sum.8  0.0167628398049 0.0167628398049
## features.properties.sum.9   0.234736140147  0.234736140147
## features.properties.sum.10 0.0123072515574 0.0123072515574
## features.properties.sum.11 0.0490761861993 0.0490761861993
## features.properties.sum.12  0.181770894446  0.181770894446
## features.properties.sum.13  0.187241197168  0.187241197168
## features.properties.sum.14 0.0822324117868 0.0822324117868
## features.properties.sum.15 0.0811335051897 0.0811335051897
## features.properties.sum.16 0.0517317014976 0.0517317014976
## features.properties.sum.17 0.0342342838388 0.0342342838388
## features.properties.sum.18  0.025537434093  0.025537434093
## features.properties.sum.19   0.14556645363   0.14556645363
## features.properties.sum.20 0.0969989514597 0.0969989514597
## features.properties.sum.21 0.0133418051918 0.0133418051918
## features.properties.sum.22 0.0876500187874 0.0876500187874
## features.properties.sum.23 0.0722716222512 0.0722716222512
## features.properties.sum.24 0.0460606415757 0.0460606415757
## features.properties.sum.25 0.0863192881262 0.0863192881262
## features.properties.sum.26 0.0786716671606 0.0786716671606
## features.properties.sum.27 0.0996942476728 0.0996942476728
## features.properties.sum.28 0.0926249593749 0.0926249593749
## features.properties.sum.29  0.107310516435  0.107310516435
## features.properties.sum.30  0.125452215699  0.125452215699
## features.properties.sum.31 0.0370788979097 0.0370788979097
## features.properties.sum.32  0.121432954093  0.121432954093
## features.properties.sum.33 0.0156484103605 0.0156484103605
## features.properties.sum.34 0.0637414139919 0.0637414139919
## features.properties.sum.35  0.163304691787  0.163304691787
## features.properties.sum.36 0.0228456773695 0.0228456773695
## features.properties.sum.37 0.0161851633664 0.0161851633664
## features.properties.sum.38 0.0165129804368 0.0165129804368
## features.properties.sum.39 0.0198858464782 0.0198858464782
## features.properties.sum.40  0.121994691845  0.121994691845
## features.properties.sum.41 0.0391150993622 0.0391150993622
## features.properties.sum.42 0.0305984387632 0.0305984387632
## features.properties.sum.43 0.0225271588162 0.0225271588162
## features.properties.sum.44 0.0661863412709 0.0661863412709
## features.properties.sum.45 0.0641183674004 0.0641183674004
## features.properties.sum.46 0.0711972768362 0.0711972768362
## features.properties.sum.47 0.0127425943588 0.0127425943588
## features.properties.sum.48  0.130168840973  0.130168840973
## features.properties.sum.49 0.0425520003997 0.0425520003997
## features.properties.sum.50 0.0157886226892 0.0157886226892
## features.properties.sum.51 0.0155196563794 0.0155196563794
## features.properties.sum.52 0.0181153020415 0.0181153020415
## features.properties.sum.53 0.0346999506794 0.0346999506794
## features.properties.sum.54  0.071272988267  0.071272988267
## features.properties.sum.55 0.0311922006569 0.0311922006569
## features.properties.sum.56 0.0269613307305 0.0269613307305
## features.properties.sum.57 0.0182961256272 0.0182961256272
## features.properties.sum.58 0.0474476041426 0.0474476041426
## features.properties.sum.59  0.030925134966  0.030925134966
## features.properties.sum.60 0.0990839862833 0.0990839862833
## features.properties.sum.61 0.0316396276002 0.0316396276002
## features.properties.sum.62 0.0151640809662 0.0151640809662
## features.properties.sum.63 0.0261707721535 0.0261707721535
## features.properties.sum.64 0.0531454414338 0.0531454414338
## features.properties.sum.65  0.108143702435  0.108143702435
## features.properties.sum.66 0.0585930577914 0.0585930577914
## features.properties.sum.67  0.232784007153  0.232784007153
## features.properties.sum.68  0.189817275455  0.189817275455
## features.properties.sum.69  0.157189625326  0.157189625326
## features.properties.sum.70 0.0937245066846 0.0937245066846
## features.properties.sum.71  0.128313247654  0.128313247654
## features.properties.sum.72   0.11302848218   0.11302848218
## features.properties.sum.73 0.0630994631392 0.0630994631392
## features.properties.sum.74 0.0559590444049 0.0559590444049
## features.properties.sum.75 0.0578071886107 0.0578071886107
## features.properties.sum.76 0.0229483888793 0.0229483888793
## features.properties.sum.77 0.0126627103916 0.0126627103916
## features.properties.sum.78 0.0139952832236 0.0139952832236
## features.properties.sum.79 0.0548194450787 0.0548194450787
## features.properties.sum.80  0.224397707218  0.224397707218
## features.properties.sum.81 0.0476141336392 0.0476141336392
## features.properties.sum.82 0.0443321397721 0.0443321397721
## features.properties.sum.83 0.0490302735314 0.0490302735314
## features.properties.sum.84 0.0920502698629 0.0920502698629
## features.properties.sum.85  0.343230877798  0.343230877798
## features.properties.sum.86 0.0303463589963 0.0303463589963
## features.properties.sum.87 0.0304940656141 0.0304940656141
## features.properties.sum.88 0.0301132769341 0.0301132769341
## features.properties.sum.89 0.0145008622673 0.0145008622673
## features.properties.sum.90 0.0173113872952 0.0173113872952

Podemos obtener todos los glaciares de Chile y podemos obtener sus areas. A travaés de nuestros proceso de clasificación hemos podido determinar cantidades que debiesen ser glaciares en color rojo y cantidades que no son glaciares con valor de 1. Nuestro proceso de analisis nos llevo a la obtencion de estas cantidades cuyas magnitudes desconocemos. Pero lo anterior no es problema. Debemos seguir el siguiente procedimiento:

Calcular el porcentaje de rojo que hay dentro de la relación rojo-verde
Llamemos el porcentaje del punto 1 β.
debemos calcular el porcentaje que β representa en el area_Km2(shp). de cada glaciar.

\[ {\%rojo \over x} = {(rojo +verde) \over 100 } \]

Lo que nos interesa es obtener la superficie glaciar dentro de cada glaciar.

\[ { \%superficie\_roja \over 100} = {\%rojo \over region\_glaciar }\]

data4$porcentaje_rojo <- (100*as.numeric(data4$rojo)) / (as.numeric(data4$rojo) + as.numeric(data4$verde))
data4$porcentaje_area_glaciar <- (data4$porcentaje_rojo * as.numeric(data4$`area_Km2(shp)`)) / 100
data4

##                             codigo_gla              rojo              verde
## features.properties.sum    CL101044025  49.2274509803922                  0
## features.properties.sum.1  CL101044018  10.2509803921569   102.247058823529
## features.properties.sum.2  CL101044017                 2   124.862745098039
## features.properties.sum.3  CL101044019                 0   97.7882352941177
## features.properties.sum.4  CL101044016  1.05098039215686   74.0392156862745
## features.properties.sum.5  CL101044015                 0   34.8666666666667
## features.properties.sum.6  CL101044014  1.02352941176471   29.9019607843137
## features.properties.sum.7  CL101044013                 0   40.1607843137255
## features.properties.sum.8  CL101044012                 0   13.8980392156863
## features.properties.sum.9  CL101044011                 0   265.913725490196
## features.properties.sum.10 CL101044010                 0   5.82352941176471
## features.properties.sum.11 CL101044007                 0   57.3803921568627
## features.properties.sum.12 CL101044008                 0   201.643137254902
## features.properties.sum.13 CL101044006                 0   221.235294117647
## features.properties.sum.14 CL101042002                 0   81.0078431372549
## features.properties.sum.15 CL101042001  2.75294117647059   77.1843137254902
## features.properties.sum.16 CL101042003                 0   63.2117647058824
## features.properties.sum.17 CL101042005  1.00392156862745    32.878431372549
## features.properties.sum.18 CL101042004                 0    20.121568627451
## features.properties.sum.19 CL101610012                 0   154.098039215686
## features.properties.sum.20 CL101610013                 0   110.176470588235
## features.properties.sum.21 CL101610011                 0   6.61960784313725
## features.properties.sum.22 CL101610010                 0   94.2666666666667
## features.properties.sum.23 CL101610009                 0   72.7764705882353
## features.properties.sum.24 CL101610008                 0   41.6274509803922
## features.properties.sum.25 CL101610005                 0   78.9098039215686
## features.properties.sum.26 CL101610004                 0   63.0549019607843
## features.properties.sum.27 CL101610002                 0   113.752941176471
## features.properties.sum.28 CL101610003                 0   97.6431372549019
## features.properties.sum.29 CL101610001                 0    114.43137254902
## features.properties.sum.30 CL101611002                 0   159.976470588235
## features.properties.sum.31 CL101720004                 0   30.7843137254902
## features.properties.sum.32 CL101720003                 0                129
## features.properties.sum.33 CL101720001                 0   9.56470588235294
## features.properties.sum.34 CL101720002                 0   73.6862745098039
## features.properties.sum.35 CL101720008                 0   188.541176470588
## features.properties.sum.36 CL101720005                 0   22.3333333333333
## features.properties.sum.37 CL101720006                 0   15.2666666666667
## features.properties.sum.38 CL101720009                 0   13.2156862745098
## features.properties.sum.39 CL101720007                 0   9.65490196078431
## features.properties.sum.40 CL101730001                 0   129.050980392157
## features.properties.sum.41 CL101730002                 0   35.5254901960784
## features.properties.sum.42 CL101730009                 0    21.278431372549
## features.properties.sum.43 CL101730008                 0   19.0705882352941
## features.properties.sum.44 CL101730011                 0   53.4156862745098
## features.properties.sum.45 CL101730012 0.203921568627451   72.8862745098039
## features.properties.sum.46 CL101730013                 0   63.9176470588235
## features.properties.sum.47 CL101730010                 0   7.74117647058823
## features.properties.sum.48 CL101730014                 0   144.137254901961
## features.properties.sum.49 CL101730016                 0   25.1607843137255
## features.properties.sum.50 CL101730017                 0   9.94901960784314
## features.properties.sum.51 CL101730015                 0   9.06666666666666
## features.properties.sum.52 CL101730018                 0   11.5607843137255
## features.properties.sum.53 CL101730004                 0   33.2745098039216
## features.properties.sum.54 CL101730003                 0   79.1529411764706
## features.properties.sum.55 CL101730006                 0   27.6901960784314
## features.properties.sum.56 CL101730007                 0   24.5843137254902
## features.properties.sum.57 CL101730005                 0   13.2509803921569
## features.properties.sum.58 CL101044001                 2   47.4078431372549
## features.properties.sum.59 CL101044002                 0   27.0235294117647
## features.properties.sum.60 CL101044003 0.156862745098039   97.0078431372549
## features.properties.sum.61 CL101044004                 0   31.4117647058824
## features.properties.sum.62 CL101044009                 0   13.6901960784314
## features.properties.sum.63 CL101044005  3.29411764705882   18.8235294117647
## features.properties.sum.64 CL101040008                 0   51.1137254901961
## features.properties.sum.65 CL101611001                 0   128.905882352941
## features.properties.sum.66 CL101610024                 0   53.6862745098039
## features.properties.sum.67 CL101610023                 0    256.78431372549
## features.properties.sum.68 CL101610022                 0   229.074509803922
## features.properties.sum.69 CL101610021                 0   180.870588235294
## features.properties.sum.70 CL101610020                 0   106.007843137255
## features.properties.sum.71 CL101610018                 0    145.83137254902
## features.properties.sum.72 CL101610017                 0   119.317647058824
## features.properties.sum.73 CL101610016                 0   63.3176470588235
## features.properties.sum.74 CL101040006                 0   53.2901960784314
## features.properties.sum.75 CL101040005                 0   55.3372549019608
## features.properties.sum.76 CL101040004   3.6078431372549   9.25882352941177
## features.properties.sum.77 CL101040003                 0   7.43529411764706
## features.properties.sum.78 CL101610015                 0   9.92156862745098
## features.properties.sum.79 CL101610014                 0   59.9921568627451
## features.properties.sum.80 CL101610019                 0   253.152941176471
## features.properties.sum.81 CL101040002                 0   50.8196078431372
## features.properties.sum.82 CL101040007  3.75686274509804   45.8470588235294
## features.properties.sum.83 CL101040001  23.7176470588235    24.756862745098
## features.properties.sum.84 CL101040009  45.1254901960784               54.4
## features.properties.sum.85 CL101044024  410.019607843137 0.0549019607843137
## features.properties.sum.86 CL101070001  18.7686274509804   10.3019607843137
## features.properties.sum.87 CL101044020  30.8039215686274                  0
## features.properties.sum.88 CL101044022  24.9058823529412                  0
## features.properties.sum.89 CL101044021  15.3411764705882                  0
## features.properties.sum.90 CL101044023   11.956862745098                  0
##                              area_Km2(shp)              NA porcentaje_rojo
## features.properties.sum    0.0397270010711 0.0397270010711     100.0000000
## features.properties.sum.1   0.100876482862  0.100876482862       9.1121414
## features.properties.sum.2   0.109852809052  0.109852809052       1.5765070
## features.properties.sum.3  0.0873709651722 0.0873709651722       0.0000000
## features.properties.sum.4  0.0639482581884 0.0639482581884       1.3996240
## features.properties.sum.5  0.0390825631323 0.0390825631323       0.0000000
## features.properties.sum.6  0.0327605622869 0.0327605622869       3.3096627
## features.properties.sum.7  0.0382199791074 0.0382199791074       0.0000000
## features.properties.sum.8  0.0167628398049 0.0167628398049       0.0000000
## features.properties.sum.9   0.234736140147  0.234736140147       0.0000000
## features.properties.sum.10 0.0123072515574 0.0123072515574       0.0000000
## features.properties.sum.11 0.0490761861993 0.0490761861993       0.0000000
## features.properties.sum.12  0.181770894446  0.181770894446       0.0000000
## features.properties.sum.13  0.187241197168  0.187241197168       0.0000000
## features.properties.sum.14 0.0822324117868 0.0822324117868       0.0000000
## features.properties.sum.15 0.0811335051897 0.0811335051897       3.4438776
## features.properties.sum.16 0.0517317014976 0.0517317014976       0.0000000
## features.properties.sum.17 0.0342342838388 0.0342342838388       2.9629630
## features.properties.sum.18  0.025537434093  0.025537434093       0.0000000
## features.properties.sum.19   0.14556645363   0.14556645363       0.0000000
## features.properties.sum.20 0.0969989514597 0.0969989514597       0.0000000
## features.properties.sum.21 0.0133418051918 0.0133418051918       0.0000000
## features.properties.sum.22 0.0876500187874 0.0876500187874       0.0000000
## features.properties.sum.23 0.0722716222512 0.0722716222512       0.0000000
## features.properties.sum.24 0.0460606415757 0.0460606415757       0.0000000
## features.properties.sum.25 0.0863192881262 0.0863192881262       0.0000000
## features.properties.sum.26 0.0786716671606 0.0786716671606       0.0000000
## features.properties.sum.27 0.0996942476728 0.0996942476728       0.0000000
## features.properties.sum.28 0.0926249593749 0.0926249593749       0.0000000
## features.properties.sum.29  0.107310516435  0.107310516435       0.0000000
## features.properties.sum.30  0.125452215699  0.125452215699       0.0000000
## features.properties.sum.31 0.0370788979097 0.0370788979097       0.0000000
## features.properties.sum.32  0.121432954093  0.121432954093       0.0000000
## features.properties.sum.33 0.0156484103605 0.0156484103605       0.0000000
## features.properties.sum.34 0.0637414139919 0.0637414139919       0.0000000
## features.properties.sum.35  0.163304691787  0.163304691787       0.0000000
## features.properties.sum.36 0.0228456773695 0.0228456773695       0.0000000
## features.properties.sum.37 0.0161851633664 0.0161851633664       0.0000000
## features.properties.sum.38 0.0165129804368 0.0165129804368       0.0000000
## features.properties.sum.39 0.0198858464782 0.0198858464782       0.0000000
## features.properties.sum.40  0.121994691845  0.121994691845       0.0000000
## features.properties.sum.41 0.0391150993622 0.0391150993622       0.0000000
## features.properties.sum.42 0.0305984387632 0.0305984387632       0.0000000
## features.properties.sum.43 0.0225271588162 0.0225271588162       0.0000000
## features.properties.sum.44 0.0661863412709 0.0661863412709       0.0000000
## features.properties.sum.45 0.0641183674004 0.0641183674004       0.2789999
## features.properties.sum.46 0.0711972768362 0.0711972768362       0.0000000
## features.properties.sum.47 0.0127425943588 0.0127425943588       0.0000000
## features.properties.sum.48  0.130168840973  0.130168840973       0.0000000
## features.properties.sum.49 0.0425520003997 0.0425520003997       0.0000000
## features.properties.sum.50 0.0157886226892 0.0157886226892       0.0000000
## features.properties.sum.51 0.0155196563794 0.0155196563794       0.0000000
## features.properties.sum.52 0.0181153020415 0.0181153020415       0.0000000
## features.properties.sum.53 0.0346999506794 0.0346999506794       0.0000000
## features.properties.sum.54  0.071272988267  0.071272988267       0.0000000
## features.properties.sum.55 0.0311922006569 0.0311922006569       0.0000000
## features.properties.sum.56 0.0269613307305 0.0269613307305       0.0000000
## features.properties.sum.57 0.0182961256272 0.0182961256272       0.0000000
## features.properties.sum.58 0.0474476041426 0.0474476041426       4.0479403
## features.properties.sum.59  0.030925134966  0.030925134966       0.0000000
## features.properties.sum.60 0.0990839862833 0.0990839862833       0.1614400
## features.properties.sum.61 0.0316396276002 0.0316396276002       0.0000000
## features.properties.sum.62 0.0151640809662 0.0151640809662       0.0000000
## features.properties.sum.63 0.0261707721535 0.0261707721535      14.8936170
## features.properties.sum.64 0.0531454414338 0.0531454414338       0.0000000
## features.properties.sum.65  0.108143702435  0.108143702435       0.0000000
## features.properties.sum.66 0.0585930577914 0.0585930577914       0.0000000
## features.properties.sum.67  0.232784007153  0.232784007153       0.0000000
## features.properties.sum.68  0.189817275455  0.189817275455       0.0000000
## features.properties.sum.69  0.157189625326  0.157189625326       0.0000000
## features.properties.sum.70 0.0937245066846 0.0937245066846       0.0000000
## features.properties.sum.71  0.128313247654  0.128313247654       0.0000000
## features.properties.sum.72   0.11302848218   0.11302848218       0.0000000
## features.properties.sum.73 0.0630994631392 0.0630994631392       0.0000000
## features.properties.sum.74 0.0559590444049 0.0559590444049       0.0000000
## features.properties.sum.75 0.0578071886107 0.0578071886107       0.0000000
## features.properties.sum.76 0.0229483888793 0.0229483888793      28.0402316
## features.properties.sum.77 0.0126627103916 0.0126627103916       0.0000000
## features.properties.sum.78 0.0139952832236 0.0139952832236       0.0000000
## features.properties.sum.79 0.0548194450787 0.0548194450787       0.0000000
## features.properties.sum.80  0.224397707218  0.224397707218       0.0000000
## features.properties.sum.81 0.0476141336392 0.0476141336392       0.0000000
## features.properties.sum.82 0.0443321397721 0.0443321397721       7.5737212
## features.properties.sum.83 0.0490302735314 0.0490302735314      48.9280803
## features.properties.sum.84 0.0920502698629 0.0920502698629      45.3406360
## features.properties.sum.85  0.343230877798  0.343230877798      99.9866117
## features.properties.sum.86 0.0303463589963 0.0303463589963      64.5622555
## features.properties.sum.87 0.0304940656141 0.0304940656141     100.0000000
## features.properties.sum.88 0.0301132769341 0.0301132769341     100.0000000
## features.properties.sum.89 0.0145008622673 0.0145008622673     100.0000000
## features.properties.sum.90 0.0173113872952 0.0173113872952     100.0000000
##                            porcentaje_area_glaciar
## features.properties.sum               0.0397270011
## features.properties.sum.1             0.0091920077
## features.properties.sum.2             0.0017318372
## features.properties.sum.3             0.0000000000
## features.properties.sum.4             0.0008950352
## features.properties.sum.5             0.0000000000
## features.properties.sum.6             0.0010842641
## features.properties.sum.7             0.0000000000
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## features.properties.sum.9             0.0000000000
## features.properties.sum.10            0.0000000000
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## features.properties.sum.16            0.0000000000
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## features.properties.sum.56            0.0000000000
## features.properties.sum.57            0.0000000000
## features.properties.sum.58            0.0019206507
## features.properties.sum.59            0.0000000000
## features.properties.sum.60            0.0001599612
## features.properties.sum.61            0.0000000000
## features.properties.sum.62            0.0000000000
## features.properties.sum.63            0.0038977746
## features.properties.sum.64            0.0000000000
## features.properties.sum.65            0.0000000000
## features.properties.sum.66            0.0000000000
## features.properties.sum.67            0.0000000000
## features.properties.sum.68            0.0000000000
## features.properties.sum.69            0.0000000000
## features.properties.sum.70            0.0000000000
## features.properties.sum.71            0.0000000000
## features.properties.sum.72            0.0000000000
## features.properties.sum.73            0.0000000000
## features.properties.sum.74            0.0000000000
## features.properties.sum.75            0.0000000000
## features.properties.sum.76            0.0064347814
## features.properties.sum.77            0.0000000000
## features.properties.sum.78            0.0000000000
## features.properties.sum.79            0.0000000000
## features.properties.sum.80            0.0000000000
## features.properties.sum.81            0.0000000000
## features.properties.sum.82            0.0033575927
## features.properties.sum.83            0.0239895716
## features.properties.sum.84            0.0417361778
## features.properties.sum.85            0.3431849251
## features.properties.sum.86            0.0195922938
## features.properties.sum.87            0.0304940656
## features.properties.sum.88            0.0301132769
## features.properties.sum.89            0.0145008623
## features.properties.sum.90            0.0173113873

Logica de la informacion temporal:

region_tara <- st_read("rrss/Region_01.shp", 
        quiet = TRUE) %>% 
        sf_as_ee()

glaciar_tara <- st_read("GlaciaresxRegion/Simple_TARAPACA.shp",
        quiet = TRUE) %>%
        sf_as_ee()

fn_cart <- function(a,m){

i_cuatrimestre <- switch(m, "01","05","09")
f_cuatrimestre <- switch(m, "04","08","12")


fecha_1 <- paste("20",a,"-",i_cuatrimestre,"-01", sep = "")
fecha_2 <- paste("20",a,"-",f_cuatrimestre,"-01", sep = "")
tara <- region_tara$geometry()


start <- ee$Date(fecha_1)
finish <- ee$Date(fecha_2)
cc <- 20

sentinel1 = ee$ImageCollection('COPERNICUS/S2')$filterDate(start, finish)$filterBounds(tara)$
  filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cc))

first <- sentinel1$median()

vizParams <- list(
  bands = c("B8","B5" , "B3"),
  #  bands = c("B2", "B3"),
  min = 100,
  max = 1000,
  gamma = 2
)

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

ndgi <- getNDGI(first)

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



glaciar_tara <- glaciar_tara$geometry()

glaciares_adm_con_ngvi <- ndgi$clip(glaciar_tara)

tara_sin_glacia <- tara$difference(glaciar_tara, ee$ErrorMargin(1))

ndvi_tarapaca_sin_glaciares <- ndgi$clip(tara_sin_glacia)

limites_glaciares_con_rasters <- ndgi$clip(glaciar_tara)

# region_arica <- st_read("Regiones_separadas/Region_15.shp", quiet = TRUE)
# region_arica

muestras_in <- ndgi$sampleRegions(
    collection = ee$Feature(glaciar_tara),
    scale = 100,
    tileScale = 16,
    geometries = TRUE
  )

muestras_in_gee_rojas = muestras_in$filter(ee$Filter$eq('nd', 0))




ndvi_tara_sin_glaciares <- ndgi$clip(tara_sin_glacia)


muestras_in <- ndgi$sampleRegions(
    collection = ee$Feature(ndvi_tara_sin_glaciares),
    scale = 10000,
    tileScale = 16,
    geometries = TRUE
  )

muestras_out_gee_verdes = muestras_in$filter(ee$Filter$eq('nd', 1))


union_total <- muestras_out_gee_verdes$merge(muestras_in_gee_rojas)

region_arica <- st_read("rrss/Region_01.shp", 
        quiet = TRUE) %>% 
        sf_as_ee()
arica <- region_arica$geometry()

start <- ee$Date(fecha_1)
finish <- ee$Date(fecha_2)
cc <- 20

sentinel1 = ee$ImageCollection('COPERNICUS/S2')$filterDate(start, finish)$filterBounds(arica)$
  filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cc))

first <- sentinel1$median()

# Use these bands for prediction.
bands <- c("B2", "B3", "B4", "B8")

# This property of the table stores the land cover labels.
label <- "nd"

# Overlay the points on the imagery to get training.
training <- first$select(bands)$sampleRegions(
  collection = union_total,
  properties = list(label),
  scale = 10
)

# Train a CART classifier with default parameters.
trained <- ee$Classifier$smileRandomForest(10)$train(training, label, bands)

# Classify the image with the same bands used for training.
classified1 <- first$select(bands)$classify(trained)


periodo <- paste(fecha_1," / ",fecha_2," ", sep = "")
print(periodo)

###################################################################
mask_0 <- read_sf("GlaciaresxRegion/Simple_TARAPACA.shp")


reg_15_2 <- mask_0 %>% dplyr::select(COD_GLA,AREA_Km2)
varia <- reg_15_2 %>%
sf_as_ee()

region_0 <- varia$geometry()
sale_4 <- classified1$clip(region_0)

puntos_r <- sale_4$select('classification')$eq(0)

areas_rojas = puntos_r$reduceRegions(
reducer= ee$Reducer$sum(),
collection= varia,
scale= 30
)

rojas <- areas_rojas$getInfo()

rojas1 <- as.data.frame(rojas)



puntos_v <- sale_4$select('classification')$eq(1)

areas_verdes = puntos_v$reduceRegions(
reducer= ee$Reducer$sum(),
collection= varia,
scale= 30
)

verdes <- areas_verdes$getInfo()

verdes1 <- as.data.frame(verdes)


###########

r_v <- rbind(colnames(rojas1),rojas1,verdes1)
r_v <- r_v[1:4,]
r_v <- r_v[-3,]
r_v <- t(r_v)
r_v <- as.data.frame(r_v)
names(r_v)[1] <- "col1"
names(r_v)[2] <- "col2"
names(r_v)[3] <- "col3"
r_v$col4 <- r_v$col3
r_v$col5 <- r_v$col3

###########

data1 <- filter(r_v, grepl("features.properties.COD_GLA",col1))
data1 <- as.data.frame(data1[,2])

data2 <- filter(r_v, grepl("features.properties.sum",col1))
data2 <- as.data.frame(data2[,c(2,3)])

data3 <- filter(r_v, grepl("features.properties.AREA_Km2",col1))
data3 <- as.data.frame(data3[,c(2,3)])

data4 <- cbind(data1,data2,data3)
data4 <- data4[,-c(4)]

colnames(data4) <- c("codigo_gla", "rojo", "verde", "area_Km2(shp)")

nombre <- paste("arica_",fecha_1,"_",fecha_2,".xlsx", sep = "")

data4$porcentaje_rojo <- (100*as.numeric(data4$rojo)) / (as.numeric(data4$rojo) + as.numeric(data4$verde))
data4$porcentaje_area_glaciar <- (data4$porcentaje_rojo * as.numeric(data4$`area_Km2(shp)`)) / 100

data4$fecha <- periodo

write_xlsx(data4,nombre)





}

# for (a in 18:18) {
# for (m in 2:3) {
# 
# fn_cart(a,m)
# 
# }
# }

Vamos a exponer la data de dos cuatrimetres, el segundo y el tercero ambos para el año 2018

segundo_cuatri_18 <- fn_cart(18,2)

## Warning: Ops.ee.image.Image will be deprecated in rgee v.1.1.0. Please install
## rgeeExtra (https://github.com/r-earthengine/rgeeExtra). Deeply sorry for the
## inconveniences.

## [1] "2018-05-01 / 2018-08-01 "

tercer_cuatri_18 <- fn_cart(18,3)

## Warning: Ops.ee.image.Image will be deprecated in rgee v.1.1.0. Please install
## rgeeExtra (https://github.com/r-earthengine/rgeeExtra). Deeply sorry for the
## inconveniences.

## [1] "2018-09-01 / 2018-12-01 "

Analisis temporal

library(readxl)

     
a <- read_xlsx("arica_2018-09-01_2018-12-01.xlsx")
b <- read_xlsx("arica_2018-05-01_2018-08-01.xlsx")

ab <- rbind(b,a)

write_xlsx(ab,"ab.xlsx")

kbl(ab) %>%
 kable_styling(bootstrap_options = c("striped", "hover")) %>%
 kable_paper() %>%
 scroll_box(width = "100%", height = "300px")

codigo_gla	rojo	verde	area_Km2(shp)	porcentaje_rojo	porcentaje_area_glaciar	fecha
CL101044025	0.666666666666667	48.5607843137255	0.0397270010711	1.3542579	0.0005380	2018-05-01 / 2018-08-01
CL101044018	14.8274509803922	100.4	0.100876482862	12.8679849	0.0129808	2018-05-01 / 2018-08-01
CL101044017	31.8039215686274	100.039215686275	0.109852809052	24.1225461	0.0264993	2018-05-01 / 2018-08-01
CL101044019	37.2980392156863	65.1764705882353	0.0873709651722	36.3973824	0.0318007	2018-05-01 / 2018-08-01
CL101044016	12.3058823529412	66.5803921568627	0.0639482581884	15.5995228	0.0099756	2018-05-01 / 2018-08-01
CL101044015	6.98039215686275	30.4666666666667	0.0390825631323	18.6406954	0.0072853	2018-05-01 / 2018-08-01
CL101044014	1.61176470588235	28.8	0.0327605622869	5.2998066	0.0017362	2018-05-01 / 2018-08-01
CL101044013	4.10588235294118	36.0549019607843	0.0382199791074	10.2236110	0.0039075	2018-05-01 / 2018-08-01
CL101044012	7.16470588235294	8.88627450980392	0.0167628398049	44.6371854	0.0074825	2018-05-01 / 2018-08-01
CL101044011	47.1137254901961	228.541176470588	0.234736140147	17.0915609	0.0401201	2018-05-01 / 2018-08-01
CL101044010	1.21176470588235	5.6078431372549	0.0123072515574	17.7688327	0.0021869	2018-05-01 / 2018-08-01
CL101044007	25.3529411764706	33.3764705882353	0.0490761861993	43.1690705	0.0211857	2018-05-01 / 2018-08-01
CL101044008	76.5843137254902	134.070588235294	0.181770894446	36.3553437	0.0660834	2018-05-01 / 2018-08-01
CL101044006	47.4509803921569	179.592156862745	0.187241197168	20.8995440	0.0391326	2018-05-01 / 2018-08-01
CL101042002	1.61176470588235	79.3960784313725	0.0822324117868	1.9896403	0.0016361	2018-05-01 / 2018-08-01
CL101042001	3.50196078431373	78.0705882352941	0.0811335051897	4.2930628	0.0034831	2018-05-01 / 2018-08-01
CL101042003	8.11764705882353	55.4	0.0517317014976	12.7801445	0.0066114	2018-05-01 / 2018-08-01
CL101042005	2.22745098039216	33.0588235294118	0.0342342838388	6.3125139	0.0021610	2018-05-01 / 2018-08-01
CL101042004	1.03529411764706	19.121568627451	0.025537434093	5.1361868	0.0013117	2018-05-01 / 2018-08-01
CL101610012	11.6745098039216	144.423529411765	0.14556645363	7.4789599	0.0108869	2018-05-01 / 2018-08-01
CL101610013	1	108.176470588235	0.0969989514597	0.9159483	0.0008885	2018-05-01 / 2018-08-01
CL101610011	0	6.61960784313725	0.0133418051918	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101610010	0	92.635294117647	0.0876500187874	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101610009	0	72.7764705882353	0.0722716222512	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101610008	0	41.6274509803922	0.0460606415757	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101610005	0	78.9098039215686	0.0863192881262	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101610004	0	63.0549019607843	0.0786716671606	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101610002	3.47843137254902	113.752941176471	0.0996942476728	2.9671506	0.0029581	2018-05-01 / 2018-08-01
CL101610003	0	97.6431372549019	0.0926249593749	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101610001	2.25882352941176	115.494117647059	0.107310516435	1.9182736	0.0020585	2018-05-01 / 2018-08-01
CL101611002	13.2980392156863	149.203921568627	0.125452215699	8.1833100	0.0102661	2018-05-01 / 2018-08-01
CL101720004	17.9490196078431	15.0549019607843	0.0370788979097	54.3845057	0.0201652	2018-05-01 / 2018-08-01
CL101720003	2.16078431372549	127.317647058824	0.121432954093	1.6688373	0.0020265	2018-05-01 / 2018-08-01
CL101720001	1.93725490196078	8.30588235294118	0.0156484103605	18.9127106	0.0029595	2018-05-01 / 2018-08-01
CL101720002	0.125490196078431	73.5607843137255	0.0637414139919	0.1703034	0.0001086	2018-05-01 / 2018-08-01
CL101720008	12.8588235294118	180.247058823529	0.163304691787	6.6589497	0.0108744	2018-05-01 / 2018-08-01
CL101720005	5.58823529411765	17.7450980392157	0.0228456773695	23.9495798	0.0054714	2018-05-01 / 2018-08-01
CL101720006	0.403921568627451	14.2588235294118	0.0161851633664	2.7547473	0.0004459	2018-05-01 / 2018-08-01
CL101720009	4.82352941176471	10.3921568627451	0.0165129804368	31.7010309	0.0052348	2018-05-01 / 2018-08-01
CL101720007	6.84705882352941	2.8	0.0198858464782	70.9756098	0.0141141	2018-05-01 / 2018-08-01
CL101730001	32.1019607843137	100.054901960784	0.121994691845	24.2908012	0.0296335	2018-05-01 / 2018-08-01
CL101730002	35.5254901960784	3	0.0391150993622	92.2129479	0.0360692	2018-05-01 / 2018-08-01
CL101730009	1.26274509803922	20.2313725490196	0.0305984387632	5.8748404	0.0017976	2018-05-01 / 2018-08-01
CL101730008	10.7490196078431	11.2862745098039	0.0225271588162	48.7809219	0.0109890	2018-05-01 / 2018-08-01
CL101730011	21.843137254902	36.3098039215686	0.0661863412709	37.5615348	0.0248606	2018-05-01 / 2018-08-01
CL101730012	22.8156862745098	47.9254901960784	0.0641183674004	32.2523421	0.0206797	2018-05-01 / 2018-08-01
CL101730013	34.3607843137255	35.3098039215686	0.0711972768362	49.3189238	0.0351137	2018-05-01 / 2018-08-01
CL101730010	4.42745098039216	3.31372549019608	0.0127425943588	57.1935157	0.0072879	2018-05-01 / 2018-08-01
CL101730014	62.5803921568628	95.2039215686275	0.130168840973	39.6619858	0.0516275	2018-05-01 / 2018-08-01
CL101730016	9.5921568627451	16.5686274509804	0.0425520003997	36.6661670	0.0156022	2018-05-01 / 2018-08-01
CL101730017	3.30196078431373	7.6156862745098	0.0157886226892	30.2442529	0.0047752	2018-05-01 / 2018-08-01
CL101730015	0	9.06666666666666	0.0155196563794	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101730018	2.67058823529412	8.88235294117647	0.0181153020415	23.1160896	0.0041875	2018-05-01 / 2018-08-01
CL101730004	14.5333333333333	21.7960784313725	0.0346999506794	40.0043178	0.0138815	2018-05-01 / 2018-08-01
CL101730003	31.8588235294118	53.9921568627451	0.071272988267	37.1094464	0.0264490	2018-05-01 / 2018-08-01
CL101730006	5.6156862745098	22.0745098039216	0.0311922006569	20.2804135	0.0063259	2018-05-01 / 2018-08-01
CL101730007	10.2549019607843	14.843137254902	0.0269613307305	40.8593750	0.0110162	2018-05-01 / 2018-08-01
CL101730005	0	13.2509803921569	0.0182961256272	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101044001	0	49.4078431372549	0.0474476041426	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101044002	2.73333333333333	24.2901960784314	0.030925134966	10.1146423	0.0031280	2018-05-01 / 2018-08-01
CL101044003	7.8156862745098	89.3568627450981	0.0990839862833	8.0431010	0.0079694	2018-05-01 / 2018-08-01
CL101044004	2.23137254901961	30.1803921568627	0.0316396276002	6.8844525	0.0021782	2018-05-01 / 2018-08-01
CL101044009	0.470588235294118	13.6901960784314	0.0151640809662	3.3231792	0.0005039	2018-05-01 / 2018-08-01
CL101044005	0	22.1176470588235	0.0261707721535	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101040008	27.3333333333333	23.7803921568627	0.0531454414338	53.4755255	0.0284198	2018-05-01 / 2018-08-01
CL101611001	5.51764705882353	126.678431372549	0.108143702435	4.1738357	0.0045137	2018-05-01 / 2018-08-01
CL101610024	18.6392156862745	39.5490196078431	0.0585930577914	32.0326190	0.0187689	2018-05-01 / 2018-08-01
CL101610023	44.5764705882353	223.388235294118	0.232784007153	16.6352022	0.0387241	2018-05-01 / 2018-08-01
CL101610022	102.454901960784	142.329411764706	0.189817275455	41.8551746	0.0794484	2018-05-01 / 2018-08-01
CL101610021	67.7450980392157	121.549019607843	0.157189625326	35.7882743	0.0562555	2018-05-01 / 2018-08-01
CL101610020	44.5921568627451	67.6352941176471	0.0937245066846	39.7337340	0.0372402	2018-05-01 / 2018-08-01
CL101610018	83.9176470588235	76.3960784313725	0.128313247654	52.3458904	0.0671667	2018-05-01 / 2018-08-01
CL101610017	42.9176470588235	81.8313725490196	0.11302848218	34.4031939	0.0388854	2018-05-01 / 2018-08-01
CL101610016	30.843137254902	36.1725490196078	0.0630994631392	46.0237580	0.0290407	2018-05-01 / 2018-08-01
CL101040006	21.2352941176471	37.0666666666667	0.0559590444049	36.4229502	0.0203819	2018-05-01 / 2018-08-01
CL101040005	7.51764705882353	49.4470588235294	0.0578071886107	13.1970260	0.0076288	2018-05-01 / 2018-08-01
CL101040004	2.42745098039216	10.7960784313725	0.0229483888793	18.3570581	0.0042126	2018-05-01 / 2018-08-01
CL101040003	3.58823529411765	3.84705882352941	0.0126627103916	48.2594937	0.0061110	2018-05-01 / 2018-08-01
CL101610015	3.21176470588235	7.48627450980392	0.0139952832236	30.0219941	0.0042017	2018-05-01 / 2018-08-01
CL101610014	31.2980392156863	32.078431372549	0.0548194450787	49.3843203	0.0270722	2018-05-01 / 2018-08-01
CL101610019	28.4941176470588	239.41568627451	0.224397707218	10.6357129	0.0238663	2018-05-01 / 2018-08-01
CL101040002	1	50.6627450980392	0.0476141336392	1.9356308	0.0009216	2018-05-01 / 2018-08-01
CL101040007	5.91372549019608	38.4470588235294	0.0443321397721	13.3309760	0.0059099	2018-05-01 / 2018-08-01
CL101040001	8.51764705882353	40.956862745098	0.0490302735314	17.2162334	0.0084412	2018-05-01 / 2018-08-01
CL101040009	0.286274509803922	101.360784313726	0.0920502698629	0.2816358	0.0002592	2018-05-01 / 2018-08-01
CL101044024	85.3019607843137	343.8	0.343230877798	19.8791822	0.0682315	2018-05-01 / 2018-08-01
CL101070001	0	0	0.0303463589963	NA	NA	2018-05-01 / 2018-08-01
CL101044020	6.49803921568627	25.4274509803922	0.0304940656141	20.3537649	0.0062067	2018-05-01 / 2018-08-01
CL101044022	0	24.9058823529412	0.0301132769341	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101044021	0.258823529411765	15.0823529411765	0.0145008622673	1.6871166	0.0002446	2018-05-01 / 2018-08-01
CL101044023	0	11.956862745098	0.0173113872952	0.0000000	0.0000000	2018-05-01 / 2018-08-01
CL101044025	0	0	0.0397270010711	NA	NA	2018-09-01 / 2018-12-01
CL101044018	0	0	0.100876482862	NA	NA	2018-09-01 / 2018-12-01
CL101044017	0	0	0.109852809052	NA	NA	2018-09-01 / 2018-12-01
CL101044019	0	0	0.0873709651722	NA	NA	2018-09-01 / 2018-12-01
CL101044016	0	0	0.0639482581884	NA	NA	2018-09-01 / 2018-12-01
CL101044015	0	0	0.0390825631323	NA	NA	2018-09-01 / 2018-12-01
CL101044014	0	0	0.0327605622869	NA	NA	2018-09-01 / 2018-12-01
CL101044013	0	1.14509803921569	0.0382199791074	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044012	0	13.8980392156863	0.0167628398049	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044011	9.50588235294118	265.913725490196	0.234736140147	3.4514182	0.0081017	2018-09-01 / 2018-12-01
CL101044010	0	5.82352941176471	0.0123072515574	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044007	0	57.3803921568627	0.0490761861993	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044008	0	201.643137254902	0.181770894446	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044006	4	221.235294117647	0.187241197168	1.7759206	0.0033253	2018-09-01 / 2018-12-01
CL101042002	0	81.0078431372549	0.0822324117868	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101042001	0.00392156862745098	79.9372549019608	0.0811335051897	0.0049056	0.0000040	2018-09-01 / 2018-12-01
CL101042003	0	63.2117647058824	0.0517317014976	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101042005	0	34.8823529411765	0.0342342838388	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101042004	0	20.121568627451	0.025537434093	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610012	0	154.098039215686	0.14556645363	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610013	0	110.176470588235	0.0969989514597	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610011	1	6.61960784313725	0.0133418051918	13.1240350	0.0017510	2018-09-01 / 2018-12-01
CL101610010	0.0352941176470588	94.2666666666667	0.0876500187874	0.0374267	0.0000328	2018-09-01 / 2018-12-01
CL101610009	1.96470588235294	72.7764705882353	0.0722716222512	2.6286794	0.0018998	2018-09-01 / 2018-12-01
CL101610008	1.49019607843137	41.6274509803922	0.0460606415757	3.4561164	0.0015919	2018-09-01 / 2018-12-01
CL101610005	0	78.3176470588235	0.0863192881262	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610004	0	63.0549019607843	0.0786716671606	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610002	4.03529411764706	113.752941176471	0.0996942476728	3.4258889	0.0034154	2018-09-01 / 2018-12-01
CL101610003	0	97.6431372549019	0.0926249593749	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610001	0	115.494117647059	0.107310516435	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101611002	30.6	120.443137254902	0.125452215699	20.2591131	0.0254155	2018-09-01 / 2018-12-01
CL101720004	0	30.7843137254902	0.0370788979097	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720003	0	129	0.121432954093	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720001	0	9.56470588235294	0.0156484103605	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720002	0	73.6862745098039	0.0637414139919	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720008	0	188.070588235294	0.163304691787	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720005	0	22.3333333333333	0.0228456773695	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720006	0	15.2666666666667	0.0161851633664	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720009	0	13.2156862745098	0.0165129804368	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101720007	0	9.65490196078431	0.0198858464782	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730001	0	129.050980392157	0.121994691845	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730002	0.345098039215686	35.5254901960784	0.0391150993622	0.9620641	0.0003763	2018-09-01 / 2018-12-01
CL101730009	0.368627450980392	20.9098039215686	0.0305984387632	1.7323996	0.0005301	2018-09-01 / 2018-12-01
CL101730008	0	19.0705882352941	0.0225271588162	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730011	0	53.4156862745098	0.0661863412709	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730012	0	73.0901960784314	0.0641183674004	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730013	0	63.9176470588235	0.0711972768362	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730010	0	7.74117647058823	0.0127425943588	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730014	1	144.137254901961	0.130168840973	0.6890030	0.0008969	2018-09-01 / 2018-12-01
CL101730016	0	25.1607843137255	0.0425520003997	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730017	0	9.94901960784314	0.0157886226892	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730015	0	9.06666666666666	0.0155196563794	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730018	0	11.5607843137255	0.0181153020415	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730004	0	33.2745098039216	0.0346999506794	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730003	0	79.1529411764706	0.071272988267	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730006	0	27.6901960784314	0.0311922006569	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730007	0	24.5843137254902	0.0269613307305	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101730005	0	13.2509803921569	0.0182961256272	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044001	0	49.4078431372549	0.0474476041426	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044002	0	27.0235294117647	0.030925134966	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044003	0	97.164705882353	0.0990839862833	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044004	0	31.4117647058824	0.0316396276002	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044009	0	13.6901960784314	0.0151640809662	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101044005	0	22.1176470588235	0.0261707721535	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101040008	1.86274509803922	51.1137254901961	0.0531454414338	3.5161744	0.0018687	2018-09-01 / 2018-12-01
CL101611001	0	128.905882352941	0.108143702435	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610024	0	53.6862745098039	0.0585930577914	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610023	1.21960784313726	254.564705882353	0.232784007153	0.4768110	0.0011099	2018-09-01 / 2018-12-01
CL101610022	2	229.074509803922	0.189817275455	0.8655217	0.0016429	2018-09-01 / 2018-12-01
CL101610021	5.35294117647059	180.870588235294	0.157189625326	2.8744709	0.0045184	2018-09-01 / 2018-12-01
CL101610020	15.8392156862745	106.007843137255	0.0937245066846	12.9992598	0.0121835	2018-09-01 / 2018-12-01
CL101610018	17.2156862745098	143.988235294118	0.128313247654	10.6794463	0.0137031	2018-09-01 / 2018-12-01
CL101610017	0	119.317647058824	0.11302848218	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610016	2.66274509803922	63.3176470588235	0.0630994631392	4.0356612	0.0025465	2018-09-01 / 2018-12-01
CL101040006	11.1450980392157	53.2901960784314	0.0559590444049	17.2965735	0.0096790	2018-09-01 / 2018-12-01
CL101040005	7.9921568627451	55.3372549019608	0.0578071886107	12.6199765	0.0072953	2018-09-01 / 2018-12-01
CL101040004	3.95294117647059	13.2235294117647	0.0229483888793	23.0136986	0.0052813	2018-09-01 / 2018-12-01
CL101040003	0	7.43529411764706	0.0126627103916	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610015	0	9.92156862745098	0.0139952832236	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101610014	0.874509803921569	59.0156862745098	0.0548194450787	1.4601886	0.0008005	2018-09-01 / 2018-12-01
CL101610019	0	250.643137254902	0.224397707218	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101040002	0	50.8196078431372	0.0476141336392	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101040007	0	49.5803921568627	0.0443321397721	0.0000000	0.0000000	2018-09-01 / 2018-12-01
CL101040001	0.819607843137255	49.4745098039216	0.0490302735314	1.6296296	0.0007990	2018-09-01 / 2018-12-01
CL101040009	1.63529411764706	101.011764705882	0.0920502698629	1.5931232	0.0014665	2018-09-01 / 2018-12-01
CL101044024	0	0	0.343230877798	NA	NA	2018-09-01 / 2018-12-01
CL101070001	0	0	0.0303463589963	NA	NA	2018-09-01 / 2018-12-01
CL101044020	0	0	0.0304940656141	NA	NA	2018-09-01 / 2018-12-01
CL101044022	0	0	0.0301132769341	NA	NA	2018-09-01 / 2018-12-01
CL101044021	0	0	0.0145008622673	NA	NA	2018-09-01 / 2018-12-01
CL101044023	0	0	0.0173113872952	NA	NA	2018-09-01 / 2018-12-01

# tercer_cuatri_18 <- fn_cart(18,3)
# 
# union <- rbind(segundo_cuatri_18,tercer_cuatri_18)
# 
# kbl(union) %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# kable_paper() %>%
# scroll_box(width = "100%", height = "300px")
# 
# write_xlsx(union,"union.xlsx")