Taller Interpolación
1. En primer lugar descargamos e instalamos cada uno de los paquetes que vamos a usar durante esta actividad, para luego cargarlas librerias.
install.packages( c( "sf","sp", "stars","leaflet", "gstat","automap","raster", "RColorBrewer"), repos = "https://cran.r-project.org")## Installing packages into 'C:/Users/ovall/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'sf' successfully unpacked and MD5 sums checked
## package 'sp' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'sp'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problema al copiar
## C:\Users\ovall\AppData\Local\R\win-library\4.3\00LOCK\sp\libs\x64\sp.dll a
## C:\Users\ovall\AppData\Local\R\win-library\4.3\sp\libs\x64\sp.dll: Permission
## denied## Warning: restored 'sp'## package 'stars' successfully unpacked and MD5 sums checked
## package 'leaflet' successfully unpacked and MD5 sums checked
## package 'gstat' successfully unpacked and MD5 sums checked
## package 'automap' successfully unpacked and MD5 sums checked
## package 'raster' successfully unpacked and MD5 sums checked
## package 'RColorBrewer' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\ovall\AppData\Local\Temp\Rtmp8M0WMi\downloaded_packagespaquetes = c('terra', 'sp', 'leafem')
install.packages(paquetes, repos = "https://cran.r-project.org")## Installing packages into 'C:/Users/ovall/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'terra' successfully unpacked and MD5 sums checked
## package 'sp' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'sp'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problema al copiar
## C:\Users\ovall\AppData\Local\R\win-library\4.3\00LOCK\sp\libs\x64\sp.dll a
## C:\Users\ovall\AppData\Local\R\win-library\4.3\sp\libs\x64\sp.dll: Permission
## denied## Warning: restored 'sp'## package 'leafem' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\ovall\AppData\Local\Temp\Rtmp8M0WMi\downloaded_packagesVerificamos las librerias.
library(terra)## Warning: package 'terra' was built under R version 4.3.3## terra 1.8.21library(sp)## Warning: package 'sp' was built under R version 4.3.3library(leafem)## Warning: package 'leafem' was built under R version 4.3.3library(sf)## Warning: package 'sf' was built under R version 4.3.3## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUElibrary(stars)## Warning: package 'stars' was built under R version 4.3.3## Loading required package: abind## Warning: package 'abind' was built under R version 4.3.3library(leaflet)## Warning: package 'leaflet' was built under R version 4.3.3library(gstat)## Warning: package 'gstat' was built under R version 4.3.3library(automap)## Warning: package 'automap' was built under R version 4.3.3library(raster)## Warning: package 'raster' was built under R version 4.3.3library(RColorBrewer)
library(sp)2. Verificamos que nuestro directorio de trabajo sea correcto, y de no serlo lo ajustamos usando las siguientes funciones:
getwd()## [1] "C:/Users/ovall/OneDrive/Documentos/introduccion_R/taller R Ivan"3. Declaramos las variables que vamos a usar
ruta_aoi="./municipios_muestreo.shp"
ruta_puntos="./puntos_muestreo.shp"
ruta_raster="./dem_srtm_9377.tif"4. Lectura y visualizacion de los archivos SHP y TIF, leer y almacenar el DEM como un archivo de R y guardarlo en la variable raster_dem
raster_dem=read_stars(ruta_raster, RasterIO = list(bands = 1))
raster_dem## stars object with 2 dimensions and 1 attribute
## attribute(s), summary of first 1e+05 cells:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## dem_srtm_9377.tif -247 0 0 131.274 0 1673
## dimension(s):
## from to offset delta refsys point x/y
## x 1 1754 4966240 29.78 MAGNA-SIRGAS 2018 / Orige... FALSE [x]
## y 1 1428 2297460 -29.78 MAGNA-SIRGAS 2018 / Orige... FALSE [y] plot(raster_dem)
crs_raster=st_crs(raster_dem)
crs_raster## Coordinate Reference System:
## User input: MAGNA-SIRGAS 2018 / Origen-Nacional
## wkt:
## PROJCRS["MAGNA-SIRGAS 2018 / Origen-Nacional",
## BASEGEOGCRS["MAGNA-SIRGAS 2018",
## DATUM["Marco Geocentrico Nacional de Referencia 2018",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",20046]],
## CONVERSION["Colombia Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",4,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",-73,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",0.9992,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",5000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",2000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["Cadastre, topographic mapping."],
## AREA["Colombia - onshore and offshore. Includes San Andres y Providencia, Malpelo Islands, Roncador Bank, Serrana Bank and Serranilla Bank."],
## BBOX[-4.23,-84.77,15.51,-66.87]],
## ID["EPSG",9377]] puntos = st_read(ruta_puntos)## Reading layer `puntos_muestreo' from data source
## `C:\Users\ovall\OneDrive\Documentos\introduccion_R\taller R Ivan\puntos_muestreo.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 67 features and 16 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 4968206 ymin: 2257635 xmax: 5005157 ymax: 2292361
## Projected CRS: MAGNA-SIRGAS_Origen-Nacionalcrs_puntos=st_crs(puntos)
crs_puntos## Coordinate Reference System:
## User input: MAGNA-SIRGAS_Origen-Nacional
## wkt:
## PROJCRS["MAGNA-SIRGAS_Origen-Nacional",
## BASEGEOGCRS["GCS_MAGNA-SIRGAS_2018",
## DATUM["Marco Geocentrico Nacional de Referencia 2018",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]],
## ID["EPSG",1329]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["Degree",0.0174532925199433]]],
## CONVERSION["unnamed",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",4,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",-73,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",0.9992,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",5000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",2000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["(E)",east,
## ORDER[1],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]],
## AXIS["(N)",north,
## ORDER[2],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]]]5 Realizamos la Comparacion de los sistemas de refrencia.
if (identical(crs_raster, crs_puntos)) {
cat("El sistema de referencia de coordenadas del raster y de los puntos es el mismo:\n")
print(crs_raster)
} else {
cat("Los sistemas de referencia de coordenadas son diferentes.\n")
cat("Raster:\n")
print(crs_raster)
cat("Puntos:\n")
print(crs_puntos)
}## Los sistemas de referencia de coordenadas son diferentes.
## Raster:
## Coordinate Reference System:
## User input: MAGNA-SIRGAS 2018 / Origen-Nacional
## wkt:
## PROJCRS["MAGNA-SIRGAS 2018 / Origen-Nacional",
## BASEGEOGCRS["MAGNA-SIRGAS 2018",
## DATUM["Marco Geocentrico Nacional de Referencia 2018",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",20046]],
## CONVERSION["Colombia Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",4,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",-73,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",0.9992,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",5000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",2000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["Cadastre, topographic mapping."],
## AREA["Colombia - onshore and offshore. Includes San Andres y Providencia, Malpelo Islands, Roncador Bank, Serrana Bank and Serranilla Bank."],
## BBOX[-4.23,-84.77,15.51,-66.87]],
## ID["EPSG",9377]]
## Puntos:
## Coordinate Reference System:
## User input: MAGNA-SIRGAS_Origen-Nacional
## wkt:
## PROJCRS["MAGNA-SIRGAS_Origen-Nacional",
## BASEGEOGCRS["GCS_MAGNA-SIRGAS_2018",
## DATUM["Marco Geocentrico Nacional de Referencia 2018",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]],
## ID["EPSG",1329]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["Degree",0.0174532925199433]]],
## CONVERSION["unnamed",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",4,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",-73,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",0.9992,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",5000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",2000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["(E)",east,
## ORDER[1],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]],
## AXIS["(N)",north,
## ORDER[2],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]]]puntos_reproj <- st_transform(puntos, crs_raster)
(puntos_reproj)## Simple feature collection with 67 features and 16 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 4968206 ymin: 2257635 xmax: 5005157 ymax: 2292361
## Projected CRS: MAGNA-SIRGAS 2018 / Origen-Nacional
## First 10 features:
## Department Municipali Latitude.D Longitude. Altitude.m pH EC.dSm NH4.ppm
## 1 Santander Confines 6.331580 -73.25827 1533 4.8 0.08 10.3
## 2 Santander Confines 6.345463 -73.28759 1489 4.9 0.07 10.3
## 3 Santander Confines 6.357339 -73.28138 1482 4.8 0.08 18.2
## 4 Santander Confines 6.360368 -73.24903 1581 4.7 0.07 6.7
## 5 Santander Confines 6.340922 -73.20868 1878 4.8 0.15 24.0
## 6 Santander Confines 6.357226 -73.23005 1742 4.8 0.09 11.7
## 7 Santander Confines 6.339062 -73.23301 1752 4.9 0.09 9.4
## 8 Santander Confines 6.402768 -73.22524 1715 4.8 0.08 9.4
## 9 Santander Confines 6.365608 -73.21098 1878 4.8 0.10 19.3
## 10 Santander Paramo 6.384360 -73.19552 1912 4.9 0.12 19.1
## NO3.ppm K2O.ppm P2O5.ppm SOC.pct Sand.pct Silt.pct Clay.pct Slope.pct
## 1 8.4 173.5 10.1 2.46 40.7 32.6 26.6 10.5
## 2 5.4 122.9 8.9 1.62 51.2 26.1 22.6 9.2
## 3 0.9 119.3 6.2 2.95 51.9 35.4 12.7 7.4
## 4 2.3 181.9 12.1 1.02 37.3 36.1 26.5 12.6
## 5 12.3 130.1 7.3 2.48 65.6 30.3 4.1 8.5
## 6 1.2 131.3 4.8 1.60 40.8 46.5 12.6 7.5
## 7 2.0 73.5 11.5 3.65 55.0 42.5 2.5 5.6
## 8 5.9 194.0 6.6 2.12 33.7 45.4 20.9 13.7
## 9 0.3 273.5 3.4 3.81 64.4 24.9 10.6 1.5
## 10 0.1 128.9 18.8 3.14 39.1 50.7 10.3 15.8
## geometry
## 1 POINT (4971447 2257635)
## 2 POINT (4968206 2259171)
## 3 POINT (4968893 2260483)
## 4 POINT (4972470 2260816)
## 5 POINT (4976930 2258665)
## 6 POINT (4974568 2260467)
## 7 POINT (4974240 2258460)
## 8 POINT (4975101 2265500)
## 9 POINT (4976677 2261393)
## 10 POINT (4978386 2263464)6. Interpolamos el valor de pH usando IDW
var="pH"
puntos[var]## Simple feature collection with 67 features and 1 field
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 4968206 ymin: 2257635 xmax: 5005157 ymax: 2292361
## Projected CRS: MAGNA-SIRGAS_Origen-Nacional
## First 10 features:
## pH geometry
## 1 4.8 POINT (4971447 2257635)
## 2 4.9 POINT (4968206 2259171)
## 3 4.8 POINT (4968893 2260483)
## 4 4.7 POINT (4972470 2260816)
## 5 4.8 POINT (4976930 2258665)
## 6 4.8 POINT (4974568 2260467)
## 7 4.9 POINT (4974240 2258460)
## 8 4.8 POINT (4975101 2265500)
## 9 4.8 POINT (4976677 2261393)
## 10 4.9 POINT (4978386 2263464)7. Definimos el modelo de interpolacion
st_crs(puntos_reproj) ## Coordinate Reference System:
## User input: MAGNA-SIRGAS 2018 / Origen-Nacional
## wkt:
## PROJCRS["MAGNA-SIRGAS 2018 / Origen-Nacional",
## BASEGEOGCRS["MAGNA-SIRGAS 2018",
## DATUM["Marco Geocentrico Nacional de Referencia 2018",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",20046]],
## CONVERSION["Colombia Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",4,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",-73,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",0.9992,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",5000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",2000000,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["Cadastre, topographic mapping."],
## AREA["Colombia - onshore and offshore. Includes San Andres y Providencia, Malpelo Islands, Roncador Bank, Serrana Bank and Serranilla Bank."],
## BBOX[-4.23,-84.77,15.51,-66.87]],
## ID["EPSG",9377]]crs(raster_dem)## [1] NAg = gstat(formula = pH ~ 1, data = puntos_reproj,set=list(idp=2))
z = predict(g, raster_dem)## [inverse distance weighted interpolation]print(z)## stars object with 2 dimensions and 2 attributes
## attribute(s), summary of first 1e+05 cells:
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## var1.pred 5.04681 5.130046 5.162891 5.174822 5.238671 5.283381 0e+00
## var1.var NA NA NA NaN NA NA 1e+05
## dimension(s):
## from to offset delta refsys x/y
## x 1 1754 4966240 29.78 MAGNA-SIRGAS 2018 / Orige... [x]
## y 1 1428 2297460 -29.78 MAGNA-SIRGAS 2018 / Orige... [y]8. Dado que para el modelo IDW no generamos un atributo de varianza, usaremos solamente el primer atributo de z y lo almacenamos en la variable z
z_idw = z["var1.pred",,]9. procedemos con la visualizacion
names(z) = "pH"
b = seq(4, 7.5, 0.1)10. Creamos el grafico de la prediccion
plot(z_idw, breaks = b, col = hcl.colors(length(b)-1, "Spectral"), reset = FALSE)
plot(st_geometry(puntos_reproj), pch = 3, add = TRUE)
contour(z_idw, breaks = b, add = TRUE)
Se procede a agregar los puntos de muetreo al grafico
11.Almacenar el archivo raster en archivo geotiff
interpolacion_idw="./ph_idw.tif" write_stars(z_idw, dsn = interpolacion_idw)12. Interpolamos el valor de pH usando Kriging
v_emp_ok = variogram(pH ~ 1, puntos_reproj)
plot(v_emp_ok)
13 Ajustamos el variograma empÃrico a una función
v_mod_ok = autofitVariogram(pH ~ 1, as(puntos_reproj, "Spatial"))
plot(v_mod_ok)
14. Definimos el modelo para la interpolacion
g2 = gstat(formula = pH ~ 1, model = v_mod_ok$var_model,data = puntos_reproj)15. Generamos la prediccion
z_kr=predict(g2,raster_dem)## [using ordinary kriging]print(z_kr)## stars object with 2 dimensions and 2 attributes
## attribute(s), summary of first 1e+05 cells:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## var1.pred 4.973535 5.0923192 5.1475910 5.1348409 5.18763 5.238132
## var1.var 0.604284 0.7349258 0.8883815 0.9006311 1.04760 1.302663
## dimension(s):
## from to offset delta refsys x/y
## x 1 1754 4966240 29.78 MAGNA-SIRGAS 2018 / Orige... [x]
## y 1 1428 2297460 -29.78 MAGNA-SIRGAS 2018 / Orige... [y]16. Visualizamos la superficie de la prediccion
prediccion = z_kr["var1.pred",,]
names(prediccion) = "pH"
b_predict = seq(4, 7, 0.1)
plot(prediccion, breaks = b_predict, col = hcl.colors(length(b_predict)-1, "Spectral"), reset = FALSE)
17. Ahora visualizamos la superficie de la varianza
varianza = z_kr["var1.var",,]
names(varianza) = "varianza pH"
b_var = seq(0.1, 1.4, 0.1)
plot(varianza, breaks = b_var, col = hcl.colors(length(b_var)-1, "Spectral"), reset = FALSE)
plot(st_geometry(puntos_reproj), pch = 3, add = TRUE)
cv2 = gstat.cv(g2)## [using ordinary kriging]
## [using ordinary kriging]
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## [using ordinary kriging]cv2 = st_as_sf(cv2)sp::bubble(as(cv2[, "residual"], "Spatial"))
## Calculamos el RMSE de la interpolacion por el metodo de Kriging
sqrt(sum((cv2$var1.pred - cv2$observed)^2) / nrow(cv2))## [1] 0.7066087cv3 = gstat.cv(g)## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
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## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]cv3 = st_as_sf(cv3)sp::bubble(as(cv3[, "residual"], "Spatial"))
## Calculamos el RMSE de la interpolacion por el metodo de IDW
sqrt(sum((cv3$var1.pred - cv3$observed)^2) / nrow(cv3))## [1] 0.7196336