Taller parcial juan D Gaitan
Interpolacion IDW
Instalacion librerias
library(sf)
library(stars)
library(leaflet)
library(gstat)
library(automap)
library(raster)
library(RColorBrewer)Verificacion y establecimiento ruta archivos
getwd()
setwd("/Users/Geomatica/Juandag")Añadir variables
ruta_aoi="./municipios_muestreo.shp"
ruta_puntos="./puntos_muestreo.shp"
ruta_raster="./dem_srtm_9377.tif"Visualizar los archivos de datos (shp y tif) (tablas atributos)
raster_dem=read_stars(ruta_raster, RasterIO = list(bands = 1))
raster_demstars 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): plot(raster_dem)downsample set to 2
puntos = st_read(ruta_puntos)Reading layer `puntos_muestreo' from data source `/Users/carlitos/Documents/University/Geomatica/drive-download-20250223T004732Z-001/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-Nacional gs_crs = st_crs(raster_dem)
puntos = st_transform(puntos, crs = gs_crs)
(puntos)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 NO3.ppm K2O.ppm P2O5.ppm SOC.pct Sand.pct Silt.pct Clay.pct Slope.pct geometry
1 Santander Confines 6.331580 -73.25827 1533 4.8 0.08 10.3 8.4 173.5 10.1 2.46 40.7 32.6 26.6 10.5 POINT (4971447 2257635)
2 Santander Confines 6.345463 -73.28759 1489 4.9 0.07 10.3 5.4 122.9 8.9 1.62 51.2 26.1 22.6 9.2 POINT (4968206 2259171)
3 Santander Confines 6.357339 -73.28138 1482 4.8 0.08 18.2 0.9 119.3 6.2 2.95 51.9 35.4 12.7 7.4 POINT (4968893 2260483)
4 Santander Confines 6.360368 -73.24903 1581 4.7 0.07 6.7 2.3 181.9 12.1 1.02 37.3 36.1 26.5 12.6 POINT (4972470 2260816)
5 Santander Confines 6.340922 -73.20868 1878 4.8 0.15 24.0 12.3 130.1 7.3 2.48 65.6 30.3 4.1 8.5 POINT (4976930 2258665)
6 Santander Confines 6.357226 -73.23005 1742 4.8 0.09 11.7 1.2 131.3 4.8 1.60 40.8 46.5 12.6 7.5 POINT (4974568 2260467)
7 Santander Confines 6.339062 -73.23301 1752 4.9 0.09 9.4 2.0 73.5 11.5 3.65 55.0 42.5 2.5 5.6 POINT (4974240 2258460)
8 Santander Confines 6.402768 -73.22524 1715 4.8 0.08 9.4 5.9 194.0 6.6 2.12 33.7 45.4 20.9 13.7 POINT (4975101 2265500)
9 Santander Confines 6.365608 -73.21098 1878 4.8 0.10 19.3 0.3 273.5 3.4 3.81 64.4 24.9 10.6 1.5 POINT (4976677 2261393)
10 Santander Paramo 6.384360 -73.19552 1912 4.9 0.12 19.1 0.1 128.9 18.8 3.14 39.1 50.7 10.3 15.8 POINT (4978386 2263464)Verificacion sistema de coordenadas puntos reproyectados
st_crs(puntos)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]] st_crs(raster_dem)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]] 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)
}El sistema de referencia de coordenadas del raster y de los puntos es el mismo:
Coordinate Reference System:
User input: MAGNA-SIRGAS / Origen-Nacional
wkt:
PROJCRS["MAGNA-SIRGAS / Origen-Nacional",
BASEGEOGCRS["MAGNA-SIRGAS",
DATUM["Marco Geocentrico Nacional de Referencia",
ELLIPSOID["GRS 1980",6378137,298.257222101,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4686]],
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."],
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_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 / Origen-Nacional
First 10 features:
Department Municipali Latitude.D Longitude. Altitude.m pH EC.dSm NH4.ppm NO3.ppm K2O.ppm P2O5.ppm SOC.pct Sand.pct Silt.pct Clay.pct Slope.pct geometry
1 Santander Confines 6.331580 -73.25827 1533 4.8 0.08 10.3 8.4 173.5 10.1 2.46 40.7 32.6 26.6 10.5 POINT (4971447 2257635)
2 Santander Confines 6.345463 -73.28759 1489 4.9 0.07 10.3 5.4 122.9 8.9 1.62 51.2 26.1 22.6 9.2 POINT (4968206 2259171)
3 Santander Confines 6.357339 -73.28138 1482 4.8 0.08 18.2 0.9 119.3 6.2 2.95 51.9 35.4 12.7 7.4 POINT (4968893 2260483)
4 Santander Confines 6.360368 -73.24903 1581 4.7 0.07 6.7 2.3 181.9 12.1 1.02 37.3 36.1 26.5 12.6 POINT (4972470 2260816)
5 Santander Confines 6.340922 -73.20868 1878 4.8 0.15 24.0 12.3 130.1 7.3 2.48 65.6 30.3 4.1 8.5 POINT (4976930 2258665)
6 Santander Confines 6.357226 -73.23005 1742 4.8 0.09 11.7 1.2 131.3 4.8 1.60 40.8 46.5 12.6 7.5 POINT (4974568 2260467)
7 Santander Confines 6.339062 -73.23301 1752 4.9 0.09 9.4 2.0 73.5 11.5 3.65 55.0 42.5 2.5 5.6 POINT (4974240 2258460)
8 Santander Confines 6.402768 -73.22524 1715 4.8 0.08 9.4 5.9 194.0 6.6 2.12 33.7 45.4 20.9 13.7 POINT (4975101 2265500)
9 Santander Confines 6.365608 -73.21098 1878 4.8 0.10 19.3 0.3 273.5 3.4 3.81 64.4 24.9 10.6 1.5 POINT (4976677 2261393)
10 Santander Paramo 6.384360 -73.19552 1912 4.9 0.12 19.1 0.1 128.9 18.8 3.14 39.1 50.7 10.3 15.8 POINT (4978386 2263464)Interpolacion por 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 2018 / 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)
st_crs(puntos_reproj) Coordinate Reference System:
User input: MAGNA-SIRGAS / Origen-Nacional
wkt:
PROJCRS["MAGNA-SIRGAS / Origen-Nacional",
BASEGEOGCRS["MAGNA-SIRGAS",
DATUM["Marco Geocentrico Nacional de Referencia",
ELLIPSOID["GRS 1980",6378137,298.257222101,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4686]],
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."],
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] NA
g = 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):Formula para IDW
g = gstat(formula = pH ~ 1, data = puntos_reproj,set=list(idp=2))Definicion parametros
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):z = z["var1.pred",,]
Visualizacion de la interpolacion por IDW
names(z) = "pH"
b = seq(4, 7.5, 0.1)plot(z, breaks = b, col = hcl.colors(length(b)-1, "Spectral"), reset = FALSE)downsample set to 2 plot(st_geometry(puntos_reproj), pch = 3, add = TRUE) contour(z, breaks = b, add = TRUE)
Designacion variables
interpolacion_idw="./ph_idw.tif"Almacenar raster en geotiff
write_stars(z, dsn = interpolacion_idw)Zonas aptas
zona_optima = prediccion
zona_optima[zona_optima < 6.0 | zona_optima > 6.8] <- NA
plot(zona_optima, col = hcl.colors(10, "greens"), main = "Zonas óptimas para jitomate")downsample set to 2
Interpolacion metodo kriging
Variograma desde empirico
v_emp_ok = variogram(pH ~ 1, puntos_reproj)
plot(v_emp_ok)
Ajuste funcion en variograma
v_mod_ok = autofitVariogram(pH ~ 1, as(puntos_reproj, "Spatial"))
plot(v_mod_ok)
Formula interpolacion Kriging
g2 = gstat(formula = pH ~ 1, model = v_mod_ok$var_model, data = puntos_reproj)Prediccion kriging
z = predict(g2, raster_dem)[using ordinary kriging] 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.
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):Visualizacion por kriging
prediccion = z["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)downsample set to 2
Visualizacion varianza
varianza = z["var1.var",,]
names(varianza) = "varianza pH"
b_var = seq(0.1, 1.4, 0.1)Distribucion puntos varianza
plot(varianza, breaks = b_var, col = hcl.colors(length(b_var)-1, "Spectral"), reset = FALSE)downsample set to 2 plot(st_geometry(puntos_reproj), pch = 3, add = TRUE)
Asignacion formulas IDW y Kriging (Grafico de burbujas)
cv2 = gstat.cv(g2)
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sp::bubble(as(cv2[, "residual"], "Spatial"))
cv3 = gstat.cv(g)
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|=================================================================================================================================================================== | 95%[inverse distance weighted interpolation]
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|====================================================================================================================================================================== | 97%[inverse distance weighted interpolation]
|
|======================================================================================================================================================================== | 98%[inverse distance weighted interpolation]
|
|===========================================================================================================================================================================| 100%[inverse distance weighted interpolation] cv3 = st_as_sf(cv3)
sp::bubble(as(cv3[, "residual"], "Spatial"))
NACalculo de error medio cuadratico
sqrt(sum((cv2$var1.pred - cv2$observed)^2) / nrow(cv2))[1] 0.7066087sqrt(sum((cv3$var1.pred - cv3$observed)^2) / nrow(cv3))[1] 0.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