informe2_vichada

INTRODUCCION

El informe actual describe el proceso de carga, transformación, análisis e interpolación de datos espaciales en el municipio de Vichada, centrándose en la estimación del contenido de carbono orgánico en el suelo (SOC). Se utilizan varias bibliotecas en R que son especializadas en la manipulación de datos geoespaciales, la interpolación y la visualización interactiva. El objetivo principal es representar de forma espacial los valores de SOC mediante técnicas de interpolación, concretamente los métodos de Inverse Distance Weighting (IDW) y kriging ordinaria (OK). Estos enfoques permiten estimar valores en zonas donde no hay mediciones directas, ofreciendo una mejor comprensión de la distribución del SOC en la área de estudio. Para mostrar los resultados, se crean mapas interactivos que facilitan la comparación entre ambas técnicas de interpolación. En estos mapas se presentan los datos originales de SOC. y los resultados de las estimaciones, lo que facilita la interpretación espacial de los datos y la elección del método más adecuado para el estudio.

se limpia el espacio de trabajo

##rm(list=ls())

INSTALACIÓN Y CARGA DE LIBRERÍAS

Para ejecutar el código, se requiere la instalación y carga de varias librerías especializadas en el manejo de datos espaciales:

library(sp)
library(terra)

## terra 1.8.10

library(sf)

## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE

library(stars)

## Cargando paquete requerido: abind

library(gstat)
library(automap)
library(leaflet)
library(leafem)
library(ggplot2)
library(dplyr)

## 
## Adjuntando el paquete: 'dplyr'

## The following objects are masked from 'package:terra':
## 
##     intersect, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(RColorBrewer)

CARGA DE DATOS

Se listan los archivos disponibles en la carpeta de trabajo:

list.files(path="C:/Users/Danna Abril/Desktop/Proyecto 1/PROYECTO 5", pattern = "*.gpkg")

## [1] "mun_vichada.gpkg"      "municipios 2.gpkg"     "soc_vichada.gpkg"     
## [4] "suelos_vichadaaa.gpkg"

Los datos de interés se cargan a partir de archivos GeoPackage:

samples <- sf::st_read("C:/Users/Danna Abril/Desktop/Proyecto 1/PROYECTO 5/soc_vichada.gpkg")

## Reading layer `soc_vichada' from data source 
##   `C:\Users\Danna Abril\Desktop\Proyecto 1\PROYECTO 5\soc_vichada.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 1870 features and 1 field
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -71.13305 ymin: 2.743927 xmax: -67.44597 ymax: 6.320879
## Geodetic CRS:  +proj=longlat +datum=WGS84 +no_defs

munic <- sf::st_read("C:/Users/Danna Abril/Desktop/Proyecto 1/PROYECTO 5/mun_vichada.gpkg")

## Multiple layers are present in data source C:\Users\Danna Abril\Desktop\Proyecto 1\PROYECTO 5\mun_vichada.gpkg, reading layer `municipios_colombia'.
## Use `st_layers' to list all layer names and their type in a data source.
## Set the `layer' argument in `st_read' to read a particular layer.

## Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, :
## automatically selected the first layer in a data source containing more than
## one.

## Reading layer `municipios_colombia' from data source 
##   `C:\Users\Danna Abril\Desktop\Proyecto 1\PROYECTO 5\mun_vichada.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 4 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.07793 ymin: 2.737109 xmax: -67.4098 ymax: 6.324317
## Geodetic CRS:  MAGNA-SIRGAS

Estos contienen información sobre los puntos de muestreo de SOC (contenido de carbono orgánico en el suelo) y los límites municipales de Vichada.

TRANSFORMACIÓN DE DATOS

Se transforman las coordenadas del municipio al sistema de referencia WGS 84:

(munic_wgs84 <- st_transform(munic, crs = 4326))

## Simple feature collection with 4 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.07793 ymin: 2.737109 xmax: -67.4098 ymax: 6.324317
## Geodetic CRS:  WGS 84
##   dpto_ccdgo mpio_ccdgo mpio_cdpmp dpto_cnmbr     mpio_cnmbr
## 1         99        001      99001    VICHADA PUERTO CARREÑO
## 2         99        524      99524    VICHADA   LA PRIMAVERA
## 3         99        624      99624    VICHADA  SANTA ROSALÍA
## 4         99        773      99773    VICHADA       CUMARIBO
##                             mpio_crslc mpio_tipo mpio_narea mpio_nano
## 1       Decreto 1594 de Ago 5  de 1974 MUNICIPIO  12204.913      2023
## 2       Decreto 676 de Abril13 de 1987 MUNICIPIO  18569.338      2023
## 3 Ordenanza 19 de Noviembre 26 de 1993 MUNICIPIO   3691.869      2023
## 4 Ordenanza 66 de Noviembre 22 de 1996 MUNICIPIO  65597.251      2023
##   shape_Leng shape_Area                           geom
## 1   5.475145  0.9859138 MULTIPOLYGON (((-67.80972 6...
## 2   8.080061  1.5062235 MULTIPOLYGON (((-69.03359 6...
## 3   3.805847  0.2999597 MULTIPOLYGON (((-70.65378 5...
## 4  18.794677  5.3085834 MULTIPOLYGON (((-68.47074 5...

ANÁLISIS ESTADÍSTICO

Se genera un histograma de los valores de SOC:

hist(samples$soc)

Se obtiene un resumen estadístico de los datos:

summary(samples)

##       soc                    geom     
##  Min.   : 7.607   POINT        :1870  
##  1st Qu.:11.475   epsg:NA      :   0  
##  Median :13.323   +proj=long...:   0  
##  Mean   :14.336                       
##  3rd Qu.:15.645                       
##  Max.   :49.449

Además, se redondean los valores de SOC a dos decimales:

samples$soc = round(samples$soc,2)

Se define una paleta de colores para la visualización del SOC:

pal <- colorNumeric(
  c("#E1F5C4", "#EDE574", "#F9D423", "#FC913A", "#FF4E50"),
  # colors depend on the count variable
  domain = samples$soc,
  )

Se genera un mapa interactivo con leaflet que muestra los límites municipales y los puntos de muestreo de SOC:

# This a simple visualization
# Change the code to suit your preferences
leaflet() %>%
  addPolygons(
    data = munic,
    color = "gray",
    # set the opacity of the outline
    opacity = 1,
    # set the stroke width in pixels
    weight = 1,
    # set the fill opacity
    fillOpacity = 0.2) %>%
 addCircleMarkers(
    data = samples,
    radius= 1.5, 
    label = ~soc,
    color = ~pal(soc),
    fillOpacity = 1,
    stroke = F
  ) %>%
  addLegend(
    data = samples,
    pal = pal,
    values = ~soc,
    position = "bottomleft",
    title = "SOC:",
    opacity = 0.9) %>%
  addProviderTiles("OpenStreetMap")

## Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs).
## Need '+proj=longlat +datum=WGS84'

print(st_bbox(samples))

##       xmin       ymin       xmax       ymax 
## -71.133050   2.743927 -67.445970   6.320879

*INTERPOLACIÓN ESPACIAL**

Creación del Objeto gstat En R, la función gstat() permite definir un objeto para la interpolación espacial, almacenando toda la información necesaria para realizar predicciones en ubicaciones no muestreadas.

¿Qué es un Objeto gstat? La función gstat() pertenece al paquete gstat y se utiliza para aplicar técnicas de interpolación geoestadística, como Kriging e IDW (Inverse Distance Weighting).

Usos Principales de gstat() Configurar un modelo de interpolación con los datos de entrada. Elegir el método de interpolación, como Kriging ordinario (OK), Kriging universal (UK) o IDW. Ajustar un modelo de semivarianza, estimando y modelando automáticamente el semivariograma para Kriging. Predecir valores en ubicaciones no muestreadas, generando una superficie interpolada. Tipos de Modelos que Admite gstat() Dependiendo de los argumentos proporcionados, gstat() determina automáticamente el método de interpolación a aplicar:

IDW (Distancia de Ponderación Inversa): Se usa cuando no se define un modelo de variograma. Calcula estimaciones en función de la distancia de los puntos conocidos. Kriging Ordinario (OK): Se aplica cuando se especifica un modelo de variograma, asumiendo que la media del proceso es constante en toda el área. Parámetros Clave de gstat() fórmula: Define la relación entre las variables dependientes e independientes (covariables). Para IDW: formula = soc ~ 1 Para Kriging: formula = soc ~ elev + slope datos: Datos de calibración en formato DataFrame, sf o sp. modelo: Modelo de variograma (si no se especifica, se usa IDW; si se define, se aplica Kriging).

g1 = gstat(formula = soc ~ 1, data = samples)

(rrr <- terra::rast(xmin=-71.133050 , xmax=-67.445970, ymin= 2.743927, ymax=6.320879 , nrows=370, ncols = 329, vals = 1, crs = "epsg:4326"))

## class       : SpatRaster 
## dimensions  : 370, 329, 1  (nrow, ncol, nlyr)
## resolution  : 0.01120693, 0.009667438  (x, y)
## extent      : -71.13305, -67.44597, 2.743927, 6.320879  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## name        : lyr.1 
## min value   :     1 
## max value   :     1

Se convierte este SpatRaster en un objeto stars:

stars.rrr <- stars::st_as_stars(rrr)

Se aplica el modelo g1 para predecir valores de soc en todas las celdas del raster stars.rrr.y genera una nueva capa raster con valores interpolados de SOC.

z1 = predict(g1, stars.rrr)

## [inverse distance weighted interpolation]

Muestra la estructura del objeto z1, incluyendo la resolución y los valores de SOC interpolados.

z1

## stars object with 2 dimensions and 2 attributes
## attribute(s):
##                Min.  1st Qu.   Median     Mean  3rd Qu.     Max.   NA's
## var1.pred  7.890849 12.92884 14.09295 14.41072 15.37706 49.32637      0
## var1.var         NA       NA       NA      NaN       NA       NA 121730
## dimension(s):
##   from  to offset     delta refsys x/y
## x    1 329 -71.13   0.01121 WGS 84 [x]
## y    1 370  6.321 -0.009667 WGS 84 [y]

Este codigo busca las celdas con valores NA (no estimados) en z1. Sustituye estos valores NA con 0.0 para evitar problemas en la visualización o análisis posterior.

a <- which(is.na(z1[[1]]))
z1[[1]][a] = 0.0

Seguidamente se visualiza nuevamente el objeto z1 para verificar que los valores NA han sido reemplazados correctamente.

z1

## stars object with 2 dimensions and 2 attributes
## attribute(s):
##                Min.  1st Qu.   Median     Mean  3rd Qu.     Max.   NA's
## var1.pred  7.890849 12.92884 14.09295 14.41072 15.37706 49.32637      0
## var1.var         NA       NA       NA      NaN       NA       NA 121730
## dimension(s):
##   from  to offset     delta refsys x/y
## x    1 329 -71.13   0.01121 WGS 84 [x]
## y    1 370  6.321 -0.009667 WGS 84 [y]

A continuacion se asigna el nombre “soc” a la capa resultante de la interpolación para facilitar su uso en visualizaciones y análisis posteriores.

names(z1) = "soc"

Despues define una paleta de colores para representar los valores de SOC en el mapa. domain = 10:120 indica el rango de valores que se representarán con los colores seleccionados. na.color = “transparent” hace que los valores NA se muestren como transparentes.

paleta <- colorNumeric(palette = c("orange", "yellow", "cyan", "green"), domain = 10:120, na.color = "transparent")

Se crea un mapa interactivo con leaflet y agrega capas de datos. addTiles() añade un mapa base. leafem:::addGeoRaster(z1, opacity=0.7, colorOptions=…) agrega la capa raster de SOC interpolado con la paleta de colores definida. addCircleMarkers(data = samples, radius=1.5, label=~soc, color=~paleta(soc)) añade los puntos de muestreo con colores según el valor de SOC. addLegend(“bottomright”, pal=paleta, values=z1$soc, title=“IDW SOC interpolation [%]”) añade una leyenda que describe los valores del SOC interpolado. Finalmente, m imprime el mapa interactivo.

m <- leaflet() %>%
  addTiles() %>%  ##añade un mapa base.
  leafem:::addGeoRaster(
      z1,
      opacity = 0.7,                
      colorOptions = colorOptions(palette = c("orange", "yellow", "cyan", "green"), 
                                  domain = 10:100)
    ) %>% ##agrega la capa raster de SOC interpolado con la paleta de colores definida.
   addCircleMarkers(
    data = samples,
    radius= 1.5, 
    label = ~soc,
    color = ~paleta(soc),
    fillOpacity = 1,
    stroke = F
  ) %>% ##añade los puntos de muestreo con colores según el valor de SOC
    addLegend("bottomright", pal=paleta, values= z1$soc,
    title = "IDW SOC interpolation [%]"
    ) ##añade una leyenda que describe los valores del SOC interpolado.

## Warning in pal(c(r[1], cuts, r[2])): Some values were outside the color scale
## and will be treated as NA

## Warning in paleta(soc): Some values were outside the color scale and will be
## treated as NA
## Warning in paleta(soc): Some values were outside the color scale and will be
## treated as NA

## Warning in write_stars.stars(x, dsn = fl): all but first attribute are ignored

m  # Imprime el mapa

El variograma experimental es una herramienta clave en la geoestadística, ya que describe cómo varía la variable con la distancia.

Se calcula el variograma experimental de la variable soc (contenido de carbono orgánico en el suelo) usando la función variogram() de la librería gstat.

v_emp_ok = variogram(soc ~ 1, data=samples)

Se grafica el variograma experimental obtenido en el paso anterior. Esto permite visualizar cómo la variabilidad del SOC cambia con la distancia y ayuda a elegir un modelo adecuado de variograma para el kriging.

plot(v_emp_ok)

Se ajusta automáticamente un modelo de variograma utilizando la función autofitVariogram() de la librería automap. Este paso permite obtener un modelo de variograma teórico que luego se usará en la interpolación por kriging.

#make sure you understand the parameters needed to run this fitting function
v_mod_ok = automap::autofitVariogram(soc ~ 1, as(samples, "Spatial"))

se utiliza la funcion show.vgms() obteniendo la grafica de 17 modelos y la funcion autofitVariogram compara con cada uno de ellos con el valor experimental para identifir cual se aproxima mas al valor experimental.

show.vgms()

Se grafica el modelo de variograma ajustado junto con el variograma experimental. Esto permite comparar si el modelo seleccionado representa bien los datos observados.

plot(v_mod_ok)

Se muestra el modelo de variograma ajustado en formato de tabla, indicando los parámetros estimados como el efecto pepita (nugget), el umbral (sill) y el rango (range)

v_mod_ok$var_model

##   model     psill    range kappa
## 1   Nug  1.699667 0.000000   0.0
## 2   Ste 13.761430 9.547541   0.2

Se crea un objeto gstat para el kriging ordinario (OK - Ordinary Kriging), este modelo se usará para predecir valores de SOC en ubicaciones sin datos, tambien se aplica el modelo de kriging ordinario (g2) para predecir los valores de soc en una malla espacial (stars.rrr).

g2 = gstat(formula = soc ~ 1, model = v_mod_ok$var_model, data = samples)
z2= predict(g2, stars.rrr)

## [using ordinary kriging]

Manejo de valores NA en la interpolación

a <- which(is.na(z2[[1]])) ##Identifica las celdas con valores NA en el raster interpolado.
z2[[1]][a] = 0.0 ##Reemplaza los valores NA por 0.0 para evitar problemas en la visualización.
names(z2) = "soc" ##Renombra la capa del raster como "soc".

# Extraer los valores de Z2 y de samples$soc
valores_z2 <- as.vector(z2$soc)
valores_samples <- samples$soc

# Crear un data frame con ambas variables
df_hist <- data.frame(
  Tipo = rep(c("Z2", "Samples"), c(length(valores_z2), length(valores_samples))),
  Valores = c(valores_z2, valores_samples)
)

# Graficar histogramas en un solo panel
ggplot(df_hist, aes(x = Valores, fill = Tipo)) +
  geom_histogram(position = "identity", alpha = 0.5, bins = 15) +
  facet_wrap(~Tipo, scales = "free") +
  theme_minimal() +
  labs(title = "Distribución de Valores",
       x = "Carbono Organico Disponible - SOC",
       y = "Cantidad de valres")

range(z2$soc, na.rm = TRUE)

## [1]  9.347075 40.099604

range(samples$soc, na.rm = TRUE)

## [1]  7.61 49.45

Se genera un mapa interactivo con leaflet que muestra los resultados de la interpolación por kriging:

m <- leaflet() %>%
  addTiles() %>%  
  leafem:::addGeoRaster(
      z2,
      opacity = 0.7,                
      colorOptions = colorOptions(palette = c("orange", "yellow", "cyan", "green"), 
                                  domain = 10:100)
    ) %>%
   addCircleMarkers(
    data = samples,
    radius= 1.5, 
    label = ~soc,
    color = ~paleta(soc),
    fillOpacity = 1,
    stroke = F
  ) %>%
    addLegend("bottomright", pal = paleta, values= z2$soc,
    title = "OK SOC interpolation [%]"
    )

## Warning in pal(c(r[1], cuts, r[2])): Some values were outside the color scale
## and will be treated as NA

## Warning in paleta(soc): Some values were outside the color scale and will be
## treated as NA
## Warning in paleta(soc): Some values were outside the color scale and will be
## treated as NA

## Warning in write_stars.stars(x, dsn = fl): all but first attribute are ignored

m 

Se define una configuración de colores para la interpolación:

colores <- colorOptions(palette = c("orange", "yellow", "cyan", "green"), domain = 10:100, na.color = "transparent")

Posteriormente se genera un mapa interactivo que permite comparar ambas interpolaciones:

m <- leaflet() %>%
  addTiles() %>%  
  addGeoRaster(z1, colorOptions = colores, opacity = 0.8, group= "IDW")  %>%
  addGeoRaster(z2, colorOptions = colores, opacity = 0.8, group= "OK")  %>%
  # Add layers controls
  addLayersControl(
    overlayGroups = c("IDW", "OK"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>% 
    addLegend("bottomright", pal = paleta, values= z1$soc,
    title = "Soil organic carbon [%]"
)

## Warning in pal(c(r[1], cuts, r[2])): Some values were outside the color scale
## and will be treated as NA

## Warning in write_stars.stars(x, dsn = fl): all but first attribute are ignored
## Warning in write_stars.stars(x, dsn = fl): all but first attribute are ignored

Se imprime el mapa

m  # Print the map

EVALUACIÓN DEL MODELO DE KRIGING

Desde la consola se ejecutó:

### 
## este codigo se corrio desde la consola
cv2 = gstat.cv(g2)

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## [using ordinary kriging]
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## [using ordinary kriging]
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## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
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## [using ordinary kriging]
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## [using ordinary kriging]
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Se analizan las columnas del objeto cv2 con:

colnames(cv2@data)

## [1] "var1.pred" "var1.var"  "observed"  "residual"  "zscore"    "fold"

Se convierte cv2 en un objeto espacial:

cv2_sp <- as(cv2, "Spatial")

Se visualizan los residuos del modelo de kriging con un gráfico de burbujas:

las burbujas emergen puntos de color morado y otros de color verde, que ilustran la diferencia (error) entre los datos reales y los estimados mediante la interpolación con Kriging Ordinario (OK). Pero, ¿qué representan estos valores?

Los colores: * Verde: Residuales positivos (sobrestimaciones del modelo). * Magenta: Residuales negativos (subestimaciones del modelo). Tamaño de las burbujas:

cv2_sp <- as(cv2, "SpatialPointsDataFrame")
sp::bubble(cv2_sp["residual"])

sqrt(sum((cv2$var1.pred - cv2$observed)^2) / nrow(cv2))

## [1] 3.83301

Se realiza el mismo proceso con los resultados de interpolación IDW (g1).

### 
## Se corrio desde consola
cv1 = gstat.cv(g1)

## [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]
## [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]
## [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]
## [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]
## [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]
## [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]
## [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]
## [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]
## [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]
## [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]
## [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]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]

# run from the console
cv1 = st_as_sf(cv1)

Grafiquemos los residuos:

sp::bubble(as(cv1[, "residual"], "Spatial"))

sqrt(sum((cv1$var1.pred - cv1$observed)^2) / nrow(cv1))

## [1] 3.82062

EXPORTACIÓN DE RESULTADOS

Se guarda la interpolación IDW en un archivo raster TIFF:

write_stars(
  z1, dsn = "C:/Users/Danna Abril/Desktop/Proyecto 1/PROYECTO 5/IDW_soc_stder.tif", layer = 1
)

write_stars(
  z2, dsn = "C:/Users/Danna Abril/Desktop/Proyecto 1/PROYECTO 5/OK_soc_stder.tif", layer = 1
)

CONCLUSIÓN

Este estudio facilita la exploración y visualización interactiva de datos geoespaciales. La transformación y el análisis de estos datos ofrecen información relevante sobre la distribución espacial del SOC en el Municipio de Vichada. Asimismo, la aplicación de técnicas de interpolación como IDW y kriging ordinaria permite obtener estimaciones detalladas de los valores de SOC en zonas sin mediciones directas, lo que es útil para la planificación y la toma de decisiones en la gestión de suelos.

La implementación de mapas interactivos se convierte en una herramienta eficaz para visualizar y comparar los distintos métodos de interpolación, lo que posibilita evaluar sus diferencias y escoger el método más adecuado según las exigencias del estudio.