Analisis y porcesamiento de Informacion de la Elevación de Norte de Santander

1. Introducción

Este cuaderno ilustra varias funcionalidades para obtener, procesar y visualizar modelos digitales de elevación (DEM) en R.En específico del departamento de Norte de Santander.

2. Configuración

Los paquetes necesarios deben instalarse previamente desde la consola.

2.1 Limpiar la memoria:

rm(list=ls())

2.2 Cargar las Librerias:

library(elevatr)

## elevatr v0.99.0 NOTE: Version 0.99.0 of 'elevatr' uses 'sf' and 'terra'.  Use 
## of the 'sp', 'raster', and underlying 'rgdal' packages by 'elevatr' is being 
## deprecated; however, get_elev_raster continues to return a RasterLayer.  This 
## will be dropped in future versions, so please plan accordingly.

library(sf)

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

library(leaflet)
library(terra)

## terra 1.8.15

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

3. Importar Documentos

list.files("C:/Users/León Azul/Desktop/Trabajos Universidad Mateo/Geomática Básica 2024-2/Examen 2/Datos")

## [1] "Elevación_Nte_de_Santander.tif" "Elevacisn_Nte_de_Santander.tif"
## [3] "Municipios .gpkg"               "Municipios .gpkg-shm"          
## [5] "Municipios .gpkg-wal"           "Municipios1.gpkg"              
## [7] "NteSantander.gpkg"

munic <-  sf::st_read("C:/Users/León Azul/Desktop/Trabajos Universidad Mateo/Geomática Básica 2024-2/Examen 2/Datos/Municipios1.gpkg")

## Reading layer `municipios___municipios_colombia' from data source 
##   `C:\Users\León Azul\Desktop\Trabajos Universidad Mateo\Geomática Básica 2024-2\Examen 2\Datos\Municipios1.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 40 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.63379 ymin: 6.872201 xmax: -72.04761 ymax: 9.290847
## Geodetic CRS:  MAGNA-SIRGAS

4. Borrar Columnas Inutiles

4.1 Se utilizo qgis para eliminar las columnas sobrantes

5. Obtener las cabeceras Municipales

(centers <- st_centroid(munic))

## Warning: st_centroid assumes attributes are constant over geometries

centers$x = st_coordinates(centers)[,1]

centers$y = st_coordinates(centers)[,2]

centers

5. Información sobre el CRS

(Nte_Santander = st_crs(st_geometry(munic)))

## Coordinate Reference System:
##   User input: MAGNA-SIRGAS 
##   wkt:
## GEOGCRS["MAGNA-SIRGAS",
##     DATUM["Marco Geocentrico Nacional de Referencia",
##         ELLIPSOID["GRS 1980",6378137,298.257222101,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     USAGE[
##         SCOPE["Horizontal component of 3D system."],
##         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",4686]]

6. Área

munic$area_m2 = sf::st_area(munic)

6.1 El área expresada en m^2:

munic$area = munic$area_m2/1000000
munic$area = signif(munic$area, digits = 6)

6.2 Tabla de Atributos con el Área:

munic

7. Obtener los datos de elevación

elevation <- get_elev_raster(munic, z = 10)

## Mosaicing & Projecting

## Note: Elevation units are in meters.

elevation

## class      : RasterLayer 
## dimensions : 4081, 3092, 12618452  (nrow, ncol, ncell)
## resolution : 0.0006822713, 0.0006822713  (x, y)
## extent     : -73.82812, -71.71854, 6.664713, 9.449062  (xmin, xmax, ymin, ymax)
## crs        : +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs 
## source     : file293022f320ba.tif 
## names      : file293022f320ba

writeRaster(elevation, "C:/Users/León Azul/Desktop/Trabajos Universidad Mateo/Geomática Básica 2024-2/Examen 2/Datos/Elevación_Nte_de_Santander.tif", overwrite=TRUE)

(elevation2 <- terra::rast(elevation))

## class       : SpatRaster 
## dimensions  : 4081, 3092, 1  (nrow, ncol, nlyr)
## resolution  : 0.0006822713, 0.0006822713  (x, y)
## extent      : -73.82812, -71.71854, 6.664713, 9.449062  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs 
## source      : file293022f320ba.tif 
## name        : file293022f320ba

8. Visualizar los datos de elevación

8.1 Paleta de Color:

pal <- colorNumeric(c("cyan", "forestgreen","yellow","tan","orange", "brown"), values(elevation),
  na.color = "transparent")

8.2 Reducir la resolución espacial de los datos:

(elevation3 <- terra::aggregate(elevation2, fact = 2))

## |---------|---------|---------|---------|=========================================                                          

## class       : SpatRaster 
## dimensions  : 2041, 1546, 1  (nrow, ncol, nlyr)
## resolution  : 0.001364543, 0.001364543  (x, y)
## extent      : -73.82812, -71.71854, 6.66403, 9.449062  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs 
## source(s)   : memory
## name        : file293022f320ba 
## min value   :             -391 
## max value   :             4737

8.3 Recortar el objeto de elevación3:

elevation4 <- terra::crop(elevation3, munic, mask=TRUE)

8.4 Trazar:

library(terra)
library(tmap)

elevation_raster <- rast("C:/Users/León Azul/Desktop/Trabajos Universidad Mateo/Geomática Básica 2024-2/Examen 2/Datos/Elevación_Nte_de_Santander.tif")

tm_shape(elevation_raster) +
  tm_raster(palette = terrain.colors(10), title = "Elevación (m)") +
  tm_layout(legend.outside = TRUE)

## 

## ── tmap v3 code detected ───────────────────────────────────────────────────────

## [v3->v4] `tm_tm_raster()`: migrate the argument(s) related to the scale of the
## visual variable `col` namely 'palette' (rename to 'values') to col.scale =
## tm_scale(<HERE>).
## [v3->v4] `tm_raster()`: migrate the argument(s) related to the legend of the
## visual variable `col` namely 'title' to 'col.legend = tm_legend(<HERE>)'
## SpatRaster object downsampled to 3633 by 2753 cells.

## 9. Leer datos de entrada

library(elevatr)
library(sf)
library(leaflet)
library(terra)
library(MultiscaleDTM)
library(exactextractr)

(dem = terra::rast("C:/Users/León Azul/Desktop/Trabajos Universidad Mateo/Geomática Básica 2024-2/Examen 2/Datos/Elevación_Nte_de_Santander.tif"))

## class       : SpatRaster 
## dimensions  : 4081, 3092, 1  (nrow, ncol, nlyr)
## resolution  : 0.0006822713, 0.0006822713  (x, y)
## extent      : -73.82812, -71.71854, 6.664713, 9.449062  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=GRS80 +no_defs 
## source      : Elevación_Nte_de_Santander.tif 
## name        : Elevación_Nte_de_Santander 
## min value   :                      -1222 
## max value   :                       4749

9.1 Reducir la resolución del DEM:

dem2 = terra::aggregate(dem,2, "mean")

## |---------|---------|---------|---------|=========================================                                          

9.2 Leer los municipios:

munic

st_crs(munic) # CRS del shapefile

## Coordinate Reference System:
##   User input: MAGNA-SIRGAS 
##   wkt:
## GEOGCRS["MAGNA-SIRGAS",
##     DATUM["Marco Geocentrico Nacional de Referencia",
##         ELLIPSOID["GRS 1980",6378137,298.257222101,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     USAGE[
##         SCOPE["Horizontal component of 3D system."],
##         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",4686]]

crs(dem2)     # CRS del raster

## [1] "GEOGCRS[\"unknown\",\n    DATUM[\"Unknown based on GRS 1980 ellipsoid using towgs84=0,0,0,0,0,0,0\",\n        ELLIPSOID[\"GRS 1980\",6378137,298.257222101004,\n            LENGTHUNIT[\"metre\",1],\n            ID[\"EPSG\",7019]]],\n    PRIMEM[\"Greenwich\",0,\n        ANGLEUNIT[\"degree\",0.0174532925199433,\n            ID[\"EPSG\",9122]]],\n    CS[ellipsoidal,2],\n        AXIS[\"latitude\",north,\n            ORDER[1],\n            ANGLEUNIT[\"degree\",0.0174532925199433,\n                ID[\"EPSG\",9122]]],\n        AXIS[\"longitude\",east,\n            ORDER[2],\n            ANGLEUNIT[\"degree\",0.0174532925199433,\n                ID[\"EPSG\",9122]]]]"

munic <- st_transform(munic, crs(dem2))

9.3 Recortar el objeto dem2 a los límites de Norte de Santander:

dem3 <- terra::crop(dem2, munic, mask = TRUE)
dem_plane <- project(dem3, "EPSG:9377")

10. Transformar coordenadas

(munic_plane = sf::st_transform(munic, "EPSG:9377"))

11. Computar atributos del terreno

(slp_asp = MultiscaleDTM::SlpAsp(
  dem_plane,
  w = c(3, 3),
  unit = "degrees",
  method = "queen",
  metrics = c("slope", "aspect"),
  na.rm = TRUE,
  include_scale = FALSE,
  mask_aspect = TRUE
))

## class       : SpatRaster 
## dimensions  : 1775, 1163, 2  (nrow, ncol, nlyr)
## resolution  : 150.6407, 150.6407  (x, y)
## extent      : 4929942, 5105137, 2317391, 2584779  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## names       :        slope,   aspect 
## min values  :  0.003311737,   0.0000 
## max values  : 75.537534350, 359.9999

(slope = subset(slp_asp, 1))

## class       : SpatRaster 
## dimensions  : 1775, 1163, 1  (nrow, ncol, nlyr)
## resolution  : 150.6407, 150.6407  (x, y)
## extent      : 4929942, 5105137, 2317391, 2584779  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## name        :        slope 
## min value   :  0.003311737 
## max value   : 75.537534350

11.1 ¿Cuál es la distribución de los valores de pendiente?

terra::hist(slope, 
     main = "Nte_Santander's slope", 
     xlab = "Slope (in degrees)")

(aspect =subset(slp_asp, 2))

## class       : SpatRaster 
## dimensions  : 1775, 1163, 1  (nrow, ncol, nlyr)
## resolution  : 150.6407, 150.6407  (x, y)
## extent      : 4929942, 5105137, 2317391, 2584779  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## name        :   aspect 
## min value   :   0.0000 
## max value   : 359.9999

terra::hist(aspect, 
     main = "Nte_Santander's aspect", 
     xlab = "Aspect (in degrees)")

(slope_perc = tan(slope*(pi/180))*100)

## class       : SpatRaster 
## dimensions  : 1775, 1163, 1  (nrow, ncol, nlyr)
## resolution  : 150.6407, 150.6407  (x, y)
## extent      : 4929942, 5105137, 2317391, 2584779  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## name        :        slope 
## min value   : 5.780071e-03 
## max value   : 3.877189e+02

11.2 Convertimos los grados de pendiente en porcentaje de pendiente:

terra::hist(slope_perc, 
     main = "Nte_Santander's slope", 
     xlab = "Slope (in percentage)")

terra::hist(slope_perc)

## 12. Calcular estadísticas zonales:

m <- c(0, 3, 1,  
       3, 7, 2,  
       7, 12,  3, 
       12, 25, 4, 
       25, 50, 5, 
       50, 75, 6,
       75, 160, 7)
m <- matrix(m, ncol=3, byrow = TRUE)
rc <- classify(slope_perc, m, right=TRUE)

(munic$mean_slope <- exactextractr::exact_extract(slope_perc, munic, 'mean'))

## Warning in .local(x, y, ...): Polygons transformed to raster CRS (EPSG:9377)

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##  [1]  8.6702213 26.9235306 44.9074860 34.2686043 43.6437950 35.0278358
##  [7] 35.4857483 30.9476299 41.6343918 28.3918533 41.6895752 27.8760204
## [13] 32.0564842 21.0131588 18.4156361 34.5212708 34.9811592 29.1976566
## [19] 37.4988747 15.7919779 26.5164795 19.6292419 37.1167183 33.4874992
## [25] 28.8898964 34.7548332 37.1878052  0.6774865 31.2936707 39.9569283
## [31] 33.4410667 15.2708139 26.6202831 25.2895069 29.5764160 31.8660564
## [37]  8.1980619 36.7857590 39.5793037 18.2338161

hist(munic$mean_slope, 
     main = "Nte_Santander's mean slope", 
     xlab = "Slope (in percentage)")

(munic$class <- exactextractr::exact_extract(rc, munic, 'mode'))

## Warning in .local(x, y, ...): Polygons transformed to raster CRS (EPSG:9377)

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##  [1] 1 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 1 5 4 5 5 5 5 5 1 5 5 5 4 4 4 5 5 1 5
## [39] 5 5

hist(munic$class, 
     main = "Nte_Santander's reclassified slope", 
     xlab = "Slope (as a category)")

### 12.1 Transformemos la pendiente nuevamente a coordenadas geográficas:

(rc.geo = project(rc, "EPSG:4326"))

## class       : SpatRaster 
## dimensions  : 1774, 1169, 1  (nrow, ncol, nlyr)
## resolution  : 0.001364545, 0.001364545  (x, y)
## extent      : -73.63815, -72.043, 6.871343, 9.292045  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## name        :    slope 
## min value   :   1.0000 
## max value   : 366.9063

(slope.geo = project(slope_perc, "EPSG:4326"))

## class       : SpatRaster 
## dimensions  : 1774, 1169, 1  (nrow, ncol, nlyr)
## resolution  : 0.001364545, 0.001364545  (x, y)
## extent      : -73.63815, -72.043, 6.871343, 9.292045  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## name        :        slope 
## min value   :   0.03222043 
## max value   : 366.90628052

13. Trazado de pendiente del terreno

13.1 Paleta de colores para pendiente:

palredgreen <- colorNumeric(c("darkseagreen3","yellow2", "orange", "brown2", "darkred"), values(slope.geo),
  na.color = "transparent")

leaflet(munic) %>% addTiles() %>% setView(-72.36, 6.99, 9.30) %>% 
    addPolygons(color = "gray", weight = 1.0, smoothFactor = 0.5,
    opacity = 0.4, fillOpacity = 0.10,
    popup = paste("Municipio: ", munic$mpio_cnmbr, "<br>",
                          "Slope class: ", munic$class, "<br>")) %>%
  addRasterImage(slope.geo, colors = palredgreen, opacity = 0.8)  %>%
  addLegend(pal = palredgreen, values = values(slope.geo),
    title = "Terrain slope in Cordoba (%)")

14. Visualización alternativa de la pendiente del terreno

library(leaflet)

# Crear paleta de colores para la pendiente
palredgreen <- colorNumeric(
  palette = c("darkseagreen3", "yellow2", "orange", "brown2", "darkred"),
  domain = values(slope.geo),
  na.color = "transparent"
)

# Crear mapa interactivo con Leaflet
leaflet(munic) %>%
  addTiles() %>%
  setView(-72.36, 6.99, zoom = 9.30) %>% 
  addPolygons(
    color = "gray", weight = 1.0, smoothFactor = 0.5,
    opacity = 0.4, fillOpacity = 0.10,
    popup = paste(
      "Municipio: ", munic$mpio_cnmbr, "<br>",
      "Slope class: ", munic$class, "<br>",
      "Mean slope: ", round(munic$mean_slope, 2), "%"
    )
  ) %>%
  addRasterImage(slope.geo, colors = palredgreen, opacity = 0.8) %>%
  addLegend(
    pal = palredgreen, values = values(slope.geo),
    title = "Terrain slope in degrees"
  ) %>%
  addLabelOnlyMarkers(
    data = munic,
    lng = centers$x, lat = centers$y,
    label = ~paste(mpio_cnmbr, "(", round(mean_slope, 2), "°)"),
    labelOptions = labelOptions(
      noHide = TRUE, direction = "top",
      textOnly = TRUE, style = list("font-weight" = "bold")
    )
  )

14. Visualizando el aspecto del terreno

14.1 Asegurar el cálculo de “mean_aspect”:

munic$mean_aspect <- exactextractr::exact_extract(aspect, munic, 'mean')

## Warning in .local(x, y, ...): Polygons transformed to raster CRS (EPSG:9377)

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14.2 Trazar el mapa:

library(leaflet)

# Definir la paleta de colores para el aspecto (orientación del terreno)
pal_aspect <- colorNumeric(
  palette = c("red", "orange", "yellow", "green", "cyan", "blue", "magenta", "purple"),
  domain = values(aspect),
  na.color = "transparent"
)

# Crear el mapa con Leaflet
leaflet(munic) %>%
  addTiles() %>%
  setView(-72.36, 6.99, zoom = 9.30) %>%
  addPolygons(
    color = "gray", weight = 1.0, smoothFactor = 0.5,
    opacity = 0.4, fillOpacity = 0.10,
    popup = paste(
      "Municipio: ", munic$mpio_cnmbr, "<br>",
      "Aspect (°): ", round(munic$mean_aspect, 2)
    )
  ) %>%
  addRasterImage(aspect, colors = pal_aspect, opacity = 0.8) %>%
  addLegend(
    pal = pal_aspect, values = values(aspect),
    title = "Aspect (°)"
  )

15. Análisis de la Información:

La elevación del territorio en el departamento de Norte de Santander, Colombia, es el resultado de complejos procesos geológicos asociados a la dinámica tectónica de la región andina. Este territorio se encuentra en la zona de interacción entre las placas tectónicas de Nazca, Caribe y Suramericana, lo que ha dado lugar a la formación de la Cordillera Oriental de los Andes. El levantamiento orogénico en esta región ha sido impulsado por la subducción de la placa de Nazca bajo la placa Suramericana, generando un proceso de compresión y fracturación de la corteza terrestre que ha elevado grandes masas de roca a lo largo de millones de años. La interacción de fallas geológicas activas, como la Falla de Bucaramanga-Santa Marta y la de Chitagá, contribuye a la continua actividad sísmica y al modelado del relieve montañoso característico del departamento. Además de los procesos tectónicos, la erosión diferencial y la actividad volcánica han influido en la topografía de Norte de Santander. La acción de agentes externos como el agua y el viento ha esculpido el paisaje, favoreciendo la formación de valles y cañones profundos, como el del río Pamplonita. La existencia de depósitos sedimentarios en algunas áreas sugiere que el territorio ha experimentado variaciones en su elevación debido a la acumulación y erosión de materiales a lo largo del tiempo geológico. Los estudios geológicos han identificado la presencia de antiguas cuencas marinas que, debido a los movimientos tectónicos, fueron progresivamente levantadas.Desde un enfoque científico, la elevación de Norte de Santander también ha tenido efectos en su clima y biodiversidad. Los diferentes pisos térmicos que resultan de la variación altitudinal han generado una diversidad de ecosistemas, desde bosques húmedos tropicales en las zonas bajas hasta páramos en las áreas más elevadas. Esta variabilidad geográfica ha permitido el desarrollo de una flora y fauna adaptada a distintas condiciones ambientales. Asimismo, los cambios en la altitud han influido en los patrones de precipitación y temperatura, haciendo que algunas zonas sean más propensas a la erosión y graduales. En conclusión, la elevación de Norte de Santander es el producto de una interacción compleja entre procesos geológicos internos y externos que han moldeado su alivio a lo largo de millones de años.

16. Bibliografía:

- Lizarazo, I., 2025. Geomorphometric terrain attributes in R. Available at https://rpubs.com/ials2un/geomorphometric.

- Servicio Geológico Colombiano.Mapa geológico generalizado del Departamento de Norte de Santander

- RestrepModelos orogénicos de tectónica de placas en los Andes colombianos . *Geología NorandíGeología Norandina , 1, 5-20.

- Departamento Administrativo Nacional de Estadística (DANE)