Tabla de contenido

1. Limpieza

Primero para poder empezar realizamos Limpieza del cuaderno

rm(list=ls())

2. Carga de Libreria

Descargamos la libreria que vamos a usar para el presente proyecto

warning=FALSE
library(elevatr)
## Warning: package 'elevatr' was built under R version 4.3.3
## 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)
## 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 TRUE
library(leaflet)
## Warning: package 'leaflet' was built under R version 4.3.3
library(terra)
## Warning: package 'terra' was built under R version 4.3.3
## terra 1.8.15
library(MultiscaleDTM)
## Warning: package 'MultiscaleDTM' was built under R version 4.3.3
library(exactextractr)
## Warning: package 'exactextractr' was built under R version 4.3.3

3.Lectura de Datos

Llamamos los datos del proyecto anterior

(dem = terra::rast("elev_Valledc_z10.tif"))
## class       : SpatRaster 
## dimensions  : 3580, 3077, 1  (nrow, ncol, nlyr)
## resolution  : 0.0006856326, 0.0006856326  (x, y)
## extent      : -77.69531, -75.58562, 2.811443, 5.266008  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=GRS80 +no_defs 
## source      : elev_Valledc_z10.tif 
## name        : elev_Valledc_z10 
## min value   :            -2050 
## max value   :             5373

Reducimos para evitar llenar la memoria

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

Leemos el Municipio en extension .gpkg

(munic <-  sf::st_read("mun_Valle.gpkg"))
## Reading layer `municipios_colombia' from data source 
##   `C:\Users\lacj7\OneDrive\Escritorio\GB2\PROYECTO_4\datos\mun_Valle.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 42 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -77.54977 ymin: 3.091239 xmax: -75.70724 ymax: 5.047394
## Geodetic CRS:  MAGNA-SIRGAS
## Simple feature collection with 42 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -77.54977 ymin: 3.091239 xmax: -75.70724 ymax: 5.047394
## Geodetic CRS:  MAGNA-SIRGAS
## First 10 features:
##    dpto_ccdgo mpio_ccdgo mpio_cdpmp          mpio_cnmbr   Area_Km2
## 1          76        001      76001                CALI  561.96643
## 2          76        020      76020              ALCALÁ   62.30168
## 3          76        036      76036           ANDALUCÍA  110.31362
## 4          76        041      76041        ANSERMANUEVO  305.09706
## 5          76        054      76054             ARGELIA   90.67825
## 6          76        100      76100             BOLÍVAR  742.38434
## 7          76        109      76109        BUENAVENTURA 6274.60975
## 8          76        111      76111 GUADALAJARA DE BUGA  824.83594
## 9          76        113      76113        BUGALAGRANDE  396.29099
## 10         76        122      76122          CAICEDONIA  166.82613
##                              geom
## 1  MULTIPOLYGON (((-76.59175 3...
## 2  MULTIPOLYGON (((-75.83262 4...
## 3  MULTIPOLYGON (((-76.22406 4...
## 4  MULTIPOLYGON (((-76.01558 4...
## 5  MULTIPOLYGON (((-76.14316 4...
## 6  MULTIPOLYGON (((-76.27584 4...
## 7  MULTIPOLYGON (((-77.2381 4....
## 8  MULTIPOLYGON (((-76.31608 3...
## 9  MULTIPOLYGON (((-76.15131 4...
## 10 MULTIPOLYGON (((-75.8539 4....
dem3 = terra::crop(dem2,munic, mask=TRUE)
## Warning: [crop] CRS do not match

4.Transformando Coordenadas

Cambiamos a las coordenadas de Colombia

(dem_plane = project(dem3, "EPSG:9377"))
## class       : SpatRaster 
## dimensions  : 1430, 1353, 1  (nrow, ncol, nlyr)
## resolution  : 152.1184, 152.1184  (x, y)
## extent      : 4494105, 4699921, 1900030, 2117560  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## name        : elev_Valledc_z10 
## min value   :        -252.9045 
## max value   :        4201.2246

Cambiamos para el vector

(munic_plane = sf::st_transform(munic, "EPSG:9377"))
## Simple feature collection with 42 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 4494194 ymin: 1900273 xmax: 4699750 ymax: 2116536
## Projected CRS: MAGNA-SIRGAS 2018 / Origen-Nacional
## First 10 features:
##    dpto_ccdgo mpio_ccdgo mpio_cdpmp          mpio_cnmbr   Area_Km2
## 1          76        001      76001                CALI  561.96643
## 2          76        020      76020              ALCALÁ   62.30168
## 3          76        036      76036           ANDALUCÍA  110.31362
## 4          76        041      76041        ANSERMANUEVO  305.09706
## 5          76        054      76054             ARGELIA   90.67825
## 6          76        100      76100             BOLÍVAR  742.38434
## 7          76        109      76109        BUENAVENTURA 6274.60975
## 8          76        111      76111 GUADALAJARA DE BUGA  824.83594
## 9          76        113      76113        BUGALAGRANDE  396.29099
## 10         76        122      76122          CAICEDONIA  166.82613
##                              geom
## 1  MULTIPOLYGON (((4600987 195...
## 2  MULTIPOLYGON (((4685860 208...
## 3  MULTIPOLYGON (((4642160 202...
## 4  MULTIPOLYGON (((4665634 209...
## 5  MULTIPOLYGON (((4651417 208...
## 6  MULTIPOLYGON (((4636542 205...
## 7  MULTIPOLYGON (((4529330 200...
## 8  MULTIPOLYGON (((4631835 199...
## 9  MULTIPOLYGON (((4650286 203...
## 10 MULTIPOLYGON (((4683364 204...

5. Atributos de Orientacion y Pendiente

llamamos la funcion SlpAsp

(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  : 1430, 1353, 2  (nrow, ncol, nlyr)
## resolution  : 152.1184, 152.1184  (x, y)
## extent      : 4494105, 4699921, 1900030, 2117560  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## names       :    slope,       aspect 
## min values  :  0.00000, 1.119667e-04 
## max values  : 74.02856, 3.599985e+02

Lo dividimos en dos partes

Capa 1:

(slope = subset(slp_asp, 1))
## class       : SpatRaster 
## dimensions  : 1430, 1353, 1  (nrow, ncol, nlyr)
## resolution  : 152.1184, 152.1184  (x, y)
## extent      : 4494105, 4699921, 1900030, 2117560  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## name        :    slope 
## min value   :  0.00000 
## max value   : 74.02856

Valores

terra::hist(slope, 
     main = "Pendiente del Valle del cauca", 
     xlab = "Pendiente (en grados)")
## Warning: [hist] a sample of 52% of the cells was used (of which 54% was NA)

Capa 2:

(aspect =subset(slp_asp, 2))
## class       : SpatRaster 
## dimensions  : 1430, 1353, 1  (nrow, ncol, nlyr)
## resolution  : 152.1184, 152.1184  (x, y)
## extent      : 4494105, 4699921, 1900030, 2117560  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## name        :       aspect 
## min value   : 1.119667e-04 
## max value   : 3.599985e+02

Valores

terra::hist(aspect, 
     main = "Orientacion del Valle", 
     xlab = "Orientacion (en grados)")
## Warning: [hist] a sample of 52% of the cells was used (of which 54% was NA)

Ahora hallamos la pendiente en porcentaje para poder clasificar las zonas

(slope_perc = tan(slope*(pi/180))*100)
## class       : SpatRaster 
## dimensions  : 1430, 1353, 1  (nrow, ncol, nlyr)
## resolution  : 152.1184, 152.1184  (x, y)
## extent      : 4494105, 4699921, 1900030, 2117560  (xmin, xmax, ymin, ymax)
## coord. ref. : MAGNA-SIRGAS 2018 / Origen-Nacional (EPSG:9377) 
## source(s)   : memory
## name        :    slope 
## min value   :   0.0000 
## max value   : 349.3986

Valores

terra::hist(slope_perc, 
     main = "Pendiente del valle del cauca", 
     xlab = "Pendiente (en porcentage)")
## Warning: [hist] a sample of 52% of the cells was used (of which 54% was NA)

6. Estadistica de la Zona

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)
warning=FALSE
(munic$mean_slope <- exactextractr::exact_extract(slope_perc, munic, 'mean'))
## Warning in .local(x, y, ...): Polygons transformed to raster CRS (EPSG:9377)
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##  [1] 19.1932716  7.0536819  6.5234451 24.4195595 29.2239342 33.5480080
##  [7] 14.4227972 29.3312092 10.4425707 19.4222984 29.9164867  0.5738842
## [13]  8.4911633 30.0080242 39.9681816 34.8505516 21.2526875 32.7035942
## [19] 30.7923889 27.2616692 10.1506596 19.0136471 19.5517540 14.8743944
## [25]  9.2575541 10.9017820 19.2761707 26.3611240 15.6934271 23.0123882
## [31] 16.8672638 15.2212105 24.7992954 20.7394142 26.5074520 26.5728569
## [37]  7.9285340 31.6442127 18.7146034 17.6516132 19.1481228  5.5441055
hist(munic$mean_slope, 
     main = "Pendiente media Valle del cauca",
     xlab = "Pendiente (en porcentaje)", 
     breaks = 10,  # Ajusta el número de barras del histograma
     xlim = c(0, 60),  # Ajusta el rango del eje X según tus datos
     col = "lightblue")

(munic$class <- exactextractr::exact_extract(rc, munic, 'mode'))
## Warning in .local(x, y, ...): Polygons transformed to raster CRS (EPSG:9377)
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##  [1] 1 2 1 5 5 5 1 5 1 5 5 1 1 5 5 5 1 5 5 5 1 1 4 1 1 1 1 1 4 5 5 1 5 5 5 5 2 5
## [39] 4 4 5 1
hist(munic$class, 
     main = "Pendiente reclasificada del Valle del Cauca", 
     xlab = "Pendiente (Como categoria)",
      breaks = 10,  
     xlim = c(0, 10),  
     col = "lightblue")

(rc.geo = project(rc, "EPSG:4326"))
## class       : SpatRaster 
## dimensions  : 1439, 1357, 1  (nrow, ncol, nlyr)
## resolution  : 0.001371234, 0.001371234  (x, y)
## extent      : -77.56098, -75.70021, 3.085094, 5.058299  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## name        :   slope 
## min value   :   0.000 
## max value   : 292.879
(slope.geo = project(slope_perc, "EPSG:4326"))
## class       : SpatRaster 
## dimensions  : 1439, 1357, 1  (nrow, ncol, nlyr)
## resolution  : 0.001371234, 0.001371234  (x, y)
## extent      : -77.56098, -75.70021, 3.085094, 5.058299  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## name        :    slope 
## min value   :   0.0000 
## max value   : 297.0677

7. Representacion Grafica

palredgreen <- colorNumeric(c("darkseagreen3","yellow2", "orange", "brown2", "darkred"), values(slope.geo),
  na.color = "transparent")
leaflet(munic) %>% 
  addTiles() %>% 
  setView(-76.5, 3.5, 7) %>%  # Coordenadas del Valle del Cauca con un zoom más amplio
  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 Valle del Cauca (%)")
## 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'

8. Alternativa Pendiente y clase de Pendiente

munic$longitude <- st_coordinates(st_centroid(munic))[,1]
## Warning: st_centroid assumes attributes are constant over geometries
munic$latitude <- st_coordinates(st_centroid(munic))[,2]
## Warning: st_centroid assumes attributes are constant over geometries
# Convertir la pendiente de porcentaje a grados
slope_deg <- atan(slope*(pi/180)) * (180/pi)

# Calcular la pendiente promedio en grados por municipio
munic$mean_slope_deg <- exactextractr::exact_extract(slope_deg, munic, 'mean')
## Warning in .local(x, y, ...): Polygons transformed to raster CRS (EPSG:9377)
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# Crear el mapa
leaflet(munic) %>% 
  addTiles() %>% 
  setView(-76.5, 3.5, 7) %>%  # Coordenadas del Valle del Cauca, las mismas de antes
  addPolygons(color = "gray", weight = 1.0, smoothFactor = 0.5,
    opacity = 0.4, fillOpacity = 0.10,
    popup = paste("Municipio: ", munic$mpio_cnmbr, "<br>",
                  "clase de Pendiente: ", munic$class, "<br>",
                  "pendiente promedio: ", round(munic$mean_slope_deg, 1), "°", "<br>")) %>%
  addRasterImage(slope.geo, colors = palredgreen, opacity = 0.8) %>%
  addLegend(pal = palredgreen, values = values(slope.geo),
    title = "Pendiente del terreno Valle del Cauca (%)") %>%
  addLabelOnlyMarkers(
    lng = munic$longitude,  # Coordenadas de los municipios
    lat = munic$latitude,   # Coordenadas de los municipios
    label = paste(munic$mpio_cnmbr, ": ", round(munic$mean_slope_deg, 1), "°", sep = ""),
    labelOptions = labelOptions(noHide = TRUE, direction = "auto", textsize = "12px")
  )
## 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'

9. Representacion grafica de la orientacion

# Crear la paleta de colores para el aspecto
pal_aspect <- colorNumeric(
  palette = c("blue", "green", "yellow", "orange", "red", "pink", "purple", "lightblue"),
  domain = values(aspect), 
  na.color = "transparent"
)

# Crear el mapa con leaflet
leaflet(munic) %>%
  addTiles() %>%
  setView(-76.5, 3.5, 7) %>%  # Coordenadas del Valle del Cauca
  addPolygons(color = "gray", weight = 1.0, smoothFactor = 0.5,
              opacity = 0.4, fillOpacity = 0.10,
              popup = paste("Municipio: ", munic$mpio_cnmbr, "<br>",
                            "Clase de Pendiente: ", munic$class, "<br>")) %>%
  addRasterImage(aspect, colors = pal_aspect, opacity = 0.8) %>%
  addLegend(pal = pal_aspect, values = values(aspect),
            title = "Direccion terreno basado en DTM (Grados)", opacity = 0.7) %>%
  addControl(
    html = "<div style='background-color: white; padding: 10px; border-radius: 5px; border: 2px solid black; opacity: 0.8; font-size: 14px;'>
              <b>Direction Legend</b><br>
              <span style='color: blue;'>Norte</span><br>
              <span style='color: green;'>Noreste</span><br>
              <span style='color: yellow;'>Este</span><br>
              <span style='color: orange;'>Sureste</span><br>
              <span style='color: red;'>Sur</span><br>
              <span style='color: pink;'>Suroeste</span><br>
              <span style='color: purple;'>Oeste</span><br>
              <span style='color: lightblue;'>Noroeste</span><br>
            </div>",
    position = "bottomright",
    className = "leaflet-control-legend"
  )
## 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'

10. Bibliografia

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

sessionInfo()
## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22631)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Spain.utf8  LC_CTYPE=Spanish_Spain.utf8   
## [3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Spain.utf8    
## 
## time zone: America/Bogota
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] exactextractr_0.10.0 MultiscaleDTM_0.9.1  terra_1.8-15        
## [4] leaflet_2.2.2        sf_1.0-19            elevatr_0.99.0      
## 
## loaded via a namespace (and not attached):
##  [1] s2_1.1.7           sass_0.4.9         generics_0.1.3     class_7.3-22      
##  [5] KernSmooth_2.23-21 lattice_0.21-8     digest_0.6.37      magrittr_2.0.3    
##  [9] rgl_1.3.17         evaluate_1.0.1     grid_4.3.1         fastmap_1.2.0     
## [13] jsonlite_1.8.9     e1071_1.7-16       DBI_1.2.3          promises_1.3.2    
## [17] purrr_1.0.2        scales_1.3.0       crosstalk_1.2.1    codetools_0.2-19  
## [21] jquerylib_0.1.4    cli_3.6.3          shiny_1.10.0       rlang_1.1.4       
## [25] units_0.8-5        munsell_0.5.1      base64enc_0.1-3    cachem_1.1.0      
## [29] yaml_2.3.10        raster_3.6-31      tools_4.3.1        dplyr_1.1.4       
## [33] colorspace_2.1-1   httpuv_1.6.15      png_0.1-8          vctrs_0.6.5       
## [37] R6_2.5.1           mime_0.12          proxy_0.4-27       lifecycle_1.0.4   
## [41] classInt_0.4-11    htmlwidgets_1.6.4  pkgconfig_2.0.3    progressr_0.15.1  
## [45] bslib_0.8.0        pillar_1.10.0      later_1.4.1        glue_1.8.0        
## [49] Rcpp_1.0.13-1      tidyselect_1.2.1   xfun_0.49          tibble_3.2.1      
## [53] rstudioapi_0.17.1  knitr_1.49         farver_2.1.2       xtable_1.8-4      
## [57] htmltools_0.5.8.1  rmarkdown_2.29     wk_0.9.4           compiler_4.3.1    
## [61] sp_2.1-4