M1U2

Análisis Cultivo de caña de azúcar

Mapas de aptitud

##Temperatura promedio

Procedemos a importar las librerías

require(raster)

## Loading required package: raster

## Loading required package: sp

require(gdalcubes)

## Loading required package: gdalcubes

## 
## Attaching package: 'gdalcubes'

## The following objects are masked from 'package:raster':
## 
##     animate, crop, extent, nbands

## The following object is masked from 'package:sp':
## 
##     dimensions

require(gdalraster)

## Loading required package: gdalraster

## GDAL 3.8.2, released 2023/16/12, GEOS 3.11.2, PROJ 9.3.1

## 
## Attaching package: 'gdalraster'

## The following objects are masked from 'package:raster':
## 
##     calc, rasterize

require(gdalUtilities)

## Loading required package: gdalUtilities

## 
## Attaching package: 'gdalUtilities'

## The following object is masked from 'package:gdalraster':
## 
##     ogr2ogr

require(sp)
require(rgdax)

## Loading required package: rgdax

## Loading required package: digest

## Loading required package: jsonlite

## Loading required package: RCurl

## Loading required package: httr

## Loading required package: plyr

require(tinytex)

## Loading required package: tinytex

En seguida importamos las imágenes

files=list.files("C:/Users/diaramos/Documents/MCD/M1U2/wc2.1_10m_tavg/",full.names = TRUE)
temperaturas=stack(files)
temperaturas

## class      : RasterStack 
## dimensions : 1080, 2160, 2332800, 12  (nrow, ncol, ncell, nlayers)
## resolution : 0.1666667, 0.1666667  (x, y)
## extent     : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## crs        : +proj=longlat +datum=WGS84 +no_defs 
## names      : wc2.1_10m_tavg_01, wc2.1_10m_tavg_02, wc2.1_10m_tavg_03, wc2.1_10m_tavg_04, wc2.1_10m_tavg_05, wc2.1_10m_tavg_06, wc2.1_10m_tavg_07, wc2.1_10m_tavg_08, wc2.1_10m_tavg_09, wc2.1_10m_tavg_10, wc2.1_10m_tavg_11, wc2.1_10m_tavg_12 
## min values :         -45.88400,         -44.80000,         -57.92575,         -64.19250,         -64.81150,         -64.35825,         -68.46075,         -66.52250,         -64.56325,         -55.90000,         -43.43475,         -45.32700 
## max values :          34.00950,          32.82425,          32.90950,          34.19375,          36.25325,          38.35550,          39.54950,          38.43275,          35.79000,          32.65125,          32.78800,          32.82525

names(temperaturas)=month.name
plot(temperaturas)

temperaturas_cond=temperaturas>22.5&temperaturas<28
plot(temperaturas_cond)

Obtenemos mapas binarios donde laz zonas en verde cumplen la condición.

aptitud=sum(temperaturas_cond)/12*100
plot(aptitud)