Monitoreo

library(rasterVis)

## Warning: package 'rasterVis' was built under R version 4.0.5

## Loading required package: lattice

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.5

library(raster)

## Warning: package 'raster' was built under R version 4.0.5

## Loading required package: sp

## Warning: package 'sp' was built under R version 4.0.5

library(stars)

## Warning: package 'stars' was built under R version 4.0.5

## Loading required package: abind

## Warning: package 'abind' was built under R version 4.0.3

## Loading required package: sf

## Warning: package 'sf' was built under R version 4.0.5

## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE

library(rgdal)

## Warning: package 'rgdal' was built under R version 4.0.5

## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
## 
## rgdal: version: 1.5-28, (SVN revision 1158)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/DELL/Documents/R/win-library/4.0/rgdal/gdal
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/DELL/Documents/R/win-library/4.0/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-6
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/DELL/Documents/R/win-library/4.0/rgdal/proj

library(RColorBrewer)

## Warning: package 'RColorBrewer' was built under R version 4.0.3

#TEMPERATURA

temp_san<- "C:/MODELACION AMBIENTAL/Preguntas/PREGUNTA_2"
temp_san<- list.files(temp_san,full.names = TRUE,pattern = ".tif$")

compilar_temp_san<- stack(temp_san)
compilar_temp_san1<- compilar_temp_san*0.02
compilar_temp_san2<- compilar_temp_san1-273.15

plot(compilar_temp_san2[[30:40]])

Enero2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016017_aid0001,fun=mean)


Febrero2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016041_aid0001,fun=mean)

Marzo2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016065_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016073_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016089_aid0001,fun=mean)


Abril2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016097_aid0001,
                       compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016105_aid0001,
                       compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016113_aid0001,fun=mean)

Mayo2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016121_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016129_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016145_aid0001,fun=mean)


Junio2016<- overlay ( compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016161_aid0001,
                       compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016169_aid0001,
                       compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016177_aid0001,fun=mean)


Julio2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016185_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016201_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016209_aid0001,fun=mean)


Agosto2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016217_aid0001,
                       compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016225_aid0001,
                       compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016241_aid0001,fun=mean)

Septiembre2016 <- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016249_aid0001,fun=mean)

Octubre2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016281_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016289_aid0001,
                     compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016297_aid0001,fun=mean)


Noviembre2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016305_aid0001,
                      compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016313_aid0001,
                      compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016321_aid0001,fun=mean)

Diciembre2016<- overlay (compilar_temp_san2$MOD11A2.006_LST_Day_1km_doy2016345_aid0001,fun=mean)





NDVI_HARV_stack <- stack(Enero2016, Febrero2016, Marzo2016,
                          Abril2016, Mayo2016, Junio2016, 
                          Julio2016, Agosto2016, Septiembre2016,
                          Octubre2016, Noviembre2016, Diciembre2016)
levelplot(NDVI_HARV_stack)

cols <- colorRampPalette(brewer.pal(9,"YlOrRd"))

names(NDVI_HARV_stack)<-c('Enero2016','Febrero2016','Marzo2016',
                          'Abril2016','Mayo2016','Junio2016',
                          'Julio2016', 'Agosto2016','Septiembre2016',
                          'Octubre2016','Noviembre2016','Diciembre2016')

levelplot(NDVI_HARV_stack,main="Promedio de Temperatura 2016",
          col.regions=cols)