---
title: "Análise do impacto da vegetação no microclima"
author: Fellipe Mira Chaves
output:
flexdashboard::flex_dashboard:
theme: journal
social: menu
source_code: embed
---
```{r setup, include = FALSE}
library(flexdashboard)
library(tidyverse)
library(tmap)
require(raster)
setwd("E:/")
guara.shp <- rgdal::readOGR("2016/shp/shp_reprojetado/guara_shp_rep.shp")
bairros <- rgdal::readOGR("bairros/bairros_novo.shp")
raster_2020 <- stack("2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB1_pTOA.tif",
"2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB2_pTOA.tif",
"2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB3_pTOA.tif",
"2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB4_pTOA.tif",
"2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB5_pTOA.tif",
"2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB6_pTOA.tif",
'2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB7_pTOA.tif',
"2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/recorte_BB10_T.tif",
"2020_novo/LC08_L1TP_218076_20200812_20200822_01_T1/binario_limiar_b10.tif",
"mapbiomas_2020.tif")
names(raster_2020) <- c(paste0("B",c(1:7,10)),"resposta","clas")
raster_2020$NDVI <- (raster_2020$B5 - raster_2020$B4)/(raster_2020$B5 + raster_2020$B4)
raster_2016 <- stack("/2016/recorte_bruto/recorte_B1_pTOA.tif",
"/2016/recorte_bruto/recorte_B2_pTOA.tif",
"/2016/recorte_bruto/recorte_B3_pTOA.tif",
"/2016/recorte_bruto/recorte_B4_pTOA.tif",
"/2016/recorte_bruto/recorte_B5_pTOA.tif",
"/2016/recorte_bruto/recorte_B6_pTOA.tif",
"/2016/recorte_bruto/recorte_B7_pTOA.tif",
"/2016/recorte_bruto/recorte_B10_T.tif",
"/2016/recorte_bruto/binario_limiar_b10.tif",
"/2016/recorte_bruto/NDVI.tif",
"mapbiomas_2016.tif")
names(raster_2016) <- c(paste0("B",c(1:7,10)),"resposta","NDVI","clas")
#raster_2020$clas <- reclassify(raster_2020$clas,rcl = mat.recl)
#raster_2020$clas <- crop(raster_2020$clas, guara.shp)
#raster_2020$clas <- mask(raster_2020$clas, guara.shp)
mat.recl <- matrix(c(1,10,1,
10,15,2,
15,30,3,
30,35,4,
35,46,5),
ncol = 3,
byrow = T)
raster_2016$clas <- reclassify(raster_2016$clas,rcl = mat.recl)
raster_2016$clas <- crop(raster_2016$clas, guara.shp)
raster_2016$clas <- mask(raster_2016$clas,guara.shp)
raster_2020$clas <- reclassify(raster_2020$clas,rcl = mat.recl)
raster_2020$clas <- crop(raster_2020$clas, guara.shp)
raster_2020$clas <- mask(raster_2020$clas, guara.shp)
raster_2016$clas <- as.integer(raster_2016$clas)
raster_2020$clas <- as.integer(raster_2020$clas)
data_2016 <- data.frame(na.omit(getValues(raster_2016)))
data_2020 <- data.frame(na.omit(getValues(raster_2020)))
data_2020$clas <- as.integer(data_2020$clas)
bairro_2016 <- crop(raster_2016, bairros)
bairro_2016 <- mask(bairro_2016, bairros)
bairro_2020 <- crop(raster_2020, bairros)
bairro_2020 <- mask(bairro_2020,bairros)
data_bairro_2016 <- data.frame(na.omit(getValues(bairro_2016)))
data_bairro_2020 <- data.frame(na.omit(getValues(bairro_2020)))
df <- as.data.frame(data_bairro_2016$NDVI)
df$NDVI_2020 <- data.frame(data_bairro_2020$NDVI)
df$temp_2016 <- data.frame(data_bairro_2016$B10)
df$temp_2020 <- data.frame(data_bairro_2020$B10)
dfNDVI <- cbind(data_bairro_2016$NDVI,
data_bairro_2020$NDVI)
M_2016 <- glm(resposta~NDVI,
data = data_2016,
family = binomial(link="logit"))
raster_2016$prob_temp <- predict(raster_2016,
model = M_2016,
type = "response")
M_2020 <- glm(resposta~NDVI,
data = data_2020,
family = binomial(link="logit"))
raster_2020$prob_temp <- predict(raster_2020,
model = M_2020,
type = "response")
```
Column {data-width=500}
-----------------------------------------------------------------------
### Probabilidade da ocorrência de microclima 2016
```{r}
tmap_mode("view")
tm_shape(raster_2016$clas,
name = "Classificação: RandomForests")+
tm_raster(title = "Classes",
style='pretty',
palette = 'inferno',
legend.show = F)+
tm_shape(raster_2016$NDVI,
name = "NDVI")+
tm_raster(title = "NDVI",
style = "fisher",
legend.show = F)+
tm_shape(raster_2016$prob_temp,
name = "Temperatura")+
tm_raster(title = "Probabilidade [%]",
style = "fisher",
palette = "inferno",
legend.show = T)+
tm_shape(bairros)+
tm_borders(col="gray")+
tm_text(text = "name",col = "gray")+
tm_layout(main.title = "Mapeamento das variaveis - 2016")+
tm_view(bbox = guara.shp)
```
Column {data-width=500}
-----------------------------------------------------------------------
### Probabilidade da ocorrência de microclima 2020
```{r}
tmap_mode("view")
tm_shape(raster_2020$clas,
name = "Classificação: RandomForests")+
tm_raster(title = "Classes",
style='pretty',
palette = 'inferno',
legend.show = F)+
tm_shape(raster_2020$NDVI,
name = "NDVI")+
tm_raster(title = "NDVI",
style = "fisher",
legend.show = F)+
tm_shape(raster_2020$prob_temp,
name = "Temperatura")+
tm_raster(title = "Probabilidade [%]",
style = "fisher",
palette = "inferno")+
tm_shape(bairros)+
tm_borders(col="gray")+
tm_text(text = "name",col = "gray")+
tm_layout(main.title = "Mapeamento das variaveis - 2020")+
tm_view(bbox = guara.shp)
```
### NDVI 2020 x NDVI 2016
```{r}
a <- raster_2020$NDVI %>% as.data.frame(na.omit(getValues(.)))
names(a) <- "NDVI 2020"
b <- raster_2016$NDVI %>% as.data.frame(na.omit(getValues(.)))
names(b) <- "NDVI 2016"
a <- tibble::tibble(cbind(a,b))
b <- ggplot(a)+
geom_density(aes(x=`NDVI 2020`),col="gray",fill="khaki",alpha=0.41)+
geom_density(aes(x=`NDVI 2016`),col="gray",fill="green4",alpha=0.41)+
theme_light()
b %>% plotly::ggplotly()
```