library(sf)
## Warning: package 'sf' was built under R version 4.4.2
## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE
library(tmap)
## Warning: package 'tmap' was built under R version 4.4.2
## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')
library(tigris)
## Warning: package 'tigris' was built under R version 4.4.2
## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.
storm_channel<-st_read("C:/Users/vmq938/OneDrive - University of Texas at San Antonio/methods1/StormChannels/StormChannels.shp")
## Reading layer `StormChannels' from data source 
##   `C:\Users\vmq938\OneDrive - University of Texas at San Antonio\methods1\StormChannels\StormChannels.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 12042 features and 17 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: 2024999 ymin: 13600410 xmax: 2249487 ymax: 13824430
## Projected CRS: NAD83 / Texas South Central (ftUS)
corridor_plan<-st_read("C:/Users/vmq938/OneDrive - University of Texas at San Antonio/methods1/CorridorPlans/CorridorPlans.shp")
## Reading layer `CorridorPlans' from data source 
##   `C:\Users\vmq938\OneDrive - University of Texas at San Antonio\methods1\CorridorPlans\CorridorPlans.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 12 features and 4 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 2058688 ymin: 13665110 xmax: 2178301 ymax: 13793820
## Projected CRS: NAD83 / Texas South Central (ftUS)
#Transform the coordinate reference systems of both datasets to crs = 4326 (2')
storm_channel<-st_transform(storm_channel,crs = 4326)
corridor_plan<-st_transform(corridor_plan, crs = 4326)

#To find what parts of storm channels are within the corridor plan area, please intersect corridor plans with storm channels (2')
intersected_channel<-st_intersection(storm_channel, corridor_plan)
## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries
#Visualize the intersected storm channels with a background of Bexar County census tracts (1') 
bexar_ct<-tracts(state = "TX", county = "Bexar", cb=T)
## Retrieving data for the year 2022
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tm_shape(bexar_ct)+
  tm_borders()+
  tm_shape(intersected_channel)+
  tm_lines(col="blue")

#Read the raster file (LC09_028040_20230826.tif) Download LC09_028040_20230826.tif)Open this document with ReadSpeaker docReader, which is the land surface temperature of Dallas. (1')
library(raster)
## Loading required package: sp
LST<-raster("C:/Users/vmq938/OneDrive - University of Texas at San Antonio/methods1/LC09_028040_20230826.tif")

#Convert the Kelvin into Celsius (C = K - 273.15) and then plot the raster data. (2')
LST<-setMinMax(LST)
LST<- LST-273.15
cellStats(LST,max)
## [1] 61.28771
#Only visualize the areas with temperatures higher than 40 Degree Celsius (2').
plot(LST, main="Land Surface Temperature, Dallas")

plot(LST > 40, main = "LST>40")

knitr::opts_chunk$set(echo = TRUE)