spatialEco (version 2.0-2)

built.index: built index

Description

Remote sensing built-up index

Usage

built.index(
  green,
  red,
  nir,
  swir1,
  swir2,
  L = 0.5,
  method = c("Bouhennache", "Zha", "Xu")
)

Value

A terra raster object of the built index

Arguments

green

Green band (0.53 - 0.59mm), landsat 5&7 band 3, OLI (landsat 8) band 3

red

Red band (0.636 - 0.673mm), landsat 5&7 band 3, OLI (landsat 8) band 4

nir

Near infrared band (0.851 - 0.879mm) landsat 5&7 band 4, OLI (landsat 8) band 5

swir1

short-wave infrared band 1 (1.566 - 1.651mm), landsat 5&7 band 5, OLI (landsat 8) band 6

swir2

short-wave infrared band 2 (2.11 - 2.29mm), landsat 5&7 band 7, OLI (landsat 8) band 7

L

The L factor for the savi index

method

Method to use for index options are "Bouhennache", "Zha", "Xu"

Author

Jeffrey S. Evans

Details

This function calculates the built-up index. Three methods are available:

• Bouhennache is a new method that uses a larger portion of the VIR/NIR following OLI bands (((b3+b4+b7)-b6)/3) / (((b3+b4+b7)+b6)/3)

• Zha is the original band ratio method using TM5 ndbi = (b5 - b4) / (b5 + b4)

• Xu is a modification to eliminate noise using ETM+7 (ndbi-((savi-nndwi)/2) / (ndbi+((savi-nndwi)/2)

Generally water has the highest values where built-up areas will occur in the mid portion of the distribution. Since Bouhennache et al (2018) index exploits a larger portion of the visible (Vis) and infra red spectrum, vegetation will occur as the lowest values and barren will exhibit greater values than the vegetation and lower values than the built-up areas.

Band wavelength (nanometers) designations for landsat TM4, TM5 and ETM+7

• band-2 0.52-0.60 (green)

• band-3 0.63-0.69 (red)

• band-4 0.76-0.90 (NIR)

• band-5 1.55-1.75 (SWIR 1)

• band-7 2.09-2.35 (SWIR 2)

OLI (Landsat 8)

• band-3 0.53-0.59 (green)

• band-4 0.64-0.67 (red)

• band-5 0.85-0.88 (NIR)

• band-6 1.57-1.65 (SWIR 1)

• band-7 2.11-2.29 (SWIR 2)

References

Bouhennache, R., T. Bouden, A. Taleb-Ahmed & A. Chaddad(2018) A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery, Geocarto International 34(14):1531-1551

Xu H. (2008) A new index for delineating built-up land features in satellite imagery. International Journal Remote Sensing 29(14):4269-4276.

Zha G.Y., J. Gao, & S. Ni (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing 24(3):583-594

Examples

# \donttest{
library(terra)
 lsat <- rast(system.file("/extdata/Landsat_TM5.tif", package="spatialEco"))
   plotRGB(lsat, r=3, g=2, b=1, scale=1.0, stretch="lin")
			   
 # Using Bouhennache et al., (2018) method (needs green, red, swir1 and swir2) 
 ( bouh <- built.index(red = lsat[[3]], green = lsat[[2]], swir1 = lsat[[5]], 
                      swir2 = lsat[[6]]) )
    plotRGB(lsat, r=3, g=2, b=1, scale=1, stretch="lin")
      plot(bouh, legend=FALSE, col=rev(terrain.colors(100, alpha=0.35)), 
	       add=TRUE )

 # Using simple Zha et al., (2003) method (needs nir and swir1)
 ( zha <- built.index(nir = lsat[[4]], swir1 = lsat[[5]], method = "Zha") )
   plotRGB(lsat, r=3, g=2, b=1, scale=1, stretch="lin")
     plot(zha, legend=FALSE, col=rev(terrain.colors(100, alpha=0.35)), add=TRUE )

 # Using Xu (2008) normalized modification of Zha (needs green, red, nir and swir1)
 ( xu <- built.index(green= lsat[[2]], red = lsat[[3]], nir = lsat[[4]], 
                     swir1 = lsat[[5]], , method = "Xu") )
   plotRGB(lsat, r=3, g=2, b=1, scale=1, stretch="lin")
     plot(xu, legend=FALSE, col=rev(terrain.colors(100, alpha=0.35)), add=TRUE ) 
# }