lai: Leaf Area Index

In spatialEco: Spatial Analysis and Modelling Utilities

Leaf Area Index

Description

Remote sensing measure of LAI (leaf area per ground-unit area)

Usage

lai(ndvi, method = c("Jonckheere", "Chen"))

Arguments

ndvi	NDVI in floating point standard scale range (-1 to 1)
method	Method to use for index options c("Jonckheere", "Chen")

Details

This function calculates the Leaf Area Index (LAI) representing the amount of leaf area per unit of ground area. This is an important parameter for understanding the structure and function of vegetation, as it affects processes such as photosynthesis, transpiration, and carbon cycling. These two approaches are based on the empirical relationship between NDVI and LAI, which has been observed in many studies, and it is a widely used method for estimating LAI from remote sensing data. The formulas are derived from the fact that vegetation with higher LAI tends to have higher reflectance in the near-infrared (NIR) band and lower reflectance in the red band, resulting in higher NDVI values. But still, the exact relationship between NDVI and LAI can vary depending on factors such as vegetation type, canopy structure, and environmental conditions.

Value

A terra SpatRaster object with derived LAI vaues

Author(s)

Jeffrey S. Evans <[e-mail]>

References

Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P. (2004). A comparison of two methods to retrieve the leaf area index (LAI) from SPOT-4 HRVIR data. International Journal of Remote Sensing, 25(21):4407–4425.

Chen, J. M., Liu, R., & Ju, W. (2014). A simple and effective method for estimating leaf area index from Landsat imagery. Remote Sensing of Environment, 152:538–548.

Examples

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")
	
  ndvi <-  ( lsat[[4]] - lsat[[3]] ) / (lsat[[4]] + lsat[[3]]) 
		   
 # Using Jonckheere et al., (2004) method
 lai01 <- lai(ndvi)
   plot(lai01)

spatialEco documentation built on Nov. 18, 2023, 1:13 a.m.