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Estimation of forest AGB from ICESat-2 and Landsat

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) launched on September 15th, 2018, offers a phenomenal opportunity to obtain large-scale coverage about vegetation, through data collected along transects on the earth’s surface. This study served to examine the utility of ICESat-2 for estimating AGB using a combinatory approach with Landsat. Specifically, the objectives of this study were to: (1) Estimate AGB using ICESat-2 photon-counting lidar (PCL) data over vegetation in south-east Texas, (2) Upscale estimates to generate a wall-to-wall map of AGB using spectral metrics derived from Landsat imagery and land cover and canopy cover data from the National Land Cover Database (NLCD), (3) Validate map estimates using airborne-lidar derived AGB. Predictions equations were previously developed by relating simulated PCL metrics for 100 m segments along planned ICESat-2 tracks to reference airborne lidar-estimated AGB over Sam Houston National Forest (SHNF) in south-east Texas using linear regression analysis and then relating predicted AGB estimates to spectral metrics derived from Landsat TM imagery and land cover and canopy cover data from NLCD, with Random Forests (RF).These prediction equations are applied to actual ICESat-2 data along a 13-mile track over similar vegetation conditions in Texas to estimate and map AGB, and then validate estimates for the extent of the SHNF site. Findings from this study demonstrate how ICESat-2 can be used with Landsat data, to estimate and characterize the spatial distribution of AGB.

Lana Narine
Texas A&M University
United States

Sorin Popescu
Texas A&M University
United States

Lonesome Malambo
Texas A&M University
United States

 


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