Mapping of Forest Height in Northwest Hunan, China Using Multi-Source Satellite Data

Accurate mapping forest height at fine spatial resolution is essential for evaluating terrestrial ecosystem service. Yet, current assessments of forest height rely primarily on statistical or coarse scale model estimates, thus lack of spatial details for decision making at local scales. Recent advances in remote sensing technology provide great opportunities to fill this gap. Satellite data from radar and multispectral instruments are promising in providing spatial continuous observations. Here, we present a work that combined field measurements and satellite imagery to generate a wall-to-wall forest height map at a 30-m spatial resolution. Field plot data collected from October 2017 to April 2018 were used for model calibration and validation. A series of characteristic metrics were tested, including Landsat-8 multispectral reflectance and vegetation indices, Sentinel-1 C-band, and PALSAR-2 L-band SAR backscattering coefficients and difference index, and SRTM topographic variables. Our results indicate that a few variables from SRTM, Landsat, and Sentinel-1 show stronger relationships with forest height. We evaluated three types of models, including multiple linear regression (MLR), support vector regression (SVR), random forest (RF). Results show that RF model perform best (R
²
=0.44, RSME=4.7 m) compared with the other two methods (MLR, R
²
=0.31, RSME=5.0 m; SVR, R
²
=0.37, RSME=4.5 m).