Harmonizing GEDI and LVIS Data for Accurate and Large-Scale Mapping of Foliage Height Diversity

Abstract

Remote sensing is key for large-scale forest mapping, yet the limited integration of LiDAR sensors restricts the spatial coverage of forest attribute estimation. Our study aimed to accurately map Foliage Height Diversity (FHD) in five key North American regions, vital for ongoing research in ecosystem dynamics in western North America. We used a combination of GEDI data and LVIS data, incorporating measures of forest complexity and relative forest heights (RH25, RH50, RH75, and RH98), to predict FHD with a random forest regression in the Kluane region, southwest Yukon—a northernmost site where GEDI data are not available. This method was designed to overcome spatial coverage limitations of different sensors, enabling the production of consistent, precise, multi-temporal FHD maps across all sites. We found strong agreement between predicted and observed FHD values estimated from Airborne Laser Scanning in the Yukon (R2 = 0.72; RMSE = 0.46). Additionally, we upscaled GEDI FHD predictions in all sites by integrating Landsat imagery, ALOS PALSAR, and topographical data, resulting in high accuracy (R2 = 0.85; RMSE = 0.26). Our findings demonstrate that by harmonizing full-wave form LiDAR sensors, we can significantly expand the coverage of LiDAR data, allowing for consistent broad-scale analyses of forest attributes.