Improving UAV-Based LAI Estimation for Forests Over Complex Terrain by Reducing Topographic Effects on Multispectral Reflectance

Leaf area index (LAI) is a key parameter for characterizing the dynamics of terrestrial ecosystems and is also one of the important structural parameters that can be retrieved from remote sensing (RS) data. LAI over mountainous areas, however, is still difficult to retrieve reliably due to the topographical variation that introduces significant uncertainties into observed reflectance. In this article, we proposed a new scheme to estimate topographic influence on ratio-based vegetation indices (VIs) from diffuse radiation, which is not yet adequately considered in existing topographic correction schemes. In our scheme, unmanned aerial vehicle (UAV) light detecting and ranging (LiDAR) data were first used to model the sky view factor (SVF) and terrain view factor of target pixels in a slope coordinate system. Based on these view factors, the total incident solar radiation on slope (ISRS) was corrected, specifically for the diffuse sky irradiance and adjacent terrain-reflected irradiance over complex terrains. we then recalculated the multispectral reflectance of UAV images and evaluated the topographic effects on the normalized difference vegetation index (NDVI) because the magnitudes of correction on red and near-infrared (NIR) reflectances are quite different. Finally, large-scale LAI distribution was retrieved by empirical models developed from the relationships between terrain-corrected NDVI and field-measured LAI. Our results show that the proposed topographic correction scheme can significantly improve the LAI retrievals over a growing season. Given that forests are widely distributed in complex terrains around the globe, this study would have significance in improving the mapping of global LAI that is essential for terrestrial carbon cycle studies.