Lidar + MODIS to upscale predictions of forest biomass

Li, L., Q. Guo, S. Tao, M. Kelly, and G. Xu. 2015. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass. ISPRS Journal of Photogrammetry and Remote Sensing. 102: 198–208

Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: 1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, 2) Artificial Neural Networks, among four tested models, achieved the best performance with R²= 0.75 and root-mean-square error (RMSE) around 165 Mg/ha; 3) for MODIS-derived vegetation indices at varied spatial resolution (250 – 1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR can explain 53 – 74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R² (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB. Journal link. Pdf download.