Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest

We investigated the capabilities of a canopy height model (CHM) derived from aerial photographs using the Structure from Motion (SfM) approach to estimate aboveground biomass (AGB) in a tropical forest. Aerial photographs and airborne Light Detection and Ranging (LiDAR) data were simultaneously acquired under leaf-on canopy conditions. A 3D point cloud was generated from aerial photographs using the SfM approach and converted to a digital surface model (DSM_P). We also created a DSM from airborne LiDAR data (DSM_L). From each of DSM_P and DSM_L, we constructed digital terrain models (DTM), which are DTM_P and DTM_L, respectively. We created four CHMs, which were calculated from (1) DSM_P and DTM_P (CHM_PP); (2) DSM_P and DTM_L (CHM_PL); (3) DSM_L and DTM_P (CHM_LP); and (4) DSM_L and DTM_L (CHM_LL). Then, we estimated AGB using these CHMs. The model using CHM_LL yielded the highest accuracy in four CHMs (R² = 0.94) and was comparable to the model using CHM_PL (R² = 0.93). The model using CHM_PP yielded the lowest accuracy (R² = 0.79). In conclusion, AGB can be estimated from CHM derived from aerial photographs using the SfM approach in the tropics. However, to accurately estimate AGB, we need a more accurate DTM than the DTM derived from aerial photographs using the SfM approach.