Estimating Forest Heights and Wood Volume using a Deep Learning Approach from Gedi Waveform Data

Abstract

The Global Ecosystem Dynamics Investigation (GEDI) instrument, as all FW systems, relies on very sophisticated pre-processing steps to generate a priori metrics in order to accurately estimate forest characteristics, such as forest heights and wood volume. The ever-expanding volume of acquired GEDI data, which to September 2020 comprised more than 25 billion shots, and requiring more than 90 TB of storage space, raises new challenges in terms of adapted preprocessing methods for the suitable exploitation of such a huge and complex amount of LiDAR data. Therefore, to avoid metric computation, we leveraged deep learning techniques in order to estimate canopy dominant heights (Hdom) and wood volume (V) of Eucalyptus plantations over five different regions in Brazil. Performance comparisons were conducted between a convolutional neural network based model that uses GEDI waveform data and a previously used, metric based, Random Forest regressor (RF). Cross-validated results showed that the CNN based model compared well against the RF counterpart for both Hdom and V. Indeed, the RMSE on the estimation of Hdom from the CNN based model was 1.61 m with a coefficient of determination R2 of 0.90, while the RF model produced an accuracy on Hdom estimates of 1.45 m(R2=0.92). For V, CNN based estimates was 27.35 m3.ha-1(R2 of 0.88), while for RF, the RMSE was 27.60 m3.ha-1 (R2=0.88).