ABOVEGROUND BIOMASS ESTIMATION USING RECONSTRUCTED FEATURE OF AIRBORNE DISCRETE-RETURN LIDAR BY AUTO-ENCODER NEURAL NETWORK
Keywords: Aboveground Biomass (AGB), Estimation, Discrete-Return LiDAR, Regression, Auto-Encoder Neural Network
Abstract. Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision AGB estimation. Most of the researches estimate AGB by the feature metrics extracted from the canopy height distribution of the point cloud which calculated based on precise digital terrain model (DTM). However, if forest canopy density is high, the probability of the LiDAR signal penetrating the canopy is lower, resulting in ground points is not enough to establish DTM. Then the distribution of forest canopy height is imprecise and some critical feature metrics which have a strong correlation with biomass such as percentiles, maximums, means and standard deviations of canopy point cloud can hardly be extracted correctly. In order to address this issue, we propose a strategy of first reconstructing LiDAR feature metrics through Auto-Encoder neural network and then using the reconstructed feature metrics to estimate AGB. To assess the prediction ability of the reconstructed feature metrics, both original and reconstructed feature metrics were regressed against field-observed AGB using the multiple stepwise regression (MS) and the partial least squares regression (PLS) respectively. The results showed that the estimation model using reconstructed feature metrics improved R2 by 5.44 %, 18.09 %, decreased RMSE value by 10.06 %, 22.13 % and reduced RMSEcv by 10.00 %, 21.70 % for AGB, respectively. Therefore, reconstructing LiDAR point feature metrics has potential for addressing AGB estimation challenge in dense canopy area.