Forest Attributes Estimation Using Aerial Laser Scanner and TM Data

<em>Aim of study</em>: The aim of this study was performance of four non-parametric algorithms including the <em>k</em>-NN, SVR, RF and ANN to estimate forest volume and basal area attributes using combination of Aerial Laser Scanner and Landsat-TM data.<em>Area of study</em>: Data in small part of a mixed managed forest in the Waldkirch region, Germany.<em>Material and methods</em>: The volume/ha and basal area/ha in the 411 circular plots were estimated based on DBH and height of trees using volume functions of study area. The low density ALS raw data as first and last pulses were prepared and automatically classified into vegetation and ground returns to generate two fine resolution digital terrain and surface models after noise removing. Plot-based height and density metrics were extracted from ALS data and used both separated and combined with orthorectified and processed TM bands. The algorithms implemented with different options including <em>k</em>-NN with different distance measures, SVR with the best regularized parameters for four kernel types, RF with regularized decision tree parameters and ANN with different types of networks. The algorithm performances were validated using computing absolute and percentage RMSe and bias on unused test samples.<em>Main results</em>: Results showed that among four methods, SVR using the RBF kernel could better estimate volume/ha with lower RMSe and bias (156.02 m³ ha^–1 and 0.48, respectively) compared to others. In basal area/ha, <em>k</em>-NN could generate results with similar RMSe (11.79 m³ ha^–1) but unbiased (0.03) compared to SVR with RMSe of 11.55 m³ ha^–1 but slightly biased (–1.04).<em>Research highlights</em>: Results exposed that combining Lidar with TM data could improve estimations compared to using only Lidar or TM data.<strong>Key words</strong>: forest attributes estimation; ALS; TM; non-parametric algorithms.