Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves

rization of vegetation, airborne laser scanning (ALS) remote nsing allows for evaluating properties related to forest structure broad forest areas (Lefsky et al., 2005; Maltamo et al., 2005). ese properties can be exploited to study forest successional ages (Falkowski et al., 2009; Valbuena et al., 2013a), the risk of ild fire propagation (Andersen et al., 2005; Hall et al., 2005) or ind-throw damage (Suárez et al., 2008), or characteristics related habitat quality (Lefsky et al., 2002; Martinuzzi et al., 2009). The purpose of this study was to compare a number of state-of-the-art methods in airborne laser scan-ning (ALS) remote sensing with regards to their capacity to describe tree size inequality and other indi-cators related to forest structure. The indicators chosen were based on the analysis of the Lorenz curve: Gini coefficient (GC), Lorenz asymmetry (LA), the proportions of basal area (BALM) and stem density (NSLM) stocked above the mean quadratic diameter. Each method belonged to one of these estimation strategies: (A) estimating indicators directly; (B) estimating the whole Lorenz curve; or (C) estimating a complete tree list. Across these strategies, the most popular statistical methods for area-based approach (ABA) were used: regression, random forest (RF), and nearest neighbour imputation. The latter included distance metrics based on either RF (NN–RF) or most similar neighbour (MSN). In the case of tree list esti-mation, methods based on individual tree detection (ITD) and semi-ITD, both combined best subset regression, which CV(RMSE) for most indicators 10.49%, 10.93% and 14.07%, re number of additional advanta results, ITD approaches may b inequality in for-ested areas. SN impu-tation, were also studied. The most accurate method was direct estimation by d the lowest cross-validated coefficients of variation of their root mean squared error .80%), LA (8.76%), BALM (8.80%) and NSLM (14.60%). Similar figures [CV(RMSE) 16.09%, ely] were obtained by MSN imputation of tree lists by ABA, a method that also showed a h as better distributing the residual variance along the predictive range. In light of our y inferior to ABA with regards to describing the structural properties related to tree size