A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions

摘要: Reliable assessment of forest structural types (FSTs) aids sustainable management. We developed a methodology for the identification FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied clusters were determined in coniferous deciduous four variables obtained inventory data – quadratic mean diameter (QMD), Gini coefficient (GC), basal area larger than (BALM) density stems (N) –. Then, classification regression tree (CART) used extract empirical threshold values discriminating those clusters. Based on trees, GC BALM most important FSTs. Lower, medium high characterize single storey FSTs, multi-layered exponentially decreasing size distributions (reversed J), respectively. Within each these main FST groups, we also identified young/mature sparse/dense subtypes QMD N. Then similar predictors derived ALS maximum height (Max), L-coefficient variation (Lcv), L-skewness (Lskew), percentage penetration (cover), nearest neighbour method predict greater overall accuracy (0.87) as compared (0.72). Our proves usefulness heterogeneity across simple two-tier approach paves way toward transnational assessments structure bioregions.