A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions
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CitationSyed Adnan. Matti Maltamo. David A. Coomes. Antonio García-Abril. Yadvinder Malhi. José Antonio Manzanera. Nathalie Butt. Mike Morecroft. Rubén Valbuen. (2019). A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions. FOREST ECOLOGY AND MANAGEMENT, 433, 111-121. 10.1016/j.foreco.2018.10.057.
Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We 50 developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter (𝑄𝑀𝐷), Gini coefficient (𝐺𝐶), basal area larger than mean (𝐵𝐴𝐿𝑀) and density of stems (𝑁) –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, 𝐺𝐶 and 𝐵𝐴𝐿𝑀 were the most important variables in the identification of FSTs. Lower, medium and high values of 𝐺𝐶 and 𝐵𝐴𝐿𝑀 characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using 𝑄𝑀𝐷 and 𝑁. Then we used similar structural predictors derived from ALS – maximum height (𝑀𝑎𝑥), L-coefficient of variation (𝐿𝑐𝑣), L-skewness (𝐿𝑠𝑘𝑒𝑤), and percentage of penetration (𝑐𝑜𝑣𝑒𝑟), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions.