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

FOREST ECOLOGY AND MANAGEMENT

卷:433

Article

Adnan, Syed^1,2,3  Maltamo, Matti¹  Coomes, David A.²  Garcia-Abril, Antonio⁴  Malhi, Yadvinder⁵  Antonio Manzanera, Jose⁴  Butt, Nathalie^5,6  Morecroft, Mike⁷  Valbuena, Ruben² 

[1] Univ Eastern Finland, Fac Forest Sci, POB 111, FI-80101 Joensuu, Finland

[2] Univ Cambridge, Dept Plant Sci Forest Ecol & Conservat, Downing St, Cambridge CB2 3EA, England

[3] Natl Univ Sci & Technol, Inst Geog Informat Syst, Islamabad 44000, Pakistan

[4] Univ Politecn Madrid, Coll Forestry & Nat Environm, Res Grp SILVANET, Ciudad Univ, E-28040 Madrid, Spain

[5] Univ Oxford, Sch Geog & Environm, Environm Change Inst, Oxford OX1 3QY, England

[6] Univ Queensland, Sch Biol Sci, St Lucia, Qld 4072, Australia

[7] Nat England, Cromwell House,15 Andover Rd, Winchester SO23 7BT, Hants, England

关键词: Structural heterogeneity;    LiDAR;    Nearest neighbour imputation;    Classification and regression trees;    Forest structural types;   

DOI  :  10.1016/j.foreco.2018.10.057

来源: Elsevier

【 摘 要 】

Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We 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 (Lev), 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.

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