TY - JOUR

T1 - A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions

AU - Adnan, Syed

AU - Maltamo, Matti

AU - Coomes, David A.

AU - García-Abril, Antonio

AU - Malhi, Yadvinder

AU - Manzanera, José Antonio

AU - Butt, Nathalie

AU - Morecroft, Mike

AU - Valbuena, Rubén

PY - 2019/2/15

Y1 - 2019/2/15

N2 - 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 (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.

AB - 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 (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.

KW - Structural heterogeneity

KW - LiDAR

KW - Nearest neighbour imputation

KW - Classification and regression trees

KW - Forest structural types

U2 - 10.1016/j.foreco.2018.10.057

DO - 10.1016/j.foreco.2018.10.057

M3 - Article

VL - 433

SP - 111

EP - 121

JO - Forest Ecology and Management

JF - Forest Ecology and Management

SN - 0378-1127

ER -