Electronic versions


  • 2019 - A simple approach to forest structure classification

    Accepted author manuscript, 1 MB, PDF-document

    Embargo ends: 3/11/19

    Licence: CC BY-NC-ND Show licence



  • Syed Adnan
  • Matti Maltamo
  • David A. Coomes
  • Antonio García-Abril
  • Yadvinder Malhi
  • José Antonio Manzanera
  • Nathalie Butt
  • Mike Morecroft
  • Rubén Valbuena
    Cambridge University
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.


  • Structural heterogeneity, LiDAR, Nearest neighbour imputation, Classification and regression trees, Forest structural types
Original languageEnglish
Pages (from-to)111 - 121
JournalForest Ecology and Management
Early online date3 Nov 2018
Publication statusPublished - 15 Feb 2019
Externally publishedYes
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