Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands

Abstract

In this paper, we present a two-stage approach for characterizing the structure of Pinus sylvestris L. stands in forests of central Spain. The first stage was to delimit forest stands using eCognition and a digital canopy height model (DCHM) derived from lidar data. The polygons were then clustered ( k-means algorithm) into forest structure types based on the DCHM data within forest stands. Hypsographs of each polygon and field data validated the separability of structure types. In the study area, 112 polygons of Pinus sylvestris were segmented and classified into five forest structure types, ranging from high dense forest canopy (850 trees ha −1 and Loreýs height of 17.4 m) to scarce tree coverage (60 tree ha −1 and Loreýs height of 9.7 m). Our results indicate that the best variables for the definition and characterization of forest structure in these forests are the median and standard deviation (S.D.), both derived from lidar data. In these forest types, lidar median height and standard deviation (S.D.) varied from 15.8 m (S.D. of 5.6 m) to 2.6 m (S.D. of 4.5 m). The present approach could have an operational application in the inventory procedure and forest management plans.