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

Pascual Castaño, Isabel Cristina and García Abril, Antonio and García-Montero, Luis Gonzaga and Martín Fernández, Susana and Cohen, Warren (2008). Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands.. "Forest Ecology and Management", v. 255 (n. 11); pp. 3677-3685. ISSN 0378-1127. https://doi.org/10.1016/j.foreco.2008.02.055..

Description

Title: Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands.
Author/s:
  • Pascual Castaño, Isabel Cristina
  • García Abril, Antonio
  • García-Montero, Luis Gonzaga
  • Martín Fernández, Susana
  • Cohen, Warren
Item Type: Article
Título de Revista/Publicación: Forest Ecology and Management
Date: June 2008
Volume: 255
Subjects:
Freetext Keywords: Lidar, Forest structure, Pinus sylvestris, Mean height, Forest management.
Faculty: E.T.S.I. Montes (UPM)
Department: Economía y Gestión Forestal [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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 DCHMdata 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 Lorey´ s height of 17.4 m) to scarce tree coverage (60 tree ha_1 and Lorey´ s 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.

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