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

Pascual Castaño, Isabel Cristina; García Abril, Antonio; García-Montero, Luis Gonzaga; Martín Fernández, Susana y 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..

Descripción

Título: Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands.
Autor/es:
  • Pascual Castaño, Isabel Cristina
  • García Abril, Antonio
  • García-Montero, Luis Gonzaga
  • Martín Fernández, Susana
  • Cohen, Warren
Tipo de Documento: Artículo
Título de Revista/Publicación: Forest Ecology and Management
Fecha: Junio 2008
Volumen: 255
Materias:
Palabras Clave Informales: Lidar, Forest structure, Pinus sylvestris, Mean height, Forest management.
Escuela: E.T.S.I. Montes (UPM) [antigua denominación]
Departamento: Economía y Gestión Forestal [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (549kB) | Vista Previa

Resumen

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.

Más información

ID de Registro: 2126
Identificador DC: http://oa.upm.es/2126/
Identificador OAI: oai:oa.upm.es:2126
Identificador DOI: 10.1016/j.foreco.2008.02.055.
URL Oficial: http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235042%232008%23997449988%23690210%23FLA%23&_cdi=5042&_pubType=J&_auth=y&_acct=C000047350&_version=1&_urlVersion=0&_userid=885385&md5=323f823dd0d3a58d737386a9c525c7ea
Depositado por: Memoria Investigacion
Depositado el: 21 Abr 2010 11:55
Ultima Modificación: 20 Abr 2016 11:53
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM