- -

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

Show simple item record

Files in this item

dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.contributor.author Recio Recio, Jorge Abel es_ES
dc.contributor.author Crespo-Peremarch, Pablo es_ES
dc.contributor.author Sapena, Marta es_ES
dc.date.accessioned 2018-10-07T04:33:06Z
dc.date.available 2018-10-07T04:33:06Z
dc.date.issued 2018 es_ES
dc.identifier.issn 1010-6049 es_ES
dc.identifier.uri http://hdl.handle.net/10251/109833
dc.description.abstract [EN] Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. es_ES
dc.description.sponsorship This work was supported by the Spanish Ministerio de Economia y Competitividad and FEDER under [grant number CGL2013-46387-C2-1-R]; Fondo de Garantia Juvenil under [contract number PEJ-2014-A-45358]. en_EN
dc.language Inglés es_ES
dc.publisher Taylor & Francis es_ES
dc.relation MINECO/CGL2013-46387-C2-1-R es_ES
dc.relation.ispartof Geocarto International es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Fuel strata es_ES
dc.subject Object-based classification es_ES
dc.subject LiDAR es_ES
dc.subject WorldView-2 es_ES
dc.subject Sentinel-2 es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/10106049.2016.1265595 es_ES
dc.rights.accessRights Abierto es_ES
dc.date.embargoEndDate 2019-08-01 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Ruiz Fernández, LÁ.; Recio Recio, JA.; Crespo-Peremarch, P.; Sapena, M. (2018). An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery. Geocarto International. 33(5):443-457. doi:10.1080/10106049.2016.1265595 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1080/10106049.2016.1265595 es_ES
dc.description.upvformatpinicio 443 es_ES
dc.description.upvformatpfin 457 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 33 es_ES
dc.description.issue 5 es_ES
dc.relation.pasarela S\324972 es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES


This item appears in the following Collection(s)

Show simple item record