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Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

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Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

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dc.contributor.author Hermosilla Gómez, Txomin es_ES
dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.contributor.author Kazakova, Alexandra N.
dc.contributor.author Coops, Nicholas
dc.contributor.author Moskal, L. Monika
dc.date.accessioned 2015-11-26T16:11:58Z
dc.date.available 2015-11-26T16:11:58Z
dc.date.issued 2014
dc.identifier.issn 1049-8001
dc.identifier.uri http://hdl.handle.net/10251/58197
dc.description.abstract Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies. es_ES
dc.description.sponsorship This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation. en_EN
dc.language Inglés es_ES
dc.publisher CSIRO Publishing es_ES
dc.relation.ispartof International Journal of Wildland Fire es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1071/WF13086
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CGL2010-19591/ES/DESARROLLO DE METODOLOGIAS INTEGRADAS PARA LA ACTUALIZACION DE BASES DE DATOS DE OCUPACION DEL SUELO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//BEST%2F2012%2F235/ es_ES
dc.rights.accessRights Abierto 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 Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1071/WF13086 es_ES
dc.description.upvformatpinicio 224 es_ES
dc.description.upvformatpfin 233 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 23 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 246544 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Erasmus+ es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Generalitat Valenciana es_ES
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