DOI: 10.1016/j.jag.2016.10.007
Scopus记录号: 2-s2.0-85025578624
论文题名: Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?
作者: Gastón A ; , Ciudad C ; , Mateo-Sánchez M ; C ; , García-Viñas J ; I ; , López-Leiva C ; , Fernández-Landa A ; , Marchamalo M ; , Cuevas J ; , de la Fuente B ; , Fortin M ; -J ; , Saura S
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 55 起始页码: 1
结束页码: 8
语种: 英语
英文关键词: Brown bear
; Habitat selection
; High-resolution data
; LiDAR
; Multi-scale habitat modelling
; Remote sensing
Scopus关键词: Ursus arctos
英文摘要: Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25 ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25 ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m2) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC > 0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79939
Appears in Collections: 气候变化事实与影响
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作者单位: ETSI Montes, Forestal y del Medio Natural, Technical University of Madrid, Ciudad Universitaria s/n, Madrid, Spain; Agresta Cooperative Society, C/Duque Fernán Núñez 2, Madrid, Spain; ETSI Caminos, Canales y Puertos, Technical University of Madrid, C/Profesor Aranguren s/n, Madrid, Spain; Junta de Castilla y León, Servicio Territorial de Medio Ambiente, Plaza Reina Doña Juana 5, Segovia, Spain; Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, Ontario, Canada
Recommended Citation:
Gastón A,, Ciudad C,, Mateo-Sánchez M,et al. Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,55