globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2014.10.014
Scopus记录号: 2-s2.0-84945299027
论文题名:
Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system
作者: Vaz A; S; , Marcos B; , Goncalves J; , Monteiro A; , Alves P; , Civantos E; , Lucas R; , Mairota P; , Garcia-Robles J; , Alonso J; , Blonda P; , Lomba A; , Honrado J; P
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 37
起始页码: 106
结束页码: 113
语种: 英语
英文关键词: Land cover ; Multi-model inference ; Natura 2000 ; Very high resolution image ; Woodland quality monitoring
Scopus关键词: environmental assessment ; environmental monitoring ; habitat quality ; image resolution ; knowledge based system ; land cover ; remote sensing ; woodland ; Portugal
英文摘要: There is an increasing need of effective monitoring systems for habitat quality assessment. Methods based on remote sensing (RS) features, such as vegetation indices, have been proposed as promising approaches, complementing methods based on categorical data to support decision making. Here, we evaluate the ability of Earth observation (EO) data, based on a new automated, knowledgedriven system, to predict several indicators for oak woodland habitat quality in a Portuguese Natura 2000 site. We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitats of a landscape undergoing agricultural abandonment. Forty-three predictors were calculated, and a multimodel inference framework was applied to evaluate the predictive strength of each data set for the several quality indicators. Three indicators were mainly explained by predictors related to landscape and neighbourhood structure. Overall, competing models based on the products of the automated knowledge-driven system had the best performance to explain quality indicators, compared to models based on manually classified land cover data. The system outputs in terms of both land cover classes and spectral/landscape indices were considered in the study, which highlights the advantages of combining EO data with RS techniques and improved modelling based on sound ecological hypotheses. Our findings strongly suggest that some features of habitat quality, such as structure and habitat composition, can be effectively monitored from EO data combined with in-field campaigns as part of an integrative monitoring framework for habitat status assessment. © 2014 Elsevier B.V..
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79609
Appears in Collections:气候变化事实与影响

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作者单位: Centro de Investigacao em Biodiversidade e Recursos Geneticos da Universidade do Porto (CIBIO), InBIO - Rede de Investigacão em Biodiversidade e Biologia Evolutiva, Rua Padre Armando Quintas, Vairão, Portugal; Faculdade de Ciências da Universidade do Porto, Departamento de Biologia, Edifício FC4, Rua do Campo Alegre, S/N, Porto, Portugal; Institute of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom; Department of Agro-Environmental and Territorial Sciences, University of Bari Aldo Moro, via Orabona 4, Bari, Italy; Altamira Information, C/Còrsega, 381-387, Barcelona, Spain; Escola Superior Agrária, Instituto Politécnico de Viana do Castelo (ESA-IPVC), Refóios do Lima, Ponte de Lima, Portugal; Consiglio Nazionale delle Ricerche (CNR), Istituto di Studi sui Sistemi Intelligenti per l'Automazione (ISSIA), Via Amendola 122/D, Bari, Italy

Recommended Citation:
Vaz A,S,, Marcos B,et al. Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,37
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