Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system
| Autor: | Emilio Civantos, João Gonçalves, Ana Sofia Vaz, Paola Mairota, Javier Garcia-Robles, Joaquim Alonso, João P. Honrado, Richard Lucas, Bruno Marcos, Angela Lomba, Paulo C. Alves, António T. Monteiro, Palma Blonda |
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| Rok vydání: | 2015 |
| Předmět: |
Land cover
media_common.quotation_subject Multi-model inference Very high resolution image Woodland Management Monitoring Policy and Law Natura 2000 Quality (business) Computers in Earth Sciences Categorical variable Earth-Surface Processes Remote sensing media_common Global and Planetary Change business.industry Environmental resource management Vegetation 15. Life on land Data set Geography Habitat 13. Climate action Woodland quality monitoring business |
| Zdroj: | ITC journal 37 (2015): 106–113. doi:10.1016/j.jag.2014.10.014 info:cnr-pdr/source/autori:Vaz, Ana Sofia; Marcos, Bruno; Goncalves, Joao; Monteiro, Antonio; Alves, Paulo; Civantos, Emilio; Lucas, Richard; Mairota, Paola; Garcia-Robles, Javier; Alonso, Joaquim; Blonda, Palma; Lomba, Angela; Honrado, Joao Pradinho/titolo:Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system/doi:10.1016%2Fj.jag.2014.10.014/rivista:ITC journal/anno:2015/pagina_da:106/pagina_a:113/intervallo_pagine:106–113/volume:37 International Journal of Applied Earth Observation and Geoinformation |
| ISSN: | 1569-8432 |
| DOI: | 10.1016/j.jag.2014.10.014 |
| Popis: | 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, knowledge-driven 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 multi-model 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. |
| Databáze: | OpenAIRE |
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