RT Journal Article
T1 Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system
A1 Vaz, Ana Sofia
A1 Marcos, Bruno
A1 Gonçalves, João
A1 Monteiro, António
A1 Alves, Paulo
A1 Civantos Calzada, Emilio
A1 Lucas, Richard
A1 Mairota, Paola
A1 Garcia Robles, Javier
A1 Alonso, Joaquim
A1 Blonda, Palma
A1 Lomba, Angela
A1 Honrado, João Pradinho
AB 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.
PB Elsevier
SN 1569-8432
YR 2015
FD 2015
LK https://hdl.handle.net/20.500.14352/96235
UL https://hdl.handle.net/20.500.14352/96235
LA eng
NO Vaz, Ana Sofia, et al. «Can We Predict Habitat Quality from Space? A Multi-Indicator Assessment Based on an Automated Knowledge-Driven System». International Journal of Applied Earth Observation and Geoinformation, vol. 37, mayo de 2015, pp. 106-13. https://doi.org/10.1016/j.jag.2014.10.014.
NO This research was supported by the European Community's Seventh Framework Programme (FP7/SPA.2010.1.1-04), under grant agreement 263435 for the project “Biodiversity Multi-SOurce Monitoring System: From Space To Species (BIO_SOS)”. A.S. Vaz (Grant: PD/BD/52600/2014), J. Gonçalves (SFRH/BD/90112/2012) and A. Lomba (SFRH/BPD/80747/2011) are supported by the Portuguese Foundation for Science and Technology (FCT). A. Monteiro and E. Civantos are supported by the project “Biodiversity, Ecology and Global Change”, co-financed by North Portugal Regional Operational Programme 2007/2013 (ON.2 – O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund (ERDF).
NO Portuguese Foundation for Science and Technology
NO European Commission
DS Docta Complutense
RD 19 abr 2025