Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system

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. All rights reserved. Please cite this article in press as: Vaz, A.S., et al., Can we predict habit automated knowledge-driven system. Int. J. Appl. Earth Observ. Geoin ∗ Corresponding author at: Centro de Investigaç ão em Biodiversidade e Recursos enéticos da Universidade do Porto (CIBIO)/InBIO – Rede de Investigaç ão em Bioiversidade e Biologia Evolutiva, Rua Padre Armando Quintas, PT-4485-661 Vairão, ortugal. Tel.: +351 252660401. E-mail address: [email protected] (A.S. Vaz). ttp://dx.doi.org/10.1016/j.jag.2014.10.014 303-2434/© 2014 Elsevier B.V. All rights reserved. Introduction Assessing habitat quality, namely habitat resilience and ability to support biodiversity and ecosystem services (Buffa and Villani, 2012), is crucial for the conservation of European native woodlands (Brumelis et al., 2011; Jamoneau et al., 2011), which are expected to at quality from space? A multi-indicator assessment based on an f. (2014), http://dx.doi.org/10.1016/j.jag.2014.10.014 expand in the future (Navarro and Pereira, 2012). The assessment of woodland quality has focused on habitat composition (Buffa and Villani, 2012), structure (Liira and Sepp, 2009) or function (Thompson et al., 2013), depending on the observation extent. At ARTICLE IN PRESS G Model JAG-993; No. of Pages 8 2 A.S. Vaz et al. / International Journal of Applied Earth Observation and Geoinformation xxx (2014) xxx–xxx