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

Ana Sofia Vaz, Bruno Marcos, João Goncalves, António Monteiro, Paulo Alves, Emilio Civantos, Richard Lucas, Paola Mairota, Javier Garcia-Robles, Joaquim Alonso, Palma Blonda, Angela Lomba, João Pradinho Honrado

Research output: Contribution to journalArticlepeer-review

21 Citations (SciVal)

Abstract

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.
Original languageEnglish
Pages (from-to)106-113
Number of pages8
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume37
Early online date10 Nov 2014
DOIs
Publication statusPublished - 01 May 2015
Externally publishedYes

Keywords

  • land cover
  • multi-model inference
  • Natura 2000
  • very high resolution image
  • woodland quality monitoring

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