Biophysical characterization of protected areas globally through optimized image segmentation and classification

Javier Martínez-López*, Bastian Bertzky, Francisco Javier Bonet-García, Lucy Bastin, Grégoire Dubois

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Protected areas (PAs) need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually not comparable across regions. There are currently over 200,000 PAs in the world, and assessing these systematically according to their ecological values remains a huge challenge. However, linking remote sensing with ecological modelling can help to overcome some limitations of conservation studies, such as the sampling bias of biodiversity inventories. The aim of this paper is to introduce eHabitat+, a habitat modelling service supporting the European Commission's Digital Observatory for Protected Areas, and specifically to discuss a component that systematically stratifies PAs into different habitat functional types based on remote sensing data. eHabitat+ uses an optimized procedure of automatic image segmentation based on several environmental variables to identify the main biophysical gradients in each PA. This allows a systematic production of key indicators on PAs that can be compared globally. Results from a few case studies are illustrated to show the benefits and limitations of this open-source tool.

Original languageEnglish
Article number780
Number of pages19
JournalRemote Sensing
Volume8
Issue number9
DOIs
Publication statusPublished - 21 Sep 2016

Bibliographical note

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

Keywords

  • ecological modelling
  • free and open source software
  • habitat functional types
  • image segmentation
  • multivariate statistics
  • protected areas
  • remote sensing

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