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

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.

Publication DOI: https://doi.org/10.3390/rs8090780
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
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College of Engineering & Physical Sciences > Sustainable environment research group
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: © 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/).
Uncontrolled Keywords: ecological modelling,free and open source software,habitat functional types,image segmentation,multivariate statistics,protected areas,remote sensing,Earth and Planetary Sciences(all)
Publication ISSN: 2072-4292
Last Modified: 25 Mar 2024 08:21
Date Deposited: 15 Jun 2017 08:05
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-09-21
Accepted Date: 2016-09-12
Submitted Date: 2016-05-27
Authors: Martínez-López, Javier
Bertzky, Bastian
Bonet-García, Francisco Javier
Bastin, Lucy (ORCID Profile 0000-0003-1321-0800)
Dubois, Grégoire

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