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Remote Sens. 2016, 8(9), 780; doi:10.3390/rs8090780

Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification

1
European Commission—Joint Research Centre, Directorate for Sustainable Resources, Via Fermi 2749, 21027 Ispra, VA, Italy
2
BC3—Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the University of the Basque Country, 48940 Leioa, Spain
3
Andalusian Institute for Earth System Research, University of Granada, E-18010 Granada, Spain
4
School of Engineering and Applied Science, Aston University, B4 7ET Birmingham, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy, Xiaofeng Li and Prasad S. Thenkabail
Received: 27 May 2016 / Revised: 7 September 2016 / Accepted: 12 September 2016 / Published: 21 September 2016
View Full-Text   |   Download PDF [6838 KB, uploaded 22 September 2016]   |  

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. View Full-Text
Keywords: habitat functional types; protected areas; free and open source software; ecological modelling; remote sensing; image segmentation; multivariate statistics habitat functional types; protected areas; free and open source software; ecological modelling; remote sensing; image segmentation; multivariate statistics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Martínez-López, J.; Bertzky, B.; Bonet-García, F.J.; Bastin, L.; Dubois, G. Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification. Remote Sens. 2016, 8, 780.

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