Remote Sens. 2016, 8(9), 780; doi:10.3390/rs8090780
Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification
Javier Martínez-López 1,2,*
, Bastian Bertzky 1, Francisco Javier Bonet-García 3, Lucy Bastin 1,4 and Grégoire Dubois 1
, Bastian Bertzky 1, Francisco Javier Bonet-García 3, Lucy Bastin 1,4 and Grégoire Dubois 11
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
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-TextKeywords:
habitat functional types; protected areas; free and open source software; ecological modelling; remote sensing; image segmentation; multivariate statistics
▼
Figures
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).
Scifeed alert for new publications
Never miss any articles matching your research from any publisher- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Share & Cite This Article
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.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Related Articles
Article Metrics
Comments
[Return to top]
Remote Sens.
EISSN 2072-4292
Published by MDPI AG, Basel, Switzerland
RSS
E-Mail Table of Contents Alert