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Remote Sens. 2016, 8(10), 850; doi:10.3390/rs8100850

Towards Operational Detection of Forest Ecosystem Changes in Protected Areas

1
Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), Via Amendola 173, 70125 Bari, Italy
2
Institute of Intelligent Systems for Automation (ISSIA), National Research Council (CNR), Via Amendola 122/D-O, 70126 Bari, Italy
3
Ashoka Trust for Research in Ecology and the Environment (ATREE), 560056 Bangalore, India
4
School of Biological, Earth and Environmental Sciences, The University of New South Wales, 2052 Sidney, Australia
5
Institute of Atmospheric Pollution Research (IIA), National Research Council (CNR), Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 14 July 2016 / Revised: 21 September 2016 / Accepted: 11 October 2016 / Published: 16 October 2016
View Full-Text   |   Download PDF [4192 KB, uploaded 21 October 2016]   |  

Abstract

This paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA investigation were elaborated from the forest layer extracted from an existing Land Cover/Land Use (LC/LU) map (time T1) and a more recent (T2, with T2 > T1) single date image. The latter consist of a High Resolution (HR) Landsat 8 OLI image and a Very High Resolution (VHR) Worldview-2 image, which were analysed separately. For the Italian site, the forest layer (1:5000) was first compared to the HR Landsat 8 OLI image and then to the VHR Worldview-2 image. For the Indian site, the forest layer (1:50,000) was compared to the Landsat 8 OLI image then the changes were interpreted using Worldview-2. The changes detected through CCA, at HR only, were compared against those detected by applying a traditional NDVI image differencing technique of two Landsat scenes at T1 and T2. The accuracy assessment, concerning the change maps of the multi-spatial resolution outputs, was based on stratified random sampling. The CCA technique allowed an increase in the value of the overall accuracy: from 52% to 68% for the Italian site and from 63% to 82% for the Indian site. In addition, a significant reduction of the error affecting the stratified changed area estimation for both sites was obtained. For the Italian site, the error reduction became significant at VHR (±2 ha) in respect to HR (±32 ha) even though both techniques had comparable overall accuracy (82%) and stratified changed area estimation. The findings obtained support the conclusions that CCA technique can be a useful tool to detect and quantify changes in forest areas due to both legal and illegal interventions, including relatively inaccessible sites (e.g., tropical forest) with costs remaining rather low. The data obtained through CCA intervention could not only support the commitments undertaken by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also satisfy UN Sustainable Development Goals (SDG). View Full-Text
Keywords: earth observation data; forest change detection; forest ecosystems; very high spatial resolution; high spatial resolution; Worldview-2; Landsat 8 OLI earth observation data; forest change detection; forest ecosystems; very high spatial resolution; high spatial resolution; Worldview-2; Landsat 8 OLI
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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

Tarantino, C.; Lovergine, F.; Niphadkar, M.; Lucas, R.; Nativi, S.; Blonda, P. Towards Operational Detection of Forest Ecosystem Changes in Protected Areas. Remote Sens. 2016, 8, 850.

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