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).
Highlights
IntroductionEarth Observation (EO) data and techniques are most promising for monitoring and quantifying forest changes at multiple scales and high frequencies [1,2,3,4,5]
The present study discusses the application of the Cross Correlation Analysis (CCA) change detection technique, which can detect changes at different spatial scales using a Land Cover/Use (LC/LU) map and a sole recent image for forest monitoring
This study argues that the CCA technique can be attractive for fine scale change detection, since it can reduce change detection costs when: (a) the acquisition of several Very High Resolution (VHR) images at time T2 is too expensive; and (b) no archival VHR data are available at T1 for direct image comparison between the T1 image and a new tasked T2 image
Summary
Earth Observation (EO) data and techniques are most promising for monitoring and quantifying forest changes at multiple scales and high frequencies [1,2,3,4,5]. These techniques can provide new products and services for a wide user community including ecologists and decision makers such as those involved in the commitments of Natura 2000 site conservation [6,7,8,9].
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