An application of the cross-correlation analysis to detect changes in semi-natural grasslands to artificial structures using very high and high resolution satellite data

The paper focuses on the application of the Cross-Correlation Analysis (CCA) technique for quantifying changes of semi-natural grasslands to artificial structures at different spatial resolutions (grain) on a Mediterranean Natura 2000 site as a further test case of the results reported in [1]. In that work the CCA was applied to detect specific changes associated with agricultural intensification and fires. A semi-natural grasslands layer extracted from an existing Land Cover/Land Use map (1:5000, time T
₁
) was considered as input to the CCA jointly with a Very High Resolution (VHR) WorldView-2 satellite image (2 meters spatial resolution, time T
₂
) and with a High Resolution Landsat 8 OLS satellite image (30 meters spatial resolution, time T
₂
), with T
₂
> T
₁
, respectively, for the fine and the coarse scale analysis. The results were compared to those obtained by applying a traditional Post Classification Comparison technique to the same reference at time T1 map and an updated at time T2 map obtained by a knowledge driven classification of four multi-seasonal Worldview-2 input images, according to [1]. Also in this case the CCA technique results encouraging offering the possibility to reduce the costs of change detection when the acquisition of multi-seasonal VHR images at time T2 is too expensive or when no archive VHR image is available in the past for comparison between at time T1 and T2 images. The areas of change detected at VHR and HR were quite similar for the specific transition analyzed with larger error values in HR change images.