Detection of changes in semi-natural grasslands by cross correlation analysis with WorldView-2 images and new Landsat 8 data

Focusing on a Mediterranean Natura 2000 site in Italy, the effectiveness of the cross correlation analysis (CCA) technique for quantifying change in the area of semi-natural grasslands at different spatial resolutions (grain) was evaluated. In a fine scale analysis (2 m), inputs to the CCA were a) a semi-natural grasslands layer extracted from an existing validated land cover/land use (LC/LU) map (1:5000, time T(1)) and b) a more recent single date very high resolution (VHR) WorldView-2 image (time T(2)), with T(2) > T(1). The changes identified through the CCA were compared against those detected by applying a traditional post-classification comparison (PCC) technique to the same reference T(1) map and an updated T(2) map obtained by a knowledge driven classification of four multi-seasonal Worldview-2 input images. Specific changes observed were those associated with agricultural intensification and fires. The study concluded that prior knowledge (spectral class signatures, awareness of local agricultural practices and pressures) was needed for the selection of the most appropriate image (in terms of seasonality) to be acquired at T(2). CCA was also applied to the comparison of the existing T(1) map with recent high resolution (HR) Landsat 8 OLS images. The areas of change detected at VHR and HR were broadly similar with larger error values in HR change images.