Multi-resolution and temporal characterization of land-use classes in a Mediterranean wetland with land-cover fractions

Four different methods for analysing land-use and land-cover fractions at multiple scales, namely composite operator, t-test, Dutilleul’s modified t-test and ternary diagrams of physical models for process pathways, were applied to sets of multi-resolution images in order to evaluate the usefulness of coarse-resolution satellite data (e.g. the Moderate Resolution Imaging Spectroradiometer; MODIS) in obtaining similar results to those obtainable with moderate-resolution satellite data (e.g. Landsat). A spectral-mixture model based on three endmembers (soil, vegetation and water) was used to determine the land-cover fractions of the main land-use classes of a wetland in southeast Spain. The land-use map was produced by applying the unsupervised k-means classification method to the moderate-resolution image. Spatial and temporal changes in the mixture fractions at multiple resolutions and their corresponding land-cover fraction maps were assessed. Three different t-tests (paired-samples, independent-samples and Dutilleul’s modified t-tests) were used to evaluate the effects of pixel aggregation on land-cover fractions and land-use maps in terms of surface-area estimations. Ternary plots of land-use classes characterized by land-cover fractions were used to visualize environmental processes pathways describing temporal changes in the landscape. The results obtained with moderate- and coarse-resolution data were not significantly different from each other. Land-use and land-cover surface-area estimations were not significantly different between Landsat moderate-resolution (30 m) and Landsat resampled coarse-resolution (300 m) data. Spatial autocorrelation had an important effect when comparing Landsat moderate-resolution (30 m) with MODIS coarse-resolution (250 m) data. In order to minimize these effects Dutilleul’s modified t-test was applied for the comparison of Landsat with MODIS image data. However, this test did not reveal significant differences between both datasets, whereas with the ordinary t-test, significant differences were found, which suggest the existence of a bias by spatial autocorrelation that must be taken into account for up-scaling or down-scaling of remote-sensing data. The results suggest the possibility of using coarse-resolution images (MODIS) to characterize environmental changes with a similar accuracy to those of moderate-resolution images (Landsat), as long as potential spatial autocorrelation effects are taken into account. This finding indicates that a substantial reduction in the costs of conducting wetland management and monitoring tasks can be achieved by using free or low-cost coarse-resolution satellite images.