The environmentally sensitive area (ESA) methodology (originally proposed in the framework of MEDALUS–Mediterranean Desertification and Land Use—a series of international cooperation research projects funded by the European Union) is used worldwide to identify 'sensitive areas' that are potentially threatened by land degradation and desertification (LDD). The distinctive outcome of this approach is a multidimensional index (the ESA index) composed of partial indicators of climate, soil, vegetation, and management quality that are derived from the elaboration of 15 elementary variables. In this study, we propose (a) a major update of the ESA methodology, as presented in the MEDALUS project, for global LDD assessment, (b) a global map of ESAs to LDD, and (c) a global environmentally critical factors map. The results of the updated ESA framework confirm the efficiency and applicability of the ESA methodology in different worldwide areas, allowing for the harmonization of regional/country level studies and applications, and the more efficient use of global level datasets. In this study, we provide examples for analysis of LDD patterns and processes at a global level, as well as for identification of the main risk factors over time and space. Global‐ESA and global‐environmentally critical factors maps also support regional‐scale knowledge on LDD processes and sustainable land management practices for LDD mitigation. High‐resolution illustrative maps and other information are available on a dedicated website (http://web.unibas.it/global-esa/).

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.

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