Digital Elevation Models (DEMs) including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available for nearly the entire earth’s surface. DEMs that are usually subject to errors need to be evaluated using reference elevation data of higher accuracy. This work was performed to assess the vertical accuracy of the ASTER GDEM version 2, (ASTER GDEM2), the Consultative Group on International Agriculture Research-Consortium for Spatial Information (CGIAR-CSI) SRTM version 4.1 (SRTM v4.1) and the systematic subsample GMTED2010, at their original spatial resolution, using Global Navigation Satellite Systems (GNSS) validation points. Two test sites, the Anaguid Saharan platform in southern Tunisia and the Tebessa basin in north eastern Algeria, were chosen for accuracy assessment of the above mentioned DEMs, based on geostatistical and statistical measurements. Within the geostatistical approach, empirical variograms of each DEM were compared with those of the GPS validation points. Statistical measures were computed from the elevation differences between the DEM pixel value and the corresponding GPS point. For each DEM, a Root Mean Square Error (RMSE) was determined for model validation. In addition, statistical tools such as frequency histograms and Q-Q plots were used to evaluate error distributions in each DEM. The results indicate that the vertical accuracy of SRTM model is much higher than ASTER GDEM2 and GMTED2010 for both sites. In Anaguid test site, the vertical accuracy of SRTM is estimated 3.6 m (in terms of RMSE) 5.3 m and 4.5 m for the ASTERGDEM2 and GMTED2010 DEMs, respectively. In Tebessa test site, the overall vertical accuracy shows a RMSE of 9.8 m, 8.3 m and 9.6 m for ASTER GDEM 2, SRTM and GMTED2010 DEM, respectively. This work is the first study to report the lower accuracy of ASTER GDEM2 compared to the GMTED2010 data.

This study aims to assess the performance of the Sentinel-2 and Landsat 8 sensors to map forest fires. We choose two fire events, the Haddad fire and the Sidi Ferdjani fire, in northwestern Tunisia in 2017. Several spectral indices were derived from each sensor and the performance of each spectral index was assessed. A validation exercise was undertaken for each fire to compare the spatial matching between the burned area retrieved from each spectral index and its homologue obtained from the Emergency Management Service (EMS). Our results indicate that ΔNBR and its relativized version RBR derived from both sensors exhibit the highest discrimination power (M-statistic values >2.5). The Sentinel sensor is slightly more efficient than the Landsat 8 in mapping burned scars, but both sensors produce acceptable results. We conclude that both sensors could be a good alternative to EFFIS data, particularly when there is a need to detect details inside the burned areas.