Remote sensing and geographic information system (GIS) methods were used for karst research in the coastal area of Northwest Morocco near the city of Safi in order to identify karst landscapes, to describe karst features and to detect geological structures relevant to karst development. The aim of this study was to investigate the use of different satellite data, such as Landsat, RapidEye and IKONOS imagery, as well as ASTER- and SRTM-derived digital elevation models (DEMs) for the analysis of karst features. Dolines were identified by visual interpretations based on high resolution satellite imagery and aerial photographs. Digital image processing of the satellite data, such as deriving vegetation and water index images, helped to identify regions with relatively higher surface water input, where karstification processes might be more intense than in surrounding areas. ArcGIS-integrated weighted overlay tools were used for this purpose as well by aggregating of morphometric, causal factors (lowest and flattest areas) influencing the susceptibility to higher surface water input. Lineament analysis based on the different satellite data contributed to the detection of near-surface fault and fracture zones with potential influence on dissolution processes in sub-terrain waterways.

The potential contribution of remote sensing and GIS techniques to earthquake hazard analysis was investigated in SW-Haiti in order to improve the systematic, standardized inventory of those areas that are more susceptible to earthquake ground motions or to earthquake related secondary effects such as landslides, liquefaction, soil amplifications, compaction or even tsunami-waves. Geophysical, topographical, geological data and satellite images were collected, processed, and integrated into a spatial database using Geoinformation Systems (GIS) and image processing techniques. The GIS integrated evaluation of satellite imageries, of digital topographic data and of various open-source geodata can contribute to the acquisition of those specific tectonic, geomorphologic/topographic settings influencing local site conditions in Haiti and, thus, to a first data base stock. Using the weighted overlay techniques in GIS susceptibility maps were produced indicating areas where causal factors influencing surface-near earthquake shock occur aggregated and interfering each other and, thus, rise the susceptibility to soil amplification. This approach was used as well to create landslide and flooding susceptibility maps.

The Koyna-Warna area in Maharashtra, Western India, is one of the world’s best examples of reservoir-induced seismicity. The occurrence of earthquakes in the vicinity of Koyna Dam (Shivaji Sagar Lake) started since 1962, soon after the initiation of water impoundment. To understand the tectonics and seismicity of the region, recent Landsat 8 images and Sentinel radar data were evaluated in GIS, as well as SRTM and ASTER Digital Elevation Model data (DEM) and the DEM-derived morphometric maps. Geophysical and climate data were also included in the GIS database. The analysis of satellite data contributed towards understanding the tectonic framework of the Koyna reservoir area by visual lineament analysis. Thus, Landsat 8 and Sentinel radar data brought out the structural pattern and made visible larger fault zones. The detailed lineament analysis detected areas, presumed to have rocks of relatively higher permeability, supporting intrusion and infiltration of surface water. The resulting maps of weighted overlay procedures derived from causal morphometric factors that influence the susceptibility to ground motion revealed areas with higher, medium and lower susceptibility to soil amplification.

Springs located at the historical sites of Wadi Araba (Eastern Desert of Egypt) and emerging from the escarpments of the Northern and Southern Galala Plateaus were investigated. A combination of methods, including hydrochemistry, stable and radioisotope composition, and structural analyses based on satellite data, provided information about the structure of the subsurface and the derived groundwater flow paths. Satellite images reveal karst features within the northern plateau, e.g. conical landforms. Karstic caves were documented along both escarpments. Chemical analysis of floodwater from Wadi Araba indicates higher concentrations of terrestrial salts compared to floodwaters from central and southern parts of the desert. δ¹⁸O and δ²H signatures in spring waters resemble those of floodwater and fall on the global meteoric water line, confirming their fast infiltration with minor influence of evaporation. The aquifer feeding the springs of the Northern Galala Plateau has low retention and the springs dry out quickly, even after heavy rainfall. Contrastingly,³H activities in springs emerging from the Southern Galala Plateau refer to much slower subsurface passage. With respect to³H content (3.8 TU) in recent flood waters, the spring water at Southern Galala Plateau contains about 40% recently recharged groundwater. However, its largest spring—the St. Antony spring—discharges water with a radiocarbon age of about 15,000 years. In combination with this spring’s constant and high discharge over a period of several months, that age estimate suggests a large reservoir with moderate to high retention.

The most frequent disasters in Western Saudi Arabia are flash floods, earthquakes and volcanism, especially submarine volcanism potentially causing tsunamis in the Red Sea and submarine mass movements, dust storms and droughts. As the consequences and effects of the climate change are expected to have an increasing impact on the intensity and occurrence of geohazards as flash floods, length of drought periods, or dust storms, the systematic, continuous monitoring of these hazards and affected areas using satellite data and integration of the results into a geographic information systems (GIS) database is an important issue for hazard preparedness and risk assessment. Visual interpretation and digital image processing of optical aerial and satellite images, as well as of radar images, combined with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topographic Mission (SRTM) and Advanced Land Observing Satellite (ALOS) PALSAR DEM data are used in this study for the mapping and inventory of areas prone to geohazards, such as flash floods or tsunami flooding. Causal or critical environmental factors influencing the disposition to be affected by hazards can be analyzed interactively in a GIS database. How remote sensing and GIS methods can contribute to the detection and continuously, standardized monitoring of geohazards in Western Saudi Arabia as part of a natural hazard geodatabase is demonstrated by several examples, such as the detection of areas prone to hydrological hazards, such as flash floods causing flooding of roads and settlements, the outlining of coastal areas of the Red Sea prone to tsunami flooding and storm surge, the mapping of traces of recent volcanic activity, and of fault/fracture zones and structural features, especially of ring structures.

The violent storms of 22–30 November 2014, resulted in flash floods and wadi floods (rivers) in large parts of Southern Morocco, at the foot of the Atlas Mountains. The Guelmim area was the most affected part with at least 32 fatalities and damages due to inundations. The flooding hazard in the Guelmim region initiated this study in order to investigate the use of remote sensing and geographic information system (GIS) for the detection and identification of areas most likely to be flooded in the future again due to their morphologic properties during similar weather conditions. By combining morphometric analysis and visual interpretation based on Landsat 8 satellite data and derived images such as water index (NDWI) images, areas with relatively higher soil moisture and recently deposited sediments were identified. The resulting maps of weighted overlay procedures, aggregating causal, morphometric factors influencing the susceptibility to flooding (lowest height levels, flattest areas), allowed for the distinguishing of areas with higher, medium and lower susceptibility to flooding. Thus, GIS and remote sensing tools contribut to the recognition and mapping of areas and infrastructure prone to flooding in the Guelmim area.