Khodaei & Nassery (2013) opine that rainfall distribution is not uniform in all seasons in arid and semi-arid regions of the world. They proceed to mention that in such regions, the water demands for domestic, agricultural, and industrial usage are provided by groundwater, which is contained in hard rock and karstic aquifers. The decrease of groundwater over the years has forced experts to identify secondary porosity zones by studying some surface features such as lineaments, drainage pattern, and density, fractured and weathered zones, rock type, as well as the green vegetation of dry season. Of course, studying, defining, and extracting the mentioned groundwater indicators through the use of geophysical and geological approaches is expensive and time-consuming, and this has paved the way for the use of remote sensing satellites to identify, extract, and define groundwater indicators. This article explores how remote sensing satellites were used to identify groundwater resources in the semi-arid area in the northwest of Iran. In the investigation, after the gathering data needed from topographic and geological maps, remote sensing (Landsat) was used to extract some of the key groundwater indicators including lineaments, rock type, and green vegetation of dry season. The middle infrared band of Landsat ETM, Spot, and IRS panchromatic band were selected to enhance and extract lineaments by directional filters. For the extraction of vegetation cover, Landsat 5-TM data were selected because of its multispectral abilities (Khodaei & Nassery, 2013). The enhancement and mapping of vegetation on the areas was achieved by NDVI index and supervised classification. Information on the region’s rock type was prepared from Landsat TM data through the use of image characters such as texture, tone, and drainage pattern as well as image processing techniques including false color composite, principal component analysis, and band rationing.
Using remote sensing satellite data could also prove pivotal in detecting groundwater indicators in a different location such as central Jordan. Central Jordan is different from northwest Iran in that the former’s rock sequence is a succession of shallow marine deposits originally lying in the southern depressions of the Tethyanocean and largely consisting of carbonates deformed after regression of the sea and later eroded by fluvial processes. As such, adaptations to the use of remote sensing satellite data could be needed in identifying and locating groundwater indicators such as lineament length density, drainage length density, slope steepness, elevation, and geological formations. The extraction of lineament and drainage densities in central Jordan could see the use of orthorectified Landsat 7 ETM+. Other technologies that could prove pivotal in this situation include the digital elevation model (DEM) that could be used to delineate the sub-watershed boundary of the region and derive slope and drainage watershed maps. The Landsat 7 ETM+ imagery covering the central Jordan region could be obtained from Global Ortho-rectified Landsat data (Hammouri, El-Naqa, & Barakat, 2012). The use of this image in central Jordan could be attributed to its spatial resolution of 15 meters that can be integral in the extraction of lineament features in central Jordan.
Detecting, locating, and extracting groundwater indicators such as vegetation, lineament length density, slope steepness, and rock type could also focus on the use of remote sensing imagery. This technique involves the use of remote sensing sensors, which are key devices that capture information or data about an object or scene remotely without being in physical contact. Since groundwater indictors such as vegetation, rock type, lineament, and others have their unique spectral features, they can be easily identified from remote sensing imagery, and this is dependent on the unique spectral characteristics of the groundwater indicators (Khodaei & Nassery, 2013). For instance, remote sensing imagery could be used to extract or identify green vegetation because of the vegetation’s spectral radiances in the red and near-infrared regions. The radiances in the detected vegetation could then be incorporated into the spectral vegetation indices (VI) that in direct relation with the intercepted fraction of photosynthetically active radiation. One of the remote sensing sensors that could play a crucial role in detecting and locating groundwater indicators in arid and semi-arid areas is SPOT. It is argued that images acquired through SPOT can be useful in studying, forecasting, and managing not only natural resources such as vegetation and drainage patterns but also human activities. The advantage of using SPOT imagery in detecting or locating groundwater indicators in arid areas is that it comes in a full range of resolutions from 1-kilometer scale down to 2.5-meter local scale. SPOT imagery is preferable for detecting and locating green vegetation in arid and semi-arid areas because it has a second imaging instrument known as SPOT Vegetation instrument, which helps in the collection of data at a spatial resolution of 1 kilometer and a temporal resolution of 1 day. One of the requirements in detecting groundwater indicators in arid and semi-arid areas is the observation and analysis of land surfaces as well as understanding the changes that occur on land surfaces, and this is where SPOT imagery is useful, and thus should be considered as an alternative to the use of remote sensing satellite technique.
Hammouri, N., El-Naqa, A., & Barakat, M. (2012). An integrated approach to groundwater exploration using remote sensing and geographic information system. Journal of Water Resource and Protection, 4(9), 717.
Khodaei, K., & Nassery, H. R. (2013). Groundwater exploration using remote sensing and geographic information systems in a semi-arid area (Southwest of Urmieh, Northwest of Iran). Arabian Journal of Geosciences, 6(4), 1229-1240.