Satellite Mapping and Stabilization of the Aeolian deposits of QAFCO site 5/6

Aeolian processes produce vast areas of sand and dunes in the arid region and need monitoring since they are encroaching land and degrading infrastructures. In this study, we used the satellite data of ASTER and mineral indices namely quartz index (QI) and carbonate index (CI), and identified and discriminated the sand deposits, dunes, and associated rock formations that occurred in and around the QAFCO site 5/6, Qatar. The mapping of the area using high spatial resolution WorldView-2 satellite data confirmed the presence of sand deposits, dunes, and sand encroachments in the site. Our field studies validated the satellite data results. The grain size analyses of samples showed that the deposits have predominantly sand grains (81.3 to 99.81 %). The XRD analyses of samples identified the presence of quartz, calcite, dolomite, albite, and halite minerals. These are confirmed by geochemical analyses, which showed the high concentration of SiO2, Al2O3, CaO, MgO, Na2O, CO3, SO4, Cl, and B. In addition, the study of sand stabilization by bacteria method to stop the erosion at selected places of the site showed the applicability of the technique. All results allowed us to assess the implications of the deposits and encroachments at the industry site.

Application of Hyperspectral Remote Sensing in the Longwave Infrared Region to Assess the Influence of Dust from the Desert on Soil Surface Mineralogy

Soil mineralogy can be used to study changes in the environment affecting the soil surface, such as dust from the desert through Aeolian processes, which is one of the sources that determine the mineral nature of the soil. Ground- and field-based hyperspectral longwave infrared images, acquired before and after dust dispersion on the soil surface, were processed and analyzed by applying a procedure for determining soil surface mineralogy from the emissivity spectrum, using two indices―SQCMI (the Soil Quartz Clay Mineral Index) and SCI (the Soil Carbonate Index)―to identify changes in the abundance of quartz, clay minerals and carbonates on the surface, caused by the settling dust particles. Mineralogical changes were identified, depending on the mineral composition of the dust compared to the soil surface mineralogy.

Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery

Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.