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The earth's surface linear features, expressing geological lineaments, play a key role in identifying hydrothermal alteration and mineralization zones, as well as in understanding tectonic settings of a region. The objective of this investigation is to utilize a method for extracting lineaments automatically, which will be integrated and applied to identify geological-based lineaments by making use of remote sensing data. The study will further examine the structural pattern and temporal-spatial evolution of the lineaments and establish their connection with the primary active faults present in the Kabul Block. Multi-sensor data from radar (DEM (Digital Elevation Model)-5m, Sentinel-1B GRD (Ground Range Detected)) and optical sensors (Sentinel-2 MSI (Multispectral Instrument) and ASTER) were processed using spatial and spectral filters before automatic lineament extraction. LINE-module algorithm was applied to various illuminated hill-shades of DEM-5m, filtered HH (Horizonal-Horizonal) and VH (Vertical-Horizontal) of Sentinel-1GRD and PC1 (Principle Component) of Sentinel-2A MSI, and ASTER data to detect linear surface features. Extensive testing was conducted to verify the accuracy of extracted lineaments and to exclude any artificial lineaments in the study area. The radar and optical data results were compared while taking into consideration the geological and tectonic settings of the study area to select the most appropriate extracted lineaments. The DEM-5m and Sentinel-1B GRD showed the best result for identifying lineaments, and these were found to be highly correlated with previously available data in the Kabul Block. The final results of DEM-5m and Sentinel-1B GRD were further analyzed. The extracted lineaments were found to trend predominantly in a NW-SE and NE-SW directions, which is consistent with the results of other data. Temporal evolution and spatial distribution reveal that a high density of the lineaments is associated with Paleogene and Quaternary formations, while a low density is observed in Proterozoic, Paleozoic, and Mesozoic formations. The west and southwest edges of the Kabul Block are controlled by compressive stress trending NNW-SSE, while the north and southeast margins are influenced by NE-SW and ∼ N-S trending compressive stress. [ABSTRACT FROM AUTHOR]

Lithium-cesium-tantalum (LCT) pegmatites are widely distributed in northeastern, eastern, and central Afghanistan. The largest raremetal pegmatites having Lithium (Li)-bearing minerals occur in the Nuristan pegmatite belt in Eastern Afghanistan. The main aim of this study is to detect and highlight the Li-bearing pegmatites using spectral analysis within the Southwest Konar province in the east of Afghanistan and to establish self-proposed spectral indices for the detection of Li-rich minerals. To achieve the study’s objective, we apply several algorithms, for example, false color composite, band ratio, and spectral angle mapper on ASTER, Landsat 8 OLI/TIRS, and Sentinel-2MSI data. Considering the spectral characteristics of key mineral spodumene and other diagnostic minerals, for example, lepidolite, amblygonite, and cookeite, which are considered diagnostic minerals of LCT pegmatites, particularly lithium, we utilize the ECOSTRESS Spectral Library-Version 1.0 for the interpretation of reflectance and absorption determinants of these minerals. Based on the diagnostic characteristics of the selected Li-bearing minerals, we examine several BRs and thresholding and consequently, propose new spectral indices which show a high concentration of spodumene, cookeite, and lepidolite over the northern regions of the study area. According to the obtained results, ASTER data and thresholded BRs present effective results compared to Landsat 8 OLI/TIRS. Based on the final outputs, we define nine zones having Li-bearing pegmatites, particularly over the Chapa Dara and Dara-e-Pech districts of southwestern Konar province, which could be promising areas of interest for further detailed exploration. Lastly, we compare the 118 known Li-bearing points collected from previous studies and field observations with the newly-defined zones to validate accuracy. The study’s overall accuracy is 71.1%, indicating a good match between our findings and the reference points. [ABSTRACT FROM AUTHOR]