Lithological discrimination based on Landsat-9 OLI sensor and field observation data: The bana an-orogenic volcano-plutonic ring complex, West Cameroon line.

The western region, encompassing the an-orogenic Bana volcano-plutonic ring complex in Cameroon, underwent comprehensive exploration involving remote sensing analysis, fieldwork investigations, petrographic, and volcanological studies. The primary objective of this work was to integrate remote sensing analysis, fieldwork, and laboratory studies to achieve accurate lithological mapping for future prospective mineral explorations in the study area. Field relationships among co-occurring rock units in the area were examined, utilizing Landsat-9 OLI data. Petrographic analysis, including the use of a polarizing microscope, was conducted on various rock units (15 samples), along with volcanological processes studies. Operational Land Imager (OLI) images of Landsat 9 were processed using algorithms including False Colour Composite (FCC), Decorrelation Stretch (DS), Band Ratio (BR) composite, Principal Component Analysis (PCA), Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) methods to identify distinct rock units in the Bana ring complex. As a result, the later methods permitted to identify the petrographic units of the ring complex, which primarily comprise a volcano-plutonic sequence, along with metamorphic rocks like gneisses. The volcanic units include variety of basalts, trachytes, rhyolites and volcanic tuffs, while the plutonic units including gabbros, diorites, syenites and fine-grained granites. The findings of this study accurately at 99 % have permitted to newly setup a geologic map of the study area with implications for future mineral explorations.

Geological controls of mineralization occurrences in the Egyptian Eastern Desert using advanced integration of remote sensing and magnetic data.

This study presents a comprehensive analysis of mineralization exploration in the Egyptian Eastern Desert (ED), one of the most sought-after areas for those interested in mining industry, by integrating Landsat-9 images and geophysical magnetic data. Employing advanced techniques like Principal Component (PC) analysis, Minimum Noise Fraction (MNf) transform, and Band-Ratio (B-Ratio), the research focuses on mapping lithological units, hydrothermal alteration regions, and structural elements. Composite images derived from specific PC, and MNf bands, and B-Ratio exhibit superior lithological unit identification. The findings emphasize that there are significant variations in the types of rocks extend from the southern to the northern parts of the ED. Hydrothermal alteration mapping, guided by B-Ratio results, aids qualitative lithological discrimination. A novel false color composite image optimizes Landsat-9 B-Ratios, enhancing rock unit discrimination. Correlation analyses reveal associations between mineralization types and major lithological units, while exploration of the magnetic anomaly map highlights its role in correlating mineralization sites. Structural features, analyzed through Center for Exploration-Targeting Grid-Analysis (CET-GA) and Center for Exploration-Targeting Porphyry-Analysis (CET-GA) with Tilt Derivative of RTP (TDR) techniques, contribute to a robust association between regions with medium to high structural density and porphyry intrusions and mineralization. The study significantly supports the advanced exploration geoscience, providing insights into the geological structures and dynamics governing mineralization in the Egyptian ED.

An integrated remote sensing, petrology, and field geology analyses for Neoproterozoic basement rocks in some parts of the southern Egyptian-Nubian Shield.

The main objective of this study was to use deep learning, and convolutional neural networks (CNN), integrated with field geology to identify distinct lithological units. The Samadia-Tunduba region of the South Eastern Desert of Egypt was mapped geologically for the first time thanks to the use of processed developed CNN algorithms using Landsat 9 OLI-2, which were further enhanced by geological fieldwork, spectral measurements of field samples, and petrographic examination. According to previously published papers, a significant difference was observed in the distribution of rocks and their boundaries, as well as the previously published geological maps that were not accurately compatible with the nature of the area. The many lithologic units in the region are refined using principal component analysis, color ratio composites, and false-color composites. These techniques demonstrated the ability to distinguish between various igneous and metamorphic rock types, especially metavolcanics, metasediments, granodiorite, and biotite monzogranite. The Key structural trends, lithological units, and wadis affecting the area under study are improved by the principal component analysis approach (PC 3, 2, 1), (PC 2, 3, 4), (PC 4, 3, 2), (PC 5, 4, 3), and (PC 6, 5, 4) in RGB, respectively. The best band ratios recorded in the area are recorded the good discrimination (6/5, 4/3, and 2/1), (4/2, 6/7, and 5/6), and (3/2, 5/6, and 4/6) for RGB. The classification map achieved an overall accuracy of 95.27%, and these results from Landsat-9 data were validated by field geology and petrographical studies. The results of this survey can make a significant difference to detailed geological studies. A detailed map of the new district has been prepared through a combination of deep learning and fieldwork.

Remote sensing and monitoring of water resources: A comparative study of different indices and thresholding methods.

Water resources are essential for the ecological system and the development of civilization. Water is imperative factor for health preservation and sustaining various human activities, including industrial production, agriculture, and daily life. Remote sensing provides a cost-effective and practical means to detect and monitor water bodies, offers valuable insights into the impact of climatic events on water structures, especially in coastal lake regions. The research primarily utilizes Landsat-9 OLI-2 satellite images to evaluate the effectiveness of various water indices (WRI, NWI, MNDWI, NDWI) in combination with global automatic thresholding methods (K-Means, Zhenzhou's, Adaptive, Intermodes, Prewitt and Mendelsohn's Minimum, Maximum Entropy, Median, Concavity, Percentile, Intermeans, Kittler and Illingworth's Minimum Error, Tsai's Moments, Otsu's, Huang's fuzzy, Triangle, Mean, IsoData, Li's). The study was carried out on Lake Nazik, Lake Iznik, and Lake Beysehir, which have unique geographical characteristics, and examined the adaptability and robustness of the selected indices and thresholding methods. MNDWI consistently stands out as a robust index for water extraction, delivering accurate results across different thresholding methods in regions all three lakes. As a result of quite extensive analysis, it is obtained that MNDWI and NDWI are reliable choices for water feature extraction in various lake environments, but the specific index should consider the thresholding method and unique lake characteristics. The Minimum thresholding method stands out as the most effective thresholding technique, demonstrating impressive results across different lakes. Specifically, it achieved an average Peak Signal-to-Noise Ratio (PSNR) of 78.97 and Structural Similarity Index (SSIM) of 99.37 for Lake Nazik, 74.08 PSNR and 98.34 SSIM for Lake Iznik, and 63.96 PSNR and 93.61 SSIM for Lake Beysehir.

Fine-scale monitoring of lake ice phenology by synthesizing remote sensed and climatologic features based on high-resolution satellite constellation and modeling.

Lake ice, as a crucial component of the cryosphere, serves as a sensitive indicator of climate change. Fine-scale monitoring of spatiotemporal patterns in lake ice phenology holds significant importance in scientific research and environmental management. However, the rapid and dynamic nature of the freeze-thaw process of lake ice poses challenges to existing methods, resulting in their limited application in small lakes. In this study, we propose a novel approach of investigating ice phenology of lakes in various sizes. We conducted a case study in Hoh Xil, known for its vulnerability to climate change and a wide distribution of small lakes, to analyze the ice phenology of 372 lakes (>1 km2) during 2017-2021. Firstly, ensemble machine-learning model was developed for lake ice identification from Landsat-8/9 and Sentinel-2 A/B imagery. The accuracy evaluation reveals the overall good performance for ice extraction results based on Landsat-8/9 (97.03 %) and Sentinel-2 A/B (96.89 %). Next, the XGBoost models were employed to reconstruct ice coverages on unobserved dates for the freezeup and breakup periods, respectively. Totally, 744 XGBoost models were constructed for the study lakes, and the majority of them perform well. Based on the reconstructed daily ice coverage, phenology parameters could be extracted for examining the spatiotemporal characteristics of ice cover and possible relationships with lake sizes and terrains. From early-October to early-November, the Hoh Xil lakes freeze from the northwest to the southeast, while the breakup period starts in late-March and lasts until late-June. Moreover, the results indicate relatively small variability in freezeup-end dates among lakes, but significant differences in breakup dates, showing a greater sensitivity to temperature variations. Furthermore, ice phenology in small lakes exhibit stronger consistency with subtle climatic fluctuations. The results highlight the significant role of ice phenology in small lakes, as they dominate the overall tendency of ice phenology in Hoh Xil.

Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery.

Precise identification and spatial analysis of land salinity in China's Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity retrieval framework using a harmonized UAV and Landsat-9 multi-spectral dataset. The Kenli district of the Yellow River Delta was selected as the case study area, and a land salinity monitoring index (LSMI) was proposed based on field survey data and UAV multi-spectral image and applied to the reflectance-corrected Landsat-9 OLI image. The land salinity distribution patterns were then mapped and spatially analyzed using Moran's I and Getis-Ord GI* analysis. The results demonstrated the following: (1) The LSMI-based method can accurately retrieve land salinity content with a validation determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of 0.75, 1.89, and 2.11, respectively. (2) Land salinization affected 93.12% of the cultivated land in the study area, and the severely saline soil grade (with a salinity content of 6-8 g/kg) covered 38.41% of the total cultivated land area and was widely distributed throughout the study area. (3) Saline land exhibited a positive spatial autocorrelation with a value of 0.311 at the p = 0.000 level; high-high cluster types occurred mainly in the Kendong and Huanghekou towns (80%), while low-low cluster types were mainly located in the Dongji, Haojia, Kenli, and Shengtuo towns (88.46%). The spatial characteristics of various salinity grades exhibit significant variations, and conducting separate spatial analyses is recommended for future studies.