Imaging of hydrothermal altered zones in Wadi Al-Bana, in southern Yemen, using remote sensing techniques and very low frequency–electromagnetic data

© 2019, Saudi Society for Geosciences. Economic mineralization and hydrothermally altered zones are areas of great economic interests. This study focusses on hydrothermal altered zones of high mineralization potentials in Wadi Al-Bana, in southern Yemen. An azimuthal very low frequency–electromagnetic (AVLF-EM) data acquisition was conducted in search for mineralization in the study area. The study integrated observations from geophysical field data with others extracted from object-oriented principal component analysis (PCA) to better map and understand mineralization in the investigated area. This technique was applied to two data sets, ASTER and Landsat 8 Operational Land Imager (OLI) imagery. The results of PCA revealed high accuracy in detecting alteration minerals and for mapping zones of high concentration of these minerals. The PCA-based distribution of selected alteration zones correlated spatially with high conductivity anomalies in the subsurface that were detected by VLF measurements. Finally, a GIS model was built and successfully utilized to categorize the resulted altered zones, into three levels. [Figure not available: see fulltext.]

Role of statistical remote sensing for Inland water quality parameters prediction

Understanding the statistical relations among the Advanced Space borne Thermal Emission and Reflection Radiation (ASTER) data and observed water quality parameters, in order to develop a mathematical relation for the precise prediction of the missing data in a given area, is the main aim of the present study. This should enable to establish a spatial distribution map for each parameter of water quality for the area. The method was applied to Qaroun Lake in the Fayoum depression of Egypt.The water quality parameters obtained from ASTER data used in the present work are: Temperature, Turbidity, Hydrogen ion concentration (pH), Salinity, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Total alkalinity, Total Organic Carbon (TOC) and Ortho-phosphorus.18 water sample data were used in the study: 15 sample data for mathematical model construction, giving the relation between the ASTER values and the water quality parameters, while 3 samples data were used to test the obtained model.The SPSS software of IBM was also used in the present research for the applied statistical analysis.The analysis showed a significant correlation between the observed values and the remotely sensed data with R2 > 0.94 and sig. < 0.01 in most cases. The calculated values resulting through the obtained equation showed a high accuracy: Root mean square error (RMSE) ranging from 0.8 to 0.014 and Standard Estimated Error (SEE) ranging from 0.9 to 0.0116.ERDAS Imagine and ArcGIS packages were used for applying the obtained mathematical model and spatial distribution map to the Qaroun Lake. Keywords: Remote sensing, Regression, Inland water quality, ASTE

Quadruple stacked-based concept: A novel approach for change detection

Developing a novel change-detection approach instead classic one is imperative for better assessment of the accuracy of evaluable results. Modern generations of optical satellites, which are characterized by their high to moderate spatial and temporal resolutions enable us to continuously develop the traditional approaches. Successive changes on land with different scales, and their environmental impact needs more accurate specific approaches, which reduce the noise in the data and increase the precision of the results. Integration between many developed methods may produce a new specific one, which solves this.The present work developed a novel approach based on time series results temporally at a very short time scale “Quadruple-stacked changes concept”. The higher weight of change probability indicates a certain change. The advantage of the developed methodology is decreasing the amount of data noise; by using the weight concept of GIS layers. The NDVI “Normalized Difference Vegetation Index” was calculated for each month all over the used two years (2016 and 2021) - using Sentinel-2 imagery data - then were subjected to reclassification and weighted sum to produce the accumulated NDVI.PlanetScope imagery data were used for Quadruple image difference (4 images/2 times). The Threshold of 1/3, ½ and 1 Standard deviation were tested - this step was essential for mapping the true changes which is considered as one of the inputs of the change detection - The resulted accuracy of the real changed areas detected by the newly developed concept was 0.9662, 0.9602, and 0.9579 respectively to the used SD