Articles

Predictive Modeling in Remote Sensing Using Machine Learning Algorithms

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Predictive modeling in remote sensing using machine learning (ML) algorithms has emerged as a powerful approach for addressing various environmental and climatic challenges. This paper explores the integration of advanced ML techniques with remote sensing data to enhance predictive capabilities for applications such as land cover classification, crop yield prediction, climate change monitoring, and disaster management. We review related works and existing systems, highlighting platforms like Google Earth Engine (GEE), NASA Earth Exchange (NEX), and Sentinel Hub, which leverage cloud computing to handle large-scale data processing and model deployment. The proposed system incorporates data acquisition, preprocessing, feature extraction, model selection and training, and prediction and visualization to provide accurate and timely predictions. Future enhancements, including deep learning integration, real-time data processing, enhanced user interfaces, and collaboration with Internet of Things (IoT) devices, are discussed to further strengthen the system’s capabilities. The paper concludes by emphasizing the potential of ML algorithms in transforming remote sensing applications, supporting informed decision-making, and improving the management of Earth’s resources.

Morphological Changes on Gungata River Watershed due to Anthropogenic Interferences, a part of the Upper Rihand Basin, Chhattisgarh

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The main aims of this research are to identify the morphological changes and development of the Gungata River watershed due to the rapid growth and effects of anthropogenic activities. The natural origin of rivers is sensitive to anthropogenic interference which causes a change in channel morphological characteristics. Human activities have revamped the river geomorphology and made limitless anthropogenic geomorphic features. These features have remarkable characteristics which have sometimes been misbalanced with landscapes produced by natural processes. Modern techniques like remote sensing and GIS were used to identification of morphological changes and their historical comparison etc. High-resolution satellite imagery (LISS-I 5m.), Digital Elevation Model (CARTO DEM 30m) were used to analyze the anthropogenic geomorphic features which provide different opportunities for a better understanding of landscape processes. This research paper has been shown how anthropogenic activities interference with the morphological changes of the Gungata river watershed.

Investigation of Groundwater Potential Using Remote Sensing and Geographical Information System (GIS) Techniques in Fakai Local Government of Kebbi State, Nigeria

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Groundwater is one of the most precious natural resource which supports human health, economic development and ecological diversity. Remote sensing and Geographical Information System (GIS) Techniques have been effectively used for the investigation of the potentiality of groundwater resource in Fakai local government area. The dataset for this research work are Landsat 8 Operational land imager (OLI), ASTER DEM, Topographical map and Geological map from which the essential criteria were obtained. The study used Weighted Linear Combination approach which involves mathematical weighing and ranking of the criteria. Multi-criteria evaluation was carried out on all the criteria using the Weighted Linear Combination approach in ArcGIS 10.4. Spatial analysis was carried out on the derived result using the Suitability Index (SI) value created from pairwise comparison analysis. The suitability map for groundwater recharge in the study area was hence produced using the suitability index. The result shows four classes for the study area. The classes include highly suitable, moderately suitable, less suitable and least suitable. Thus, the area most suitable for groundwater are found most towards the northern part, around the center and some regions in the northern part of the study area this serves as an indicator that most of the study area has good potential for groundwater recharge.