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Accurate flood mapping is essential for assessing flood hazards, particularly in areas like the lower Baro flood plain in Ethiopia where floods pose significant challenges to society. This study aims to enhance flood inundation mapping by integrating the Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Artificial Neural Network (ANN) with the Hydrologic Engineering Centre-River Analysis System (HEC-RAS). Data from 14 meteorological stations and 3 streamflow stations, spanning from 2000 to 2016, including soil characteristics, Digital Elevation Model, and land use data, were used in the analysis. The combination of ANN and HEC-HMS models provided runoff values for input into the HEC-RAS model, resulting in the creation of accurate flood inundation maps. The HEC-HMS-ANN model was evaluated using statistical metrics such as Nash Sutcliffe (NSE), Root Mean Square Error (RMSE), and Correlation coefficient (R²) demonstrating excellent performance with NSE of 0.9924, RMSE of 24 m³/s, and R² of 0.9926. Calibration and validation of flood inundation outputs from HEC-RAS using the Normalized Difference Water Index (NDWI) revealed high accuracy with overlapping percentages of 90.6% and 91% during the calibration and validation phases, respectively. This integration of models significantly enhances prediction accuracy compared to traditional flood forecasting methods in the Gambella gaging station and Itang area. [ABSTRACT FROM AUTHOR]

The study aims to improve the classification and mapping of snow cover over the Himalayan region, which is essential for assessing water availability and understanding hydrological and climatic interactions. The normalized difference snow index (NDSI) is a traditional digital classification method for snow cover mapping. However, it is not always effective in differentiating snow from other features such as water bodies and shadows of mountain hills. In this study, an improved methodology for snow cover mapping was developed using an object-based classification with NDSI and normalized difference water index (NDWI) over segmented objects instead of pixels to separate snow and water with reduced noise. Shepherd segmentation was used to generate spatially homogeneous objects associated with ground cover features. The study focused on the Chandra basin in Himachal Pradesh, India, using an Indian Remote Sensing Satellite (IRS-P6) LISS-III optical image from 30-09-2016. The developed framework was tested using an object-based NDSI classification and further improved with an object-based NDSI-NDWI classification and validated against a manually digitized snow cover map. Validation showed that the object-based NDSI-NDWI classification provided a significant improvement in snow cover mapping compared to traditional NDSI classification, reducing the overestimation of snow-covered areas by up to 6.14%. The developed methodology was executed in the Python environment with efficient computing power. This study demonstrates that an integrated analysis of object-based classification with NDSI and NDWI, can significantly improve snow cover mapping by separating non-snow features. The results of this study show that they have the potential to be extended to larger regions with snow cover. [ABSTRACT FROM AUTHOR]

Environmental Impact Assessment (EIA) is a prominent technology to assess the environmental changes in the open cast Limestone mining region. However, traditional EIA methods and Limestone deposit identification involves time consuming, cost effective and manpower requirements. We cope-up these problem and incorporated with different satellite image processing techniques and field studies to estimate the impact on environment. Landsat imageries have been used to calculate NDVI, NDWI, LULC, LST for the buffer zone within a 10 km radius of the mines from the period between 1997–2017. The noise pollution of the mining region is identified using a random sample method. To access water quality analysis in the area, the Water Quality Index (WQI) is generated using 12 water quality parameters. EIA is analyzed using Analytical Hierarchy Process (AHP), Multi-Criteria Decision Analysis (MCDA) and future EIA is also predicted using empirical Bayesian Kriging approach. [ABSTRACT FROM AUTHOR]

Coastal management has a critical role in estimating the coastal environmental and socio-economic dynamics, providing various vital regional and local services. Remote sensing earth observations are essential for detecting and monitoring shorelines. UAVs combined with satellite remote sensing address the shoreline delineation problems to detect the shoreline and identify the shoreline zones. The paper presents a shoreline delineation service utilizing UAV and Sentinel 2 images within a Data Cube environment for monitoring coastal areas. The BandRatio, McFeeters, MNDWI1, and MNDWI2 algorithms have been implemented in the service to analyze the accuracy of each algorithm by comparing satellite and UAV-derived shorelines. As a case study, the Lake Sevan shoreline delineation, as one of the most incredible freshwater lakes in Eurasia, has been studied using the service. MNDWI2 algorithm showed the best accuracy for Lake Sevan shoreline delineation. [ABSTRACT FROM AUTHOR]

Drought is often one of environmental disasters replicated in Morocco. It is the result of climate variations and human activity that has affected different sectors (water resources, agriculture, ecology, and socio-economy, etc.). The normalized water differential index (NDWI) is a type of spectral water analysis based on one green band and one NIR-band representation. The NDWI was effectively used to gather information around water bodies from remote sensing data. The study area for this work is the Idriss 1st Dam in northeast Morocco, situated downstream from the drainage of the Inaouene River. This basin is mostly affected by drought risk, which will evaluate by calculating NDWI index of image time series, based on Sentinel-2 (2015 to 2020), Landsat-8 (2013 to 2020) and Google Earth Engine (2013 to 2020) as a data processing tool. For the study area drought monitoring, DrinC software is used to calculate the Standardized Precipitation Index (SPI) and the Stream-flow Drought Index (SDI).The result of this paper is mapping water bodies of Idriss 1st Dam, and evaluated drought risk severity and frequency with a high precision. [ABSTRACT FROM AUTHOR]