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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 deep pools are considered vital habitats for both aquatic and terrestrial biodiversity in arid and semi-arid rivers. These 'refugia' habitats sustain the aquatic biodiversity of local and regional importance when water flow ceases. Banas is an ecologically unique and non-perennial river in the Ganga Basin originating from the Aravalli Range and flowing through the semi-arid region of Rajasthan, India. This study maps and characterises the deep pools in the water stressed river using Sentinel-2 satellite data (2019–2022). Mapping and analysis were done using geospatial tools and field data. The composite map reported 2.18 km2 (0.6% of the total area) and 72.42 km2 (19.0% of the total area) of permanent water spread in the floodplain and reservoirs of Banas River, respectively with seasonal variations. A total of 558 contiguous habitats with varying sizes (50 to 314,422 m2) were delineated and most of them were located in the downstream of Bisalpur Dam especially along the river meandering. The composition of the area under different land use land cover classes in the riparian zone varied across the deep pools with medium land use intensity. The high proportion of vegetation and cropland near and far from the riparian buffer indicated existence of the natural and agrarian landscapes, respectively. The indications of various ecosystem services by the deep pools necessitate spatial quantification. Additionally, impact of the various anthropogenic threats on aquatic habitats recommends measures for habitat restoration and conservation planning of Banas River. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) changes hugely influence the ecological balance of an ecosystem, which adversely affects the inhabitants, making them more vulnerable to natural calamities. The LULC change studies are therefore carried out to analyze the impact of these changes on the overall ecology of an area and are very helpful in policy framing and proper management of the available natural resources. In this study, changes in the land use and land cover for a three-decade period spanning from 1992 to 2020 have been monitored in the valley of Kashmir using remotely sensed satellite data obtained from USGS/NASA’s Landsat repository. Considerable changes in the LULC patterns were observed with a significant reduction in the area covered by water (18.21%), forest (13.56%), snow/glacial cover (29.32%) and agriculture (22.37%) during the past three decades. Concurrently, expansion in the land covered by urban areas (22.33%), barren land (37.32%), plantation (14.53%) and marshes (13.21%) were noted. The calculated Normalized Difference Water Index (NDWI) confirmed an overall reduction of 51.1% in the water and glacial cover of the study area. Significant changes in the form of forest, water and glacial cover transforming into urban, marshy and barren areas can be largely accredited to increased human interference that may have serious repercussions on the environment. [ABSTRACT FROM AUTHOR]

The identification and systematic monitoring of water spread regions using remote sensing techniques necessitates precise temporal resolution datasets. Landsat data are one of the most popular data sources that provides satellite derived images of the Earth's surface for examining the satellite-based indices of a specific region. In the present research study, Normalized Difference Water Index is employed to identify the surface area of 11 system and non-system lakes in a part of Coimbatore region over the time span of 32 years (1990–2022). Based on the analysis, the water spread region in 1990 is seen to be 13.01 ha which gradually increased to 435.17 ha in 2022. This clearly indicates that there is substantial difference in the rainfall statistics over the years. Computations for cross-validation are done using other indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI). A 500 m buffer is created and calculated for vegetation and built-up indices like Chinna kulam 1 and 2, Kumaraswamy Lake, Valankulam, Singanallur and Sengulam lakes which show the decreasing trend of NDVI. Simultaneously, NDBI increased in 2000, which shows that measures were taken to tackle the encroachment of lakes in the city and the concerted efforts of the Governmental authorities and Non-governmental organizations (NGOs) were fruitful. A decreasing trend is noted in NDBI during 2011 and 2020. The microclimatic conditions and advection current of waterbodies along with boating facilities and parks in some lakes offer city dwellers a tranquil recreational area with respite from vehicular pollution and heat generation from the concrete jungle. This study will be helpful in facilitating policy decisions for future planning and sustainable development of water bodies. It will also be beneficial in devising rehabilitation measures for irrigation tanks and will aid in bringing about agrarian stability of tanks in urban locales. [ABSTRACT FROM AUTHOR]

Lake Tana is facing a big problem with water hyacinth invasion since September 2011 of which it is worst for lakes in the world that invades vast area of lakes with in limited period of time if it does not control properly. Water hyacinth is an invasive and free-floating water plant that grows into large dense vegetable mats blocks transportation of boats and sun energy transmission into Lake Bottom. Since its official recognition, GPS have been used in order to delineate the infestation on the lake, but using GPS for infestation mapping is not time and cost effective, and note safe for data collectors. Considering this reason, this study focuses on mapping of water hyacinth infestation coverage area and analyzing its possible cause on Lake Tana by applying satellite imagery and nutrient level of it. In this document, Landsat 7ETM + and Landsat 8 images were used with the conversion of DN values to TOA reflectance, and TOA were used to evaluate different spectral indices like NDWI and NDVI. These indices with dynamic threshold increasing between class variance were used to map potential water hyacinth infestation area from 2011 to 2018, and NDWI used to discriminate Lake Tana from its surrounding background feature while NDVI was used to separate open water surface from aquatic vegetation coverage areas. In addition to these indices, thermal band of Landsat images were used to discriminate water hyacinth from other features. The accuracy of satellite image classification on this study was evaluated using error matrices and achieved with the best overall accuracy and Kappa coefficient (K). Total nitrogen concentration was increased from dry month to wet month and total phosphorus was increased from wet month to dry month and potential water hyacinth was greatly found where nutrient level was highly available. To reduce infestation rate of its special soil management practice applies to reduce nutrient has to implement. In addition, there has to be buffer zone for the lake and recessional agriculture has to be stopped. [ABSTRACT FROM AUTHOR]

This study aims to predict water availability in New Capital (IKN) and its surroundings using artificial neural networks and spectral indices as predictors. The study uses Sentinel-2 A imagery from the year 2022 analyzed directly from Google Earth Engine (GEE) to calculate three spectral indices, including the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), and uses these indices as predictors in the artificial neural network model. The study carried out four simulations to determine the best prediction results, and the best results were obtained using ANN parameters: 2 hidden layers (HL); learning rate (LR) 0.01; momentum (M) 0.4; root mean square (RMS) 0.001 and iteration (I) 25,000 with overall accuracy (OA) 97.7% and kappa index 0.96. The results show that the percentage of water availability in the study area is high water/HW (0.51%), vegetation water/VW (20.41%), and non-water/NW (79.08%). The study concludes that artificial neural networks and spectral indices can effectively predict water availability. [ABSTRACT FROM AUTHOR]

Flooding stands as one of the most devastating natural occurrences, warranting immediate investigation to mitigate its destructive impact. The inundation of agricultural lands and settlements has led to adverse consequences. Remote sensing emerges as a widely applicable and expeditious method for addressing these challenges. Within the scope of this study, S1A SAR data with VH descending pass and S2 data from 01/03/2019 to 20/03/2019 and 25/03/2019 to 20/04/2019 were leveraged to assess the pre- and post-flood periods in Kermanshah province. MNDWI and NDWI techniques were employed to identify water zones in the S2 imagery, subsequently was used for validating of S1 images. The calculated RMSE and correlation coefficients yielded values of 0.27 and 0.93, respectively. It was observed that radar imagery exhibits superior quality to optical imagery in flood scenarios characterized by cloudy and rainy weather. MODIS, Hydrosheds, and SRTM satellite images were utilized as distinct filters to identify land use, permanent water bodies, and areas with a slope exceeding 5%. The findings indicated that a total of 36,849 ha of land were affected by the flood, encompassing 7073 and 4224 ha of agricultural and urban areas, respectively, which were susceptible to destruction during this period. The NDWI and MNDWI indices estimated the flooded area to be 30,179 and 32,540 ha, respectively, representing lower values compared to the results obtained from the S1 data. [ABSTRACT FROM AUTHOR]

Application of different water identification indices and their modified form with a threshold is a common practice in surface water identification from multispectral images. Implementation of the statistical features of water present in such images to improve the accuracy of existing approaches is a novel application. A dynamic threshold selection is more suitable for the detection of sediment–water. In consideration of the facts, the present study proposed a hybrid approach for automatic surface water detection. Fuzzy c-means, NDWI, and a statistical feature: gradient are used to classify and therefore identify surface water. The study area, the river basin of Sundarban, is chosen due to its nature of water bodies such as wide rivers, narrow water streams, and sediment–water. The algorithm works with minimum human interaction. The method is validated by applying on Sentinal-2 and WorldView-2 images having a spatial resolution of 10 m and 0.46 m, respectively, and is found the accuracy is 97%. [ABSTRACT FROM AUTHOR]

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul’s daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03 ha (2.89%). While there was a 1–12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65–93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33 ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization. [ABSTRACT FROM AUTHOR]

The Muriganga River, also known as channel creek, underwent morphological changes often since it is an alluvial as well as a tidal river. The present study analyses the morphological changes in the Muriganga River and its islands with the help of the Remote Sensing and Geographical Information System (GIS) and digital shoreline analysis tool (DSAS 5.0). Moreover, the computation of morphological changes was also performed on two islands, i.e. Sagar and Ghoramara, which are situated just outside the river reach. Eight cloud-free satellite images of Landsat MSS (1972–1980), Landsat TM (1988–2011) and Landsat OLI (2017–2021) have been used to investigate the river shoreline shifting and island dynamics of the Muriganga River resulted from the erosion–accretion process during the last 49 years. For the short-term study, the erosion–accretion rates are derived from one Landsat image to the next, whereas for long-term analysis, the erosion–accretion rates are estimated based on the difference between 1972 as the reference image and the succeeding images. Short-term and long-term analysis shows that the average rate of erosion is more than that of accretion in Muriganga River. It is also found that the areas of Sagar, Ghoramara, Mousuni and Pushpa islands are shrinking continuously, whereas the Niogi and Basit islands are expanding enormously. These may indicate that the shoreline erosion results in widening the river and the eroded materials are accumulated in Niogi and Basit islands. The results suggest that there is an urge for a better coastal management strategy for the erosion control scheme. This study also helps in gaining knowledge of maintaining the navigability in the Muriganga River. [ABSTRACT FROM AUTHOR]

Flood inundation mapping and satellite imagery monitoring are critical and effective responses during flood events. Mapping of a flood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India’s Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu’s algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV+VH, VH−VV, VV−VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The normalized difference water index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identified as 440.3 km2 in 2018, 268.58 km2 in 2019, 178.40 km2 in 2020, 203.79 km2 in 2021, and 321.33 km2 in 2022, respectively. The overlap of flood maps on the LULC map indicated that flooding highly affected agriculture and urban areas in these years. The approach using the near-real-time Sentinel-1 SAR imagery and GEE platform can be operationalized for periodic flood mapping, helps develop flood control measures, and helps enhance flood management. The generated annual flood inundation maps are also useful for policy development, agriculture yield estimation, crop insurance framing, etc. [ABSTRACT FROM AUTHOR]

Osun River, a UNESCO heritage site is currently under threat to unregulated mining activity along its course. Through remote sensing techniques, this study aimed to evaluate mining severity (number and size of mine pits) which was acquired and used to quantify landscape changes and degradation in water quality (turbidity) along the river. Studies were carried out using data for years 2020–2022 covering 2-km and 5-km buffer areas of the river's watershed. High-resolution Google Earth imagery and Landsat 8 imagery (OLI) were used for land-use land-cover assessment and computation of the Normalized Difference Water Index (NDWI) respectively. The mining severity assessment for 2022 revealed a total of 32 mining pits within the 5-km buffer zone, where 21 mining pits (65.63%) of the total mining activities occurred within the 2-km buffer zone. The severity assessment in terms of both the number of mines and the land area covered revealed an extensive physical mining activity, with severe impacts on surface water quality. The predominantly irregular shapes of the mine pits, indicated by the mean perimeter-to-area ratio (X̅=0.22), linear regression (y = 0.1351x + 0.6102), and coefficient of determination (R2=0.88), portend challenges for post-mining reclamation, including erosion control and terrain stabilization. The declining trend in the upper limit NDWI values over the 3 years, 2020, 2021, and 2022 (0.053, 0.017, and −0.084476 respectively), indicates a significant reduction in water quality, plausibly associated with the heightened mining activity within the 2-km buffer areas of the river. The escalating habitat degradation and imminent effects on drinking water quality and biodiversity health underscore the pressing implications of unchecked mining, eroding the ecological and cultural significance of the Osun-Oshogbo Sacred Grove, a UNESCO World Heritage Site. Urgent governmental action is crucial to enforce mining regulations, curbing these threats, and preserving the site's ecological and cultural importance. [ABSTRACT FROM AUTHOR]

Changes in global temperature have adverse influences on the environment, crop production, and public health. Temperature extreme investigation is necessary for those areas whose cropland and work cultures rely on effective climatic conditions. Therefore, identifying trends in climatic scenarios is important in determining the pattern of extreme temperatures. The previously mentioned trend analysis was based on a complete period that did not investigate the recent variability in temperature. We scrutinized a different decadal zone using the innovative trend analysis method (ITAM). We selected the annual and seasonal extreme temperature variations for 70 years (1951–2020) at 11 grid points in the Kangsabati–Silabati–Keliaghai–Dwarkeswar basins of West Bengal. The outcomes from two different time zones, 1951–2020 and 2001–2020, show some surprising results. For 1951–2020, Tmax shows negative sloping patterns in winter and summer, whereas for 2001–2020, it shows the opposite pattern: year-round, winter and summer with excessive magnitudes. Similarly, for Tmin, all the seasons from 1951 to 2020 show positive trends, but in recent decades (2001–2020), except the monsoon season, other seasons show negative trends. The normalized difference in vegetation and water indices also supports the results of trends. From the results obtained through the use of ITAM, we recognize that the results of the recent trends are more sensitive and of a higher magnitude of slope in nature than in the historical decade. This study may serve as scientific support for detecting and strategically minimizing the effects of climate change on water resources to reduce the risk of adverse weather soon. [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]

Accurate coastline position is of fundamental importance for many applications concerning coastal zone monitoring, management, and planning. For example, coastal erosion phenomena require a careful and continuous monitoring due to the dynamic nature of the coastline which can undergo sudden and significant changes in position and shape over time. Various techniques allow acquiring the coastline, among these the use of multispectral optical sensors operating from satellites is one of the most widespread. With the advent of high and very high geometric resolution (VHR) satellites, it is possible to obtain images with a pixel size of less than 1 m that allow extracting accurate coastlines. The purpose of this article is to define a fast approach to investigate the degree of accuracy of one of the most popular techniques for the automatic extraction of the coastline, based on the Normalized Difference Water Index (NDWI) use. In this study, the coastline is achieved from VHR Pléiades imagery (2 m for multispectral and 0.5 m for panchromatic). Therefore, NDWI is obtained and processed both from initial images and pan-sharpened images. The resulting coastlines are submitted to smoothing and their accuracy is therefore evaluated. For this purpose, a reference coastline is manually achieved from panchromatic image. Two different methods are proposed for coastline accuracy evaluation, both based on the geometrical analysis of the polygons generated by the intersection between the extracted coastline and the reference one. This study demonstrates that the proposed methods permit to easily evaluate the accuracy of the extracted coastline; in addition, the results confirm the effectiveness of NDWI and highlight the limited benefits of pan-sharpened images for this index application. [ABSTRACT FROM AUTHOR]

Invasive Hieracium plant species are invading the Greater Yellowstone Ecosystem. The potential distribution of orange hawkweed (Hieracium aurantiacum) and meadow hawkweed (Hieracium caespitosum) were estimated using habitat susceptibility models to assist land managers' management of these invasive plants. The objectives of this study were to: (1) develop models describing susceptibility of ecosystems to hawkweed invasion, (2) identify indicator species of orange hawkweed and meadow hawkweed, (3) determine habitat types where these invasive hawkweeds might occur, and (4) create habitat susceptibility maps for management planning and ground surveys. Models were developed using a Mahalanobis distance similarity technique from remotely sensed biotic and abiotic variables, as well as known location data for orange and meadow hawkweed. Ground validation was conducted to assess model weaknesses and subsequent model modification. Indicator plant species were identified as surrogates to determine the likelihood of hawkweed presence during ground survey. Transect data collected from areas susceptible to invasion also were used to determine habitat types where hawkweed might occur. The best model included eight variables: north–south aspect, east–west aspect, slope, NDVI, NDWI, blue spectral band, green spectral band, and precipitation. High susceptibility (65 + % likelihood of suitable habitat) consisted of 66,000 ha for meadow hawkweed and 35,000 ha for orange hawkweed, 5.0% and 2.7% of the study area, respectively. Meadow hawkweed and orange hawkweed had seven and three indicator plant species, respectively. Predicted hawkweed habitat susceptibility encompassed nine habitat types, ranging from xeric sagebrush steppe to wet forests and they overlapped except at the xeric habitat type. Habitat susceptibility models save costs and allow survey prioritization to those areas most susceptible to invasion. [ABSTRACT FROM AUTHOR]

In this study, the most appropriate algorithm and water index to determine the boundaries of the dam water surface using remote sensing (RS) techniques were investigated. Water surface boundaries of Demirkopru Dam were determined using Sentinel-2 L2A (MSI) and Landsat-8 (OLI) satellite images. Demirkopru Dam was chosen as the study area as it is suitable for floating photovoltaic (FPV) solar power plant installation. Normalized difference water index (NDWI) and modified NDWI indices were used to determine the water surface boundaries of the dam. Thirty-six classification results were obtained using K-means, maximum likelihood classification (MLC), and random forest (RF) algorithms. The best classification accuracies of the produced maps have been calculated as 80.3%, 73.1%, and 73.2% by RF, MLC, and K-means, respectively. In addition, the water coastlines determined by classifications were compared with the continuously operating reference station (CORS-TR) data in a local area by calculating the root-mean-square error (RMSE). Compared with the CORS-TR measurements of the dam coastline obtained from the images classified by the RF algorithm, the minimum RMSE values were calculated as 13.8 m and 10.1 m for Landsat and Sentinel images, respectively. While the minimum RMSE value for coastlines obtained with various layer stacks of Landsat images classified by the MLC algorithm is 36.7 m, it could not be calculated in Sentinel images due to poorer classification results. For the coastlines obtained from the images classified by the K-means algorithm, the minimum RMSE values were calculated as 14.5 m and 9.6 m for Landsat and Sentinel images, respectively. According to the comparisons based on classification accuracy and CORS-TR measurements, it is concluded that the RF algorithm performs better than others for the dam water surface. Moreover, it was determined that the NDWI presented better results when the water level was the lowest for Demirkopru Dam. Also, in this study, the MLC algorithm has better results in detecting water surfaces using Landsat images. It was concluded that the K-means algorithm is also very effective in water surface detection. In this study, various water extraction indices, algorithms and free Landsat and Sentinel images were used to extract the water surface in a selected reservoir for the FPV installation. This study guides a series of algorithms and indexes used to detect water surfaces. In addition, it has been shown that the use of RS techniques, which are more practical than classical approaches in determining water boundaries, will be more effective in planning and design in terms of engineers, investors and various organizations who will realize the FPV installation. [ABSTRACT FROM AUTHOR]

Efficient management of land use/land cover (LULC) features is vital for a balanced sustainable ecosystem. Thus, this work aimed to document the LULC changes in the less studied El Peñol-Guatapé reservoir, Antioquia, Colombia, especially in the reservoir area due to the construction of a hydro-electric power plant. For this study, Landsat images of 1977, 1986, 1997, and 2017 were used and the results indicated an increase in the settlement area and road networks by 0.10 and 0.60%, respectively, while during 1986 to 2017, cropland, plantation, dense forest, and open forest areas presented an increase of 0.52, 1.06, 2.87 and 2.61%, respectively. However, the marshy vegetation, scrub forest and fallow land decreased to − 0.51, − 3.79 and − 4.29%, respectively, in the same period. The water body before and after the completion of reservoir project denoted an increase from 13.1 km2 in 1977 to 45.7 km2 in 1986. This study provides a first-hand report on LULC dynamics in this tourism dominated municipalities that will serve as a reference for ecosystem management to reconcile the conflicts between different LULC classes in ecologically enriched regions. [ABSTRACT FROM AUTHOR]

In this study, trend analysis of the lake surface areas was performed on the Google Earth Engine (GEE) platform in the period of 1985–2022 with Landsat 5/7/8/9 (TM) (ETM +), and (OLI) satellite images. The study analyzed 10 lakes, including Acigol, Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli in the Türkiye Lakes Region. In this analysis, the normalized differentiated water index was calculated for each of the 3147 satellite images, and water surfaces were extracted from other details using Otsu's threshold method. In the study's accuracy, the overall accuracy and F1-score values were calculated to be over 90% for all lakes. Moreover, the relationship between the changes in the surface areas of the lakes was evaluated using correlation analysis, with the sea surface temperature obtained from the NOAA satellite and the evaporation, temperature, and precipitation parameters obtained from the Era-5 satellite being used. In addition, the change of the area on the lake surface was analysed using Mann–Kendall (MK), Sen's slope, and sequential MK test statistics. During the 37 years between 1985 and 2022, there was no significant change in the Acigol surface area, but a slight increasing trend was observed. Decreases of 76.07, 4.68, 41.77, 5.44, 37.56, 28.97, 78.65, 7.26, and 81.02% were determined in the lakes of Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli, respectively. The application of this method in the lakes region and monitoring these lakes, which are of great importance for Türkiye, provide valuable information in determining the lakes' organizational strategies. [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]

Recently, technological advances in UAV-mounted sensors, such as light detection and ranging (LiDAR) and multispectral sensors, have expanded the applications of unmanned aerial vehicles (UAVs) in ecosystem monitoring. LiDAR is suitable for analyzing the underlying microtopography of wetlands because it can produce a digital terrain model (DTM) with high spatial resolution. If a multispectral sensor that can also capture near-infrared wavelengths is used, it is possible to calculate the normalized difference vegetation index (NDVI), which is related to the amount of vegetation present, and the normalized difference water index (NDWI), which is related to the dryness and wetness of the soil. The purpose of this study was to understand the distribution of a disturbance-dependent species in wetlands using high spatial resolution images acquired with a consideration of phenology, and to evaluate the habitat of this disturbance-dependent species using data acquired by LiDAR and multispectral sensors. The wetland around the Omimaiko Inland Lake in Minamikomatsu, Otsu City, Shiga Prefecture, Japan, was chosen as the site for this study. I chose to examine the distribution of Euphorbia adenochlora as a disturbance-dependent species growing in the wetlands of the study area. Using high spatial resolution images acquired with a consideration of phenology, we were able to determine the distribution of the disturbance-dependent species E. adenochlora. Using the data obtained using LiDAR and multispectral sensors, we were able to evaluate its habitat and deduce its viability at six growth sites. This study aims to introduce a new way of applying UAVs in monitoring disturbance-dependent species in wetlands. [ABSTRACT FROM AUTHOR]

The threat of drought has been felt almost worldwide in recent years. It is critical to determine the causes of drought and how seasonal changes affect it. Additionally, it is necessary to determine the speed and impact area of drought, monitor drought areas, and attempt to find solutions against drought. With the developing satellite sensing systems, remote sensing methods are being used to investigate topics such as the increase and extent of drought, uncontrolled water consumption in agricultural activities, and the effects of unnatural pollutants on freshwater resources such as lakes and rivers. Using Synthetic Aperture Radar (SAR) satellite data to monitor changes in water bodies is a relatively new area of study in remote sensing. The spatial extent and seasonal change (spring and autumn) of droughts between 2017 and 2021 in Akşehir Lake were determined from Sentinel-1A SAR satellite data, and the Normalized Differential Water Index (NDWI) was calculated using Sentinel-2A optical satellite data and Standardized Precipitation Index (SPI) in this research. In addition, a different approach was applied to determine the change of wetland boundaries more accurately by converting the linear Sigma0 band to the decibel (dB) band and applying a non-linear 3 × 3 maximum filter to the dB band to Sentinel-1A data. Consequently, it has been established that Akşehir Lake, which used to have wetlands during the spring seasons but began to dry up in the autumn seasons, had completely dried up in both periods in 2021. [ABSTRACT FROM AUTHOR]

Identifying hitherto unknown palaeo-channels, especially in the arid regions of the Thar Desert, is crucial since these channels may form excellent aquifers, and are also associated with valuable ore deposits of many precious minerals. This study employed integrated C-band Synthetic Aperture Radar (SAR) of Sentinel-1A and high-resolution multispectral Sentinel-2A data of pre- and post-monsoon seasons (June and November) to delineate playas and palaeo-channels. This approach is the first of its kind for this area. The palaeo-channels were delineated through a detailed visual inspection of colour composite (CC) images of Sentinel-2A data, SAR backscatter (VH) images and fused SAR and optical images. Then, we studied the topographic profiles generated from the Shuttle Radar Topography Mission - Digital Elevation Model (SRTM-DEM) across the identified palaeo-channels, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to further confirm the existence of a palaeo-channel's course and playas. As a result, several playas and palaeo-channels in the area were successfully identified, some of which were previously unmapped and undetected. The results indicate that the post-monsoon datasets are more useful for the precise delineation of palaeo-channels due to the presence of relatively higher moisture along the palaeo-channels' courses. [ABSTRACT FROM AUTHOR]

Rivers are dynamic ecosystems with diverse habitats that require ample connectivity to ensure the flow of ecosystem services, thus empowering the sustainable development of an entire basin. Geo-spatial tools offer powerful prospects for monitoring of aquatic ecosystems. The usefulness of Sentinel-2 datasets to assess river connectivity has been explored for an un-gauged seasonal river system. The present study was undertaken in five ecologically unique river reaches viz. Wainganga, Wardha, Pranhita, Godavari-mid and Manair in Godavari Basin in the Indian Deccan Plateau to map water spread dynamics at various time scales, i.e., fortnightly, monthly, seasonal, annual and demi-decadal during 2016–2021. The maximum value of perennial water spread per square kilometre of total floodplain area (2016–2021), determined using Sentinel-2 imageries, was observed in river Wardha (0.18) followed by Pranhita (0.12) and Wainganga (0.11). The water spread showed a decreasing trend, while the number of patches in the river corridor increased over time from post-monsoon to pre-monsoon season. The copious perennial habitat with relatively larger patches, incessant flow in river Pranhita and obstructed flow, large-sized patches reported in river Wardha during summer months, hold importance in terms of providing refuge to aquatic biota. This study provides evidence for the impact of water projects on spatio-temporal water spread dynamics in Godavari Basin. The demonstrated utility of Sentinel-2 imagery coupled with gauge station measurements for river continuity assessment and deep pool mapping would aid in enhancing our understanding on environmental flow at a spatial scale, which in turn would aid in effective river management to achieve the Sustainable Development Goals. The implications of this study for sustainable environmental management and limitations are also discussed. [ABSTRACT FROM AUTHOR]

Crop type detection is of great importance in water resource allocation and planning mostly in arid and semi-arid regions of Iran. Landsat-OLI 16-day inter-annual images are invaluable sources obviating crop monitoring into issues of crop types detection, crop yield prediction, and crop pattern studies. Although many classification methods such as decision tree (DT), support vector machine (SVM), and maximum likelihood (ML) were implied for crop type mapping, recent researches often use an object-based classification approach. In this study, an object-based image analysis (OBIA) classifier based on rule-based decision tree (RBDT) and object-based nearest neighbor (OBNN) used to delineate five common crop types (includes Wheat and Barley together in one class, rice, multiple crop (MC), Alfalfa and Spring crops) in Isfahan city and nearby areas. The classification was applied in five scenarios using different vegetation indexes including normalized difference vegetation index (NDVI), normalized difference water index (NDWI), green normalized difference vegetation index GNDVI and their combination. All scenarios property and accuracy assessed both with by class separation distance matrix and confusion matrix. The overall accuracy of classification with using only one vegetation index was lower than other scenarios. It was the lowest for GNDVI rating 37% whereas combination of Indexes resulted better accuracy. In final map with combination of NDVI, GNDVI and NDWI, overall accuracy and kappa achieved to 88% and 0/83 successively. Comparing individual accuracy of different crops showed that MC crops with 66% has the lowest accuracy and Wheat-Barely crops with 94.8% individual accuracy has the Maximum accuracy. Other crop types accuracy alters between 66 and 94.8%. [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]

This study examines the environmental impacts of urban sprawl in Surat inner city in India. Urban sprawl is examined by studying land-use land-cover change from 1998 to 2016. Changes in spatial characteristics of urban land cover are further verified using Shannon's entropy. The impact of urban sprawl is examined by computing normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) values. Land surface temperature (LST) is also calculated using thermal band of multi-temporal Landsat data. Regression analysis is used to calculate the correlation between LST and NDVI, NDWI and NDBI. The study showed a negative correlation between NDVI, NDWI and LST and a positive correlation between NDBI and LST. Urban areas in Surat have grown an approximately 60.41 sq.km, a 102% increase while surface temperature in Surat inner city has risen by 5.1 °C during the study period. The study also showed that temperature increase was particularly pronounced throughout areas classified as urban and bare soil and showed an increasing trend in parts of the city with dense urban areas. [ABSTRACT FROM AUTHOR]

The present study investigates the accelerating factors for extreme flash flood at Chamoli district of Uttarakhand on 7 February 2021. The Sentinel-2A and 2B satellite data have been used to depict changes in pre-flood (16th of January) i.e., 5 years of 2016 to 2021 to post-flood (10 February, 2021) situation over the study domain. Vegetation and snow-cover from 2016 to 2021 has been obtained using Normalized Difference Vegetation Index (NDVI) classification over study area. Normalized Difference Water Index (NDWI) is used to extract the pre and post-flood water pixels for flood inundation mapping. The Cartosat-1 digital elevation model (DEM) product is used for drainage pattern and stream order mapping. Correlation between the meteorological parameters such as snowfall, wind speed and wind direction of Nanda Gunti peak during the time of flood with the flood event is analysed. The overall results indicate heavy snowfall (4.22 mm/day) over Nanda Gunti hills followed by high wind speed (23 km/hr.) that might have led to initiation of avalanche/landslide, giving rise to massive flash flood and eroded approximately 0.0263 km3 volume of landmass along with snow cover. Further, the 5 years NDVI analysis shows decrease in vegetation near Rishiganga and Alaknanda, a higher order river streams, is also crucial factor for flood intensification that caused massive destruction within the study area. The work highlights the importance of mapping of intense events and underline factors to reduce the impact and losses in case of future events. [ABSTRACT FROM AUTHOR]

Earth observation (EO) technologies and products are traditionally providing an extremely important contribution to a more efficient management of built areas and people during emergencies. Their employment could be extremely useful in monitoring and risk assessment of protected monument and sites situated in potentially vulnerable areas such as costal zones. Change detection has a crucial role in such activities, especially in case of hazardous events—on one side, this method can provide inputs in the phases of risk assessment and rapid mapping for immediate response; on the other side, over prolonged periods of time after the event, change detection can be used for purposes of option analysis of technical solutions and for overall recovery planning of the site. Workflow proposed is based on the use of Copernicus Sentinel-2 data to provide the comparison of changes occurring during extreme flooding events of river Centa, Liguria. Firstly, an investigation of NDVI and NDWI of the extreme flooding event occurring in November 2019 was conducted. The event was then put in correlation with another previously studied flooding hazard occurring in 2016 in the same area that has caused severe damages to the archaeological remains of the medieval church of San Clemente, situated within the Centa riverbed. In conclusion, the results from both years have been compared with ground truth data of topographic database in order to observe the extension of the flooded area and to provide contribution to the monitoring activities. The aim of the research was to offer some novel insight for a more informed decision-making during the risk assessment, risk management and resilience phases regarding the vulnerable built heritage sites found in coastal areas. [ABSTRACT FROM AUTHOR]

Monitoring and determining the amount of water in reservoirs is of great importance in terms of water planning and management. This study proposes a geographic information system (GIS)-based methodology to estimate the water volume changes in water reservoirs. Two specific methods are proposed using Australian National University's Digital Elevation Model (ANUDEM) raster surface and Triangulated Irregular Network (TIN) surface models, both utilizing normalized difference water index (NDWI) of Sentinel 2A satellite images for water-covered area and coastline and digital elevation model (DEM) for 3D modelling of the reservoir. The most crucial part of this study is the comprehensive evaluation of the model findings considering hydrological, meteorological and anthropogenic factors, simultaneously. Application of the proposed methods is provided for the analysis of the multi-temporal water volume changes of Bayramiç Dam Lake (Çanakkale, Turkey) in two hydrological periods covering the 2015–2016 and 2016–2017 water years. The results indicate that the TINS model produced water volume values much closer to the in situ Turkish General Directorate of State Hydraulic Works (DSI) values than the ANUDEM model. The performance of these methods was also assessed by the temporal dynamics of surface hydrological processes. Regarding the water storage dynamics, hydro-meteorological factors influence the water input, while anthropogenic factors strongly influence the water output. Water consumption for irrigation and electricity generation was found to be the most important water budget components of the total water consumption. [ABSTRACT FROM AUTHOR]

This paper presents the evaluation and management of water resources at the basin scale, focusing on small reservoirs. Due to a lack of knowledge on these untapped resources, a semiautomatic procedure for their surface area estimation is presented. Multispectral images from the Pleiades satellite were used to extract water bodies from a combination of different bands. In particular, the normalized difference water index (NDWI) was used to evaluate the presence or absence of water. This methodology was applied in a test area located in the upper Tiber River Basin. The performance for identifying and analysing small reservoirs was determined by comparing the satellite information with a reference database. The potential volume available from the investigated small reservoirs was compared with the water deficit derived from a decision support simulation model at the basin/subbasin scale, which studies water allocation for multipurpose uses. Five irrigation districts were analysed. For three of these districts, more than 60% of the annual deficit can be balanced with the volume stored in small reservoirs. To date, water scarcity conditions have been increasingly frequent, so resilience in water resource management is a key requirement. Therefore, it is essential to evaluate the recovery and reuse of small reservoirs to strongly support water use, particularly irrigation and environmental uses. [ABSTRACT FROM AUTHOR]

Rainfall plays an essential role in different water management sectors ranging from industries, farming, livestock, and domestic utilization both in rural and urban areas. The surface water bodies like rivers, lakes, ponds, and reservoirs hold a huge proportion of the rainfall. However, due to spatio-temporal variability of rainfall and limited availability of meteorological stations, accurate estimation of the hydrological components such as surface runoff, evaporation, and baseflow is a challenging task. In this study, we have used hydrological information, Soil Conservation Service Curve Number method to develop an empirical relationship between rainfall and runoff in the Telibandha Lake Catchment (TLC), situated in Raipur district of Chhattisgarh, central India. Further, we have used Landsat derived remote sensing product, Normalized Difference Water Index (NDWI) to understand its temporal variation of water spread in the TLC over the fifteen years (2001–2015). The water spread areas are assessed by using the mean monthly NDWI data set and analyzing its seasonal and annual patterns. It is found that Telibandha Lake has a maximum depth of 2.9 m and its surface area is 0.12 km2. Apart from that, the annual runoff is around 232.28 mm, which accounts for almost 22% of the estimated rainfall. The monthly runoff coefficient varies from 0 to 0.25 in TLC. Both the temporal and spatial NDWI plots show that July–September months have high NDWI values in the TLC. The seasonal NDWI showed a good relationship with mean monthly rainfall data and depicts r2 = 0.78. The findings from this study helps in determining the temporal changes of water spread within the TLC and supports in understanding the lake characteristics. [ABSTRACT FROM AUTHOR]

This study has been carried out to evaluate the river shifting and assessment of related effect on the land-use/land-cover using geographic information system (GIS) and remote sensing (RS) techniques between 1955 and 2016 for Padma River at Bangladesh. Alluvium is eroding chronically due to the rushing stream flow; therefore, people of this region are poor and government loss national assets each year that reflects the direct effect on the economy. The standard topographical map of 1955 and satellite images of 1973, 1987, 1999, 2011 and 2016 were used in the study. Impact assessment on land use/land cover around 5 km from the stream centerline of 1973 was obtained. This studied has been carried out in three steps: (1) analyzed migration rate from 1955 to 1973; (2) analyzed migration rate from 1973 to 2016 and (3) assessed migration rate impact on land-use/land-cover change using GIS and RS techniques. Normalized difference of water index technique was used for identification of land and water boundary. The maximum likelihood supervised image classification was applied to detect the land-use/land-cover type and identify erosional impact with best accuracy. Results of long-term river channel migration showed that the land area is influencing with river erosion, settlement area is increasing, vegetation area is decreasing, but sand bar, water body and cultivated land area are oscillating. The satellite images were collected during winter and dry seasons (from November to January), when stream flow is limited for a lower amount of rainfall. Maximum erosion and management plan are critical for area at section 2 right bank 1955 to 1973. On the other hand, a minor improvement of embankment is necessary at section 2 because erosion occurs from 1973 to 1987, 1973 to 1999 and 1973 to 2011 at left bank; at section 4 because erosion occurs from 1973 to 2011 at right bank; and at section 5 because erosion occurs from 1955 to 1973 at left bank. New land developments occur at section 3 left bank from 1973 to 2016; without it, the maximum occurrence of accretion number was found from 1973 to 2016 at section 2 right bank, section 3 left bank, section 4 left bank, section 5 left bank that help for river bank management, large-scale planning to prevent flood and reduce the property loss. [ABSTRACT FROM AUTHOR]

Coastline changes are caused by sea-level rise, erosion and anthropogenic activities, and have important consequences on coastal ecosystems and communities. In this study, shore line changes during the last 40 years near the Mahi estuarine belt in the Gulf of Khambhat region have been monitored using multi-temporal Landsat data from 1978 to 2018. Multi-date Landsat digital data with 10-year interval from 1978 to 2018 was downloaded from the website https://earthexplorer.usgs.gov/. The Landsat data covering the study area with 15-km buffer was extracted and analyzed for monitoring the changes in the shoreline. The Normalized Difference Water Index (NDWI) images were also generated for delineation of the shoreline. The data was analyzed using the Digital Shoreline Analysis System in ArcGIS software, which provides a set of tools permitting transects-based calculation of shoreline displacement. A total number of 10 transects from the centre of the village up to the coast line were marked on the multi-date Landsat digital data. The changes in the shoreline from the centre of the village were calculated using the End Point Rate (EPR) and temporal linear regression model to predict future shoreline position. The decadal shoreline change indicated that shoreline has continuously increased from 113.9 to 831.4 m during the 40-year period from 1978 to 2018, respectively. This indicates that total migration of shoreline towards the land area of the study village during the last 40 years (1978 to 2018) was 1590.5 m with an alarming annual rate of change of 39.76 m year−1, which is very remarkably high and alarming which may lead to any future disaster for the study village. The coastline was not significantly changing until 1988; however, change in coastline is exponentially faster from 1989 to 2018. The linear regression method was adopted for predicting the shoreline change rates over the entire period of the study from 1978 to 2018 and from 2018 to 2028 and 2038. The results of shoreline change prediction for future 10 years (2028) and 20 years (2038) indicate the average predicted transect length of 1908 m and 1543 m, respectively. With the projected estimates of shoreline change during next 20 years at the rate of almost 120 m/year, it appears that the Chokari village may get submerged in the sea water. The analysis of change in the land surface area of the Chokari village indicated that it has decreased from 12.45 to 9.63 km2, which indicates that area reduced by 2.8 km2 may be due to the erosion along the river-bed which is the result of change in the shoreline during the last 40 years. Based on these results of shoreline change analysis, it is recommended that the Government of Gujarat needs to take immediate steps to protect the Chokari village from submergence by adopting Integrated Coastal Zone management strategies. [ABSTRACT FROM AUTHOR]

Reservoir sedimentation is a momentous problem and has severe consequences on water management, flood control and production of energy. As the river flows into the reservoir, the sediments that travel along with it get deposited and thereby reduces the capacity of the reservoir. From this, it is obvious that study of sediment deposition has to be done periodically as it may affect the safety of dams and, without proper management, negatively impact the environment. This study has attempted to find out the amount of sediment deposition in Idukki reservoir, over the years. As the conventional method such as hydrographic survey is non-economical and time-consuming, the satellite remote sensing method is recommended for this analysis. LANDSAT 8 OLI/TIRS satellite images for the period 2017–2019 were used to assess the water spread area using Normalized Difference Water Index (NDWI), determine the sediment deposition and evaluate the revised capacity of the reservoir. After intensive analysis, a decline of 11.6% was observed between the cumulative capacities of the original base year (1974) and the latest (2019). The loss in live storage capacity between the years 1974 and 2019 was 0.10 km3 which is equivalent to 100 hm3. The study also evaluates the impact of the Kerala Floods 2018 in the Reservoir and also assess the need for desilting. [ABSTRACT FROM AUTHOR]

Karakorum glaciers are well known for the advancement and the formation of new glacial lakes due to accelerated climate warming. Its Shimshal valley is profoundly affected by glacier surges in the last couple of decades. Khurdopin glacier is one of the highly surging glaciers in the Karakorum region. Its continuous surge since the nineteenth century is blocking the Shimshal River and creating new lakes. Our objective is to investigate the Khurdopin glacier surge from 1999 to 2017. With this, we aim to identify possible climate and topographic controls on the glacier surge behaviour. We used Landsat 4–5, 7 and 8 satellite bands and ground observations to estimate the glacier retreat and new land cover types from the glacier surge. Our results show that the Khurdopin glacier surges every 20 years. We observed the first surge of 0.69 Km 2 in 2000 and the second surge of 0.63 Km 2 in 2017. Both events blocked the Shimshal River. We observed an increase of 1–4 °C in the glacier minimum and maximum winter temperatures. Moreover, we observed snowfall variations in the accumulation zone in the steep glacier slope with a north slope-aspect. The 64% of the glacier aspect is north that causes an increase in ice mass in the accumulation zone by receiving more snowfall in the winter season. The study output, combined with earlier findings, can further predict a surge event in the future. [ABSTRACT FROM AUTHOR]

Urban blue-green space (UBGS) is considered to be an effective way to mitigate Urban Heat Island (UHI) effects. UBGS not only cools the actual space but also influences the surrounding areas; this phenomenon is termed as UBGS cooling effect. The present study tries to anatomize the UBGS of urban Tezpur with the help of geo-technology. Landsat satellite images of Thematic Mapper (TM) and Operational Land Imager (OLI) with 30 m spatial resolution were used to investigate the UBGS scenario for the years 1993 and 2023, respectively. Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were taken into consideration for the ascertainment of UBGS and UHI. The correlation between LST and NDVI was also determined with the aid of simple regression analysis. The NDVI values for the years 1993 and 2023 are − 0.32 to 0.70 and − 0.44 to 0.50 respectively. The LST values of the town for the year 1993 are 28.76 to 20.17 and for 2023, the LST value is 29.47 to 20.36. The NDWI value indicates that the water index increased in the water bodies from the year 1993 to 2023. Though sufficient data are not available on the website, the data used in the study are free from major environmental and geometric disturbances to establish the LST, NDVI, and NDWI. However, the present work is the pioneer that used geo-spatial technology which will also help the urban planners and designers to deal with UBGS and UHI effects. [ABSTRACT FROM AUTHOR]

Assessment of land use/land cover change (LULCC) and its effects on the natural environment are important in policy decision making for sustainable environment. This study investigated and assessed the environmental impacts of artisanal and small-scale mining operations on environmental degradation resulting from such activities in selected host communities (Akoko Edo, Ijero and Ilesa). Vegetation, water, built-up expansion and land surface temperature dynamics were examined and analysed using appropriate indices for the years 1987, 2002 and 2017. A gradual decrease in the normalised difference vegetation index (NDVI) values was observed across Akoko Edo and Ijero Ekiti with a corresponding change in the highest NDVI values while Ilesa witnessed a higher NDVI value in 2017 relative to the previous years. The majority of the normalised difference water index (NDWI) values for 2017 appear to be above 0 in all the host communities. Mean land surface temperature (LST) values are in the order 24.63 °C (1986) < 25.26 °C (2002) < 26.32 °C (2017) for Ijero Ekiti study area while mean LST values are in the order 24.30 °C (1986) < 24.46 °C (2002) < 25.82 °C (2017) in Ilesa study area. Modified Normalized Difference Water Index (MNDWI) seemed a more reliable indicator as the index was able to enhance the water surfaces more clearly as indicated in the case of Akoko Edo study area. Due to its high heat capacity, high land surface temperatures were experienced over urban areas and uncultivated bare land during all the periods of study. The study showed that changes in land covers have affected the normal functioning of the habitats through urbanisation. Adequate natural resource management is therefore necessary to ensure sustainable development through effective policy formulation. [ABSTRACT FROM AUTHOR]

Remote sensing is an important tool for environmental assessment, especially in the event of disasters such as the tailings dam burst at the Córrego do Feijão mine, located in the Paraopeba River basin, Brazil. Thus, this study aimed to carry out a spectro-temporal analysis of the Paraopeba River water given the dam burst, using multispectral images from the MSI sensor onboard Sentinel-2 satellites. For this analysis, sections along the river were defined by the creation of buffers, with 10-km intervals each, starting from the origin of the burst. For each section, the average visible to near-infrared (NIR) reflectance values per band and the Normalized Difference Water Index (NDWI) were obtained. We found that the red edge and NIR bands (B5, B6, B7, B8, and B8A) showed higher reflectance values when compared to the visible bands in the months immediately after the disaster, especially in the first 20 km. In these months, negative NDWI values were also found for almost all sections downstream, demonstrating the large volume of mining tailings in the Paraopeba River. The seasonal variation of the observed values indicates the resuspension of the material deposited at the river bottom with the beginning of the rainy season. Finally, we highlight the usefulness of the MSI/Sentinel-2 red edge and NIR bands for further studies on the monitoring from space of water bodies subjected to contamination by large amounts of mud with iron ore tailings and contaminants, as occurred in the state of Minas Gerais, southeastern Brazil. [ABSTRACT FROM AUTHOR]

The Al-Asfar wetland, locally known as Lake Al-Asfar, is the largest inland wetland in the Arabian Peninsula. It is situated to the east of the Al-Ahsa Oasis. It was formed at the expense of the Umm Hishah sabkha via the discharge of increasing quantities of drainage water to the oasis. This research was conducted to assess changes in land cover using four satellite images collected from 1990 to 2020. The study used a Landsat 5 TM image (1990), a Landsat 7 ETM+ image (2000), a Landsat 8 OLI-TIRS image (2010), and a Landsat Sentinel-2 L2A image to examine land cover in the Al-Asfar wetland. The normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to evaluate the tendency of ecosystem evolution. The results indicate that for the past three decades, the wetland area has increased significantly. Approximately 15.46 km2 (24.69%) of the body of water was added during this time frame, and the vegetation areas expanded to approximately 10.74 km2 (17.15%). The changes detected in the study area can be explained by the discharge of agricultural drainage water and semi-treated water from sewage treatment plants and the spread of reed mites (Phragmites australis) which covered approximately 23.81% of the area of the Al-Asfar wetland in 2020. For these reasons, the study recommends the necessity of imperative consideration for the protection of the resources of other wetlands in Saudi Arabia to ensure their sustainable use for future generations. [ABSTRACT FROM AUTHOR]

Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas. [ABSTRACT FROM AUTHOR]

Flood is a natural calamity and causes damage of life and property devastation. The main objective of this study was to analyse flood hazard and inundation area mapping of Paravanar sub-Basin. Flood generating factors, like elevation, slope, drainage density, rainfall, soil type and land use were analyzed and delineated flood zones using Geospatial techniques and multi criteria evaluation. The flood generating factor was computed by data integration, floodplain depiction, mapping, and evaluation. Various parameters such as Normalized difference vegetation index (NDVI), Normalized difference build-up index (NDBI), Normalized difference water index (NDWI) were acquired for performing Regression Analysis. This information can be further utilized for hydro-meteorological figures and Surge forecast. These data can be used in building hazard and risk maps for pre-flood prediction. The flood hazard threats in the southeast and in between southeast and west part of the Paravanar River basin are high flood hazard threat zone. [ABSTRACT FROM AUTHOR]

Lakes are crucial elements of the Earth's hydrological cycle and play an important role in the regulation of the Earth's material exchange and energy transfer. The expansion or contraction of lakes has an important influence on water use in agricultural and biogeochemical activities. It is of great significance to establish a long time series dataset of changes in the lake area, water level, and lake volume. Based on Landsat data and Hydroweb data, this study firstly established a long time series dataset of the lake area, water level, and lake volume change of China's great lakes from 1978 to 2018. Then combined with DSAS, net shoreline movement (NSM), and linear regression rate (LRR) of the growth type lakes and the reduction type lakes were calculated, and quantitatively described the lake area change and spatial variation of lake shoreline. Finally, meteorological factors such as precipitation, evaporation, and temperature, as well as the influence of land use factors on lake area change, were analyzed. The results showed that Namtso, Selinco, Zhari Namco, Qinghai Lake, Xingkai Lake, and Taihu Lake belong to the growth type lakesl Hongze Lake, Poyang Lake, and Dongting Lake belong to the non-obvious change type lakes; and Bosten Lake and Hulun Lake belong to the reduction type lakes. Hongze Lake and Poyang Lake were greatly affected by precipitation; Xingkai Lake, Selinco, and Zhari Namco were greatly affected by evaporation and temperature. The impact of land use on lakes was mainly reflected in the following aspects: fishery breeding activities along the lakes, agricultural production activities of cultivated land around the lakes, human activities of construction land, and industrial activities of industrial land. Hydroweb/Landsat Data combined with DSAS can effectively conduct spatial analysis of changes in the lake area and provide valuable information for environmental protection of lakes in China. [ABSTRACT FROM AUTHOR]

Sand encroachment takes place when sand grains are transported by winds and collect on the glide, along watercourses. The drifts of dunes are the most important events leading to future disasters. Monitoring shoreline changes and mapping these changes at Nasser Lake using GIS and remote sensing techniques at regular periods represents the first significant step in accurately characterizing the problem. The data show a relationship between shoreline morphology, dune morphology and erosion/accretion. The analysis results show that the sands encroaching on the lake are in the shape of sand sheets, not dunes. Change detection was observed on images, and at the same time it was not possible to find rates of sand drift through remote sensing only. Nevertheless, it was found that the origin of sand is further north and that the wind, which is oriented south to southeastward, deposits its load near the lake or in it. Mitigation of dynamic sand should involve consideration of its source, corridors of movement and the site or sites of deposition. Zoning of the fluvial zone for regulatory design and assessing the potential impact of rising water levels indicates severe erosion in many locations, due to reduction in sediment supply by recharge dams and roads and the long-term process of rising water levels. [ABSTRACT FROM AUTHOR]

The paper describes experience of analysis of the high time resolution satellite image series aimed on detection of growing season framing dates. Satellite data use for growing season detection were discovered as observation gap filling technique in areas with sparse network of meteorological stations. We discovered the possibilities of growing season detection using annual series of 1-day Moderate Resolution Imaging Spectroradiometer (MODIS) composites instead of the ground-based monitoring of surface air temperature. Particularly, the study was aimed on comparison of growing season detection results based on ground and satellite observations. We attracted Google Earth Engine platform to process initial (Terra/MODIS) satellite data series, derived map series of Normalized Difference Water Index (NDWI) with a 1-day time resolution, produced annual graphs of the index in the reference points of meteorological stations, detected growing season framing dates at the reference points for the period of 2000–2017, and compared detected growing seasons with their estimations made upon the analysis of surface air temperature dynamics at the same meteorological stations. Involvement of satellite data gives the possibility to monitor growing seasons not only at the points of meteorological stations but through the whole studied area highlighting heterogeneity in spatial distribution in growing season dates and regional differences of growing season dynamics. Currently, we concluded weak quality of growing season detection based upon the 1-day NDWI series, due to the noise presented on annual graphs of NDWI. Mean observed accuracy of detection in comparison with the ground observations of surface air temperature was ~ 11.3 days, while median accuracy value was ~ 10.5. [ABSTRACT FROM AUTHOR]

Ponds, as landscape features, are known to regulate climate. Since ponds proliferate or recede due to natural or anthropogenic factors, a variation of pond numbers implies a variation of their climatic effect. Accordingly, this study investigates the impact of ponds on the local climate of the French Claise watershed. The latter was chosen because it contains a pond dense zone and a pondless zone. This repartition makes the Claise an adequate context to reveal the climatic impact of ponds even in the same landscape. To study the pond-climate effect, the parallel evolution of pond numbers variation and subsequent climatic impact must be tracked. Therefore, the remote sensing-derived Normalized Difference Water Index (NDWI) was extracted from LANDSAT images with different acquisition dates to track changes in pond numbers with time. When compared with a pond map established from aerial photography interpretation, the LANDSAT NDWI map revealed an accuracy of 85.74% for pond count and 75% for pond spatial allocation. This validation showed that NDWI is suitable for mapping the proliferation of ponds through time. In order to study the parallel evolution of the climatic effect, the land surface temperature (LST) index was extracted for each LANDSAT map. LST maps revealed that as a result of pond number variation, surface temperatures varied accordingly. A comparison of air temperatures between the ponded zone and pondless zones also revealed that pond zones had lower air temperatures than their direct surroundings. Accordingly, ponds were shown to buffer local microclimates even within the same landscape. [ABSTRACT FROM AUTHOR]

Wetland inventories especially on their spatial extent are a prerequisite for management and conservation of any wetland. The advancement in geospatial techniques has offered a wide range of methodological applications to prepare the inventories and to understand the dynamics of wetlands. The freely available Landsat imagery has been widely used in extracting spatial and temporal information about wetlands. The literature suggests that wetland has declined all over the globe over the past few decades. This study aims to prepare land use/land cover information of three wetlands of Punjab (Harike, Ropar, and Nangal) through direct on screen digitization and through digital processing using automatic digital indices as well. Evaluation of the performance of two band indices, normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) is also taken up in the present study. Landsat data of two periods-1990/91 and of 2018 are used for the study to perform two band indices. The result indicates that the NDWI and MNDWI are less time consuming and serve the purpose of mapping and monitoring wetlands with higher accuracy. [ABSTRACT FROM AUTHOR]

The article discusses remote methods for the description of the intraseasonal dynamics of soil and vegetation moisture. The field moisture content of the soil and vegetation cover is described by an integral indicator that takes into account the moisture content of the upper soil horizon (5–10 cm), grass phytomass, and leaves of trees and shrubs. The integrated field moisture demonstrates a reliable association with the normalized differential water index (NDWI); the determination coefficient R2 reaches values of 0.91 for individual classes of tracts. The most significant factors determining the loss of moisture during the summer period are the amount of photosynthetically active phytomass, the potential influx of solar radiation during the study period, and the moisture reserve in the soil and vegetation at the beginning of the growing season. These factors describe 67% of the NDWI difference between May and August 2016 in forest areas and 89% in the steppes. The results can be used to search for fire hazardous areas in the steppes and forests, as well as for the monitoring of vineyards. [ABSTRACT FROM AUTHOR]

Mapping of the surface water dynamics in parts of Upper Krishna River basin, Maharashtra State of India, was attempted in this study. It comprises an upland watershed and a major tributary of Krishna River in the Upper Krishna basin. Keeping in view that it is essential to appraise the accessibility of surface water in a basin containing the non-perennial river to deal with the water resource management, the present study area was chosen for investigations. This study modelled the surface water dynamics of the Sub-upper Krishna basin (SUKB) over a period of 17 years from 1999 to 2016 with cloud-free Landsat data sets. The technique of normalized difference water index (NDWI) was used to detect the surface water bodies of the basin. The NDWI-generated images were classified into water and non-water pixels, and an accuracy assessment was performed to find out producer's, user's, and overall accuracies of output. The NDWI method displays a good result of water surface detection with a Kappa coefficient ranging from 0.81 to 1.00 for the classified images. The surface water-mapped images were used to classify the water into permanent water, seasonal water and new permanent water. The change detection is performed and mapped using geographic information system (GIS) operations to understand the surface water dynamics from 1999 to 2016. The results illustrated the effectiveness of the NDWI approach for surface water mapping and GIS for change detection analysis, especially in detecting the changes in different times, simultaneously. [ABSTRACT FROM AUTHOR]