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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]

Over 30 million people rely on Lake Victoria for survival in Northeast African countries, including Ethiopia, Eritrea, Somalia, and Djibout. The lake faces significant challenges due to changes in land use and climate. This study used multi-source remote sensing data in the Google Earth Engine (GEE) platform to create Land Use and Land Cover (LULC), land surface temperature (LST), and Normalized Difference Water Index (NDWI) layers in the period 2000–2023 to understand the impact of LULC and climate change on Lake Victoria Basin. The land use/land cover trends before 2020 indicated an increase in the urban areas from 0.13% in 2000 to 0.16% in 2020. Croplands increased from 6.51% in 2000 to 7.88% in 2020. The water surface area averaged 61,559 square km, which has increased since 2000 with an average rate of 1.3%. The "Permanent Wetland" size change from 2000 to 2020 varied from 1.70% to 1.83%. Cropland/Natural Vegetation Mosaics rose from 12.77% to 15.01%, through 2000 to 2020. However, more than 29,000 residents were displaced in mid-2020 as the water increased by 1.21 m from the fall of 2019 to the middle of 2020. Furthermore, land-surface temperature averaged 23.98 degrees in 2000 and 23.49 in 2024. [ABSTRACT FROM AUTHOR]

The perennially ice-covered lakes of the McMurdo Dry Valleys (MDVs), Antarctica, are an important reservoir of liquid water in an arid and largely frozen environment. During the austral summer, the margins of these ice covers melt, forming a "moat" of liquid water and thin ice, allowing exchange between lake waters and the atmosphere to occur and serving as an interface between lake, soil, and stream ecosystems. The size of these moats varies from year to year. Here, we have established the first published record of moat area changes at MDVs' Lake Fryxell through time using manual traces of the moat as observed via satellite imagery. We have also tested a semi-automated approach for measuring moat area and found that it consistently underestimated the manual record, which we suspect may be due to the lower spatial resolution of images used in this versus the manual approach. Finally, we developed a predictive model based on readily available climate data, allowing moat area to be predicted beyond the limits of the satellite-based records. We found that functional moat area varies annually, potentially influencing ecosystem processes in the moats. [ABSTRACT FROM AUTHOR]

Maintaining the operability of a hydroelectric power station at a scale originally designed is being compromised by continuous reservoir sedimentation. The underlying factors include a complex mix of landscape alterations owing to natural and anthropogenic activities around dam areas, such as gully erosion, landslides, floods triggered by heavy rainfall, climate change, and construction activities. The hydropower projects in the low-to-mid mountain regions of Nepal are witnessing a combination of these phenomena, affecting their optimal performance in meeting long-term sustainable power supply targets. This paper presents a combination of geo-spatial analysis and field evaluations to identify the trends from Kulekhani-I, one of the oldest storage-type hydropower projects in Nepal, using long-term time series remote sensing satellite imagery from 1988 to 2020. Our analysis shows an expansion of the surface water content area over time, attributed mainly to high sedimentation deposition owing to multiple factors. This study has identified an urgent need for addressing the following two key contributory factors through an effective control mechanism to avoid rapid sedimentation in the reservoirs: natural—landslides and floods leading to mainly silt deposition during heavy rainfalls; and anthropogenic—road construction materials dumped directly in the reservoir. Effective implementation of a remote sensing monitoring scheme can safeguard future damages to dam environments of more recently built storage-type hydropower projects. [ABSTRACT FROM AUTHOR]

This study intends to investigate the fluvial-geomorphic change and erosion-accretion processes of two significant rivers. The 2149 km2 research area’s overall erosion and accretion were depicted using multi-temporal Landsat images and Normalized Difference Water Index, Modified Normalized Difference Water Index methods to measure and investigate change. However, focus has been placed on four unique sections that are prone to erosion, and the bank migration of the section has been measured in order to depict the actual state of erosion. The Padma-Meghna River eroded approximately 11.0, 77.0, and 13.0 km2 between 1973–1978, 1978–1989, and 1989–1994. However, 78.0 and 48.0 km2 of accretion occurred in the years between 1994 and 1999 and 1999 and 2004. Moreover, between 2004 and 2021, the river’s erosional activity was remarkably dynamic compared to deposition. Besides, 64 km2 and 4 km2 of land accretion occurred during 2008–2014 and 2014–2019, while 77 km2 and 68 km2 of land eroded during 2004–2008 and 2019–2021. The overall rates of erosion and accretion are 5.12% and 4.04%, respectively. Statistically, both banks of the Padma River eroded about 2 km from 1973 to 2021. Padma-Meghna confluences are more unstable and susceptible to change. The Meghna’s left bank moved 4.5 km east between Goshairhat and Haimchar in 1989, and in 2008, the river shifted 4.8 km west from the river of 1994. In 1989, it moved 5 km east from a previous location between Goshairhat and Haiderganj, and in 2008, it moved 6.3 km west from the 1989 river. The bank migrated 12 km eastward in 2014 from where it was in 2008 through the bar development. The Mehendiganj Upazila’s eastern side lost more than 7.4 km between 1973 and 2021, and the current major channel is simply 7.4 km west of the 1973 river. The findings of the dynamic changes might be useful to planners, developers, researchers, and government agencies as they develop and put into practice new development initiatives and river basin management.

Maintaining the operability of a hydroelectric power station at a scale originally designed is being compromised by continuous reservoir sedimentation. The underlying factors include a complex mix of landscape alterations owing to natural and anthropogenic activities around dam areas, such as gully erosion, landslides, floods triggered by heavy rainfall, climate change, and construction activities. The hydropower projects in the low-to-mid mountain regions of Nepal are witnessing a combination of these phenomena, affecting their optimal performance in meeting long-term sustainable power supply targets. This paper presents a combination of geo-spatial analysis and field evaluations to identify the trends from Kulekhani-I, one of the oldest storage-type hydropower projects in Nepal, using long-term time series remote sensing satellite imagery from 1988 to 2020. Our analysis shows an expansion of the surface water content area over time, attributed mainly to high sedimentation deposition owing to multiple factors. This study has identified an urgent need for addressing the following two key contributory factors through an effective control mechanism to avoid rapid sedimentation in the reservoirs: natural—landslides and floods leading to mainly silt deposition during heavy rainfalls; and anthropogenic—road construction materials dumped directly in the reservoir. Effective implementation of a remote sensing monitoring scheme can safeguard future damages to dam environments of more recently built storage-type hydropower projects.

The article focuses on detecting changes in vegetation and water cover in the Gai River Basin of Northeast India using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite imagery. It states that these methods, along with geographic information systems and remote sensing technology, help monitor environmental changes, such as deforestation, water body variations, and land degradation.

Integrating information on drought incidents into planning and analysis processes can assist land, water, and urban managers to prepare more effectively for water-related hazards. This research aims to spatiotemporally assess drought characteristics upstream of the Little Zab River Basin from 2004 to 2018, merging various satellite-derived and meteorological indices to overcome gauge measurement limitations. The Coefficient of Variation (CV) was primarily utilized to investigate precipitation inconsistency on an annual timescale. The Reconnaissance Drought Index (RDI), the second Modified Soil-Adjusted Vegetation Index (MSAVI2), and the Normalized Difference Water Index (NDWI) were adopted as meteorological, agricultural, and hydrological drought indices, respectively. Ultimately, the Pearson Correlation Coefficient (PCC) statistical test was applied to comprehend the relationship between the implemented variables. Findings exhibited moderate (22.4%-28.5%) CV values in the annual precipitation data. Based on RDIst results, a substantial extreme to severe drought event was recognized in the hydrological year of 2007-2008 and continued until 2008-2009 with inferior intensities at most observatories. The NDWI values displayed that the surface area of Dukan Reservoir reached its minimum extents of 133 km2 and 123 km2 in 2008 and 2009, respectively. Although mean MSAVI2 values competently detected the 2008 and 2009 drought incidents, those precipitation deficiencies later harmed the vegetation cover in 2010. There was a significant positive correlation between precipitation, RDIst, NDWI, and mean MSAVI2 values. It is concluded that meteorological drought in the research region instantly leads to hydrological drought, resulting in agricultural drought with a one-year lag. [ABSTRACT FROM AUTHOR]

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications. [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]

Because it can lead to retaliatory killing, livestock depredation by large carnivores is among the foremost threats to carnivore conservation, and it severely impacts human well‐being worldwide. Ongoing climate change can amplify these human–wildlife conflicts, but such issues are largely unexplored, though are becoming increasingly recognized. Here, we assessed how the availability of primary resources and wild prey interact to shape large carnivore selection for livestock rather than wild prey (i.e., via prey switching or apparent competition). Specifically, we combined remotely sensed estimates of primary resources (i.e., water availability and primary productivity), wild prey movement, and 7 years (2015–2021) of reports for livestock depredation by African lions (Panthera leo) in the Makgadikgadi Pans ecosystem, Botswana. Although livestock depredation did not vary between wet versus dry seasons, analyses at finer temporal scales revealed higher incidences of livestock depredation when primary production, water availability, and wild prey availability were lower, though the effects of wild prey availability were mediated by water availability. Increased precipitation also amplified livestock depredation events despite having no influence on wild prey availability. Our results suggest that livestock depredation is influenced by the diverse responses of livestock, wild prey, and lions to primary resource availability, a driver that is largely overlooked or oversimplified in studies of human–carnivore conflict. Our findings provide insight into tailoring potential conflict mitigation strategies to fine‐scale changes in resource conditions to efficiently reduce conflict and support human livelihoods. [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]

Iraq is considered to amongst those countries in the Middle Eastern region that are most exposed to the effects of climate change, which will have notable effects on wet areas and lakes. Natural or industrial water resources must be paid particular attention due to their importance in preserving environmental and biological systems, in addition to their economic and social importance. As a result of the effects of climatic change, water resources in Iraq have seen a multitude of changes. The aim of this study is to determine changes in the wetland area around AL-Razzaza Lake, Karbala province, Iraq, during the years 2000, 2005, 2010, 2015, and 2023. Landsat 5 satellite data from 2000, 2005, and 2010, and Landsat 8 and 9 data for 2015 and 2023, respectively, were used in this analysis, which was conducted using NDWI as a free, open-source program (ArcMap 10.8) to detect these changes; NDWI is a natural water anisotropy index used to detect the surface area of bodies of water in satellite images. The results revealed a clear decrease throughout the study period, as the wetland area of the lake in 2000 was 1189.7 km2, which represents a decrease of 34.3% compared to the total area of the lake (1810 km²); it decreased by 52.7% in 2005 (855.5 km²) and continued to decrease for 2010, 2015, and 2023, by 79.2%, 80%, and 85%, (376.5 km², 362.9 km², and 270.4 km², respectively). The wetland area of Al-Razzaza Lake decreased between 2000 and 2023 by 919.3 km², that is, an average of 40 km² per year. It was found that the lake wetland area sharply declined over the study period due to a lack of water surface resources via the Euphrates River, as well as climatic changes and poor water resource management. It is anticipated that the lake will lose more than half its current wetland area by 2040 if the current decline continues. These results are considered important in terms of preparing a strategic plan to preserve water bodies and wet areas in Iraq, including Al-Razzaza Lake. Remote sensing and GIS technologies have played a major and essential role in detecting such changes. [ABSTRACT FROM AUTHOR]

Under the background of global change, the lake water environment is facing a huge threat from eutrophication. The rapid increase in curly-leaf pondweed (Potamogeton crispus L.) in recent years has seriously threatened the ecological balance and the water diversion safety of the eastern route of China's South-to-North Water Diversion Project. The monitoring and control of curly-leaf pondweed is imperative in shallow lakes of northern China. Unmanned Aerial Vehicles (UAVs) have great potential for monitoring aquatic vegetation. However, merely using satellite remote sensing to detect submerged vegetation is not sufficient, and the monitoring of UAVs on aquatic vegetation is rarely systematically evaluated. In this study, taking Nansi Lake as a case, we employed Red–Green–Blue (RGB) UAV and satellite datasets to evaluate the monitoring of RGB Vegetation Indices (VIs) in pondweed and mapped the dynamic patterns of the pondweed Fractional Vegetation Coverage (FVC) in Nansi Lake. The pondweed FVC values were extracted using the RGB VIs and the machine learning method. The extraction of the UAV RGB images was evaluated by correlations, accuracy assessments and separability. The correlation between VIs and FVC was used to invert the pondweed FVC in Nansi Lake. The RGB VIs were also calculated using Gaofen-2 (GF-2) and were compared with UAV and Sentinel-2 data. Our results showed the following: (1) The RGB UAV could effectively monitor the FVC of pondweed, especially when using Support Vector Machine that (SVM) has a high ability to recognize pondweed in UAV RGB images. Two RGB VIs, RCC and RGRI, appeared best suited for monitoring aquatic plants. The correlations between four RGB VIs based on GF-2, i.e., GCC, BRI, VDVI, and RGBVI and FVCSVM calculated by the UAV (p < 0.01) were better than those obtained with other RGB VIs. Thus, the RGB VIs of GF-2 were not as effective as those of the UAV in pondweed monitoring. (2) The binomial estimation model constructed by the Normalized Difference Water Index (NDWI) of Sentinel-2 showed a high accuracy (R2 = 0.7505, RMSE = 0.169) for pondweed FVC and can be used for mapping the FVC of pondweed in Nansi Lake. (3) Combined with the Sentinel-2 time-series data, we mapped the dynamic patterns of pondweed FVC in Nansi Lake. It was determined that the flooding of pondweed in Nansi Lake has been alleviated in recent years, but the rapid increase in pondweed in part of Nansi Lake remains a challenging management issue. This study provides practical tools and methodology for the innovative remote sensing monitoring of submerged vegetation. [ABSTRACT FROM AUTHOR]

The influence of the changing land use land cover (LULC) pattern on the vegetation, water, and temperature parameters is ascertained in the study of the Vedavathi River basin. For the years 2005, 2010, 2015, and 2022 Landsat and MODIS satellite products (MOD11C3, MOD13Q1) along with the ground-based rainfall from IMD measurement were used to implement the methods of indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) along with the land surface temperature (LST), and standardized precipitation index (SPI) and analyze the situation in the basin. Since 2005 more than 2000 km2 of fallow land has been transformed into cropland. 22.35% of grassland was converted to plantation in two decades while 13% of scrub forest has been reduced to wasteland. The increase in the cropland and the changing forest area has brought an increase in the mean NDVI value while at the same time an increase in the temperature value. The years 2003 and 2004 witnessed an extreme dry conditions in most of the stations while 2010 and 2020 experienced flood-like conditions. Overall majority of the drought conditions were witnessed in the 1990s and 2000s while flood conditions were prominent in the later part of the study. The study aims to provide an understanding between all these factors and what is the intensity of influence on each other.

Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability. However, relying solely on one sensing data type may not fully capture the intricate relationship between soil properties and crop physiological processes. This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. Lee´s correlation was conducted between ECa, NDWI and NDVI data. MULTISPATI-PCA was used to assess the autocorrelation in the input variables before SSMZ delineation. The correlation results revealed two field categories based on the direct or indirect relationship between ECa and the multispectral data. On one hand, ECa values showed an inverse correlation (p < 0.01) with multispectral data (-0.25 to -0.45) for fields 1a, 1b and 4a. On the other hand, there was a direct correlation (p < 0.01) between ECa and multispectral data (0.31 to 0.51) for fields 2b and 5a. For all rice fields, MULTISPATI-PCA explained a range between 75.01 % to 95.64 % of the total variability. UML results highlighted two, three and four SSMZ, with two SSMZ being the optimal management zones number. The validation suggests that the θ data showed a significant statistical difference for two SSMZs (p < 0.05), regardless of the water content ranges. This study emphasized the importance of integrating geophysical and multispectral datasets in precision agriculture for evaluating field variability and crop conditions.

Lake Yojoa is Honduras’s only natural freshwater lake with historical, cultural, and recreational value. The lake is the core of the local economy and one of the country’s biggest tourist attractions. However, the development of the watershed during the last four decades imposed multiple anthropogenic pressures including mining, hydroelectric power generation, aquaculture, agriculture, ranching, and tourism businesses. Lake eutrophication emerges from these human-induced stressors, and climate change is the newest stressor threatening Lake Yojoa’s ecosystem health. This paper aims to investigate climate change’s effects on Lake Yojoa’s surface area over the past four decades. More specifically, this research studied lake shrinkage and recharge dynamics related to climate change, anthropocene effects, limnological risks, and the lake’s resilience. A decrease in surface area was expected after four decades of anthropogenic and climatic stressors. A Landsat imagery survey was conducted to create a time series to analyze surface area variability from 1985 to 2003, 2011, and 2013–2022. The analysis relied on the normalized difference water index model to estimate the surface areas, which ranged from 76.5 to 83 km2 and revealed a multitemporal decrease of ~6 km2. Abundant precipitation and storms have recharged Lake Yojoa through the years, while droughts and elevated temperatures have accentuated water evaporation and contributed to surface area loss. Despite intermittent flooding and droughts, Lake Yojoa’s trophic state showed resilience and consistency over time. Still, climate change and anthropogenic stressors are intensifying lake shrinkage worldwide, and without appropriate Honduran regulation, they may advance the eutrophication and shrinkage of Lake Yojoa.

The perennially ice-covered lakes of the McMurdo Dry Valleys (MDVs), Antarctica, are an important reservoir of liquid water in an arid and largely frozen environment. During the austral summer, the margins of these ice covers melt, forming a “moat” of liquid water and thin ice, allowing exchange between lake waters and the atmosphere to occur and serving as an interface between lake, soil, and stream ecosystems. The size of these moats varies from year to year. Here, we have established the first published record of moat area changes at MDVs’ Lake Fryxell through time using manual traces of the moat as observed via satellite imagery. We have also tested a semi-automated approach for measuring moat area and found that it consistently underestimated the manual record, which we suspect may be due to the lower spatial resolution of images used in this versus the manual approach. Finally, we developed a predictive model based on readily available climate data, allowing moat area to be predicted beyond the limits of the satellite-based records. We found that functional moat area varies annually, potentially influencing ecosystem processes in the moats.

As one of the largest inland lakes in China, Dongting Lake has attracted widespread attention owing to its rich natural resources, unique geographical landscape, and important ecological functions. Recently, Dongting Lake has experienced phenomena such as an early dry season and backflow during the flood season. Multi-source remote sensing data and the normalised difference water index (NDWI) threshold method were used to systematically analyse the water area of the lake from 2005 to 2020. Additionally, it employed a centre of gravity migration model and a geographic detector model to investigate the lake's evolution patterns and driving mechanisms. The research identified notable fluctuations in Dongting Lake's water area during this period, with a particularly sharp decline in 2006—from 1509.74 km2 to 815 km2, marking a decrease of 694.74 km2 and a shrinkage rate of 46.01 %. Spatial analysis indicated that the centre of gravity of these water areas changed primarily between Nandashan Town, the Dongting Lake Management Committee, Wanzihu Township, and Qingtan Township, underscoring their significant influence on lake dynamics, including runoff, surface water availability, sediment deposition, and precipitation, all of which displayed strong positive correlations (Pearson coefficients of 0.57, 0.68, and 0.63, respectively), whereas population density showed a negative correlation (Pearson coefficient of −0.56). Furthermore, the study highlighted the substantial impact of the Digital Elevation Model (DEM) and its interaction with slope and aspect on Dongting Lake's evolution, with Q values of 0.537 and 0.543, respectively, emphasising their critical roles in shaping lake area changes and providing a crucial scientific basis for enhancing the understanding and effective management of water resources in the Dongting Lake Basin through comprehensive analysis of its spatiotemporal evolution and driving mechanisms.

Abstract In this research we analyzed the morphometric parameter of Palar River basin by using the satellite data from open sources for monitoring drainage network. Palar River basin covers 1972.27 sq.km areas with elevation of 226.9 m as highest and 38.01 m lowest above mean sea level. In this research it detected that Palar River basin having 5th order stream with drainage density of 0.40 km/km2 and drainage pattern of dendritic to rectangular. We also draw aspect map, relief map and slope map for the research area by using digital elevation model (DEM) data of 30 m resolution. In this research we also employed spectral indices like normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI) for the vegetation, water and soil characteristics of the research area. In this research it found that there are major changes in land use/land cover as water bodies and land during these periods of 15 years. Highly positive correlation show between morphometric parameter and spectral indices. In this research groundwater level data for the year 2005 and 2020 are used to validation the study. This research work is very useful in developing solutions for dealing with different types of drought and management of groundwater extraction plans.

【Objective】 The Grand Canal is a complex hydraulic project in the eastern China. This paper studies the flooding and its detrimental impacts in the lowland areas of the canal. 【Method】 The research was conducted in the Pichang-Tanxin Plain, using multi-source remote sensing data and ground surveys to analyze flooding events. We calculated the normalized difference water body index (NDWI) and the normalized difference vegetation index (NDVI) before and after flooding to assess flood severity, impact on crop growth, as well as the efficacy of hydraulic facilities in mitigating flooding effects. 【Result】 The NDVI difference between 2004 and 2005 for crops in flooded and non-flooded areas was 0.5 and 0.25, respectively, indicating floodings detrimentally affected crop growth and development. NDWI analysis from 2003 to 2017 demonstrated that the Taierzhuang Control Gate project mitigated flooding impacts on nearby farmlands. Flooding also affected crops in non-inundated regions, with lowland crops more adversely impacted than those outside lowlands. The hydraulic facilities significantly reduced economic losses caused by flooding. 【Conclusion】 Remote sensing is an effective tool for monitoring inundated areas and assessing the consequences of flooding on crops. The construction of hydraulic facilities plays a crucial role in reducing the detrimental effects of flooding in lowland regions of the Grand Canal.

Drought conditions bring many problems to water resources, especially in areas of low precipitation. This study employs the Normalized Difference Water Index (NDWI) to understand the severity of drought for effective and sustainable utilization and timely response. The research uses image processing techniques to calculate surface water area with its changes for three essential lakes in Iraq (Haditha, Therthar, and Razzazah) during two time periods from 1987 to 2000, and from 2000 to 2014. Furthermore, Support Vector Machine (SVM) method was applied on Sentinel-2 images for 2024 to recognize the current changes in the three lakes. The analysis was based on the Geographic Information Systems (GIS) spatial analyst tool and Landsat images by building and executing a single map algebra expression of NDWI based on Python syntax. A drastic change occurred for the Razzazah lake surface area which increased by 2.5 % from 1987 to 2000 and decreased by 67 % from 2000 to 2014. The total decrease in Therthar and Haditha lakes from 1987 to 2014 was 28 %, and 12.6 %, respectively. There were slight changes in the geomorphological shape of Haditha Lake. While, Razzazah Lake underwent a noticeable change in shape, represented by a decrease in the northern and eastern parts of the lake. Therthar Lake has also been subjected to a reduction and change in the northern part with total drying up of lake parts in the southeast which turned to bare land. The study provides methodological guidelines for effective water management in the areas of Iraq requiring immediate interventions.

In the article, the development of the cities of the Shirvan plain, located in the central part of the Republic of Azerbaijan, and the impact of this development on the loss of biodiversity of the cities were analyzed. Currently, cities, where more than 50% of the world’s population live, are the main reasons for the change of natural landscapes. The development of cities and the intensity of urbanization accelerate these changes. Therefore, our research is dedicated to the current issue. During the research, preference was given to modern methods. Landsat 5, 8 and 9 satellite images of 6 cities located on the Shirvan plain were obtained. As a result of the processing and analysis of these satellite images, the development of the territory and population of the cities was studied, and their effects on the natural landscape, its components, vegetation and water bodies were analyzed. The boundaries of cities in 1975 and 2023 were determined and compared. As a result of the conducted studies, it was determined by the processing of the 1975 topo plans and the 2023 satellite images that the area of the cities has increased by at least 2 times. The high average annual growth of the population has led to the transformation of cities from small towns to medium and large cities in a short period of time. This causes the loss of biodiversity as soon as possible and accelerates this process. [ABSTRACT FROM AUTHOR]

Marks, S. and Potter, C., 2024. Mapping recent coastal shoreline changes in southwestern Alaska using Landsat 8 satellite imagery. Journal of Coastal Research, 40(4), 661–671. Charlotte (North Carolina), ISSN 0749-0208. Coastal communities in Alaska are frequently subjected to storm surges that erode shorelines and riverbanks, which increases flood risks around critical village infrastructure. Coastal erosion in western Alaska may be occurring currently at rates as high as 15 m per year, but the region has not been mapped in enough detail to accurately assess recent shoreline and inland riverbank erosion rates. To update the mapping of changes in all of SW Alaska's coastal margins, Landsat 8 satellite imagery was analyzed to detect trends in the normalized difference water index collected from 2013 to 2023. The normalized difference water index values range from -1 to 1, with negative values indicating majority land that includes soil and live vegetation cover and positive values indicating majority water coverage. Results showed marked coastal shoreline erosion at Chagvan Bay, Hagemeister Island, and Nanvak Bay over the past decade. Additional locations of recent bank erosion were detected on the Kuskokwim River, from its upriver mouth past the city of Bethel. Riverbank erosion was also detected in northern Nushagak Bay, and extensive surface-wetting trends around wetlands and riverbanks were detected near the village of Togiak. Moreover, many villages on the west coast of Alaska and in the Kuskokwim River Delta have recently documented climate change case studies highlighting bank erosion, indicating that a variety of related ecological disturbances are ongoing across the region. [ABSTRACT FROM AUTHOR]

An urban heat island emerged due to micro urban temperature variations are also referred to as urban heat islands or urban hot spots. The high-rise buildings along the roads form "Urban Canyons" that inhibit reflected radiation from the built-up surface. Urban heat island develops over the cities due to man-made activity and the landscape. An understanding of the urban heat island and its formation is not only helpful in understanding urban thermal characteristics but also helps in understanding human comfort. A geospatial technique has the ability to acquire updated and cost-effective data over large regions. For urban climatology studies, remote sensing and geographic information systems are an important source of information and an effective methodology. Since 1971, the city of Krishnanagar and its vicinity have been witnessing rapid urban growth. Due to its dense population, urban climate and rapid urban expansion, they cause environmental degradation. Appraisal and Impact of urbanization on micro-climate in the Krishnanagar city complex based on satellite derived parameters. For the years 1995, 2007 and 2018, several satellite image analysis approaches such as NDVI, NDWI and NDVI were computed. Significant differences in land surface temperature were observed between 1995 and 2007, as compared to 2007 and 2018. [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]

With escalating human activities and the substantial emissions of greenhouse gases, global warming intensifies. This phenomenon has led to increased occurrences of various extreme hydrological events, precipitating significant changes in lakes and rivers across the Qinghai Tibet Plateau. Therefore, accurate information extraction about and delineation of water bodies are crucial for lake monitoring. This paper proposes a methodology based on the Normalized Difference Water Index (NDWI) and Gumbel distribution to determine optimal segmentation thresholds. Focusing on Qinghai Lake, this study utilizes multispectral characteristics from the US Landsat satellite for analysis. Comparative assessments with seven alternative methods are conducted to evaluate accuracy. Employing the proposed approach, information about water bodies in Qinghai Lake is extracted over 38 years, from 1986 to 2023, revealing trends in area variation. Analysis indicates a rising trend in Qinghai Lake's area following a turning point in 2004. To investigate this phenomenon, Pearson correlation analysis of temperature and precipitation over the past 38 years is used and unveils the fact that slight precipitation impacts on area and that there is a positive correlation between temperature and area. In conclusion, this study employs remote sensing data and statistical analysis to comprehensively investigate mechanisms driving changes in Qinghai Lake's water surface area, providing insights into ecological shifts in lake systems against the backdrop of global warming, thereby offering valuable references for understanding and addressing these changes. [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]

Global warming is one of the primary causes contributing to melting glaciers and shrinking of glaciers moth. Because of the glacier retreat, more lakes increase the risk of flooding in people's homes and lives. Several studies on the surging glaciers have been conducted by researchers using various techniques, as well as with the aid of multiple models like the Normalized Differential Water Index (NDWI). The Number of glacial lakes is increasing in the Himalayan region due to climate change (rise of the temperature). Some glacial lakes are potentially dangerous so monitoring is very necessary. It is necessary to evaluate such vulnerable lakes. Therefore, current work is carried out to identify such glacial lakes present in the Teesta River Basin (Eastern Himalaya). Spatiotemporal Landsat data for the last four decades at intervals of ten years from 1990 to 2020 has been considered which was cloud-free and spatial resolution of 30 meters. The dataset mentioned above was used for lake identification and delineation. The findings indicate the presence of lakes with respective areas of 275 (18.90 km²), 337 (24.92 km²), 295 (22.96 km²), and 419 (31.44 km²). It has also been observed that the growth rate is increasing with approximate water spread from 1990 to 2000 (+129%), 2000 to 2010 (+106%), and 2010 to 2020 (+136%). The present study aimed to identify such glacial lakes based on their water spreading area, which is an essential step followed in the study of GLOF (Glacial Lake Outburst Flood) as it will be helpful in the identification of hazardous lakes. In that study, we found that eleven glacial lakes are in the potentially dangerous category situated in the upper Teesta Basin due to the presence of glaciers, which gives a clear reason for the time-to-time assessment of such lakes. By the conducted study it has been observed that the number of glacial lakes has increased, due to which water spread has also increased in the area. It can also be demonstrated that GIS (Geographical Information System), along with remote sensing, is one of the best tools for assessing and monitoring such change detection and differentiation of hazardous glacial lakes in the cryosphere, along with the supporting data. [ABSTRACT FROM AUTHOR]

Copyright of lnternational Journal of Geography & Geography Education is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of lnternational Journal of Geography & Geography Education is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many countries have shown interest in the happiness of societies due to its significant importance to citizens. This interest has been reflected through important indicators aimed at enhancing urban happiness, including economic factors such as per capita GDP, healthcare and educational services, as well as environmental factors. The research aims to explore one aspect of the reasons for the decline in the classification of Iraqi cities in the World Happiness Index. Green spaces have been the primary focus of studies in improving urban happiness, along with blue spaces. Due to instability, Iraq has experienced a decline in the ranking of its cities globally. The research utilized the NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) calculated using the Copernicus Open Access Hub, Sentinel2L1C. The study compared the reality of Iraqi cities (Mosul, Baghdad, Basra, and Najaf) with the two happiest cities in the world according to international happiness reports for 2023: Aarhus in Denmark ranked first and Amsterdam in the Netherlands ranked second. Data analysis was conducted using Land Viewer|EOS to extract the proportions of green and blue spaces for each city. The results indicated that green spaces in Iraqi cities were limited, with little attention paid to blue spaces or their preservation, resulting in their scarcity compared to Aarhus and Amsterdam. This has contributed to Iraqi cities lagging in their classification as happy cities. [ABSTRACT FROM AUTHOR]

In the article, the development of the cities of the Shirvan plain, located in the central part of the Republic of Azerbaijan, and the impact of this development on the loss of biodiversity of the cities were analyzed. Currently, cities, where more than 50% of the world’s population live, are the main reasons for the change of natural landscapes. The development of cities and the intensity of urbanization accelerate these changes. Therefore, our research is dedicated to the current issue. During the research, preference was given to modern methods. Landsat 5, 8 and 9 satellite images of 6 cities located on the Shirvan plain were obtained. As a result of the processing and analysis of these satellite images, the development of the territory and population of the cities was studied, and their effects on the natural landscape, its components, vegetation and water bodies were analyzed. The boundaries of cities in 1975 and 2023 were determined and compared. As a result of the conducted studies, it was determined by the processing of the 1975 topo plans and the 2023 satellite images that the area of the cities has increased by at least 2 times. The high average annual growth of the population has led to the transformation of cities from small towns to medium and large cities in a short period of time. This causes the loss of biodiversity as soon as possible and accelerates this process.