Normalized Difference Water Index Research Articles

Enhancing human resilience against climate change: Assessment of hydroclimatic extremes and sea level rise impacts on the Eastern Shore of Virginia, United States

Coastal regions face climate-induced threats that have likely increased over the past four decades. In this work, we quantify the future climate impacts on hydroclimatic extremes in the risk-prone, 15-m-above-sea-level Eastern Shore of Virginia (ESVA) region, utilizing the Sixth International Coupled Model Intercomparison Project (CMIP6) Assessment Report 6 (AR6) and General Circulation Models (GCMs). We incorporate historical data on demographics and disasters, land use land cover (LULC), Landsat imagery, and sea level rise (SLR) to better understand and highlight the correlation between hydroclimatic extremes and societal components in this region. The hydrological model Soil and Water Assessment Tool (SWAT), Standardized Precipitation Index (SPI), Normalized Difference Water Index (NDWI), and Interquartile Range (IQR) method have been used to evaluate the intensity and frequency of projected climate extremes, in which SLR projections under different greenhouse gas emission pathways are temporally and spatially quantified. Our findings include (1) a trend towards wetter conditions is found with an increase in the number of flood events and up to an 8.9 % rise in the severity of flood peaks compared to the 2003–2020 period; (2) current coastal high-risk regions, identified using historical data of natural disasters, demographics, and LULC, are projected to be more susceptible to future climate impacts; and (3) low-lying coastal towns and regions are identified as currently vulnerable to coastal and SLR-induced flooding and are projected to become even more susceptible by 2100. This is the first effort that provides a valuable scientific basis for anticipated shifts in future climate patterns, essential for natural hazard prevention in ESVA. It highlights the need for authorities and decision-makers to plan and implement adaptive strategies and sustainable policies for the ESVA region and other coastal areas across the United States.

Monitoring of coastline change using Sentinel-2 MSI data. A case study in Thanh Hoa Province, Vietnam

Vietnam is a coastal country with a coastline of more than 3,260 km, stretching from north to south. Coastal change in Vietnam is complicated further by the effects of climate change, including erosion and accretion, causing great impacts on infrastructure and the environment. This article presents the results of assessing coastline changes in Thanh Hoa Province (North Central region of Vietnam) from Sentinel-2 MSI satellite image data for the period 2015–23. Three Sentinel-2 MSI images taken in December 2015, December 2020 and December 2023 were used to calculate the water indices, including Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI) Argumented Normalised Difference Water Index (ANDWI) and Automated Water Extraction Index (AWEIsh), then extract the shoreline using the thresholding method and select the water index with the highest accuracy through comparing the overall accuracy and Kappa coefficient. The coastlines of 2015, 2020 and 2023 years are overlaid to evaluate the coastal changes in the study area. The results received in the study provide objective and timely information, helping managers effectively monitor and respond to coastline changes.

Bankline Migration Analysis of Brahmaputra River in Morigaon District, Assam using Automated Method

Objectives: The study examines spatio-temporal changes, forecasts bankline migration and seeks to determine its underlying causes and consequences. Methods: Bankline change detection was performed through comprehensive GPS-based field surveys and remote sensing data, validated with ground-based data. The NDWI and MNDWI used to delineate the bankline positions from 1988-2022, with temporal changes estimated using the DSAS model. Findings: The left and right riverbank have been divided into three exclusive zones to find out which zone is severely erosion and accretion prone regions for zone-wise exclusive analysis. In the analysis, it has noticed that the left bank experiencing severe bank erosion and the right bank is experiencing an accretion rate increase in the last few decades. The LRR statistics show that the erosion rate in the left bank (81.17 m/y) is significantly high with an accretion rate of 0 m/y in the observed area. On the other hand, in the right bank, the rate of erosion is 83.59 m/y and the rate of accretion is 68.45 m/y. The left bank’s erosion is notably high. Additionally, the erosion rate is severe in the zone C (118.22 m/y) of left bank line. In this zone, the erosion rate hiked by 59.58 m/y in 34 years. Novelty: This study addresses a significant gap in understanding the riverbank shifting dynamics along with its causes and socio-economic impact of the Brahmaputra River in the Morigaon district. By combining the hydrodynamic modelling and cause-effect analysis, it provides a comprehensive analysis of bankline migration patterns and their causes and consequences. Unlike previous research, which primarily focuses on bankline migration rates and land use changes, this research work uniquely explores the direct impact of riverbank shifting on local communities. The integration of advanced predictive models with detailed primary and secondary data analysis offers novel insights crucial for efficient flood management and policy making practices. Keywords: Normalised Difference Water Index (NDWI), Modified Normalized Difference Index (MNDWI), Digital Shoreline Analysis System (DSAS), EPR and LRR Model, Bankline Shift

Spatiotemporal lakes surface area changes over 35 years and potential causes in the Central Rift Valley, Ethiopia

Study RegionCentral Rift Valley (CRV) region, Ethiopia. Study FocusThe study aims to investigate the spatiotemporal change in lake water surface extents over 35 years (1986–2020) and identify potential causes. Three alternative multispectral water indices—Normalized Difference Water Index (NDWI), Automatic Water Extraction Index (AWEI), and Modified Normalized Difference Water Index (MNDWI)—derived from Landsat sensors (TM, ETM+, OLI) were utilized to extract and analyze the lake surface area. The Mann-Kendall correlation was applied to examine whether hydro-meteorological and anthropogenic variables are possible causes for changes. New Hydrological Insights for the RegionMNDWI outperforms other indices with over 96 % accuracy in mapping lake surface area. Lake Abiyata experienced the most significant reduction in surface area from 1986 to 2020 (>50 %), followed by Lake Ziway (<2 %) while Lake Langano remained relatively stable. This significant reduction positively correlates to alterations in lake water depth (R2=0.92). Factors such as increased temperature, evaporation, and water abstraction for soda ash production negatively correlated with lake surface area, while inflow from Bulbula River and mean annual rainfall showed positive correlations. Abiyata Lake's decrease in surface area is primarily attributed to water abstraction for soda-ash manufacturing and incoming river flow. If the current trends in water abstraction from the Lake and income water diversion continue, there is a high possibility that Lake Abiyata will change into a terminal lake shortly.

Geospatial Assessment of Wetland Changes in the Fringe Area of Dhaka City: Past, Present and Future Scenarios

This study examines the changes in wetlands in the fringe area of Dhaka city using geospatial assessment techniques. The wetland in the area is crucial as they serve as a buffer zone for the Dhaka Metropolitan Area (DMA) and play a significant role in maintaining the region's ecological balance. Thirty-meter NASA Landsat 4-5TM and Landsat 8 OLI satellite imagery were used to extract wetland features in the fringe area of Dhaka city. The Modified Normalized Difference Water Index (MNDWI) was applied to delineate wetland features. Finally, ArcGIS geometric computing was used to detect yearly changes, and a Cellular Automaton (CA) Markov Model was applied to predict future transitions. The research findings reveal that wetlands in the Dhaka Metropolitan Area are declining at a rate of 1.1% between 1991 and 2016, indicating the urgency of addressing this issue to ensure the sustainability of the region's natural resources. The percentage of wetlands in the fringe area has been decreasing significantly over the years, with wetlands making up only 12.98% of the fringe area in 2022, down from 28.22% in 1989. This trend is predicted to continue, with wetlands accounting for only 8.02% of the total area by 2034. The strong negative correlation coefficient between the year and wetland area suggests that the trend of decreasing wetland is likely to continue unless significant measures are taken to protect wetlands. The study highlights the importance of conserving and restoring wetlands in Dhaka's fringe areas to maintain the ecological balance of the region and support the well-being of both humans and wildlife. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(2), 2023, P 97-117

Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset

Ireland’s coastline, a critical and dynamic resource, is facing challenges such as erosion, sedimentation, and human activities. Monitoring these changes is a complex task we approach using a combination of satellite imagery and deep learning methods. However, limited research exists in this area, particularly for Ireland. This paper presents the Landsat Irish Coastal Segmentation (LICS) dataset, which aims to facilitate the development of deep learning methods for coastal water body segmentation while addressing modelling challenges specific to Irish meteorology and coastal types. The dataset is used to evaluate various automated approaches for segmentation, with U-NET achieving the highest accuracy of 95.0% among deep learning methods. Nevertheless, the Normalised Difference Water Index (NDWI) benchmark outperformed U-NET with an average accuracy of 97.2%. The study suggests that deep learning approaches can be further improved with more accurate training data and by considering alternative measurements of erosion. The LICS dataset and code are freely available to support reproducible research and further advancements in coastal monitoring efforts.

Exploring Urban Heat Distribution and Thermal Comfort Exposure Using Spatiotemporal Weighted Regression (STWR)

With rapid urbanization, many cities have experienced significant changes in land use and land cover (LULC), triggered urban heat islands (UHI), and increased the health risks of citizens’ exposure to UHI. Some studies have recognized residents’ inequitable exposure to UHI intensity. However, few have discussed the spatiotemporal heterogeneity in environmental justice and countermeasures for mitigating the inequalities. This study proposed a novel framework that integrates the population-weighted exposure model for assessing adjusted thermal comfort exposure (TCEa) and the spatiotemporal weighted regression (STWR) model for analyzing countermeasures. This framework can facilitate capturing the spatiotemporal heterogeneities in the response of TCEa to three specified land-surface and built-environment parameters (i.e., enhanced vegetation index (EVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)). Using this framework, we conducted an empirical study in the urban area of Fuzhou, China. Results showed that high TCEa was mainly concentrated in locations with dense populations and industrial regions. Although the TCEa’s responses to various land-surface and built-environment parameters differed at locations over time, the TCEa illustrated overall negative correlations with EVI and MNDWI while positive correlations with NDBI. Many exciting spatial details can be detected from the generated coefficient surfaces: (1) The influences of NDBI on TCEa may be magnified, especially in rapidly urbanizing areas. Still, they diminish to some extent, which may be related to the reduction in building construction activities caused by the COVID-19 epidemic and the gradual improvement of urbanization. (2) The influences of EVI on TCEa decline, which may be correlated with the population increase. (3) Compared with NDBI, the MNDWI had more continuous and stable significant cooling effects on TCEa. Several mitigation strategies based on the spatiotemporal heterogeneous relationships also emanated. The effectiveness of the presented framework was verified. It can help analysts effectively evaluate local thermal comfort exposure inequality and prompt timely mitigation efforts.

ASSESSMENT OF URBAN DYNAMICS – A CASE STUDY ON BERHAMPORE MUNICIPALITY AND ITS SURROUNDINGS, WEST BENGAL, INDIA

Berhampore was a former Armenian-to-British colony and historic commercial centre, had steady expansion during the colonial era, a still-ongoing trend. The natural landscape is transforming from the centre to the periphery in an extremely unpredictable way due to rapid urbanization. The current study examines the urban growth of Berhampore town in West Bengal, India, using geospatial techniques. To understand the spatiotemporal dynamics of the urban landscape from remotely sensed data, four indices are used: the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Built-up Index (BUI), and the Modified Normalized Difference Water Index (MNDWI).The result of these indices shows the unsustainable urbanization in this region. Over the course of thirty years, the built-up area increased by nearly 7.80 percent, depleting the prime vegetative cover, water bodies and, in some cases, the barren land. According to the study, it is helpful to determine current urban growth and development so that local planning authorities may control growth and development in accordance with the ecological or environmental carrying capacity of the region.

Assessing Machine Learning and Statistical Methods for Rock Glacier‐Based Permafrost Distribution in Northern Kargil Region

ABSTRACTEstimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

Assessing the land use dynamics and thermal environment using geospatial techniques in the industrial city of Chotanagpur Plateau Region, India.

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world.

Geospatial Assessment of Oil Spill’s Impact in Obio/Akpor Local Government Area Rivers State, Nigeria

Environmental degradation resulting from oil spills has emerged as a critical issue requiring immediate attention. This study uses geospatial techniques to investigate the impact of oil spills in Obio/Akpor Local Government Area of Rivers State. Landsat images, administrative map of the study area and historical oil spill data were data used in carrying out this study. The images were pre-processed through atmospheric and geometric corrections. Spatial analysis of four remote sensing indices-Normalized Difference Vegetation Index (NDVI) for assessing vegetation health, Normalized Difference Water Index (NDWI) for detecting water bodies, Land Surface Temperature (LST) for measuring surface temperature, and Normalized Difference Built-Up Index (NDBI) for identifying built-up areas-was conducted using the raster calculator in ArcGIS. The study employed the Analytical Hierarchy Process (AHP) to assign weights to these indices based their relative significance regarding oil spill impact. Subsequently, reclassification and integration of this information were done to generate Oil Spill Index (OSI) maps for 2003, 2013, and 2023. Validation of the Oil Spill Index (OSI) of various points before and after an oil spill incident was performed, specifically contrasting 2003 with 2013 and 2023 OSI values. This was to affirm the accuracy and predictive capability of the OSI maps in identifying oil spill-prone areas. Findings revealed OSI values for 2003 and 2013, indicating a range of 1.04 to 3.48 for 2003, with a noticeable increase observed in 2013, expanding the range to 3.08 to 4.72. Similarly, a comparison of OSI values between 2003 and 2023 indicates OSI values, ranging from 1.64 to 3.92 in 2003, then in 2023 after the oil spill incidents, there were notable changes in the OSI values, with the range shifting to 3.2 to 4.84. This suggests a significant increase in the impact of oil spill on the affected areas over the two-decade. This comprehensive index map serves as a tool for monitoring and assessing the effects of oil spills on the study area, thereby achieving the research objectives effectively.

Changes in monthly surface area, water level, and storage of 194 lakes and reservoirs in the Yangtze River Basin during 1990–2021 using multisource remote sensing data

Long-term, high spatiotemporal resolution of surface water area, water level, and storage changes in the Yangtze River Basin (YRB) has great scientific and practical importance for improving the management of water resources. Here, three distinct area estimations were first derived using the water classification enhancement method, automated water extraction method based on random forest, and the modified normalized difference water index. The optimized area data was determined by comparing against Sentinel-2 with the minimum root mean square error. A new area data was constructed with the optimized area as the primary data, while the remaining datasets were employed to fill in gaps. The elevation-area relationship was used to derive monthly water level. Changes in water storage were calculated by applying the pyramidal frustum formula from surface water area and water level data. Finally, a new comprehensive dataset of the monthly area, level, and storage changes in the 119 lakes and 75 reservoirs across the YRB with area larger than 10 km2 from 1990 to 2021 were first reconstructed. The spatiotemporal trends of surface water area/level/storage in lakes and reservoirs over 11 sub-basins of the YRB were quantified from 1990 to 2021, as well as before (1990–2003) and after (2003−2021) the construction of the Three Gorges Dam (TGD). During 1990–2021, there was a marked decrease in surface water area/level/storage in most of the YRB sub-basins, which contain 79 % of the lakes and 30 % of the reservoirs. After TGD was constructed, the surface water in lakes decreased by 10 %, while that of reservoirs remained consistent with the pre-construction. The surface water area/level/storage in the lower sub-basins of YRB exhibited a decline to an upward trend before and after the construction of TGD. This study provides a new comprehensive dataset for understanding the dynamic changes of water resource and climate change.

Using Artificial Neural Networks and Spectral Indices to Predict Water Availability in New Capital (IKN) and Its’ Surroundings

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.

Tracking dynamics characteristics of tidal flats using landsat time series and Google Earth Engine cloud platform

Tidal flats serve as vital spatial resources within coastal wetlands and play crucial roles in biological habitat preservation, environmental conservation, and human development. However, accurately delineating their extent from instantaneous remote sensing images poses challenges, owing to the dynamic interplay between seawater and land. Therefore, this study utilized the consistency of remote sensing satellites and developed a multi-feature decision approach based on the long-term remote sensing datasets and the Google Earth Engine (GEE) cloud platform for determining the spatial extent, classifying types, and mapping the spatial-temporal distribution of the tidal flats. Initially, a multi-temporal image stack was compiled from the filtered remote sensing images. Subsequently, the modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) were calculated to establish the maximum and minimum water surfaces using the extreme value composite algorithm (EVCA). The Otsu algorithm and spatial overlay analysis method were then utilized to accurately delineate the spatial extent of the tidal flats. Additionally, the Normalized Difference Vegetation Index (NDVI) was utilized to accurately identify tidal flat types. Post-processing involves elevation data and mathematical morphological methods to mitigate the impact of permanent water bodies. Finally, the accuracy of the extracted spatial extent and types of tidal flats was evaluated. The efficacy and practicality of the proposed method were validated using the Zhoushan Archipelago and Landsat time series in China. The results revealed the following: (1) the acquired tidal flat information exhibited evident boundaries and accurate types, achieving an overall accuracy (OA) of 94.80 % and a Kappa coefficient of 0.89; (2) the area of tidal flats in the Zhoushan Archipelago increased from 2,182.19 ha in 1985 to 3,761.21 ha in 2020, with an average annual increase of 45.12 ha and an average annual increase rate of 1.57 %; and (3) over 35 periods, the area transformed from other land types to tidal flats amounted to 2,895.74 ha, while the area converted from tidal flats to other land types totaled 1,651.90 ha. The three islands with the most rapid growth in tidal flat area were Liuheng Island, Dachangtu Island, and Daishan Island, with changes of 675.68 ha, 380.47 ha, and 362.63 ha, respectively. This method can offer an automated, swift, and accurate approach for tidal flat extraction, demonstrating high precision and practical effectiveness, thereby providing robust technical support for coastal resource surveys.

Multi-criteria decision-making techniques for groundwater potentiality mapping in arid regions: A case study of Wadi Yiba, Kingdom of Saudi Arabia

Freshwater demand is further stressed due to the impacts of climate change and the rapid expansion of the population, particularly in dry and extremely dry regions where groundwater serves as a crucial water source. This study employed various multi-criteria decision making (MCDM) techniques to identify areas with potential for groundwater in Wadi Yiba, Kingdom of Saudi Arabia (KSA). A total of 14 parameters, including Geology, land use and land cover (LULC), Drainage Density (DD), Lineament density (LD), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), rainfall, and others, were selected to create thematic layers for the delineation of groundwater potential zones (GWPZ). Using three MCDM techniques, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) methods, maps were generated to indicate the GWPZ in the study area. These maps were categorized into five categories: very low, low, medium, high, and very high. Results indicate that the GWPZ assessed by TOPSIS in Wadi Yiba categorizes 3% of the area as having very low groundwater potential, 9% as low, 29% as medium, 36% as high, and 23% as very high. In contrast, VIKOR classified the study region as 7%, 20%, 18%, 28%, and 26% for the five classes; while the EDAS study reveals a more balanced distribution, with 5.81%, 24.80%, 33.74%, 22.32%, and 13.33% for the five classes. We evaluated the accuracy of the final maps using the receiver operating characteristic-area under the curve (ROC-AUC) evaluation method. TOPSIS exhibited the highest accuracy (0.703), closely followed by VIKOR (0.701) and EDAS (0.557). TOPSIS and VIKOR were rated "excellent," while EDAS as "good". The findings are crucial for sustainable groundwater management in Wadi Yiba and underscore the importance of selecting appropriate methods in environmental modeling.

Added utility of temperature zone information in remote sensing-based large scale crop mapping

Accurate crop cover maps are beneficial for various aspects like water resources management, crop yield prediction, regulation insurance policies, and investigation of the effects of climate change. When making large-scale crop classification, regional harvest time and phenological growth differences occur due to varying temperatures along the study area, and using regional temperature differences while performing crop cover classification may increase the map accuracy. Therefore, in this study, we investigated for the first time the contribution of temperature information over large areas to the classification of agricultural products. Agricultural crop mapping is performed over Türkiye using Sentinel-2 Level-2A images with 10-m spatial resolution acquired between March 15, 2019, and October 15, 2019. In addition to spectral bands, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used as classification features. Twenty years of ERA5-Land 2-m temperature data is averaged to divide the study area into three temperature zones Low (LTZ), Medium (MTZ), and High-Temperature Zone (HTZ). Before the classification, feature selection using random forest importance is performed to select the most successful features. After that, a random forest classifier is created for each temperature zone. LTZ reached 89% overall accuracy (OA) with a 0.88 Kappa. MTZ reached 91% OA with 0.92 Kappa, and HTZ reached 94% OA with 0.94 Kappa, giving the best accuracy among the classifiers. Finally, test sets of all temperature zones are combined, and an OA of 92% with a Kappa of 0.93 is achieved with this combined test set. To test the advantage of temperature zoning, classification is also performed without the temperature zones, and it is observed that temperature zoning increases the OA and Kappa by 1%. A land cover classification map is then created using temperature zone classifiers with 34 crop classes and six non-agricultural classes.

Assessment of Continuous Growth of Glacial Lakes in the Teesta River Basin Using Semi-Automated Geospatial Approach

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.

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications.