Normalized Difference Water Index Research Articles

Predicting land cover changes in Xuan Lien Nature Reserve, Thanh Hoa province, Vietnam

The research was carried out in Xuan Lien Nature Reserve, which is a specially protected natural area and one of the five main centers of biodiversity in Vietnam. The work is aimed at analyzing changes and predicting the state of land cover in Xuan Lien Reserve. For these purposes, Landsat-7, Landsat-5 and Landsat-8 images obtained in 2000, 2010 and 2020 were studied. Normalized Difference Vegetation Index (NDVI) method was used to isolate the vegetation cover on the land areas, and Normalized Difference Water Index (NDWI) method was used. The results of the study indicate that the vegetation on the land is transforming in Xuan Lien Reserve. The area of broadleaf forests decreased by 6.2% from 2000 to 2020, and the area of wetlands increased by 4.9% due to the construction of Cua Dat hydroelectric complex in Reserve. The model of Markov chains and cellular automata was used to predict the state of the allocated lands in the study area. The predicted results of the model have been successfully confirmed using the agreement indices. According to the results of the predicted, by 2035 the area of broadleaf forests and wetlands will increase to 86.3% and 7.4%. The change in the structure of the vegetation cover of Reserve's land is explained by the measures taken by the Vietnamese government to regulate the water level in Cua Dat reservoir and expand the forest area.

Correlation analysis of landscape patterns and urban thermal environment in Kunming based on a panel data model

With the development of urbanization, the spatial and temporal distribution characteristics of the urban landscape pattern play a decisive role in the intensity of the urban heat island (UHI) effect. A panel data model was constructed to study the relationship between the UHI effect and landscape pattern in Kunming from 1995 to 2020 at four different scales. The results indicate: (1) The landscape pattern of Kunming changed obviously with time, the artificial patch increased, the natural patch decreased, and the UHI effect became more and more obvious; (2) With an increase in scale, the number of influencing variables continues to grow, and the impact of artificial patches gradually intensifies; (3) The normalized difference water index (NDWI) has the greatest influence on the UHI, and the cooling effect is more obvious with the increase in scale. Unlike previous studies, the spatial configuration of the landscape in Kunming City had a stronger effect on the UHI effect than the landscape grouping and vegetation index; (4) This paper introduces the panel data model into the discussion of the UHI for the first time, providing a new method for better understanding the changing patterns of the urban thermal environment.

MAPPING AND GEOSPATIAL DISTRIBUTION OF AÇAÍ TREES (EUTERPE OLERACEA MART) IN THE TOCANTINA-PA AMAZON

The article aims to map and analyze açaí management and planting systems in floodplain and terra firma areas in the Tocantina Amazon, specifically in the municipality of Mocajuba, Pará. To achieve this work, remote sensing and geoprocessing techniques were used, where we sought characterize the spatial and temporal distribution of açaí groves and evaluate changes in the landscape over the years. The application of the spectral indices NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index) and NDBI (Normalized Difference Construction Index) proved to be fundamental to identify areas with greater plant biomass, humidity levels and urban expansion, respectively. The results point to the importance of sustainable management practices to guarantee productivity and environmental preservation. It is concluded that the analysis of spectral indices proved to be effective in detecting variations in vegetation cover and water availability, providing crucial information for the planning and management of plantations in the region.

Spatiotemporal trends and drivers of forest cover change in Metekel Zone forest areas, Northwest Ethiopia.

The spatiotemporal dynamics of forest cover are essential for understanding the patterns and processes of forest change over time and space. This research focused on the spatiotemporal trends and drivers of forest cover change in the Metekel Zone of Northwest Ethiopia. Landsat 5, Landsat 7, and Landsat 8 imagery, covering the period from 1986 to 2019, were used for land use/cover classification. Land use/cover classification was performed using random forest (RF) and support vector machine (SVM) algorithms in the Google Earth Engine (GEE) platform, with training samples obtained through visual image interpretation. Spectral indices, such as the normalized difference vegetation index, soil-adjusted vegetation index, leaf area index, and normalized difference water index, were analyzed to examine forest cover dynamics over time. In addition, key informant interviews (KIIs) and focus group discussions (FGDs) were conducted. Findings revealed that forest cover decreased significantly from 51.37% in 1986 to 17.20% in 2019, driven largely by human activities such as agricultural expansion, increased demand for firewood, and urban expansion. Findings from spectral indices further corroborated the finding that forest cover in the study region (mainly in the southwestern part) substantially decreased from 1986 to 2019. Concerning forest depletion, the lack of local community awareness has become a key challenge. This problem is attributed to communities prioritizing immediate needs such as fuel and land for agriculture over long-term forest conservation. To combat ongoing deforestation, the Metekel Zone Administration, in collaboration with the land administration office and other stakeholders, revisited and strengthened existing forest policies and control systems. It is also suggested that community awareness, chiefly among youth, should be enhanced through the strategic expansion of formal and nonformal educational initiatives, which empower the youth as agents of change and promote the dissemination of knowledge throughout the community.

Assessing of monthly surface water changes impact on thermal human discomfort in Baghdad

In urban areas, surface water bodies play an important role in mitigating thermal discomfort, which is mainly caused by increasing air temperatures. Based on daily temperature and relative humidity data recorded by the Baghdad weather station for the two years 2018 and 2021, the monthly human discomfort index was calculated and then combined with monthly surface water areas extracted by a modified normalized difference water index using Sentinel-2A satellite imagery for the same period. The results show that the winter and most spring months of these years have no discomfort, and the summer months (July and August) in 2021 have the highest discomfort with severe thermal stress due to the large deficit in rainfall events. The monthly relationship between urban water surfaces and the level of the discomfort index was also studied, which was non-linear and followed the exponential decay function. This means that as the amount of surface water increased, the levels of the discomfort index decreased exponentially until no discomfort conditions existed.

An assessment of how street view imagery and remote-sensing data of green and blue spaces can explain variations in housing prices: a case study in Suzhou, China

ABSTRACT Urban blue and green spaces have significant influences on housing prices. In this study, we employed three data sources of blue and green space data sources (street view images, remote-sensing indicators, and land use data) and compared the variations of the three data sources of data in housing prices in Suzhou, China. The Spearman correlation showed street view data and remote sensing data reflect different perspectives of blue and green spaces. In terms of green spaces, street view greenery demonstrated the highest explanatory power for housing prices. This finding suggests that residents favour street greenery than Normalized Difference Vegetation Index (NDVI) and green space land use data. Regarding blue spaces, Normalized Difference Water Index (NDWI) exhibited the highest explanatory power for housing prices. This can be attributed to the fact that NDWI effectively captures water bodies around residential areas. The findings of this study have significant implications for urban planning and real estate development.

Easy yield mapping for precision agriculture

ABSTRACT Within precision agriculture, yield mapping is important in the evaluation of crop management and delineation of management zones. It can also be used to assess within-field yield potential, in order to guide different precision agriculture practices. However, some farmers do not have a yield monitoring system, and some who do may obtain incomplete or erroneous yield data. This study examined the accuracy with which winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden using a simple empirical relationship between Sentinel-2 (ESA, Paris, France) data, vegetation index (VI) maps and combined harvester data collected in nearby fields. The results showed that a decrease in map resolution to 40 m reduced the error in the yield maps obtained. Normalised difference water index (NDWI) was the most efficient VI, while a combination of satellite data from earlier and later plant development (booting and milk development stages) performed slightly better than data for other development stages and combinations. The best-performing model at a within-field scale (40-m resolution) had an average mean absolute error (MAE) of 0.40 tonnes ha−1 in a leave-one-field-out cross validation. When the prediction model at field-means scale was applied on 69 farms in a 1055 km2 area, MAE was 0.75 tonnes ha−1 when comparing predictions with mean yields reported by farmers in a phone survey. Therefore, if adequate combined harvester and/or mean yield data are available, a modelling framework that translates satellite imagery into yield maps on-the-fly could be made available for different stakeholders via decision support systems for precision agriculture.

Spatio-temporal analysis of habitat suitability for the endangered oriental white stork (Ciconia boyciana) in the wetland ecosystem of northeast China

Human-induced habitat transformation often leads to habitat degradation and loss, impacting suitable habitats for endangered and rare species. This study was conducted to assess suitable habitats for the endangered oriental white stork in the wetland ecosystem of northeast China, to facilitate successful species-habitat conservation. The study incorporated field surveys, literature reviews, vegetation index calculations, eDNA for assessing prey abundance and land use and land cover classification for the years 2000, 2011, and 2022. A fuzzy logic-based habitat model was developed to map suitable habitats for the oriental stork. Generalized Linear Models (GLM) and Principal Component Analysis (PCA) were used to assess the influence of various environmental variables on habitat suitability changes. Roosting locations of the oriental stork were used as reference points to evaluate the impact of environmental factors on habitat suitability. The results indicate that 20% of the area is highly suitable for the stork, 73.4% is moderately suitable, and 5.6% is less or not suitable. Habitats near lakes and rivers, with low Normalized Difference Vegetation Index (NDVI) and high Normalized Difference Water Index (NDWI), are more suitable compared to areas close to roads and built-up zones. Prey abundance, particularly fish, is a crucial determinant of oriental stork presence and survival. Marshlands, swamps, and water bodies were identified as the most suitable habitats, as they interface with lotic and lentic ecosystems providing abundant prey for the storks. Our findings emphasize the importance of ongoing efforts to protect wetland ecosystems and habitats for endangered and rare bird species. Priority should be given to habitats identified as critical roosting areas for the oriental stork.

Establishing a plastic waste map using remote sensing data in the coastal area of Thanh Hoa province (Vietnam)

Plastic pollution, particularly in oceans, is a growing environmental threat. Monitoring this pollution is challenging due to the shortage of data and tools. Remote sensing offers a promising solution by using various types of satellite and aerial images to detect and track plastic waste movement. Purpose. The study aims to establish a plastic waste map using remote sensing technology, focusing on the case study of the coastal area of Thanh Hoa province, Vietnam. Methodology. The study involves several steps, including the collection of plastic waste data (samples in the case study and indices from remote sensing images) as well as topographic and base map collection, statistics of plastic samples, grid design, calculation, interpolation, accuracy assessment, and mapping. Field surveys involved taking pictures of plastic debris along designated routes and a grid system was used to classify and calculate waste samples. Satellite images, especially Sentinel-2 MSI optical satellite imagery, are utilized to identify water areas of high plastic concentration. Findings. A map of plastic wastes in coastal areas of Thanh Hoa was created, with indices like NDWI (Normalized Difference Water Index) and FDI (Floating Debris Index) helping identify potential plastic waste signals. The resulting map reveals high concentrations of plastic waste in sea areas near the major river mouths of the Ma River basin and tourist areas, which aligns with areas of high human activity. Originality. This is the first study of plastic waste in the coastal area using remote sensing data in Thanh Hoa province, Vietnam. Practical value. This study can contribute to the effective management of marine plastic waste in Thanh Hoa while opening up opportunities for applying the method to create marine plastic waste maps throughout Vietnam and foster long-term monitoring and management.

Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery

Accurately identifying the distribution of vineyard cultivation is of great significance for the development of the grape industry and the optimization of planting structures. Traditional remote sensing techniques for vineyard identification primarily depend on machine learning algorithms based on spectral features. However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. First, this study extracted both spectral and texture features based on GF-6 satellite imagery, subsequently employing the Boruta algorithm to discern the relative significance of these remote sensing features. Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). The efficacy of various machine learning algorithms and remote sensing features in extracting vineyard information was subsequently evaluated and compared. The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). The RF algorithm significantly outperforms both the NB and SVM models in extracting vineyard information, boasting an impressive accuracy of 93.89% and a Kappa coefficient of 0.89. This marks a 12.25% increase in accuracy and a 0.11 increment in the Kappa coefficient over the NB model, as well as an 8.02% enhancement in accuracy and a 0.06 rise in the Kappa coefficient compared to the SVM model. Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. This underscores the efficacy of the RF model in harnessing the spectral and textural attributes of GF-6 imagery for the precise extraction of vineyard data, offering valuable theoretical and methodological insights for future vineyard identification and information retrieval efforts.

Groundwater potential zone mapping using AHP and geospatial techniques in the upper Narmada basin, central India

Water scarcity occurs in the agriculturally dominated Upper catchment area of Narmada River, Central India because of the overexploitation of underground water for residential, industrial, and other uses. Delineating the Ground Water Potential Zone (GWPZ) is critical to meeting the area’s water demand. Finding the Upper catchment Narmada River groundwater potential zone is the primary goal of this study. The study uses geographical methodologies based on the Analytical Hierarchical Process (AHP). To create a GWPZ map, ArcGIS 10.4 software compiles eight thematic layers, including elevation, slope, drainage density, geology, rainfall, soil texture, modified normalized difference water index, topographic wetness index, and land use/cover. There are five classifications for land use land cover map: Very low, low, moderate, high, and Very high. Each theme map in this study was given a weight based on its unique attributes and contribution to the GWP capacity. The AHP method, which takes into account each layer’s relative relevance regarding the others, was used to establish the weights. Four groups were created from the resulting groundwater potential map: excellent, good, moderate, and poor. According to the study, 26.05% of the basin was categorized as excellent, 34.59% as good, 23.97% as moderate, and 15.4% as poor groundwater potential.The results of this study further indicate that a sizable section of the Narmada River Basin has well to moderate groundwater potential, pointing to encouraging prospects for the area’s sustainable groundwater use. The study offers crucial insights for planners and policymakers to conscientiously harness groundwater resources, fostering sustainable development across diverse land uses in the fragile zone of the upper catchment of the Narmada, and it serves as a model for simulation in other sensitive river basins. The implications of the study are geared towards enhancing groundwater prospects, revitalizing fragile riverine ecosystems, and achieving the target outlined in Goal 6 of the Sustainable Development Goals (SDGs) of 2030.

Evaluation and optimization of park cooling benefits based on cumulative impact and landscape pattern

City parks can cool the surrounding environment and mitigate the urban heat island (UHI) effect, considerable improving the city’s adaptability to climate. In this study, 20 city parks in Nanjing, China, were considered, and four indexes for quantifying the cooling benefits from a cumulative impact perspective were proposed. These indexes are park cooling area (PCA), park cooling efficiency (PCE), park cooling intensity (PCI), and park cooling gradient (PCG). The results reveal the following: first, city parks have a positive impact on the surrounding thermal environment. The factors park area (PA), park perimeter (PP), landscape shape index (LSI), and normalized difference vegetation index (NDVI) determine cooling benefits. Second, PA and PP are significantly positively correlated with PCA but are significantly negatively correlated with PCE. LSI is negatively correlated with PCE, while NDVI is positively correlated with PCI and PCG. No significant correlation exists between the four cooling indexes and modified normalized difference water index(MNDWI). Finally, different parks exhibit variations in their ability to provide cooling benefits. Special or community parks are more appropriately situated in areas with constrained urban land resources. In designing comprehensive parks, the intricate boundary features and vegetation conditions need to be considered to optimize their cooling effects. Moreover, a larger number of residents are allowed to enjoy cooling services. The findings of this project will aid in the construction and optimization of city parks in future to combat the UHI effect.

Analyzing the Spatiotemporal Dynamics of Drought in Shaanxi Province

Drought, as a natural disaster with wide-ranging impacts and long duration, has an adverse effect on the global economy and ecosystems. In this paper, four remote sensing drought indices, namely the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Water Index (NDWI), are selected for drought analysis. The correlation analysis is carried out with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and based on the optimal index (CWSI), the spatiotemporal characteristics of drought in Shaanxi Province from 2001 to 2021 were studied by SEN trend analysis, Mann–Kendall test, and a center of gravity migration model. The results show that (1) the CWSI performs best in drought monitoring in Shaanxi Province and is suitable for drought studies in this region. (2) Drought in Shaanxi Province shows a decreasing trend from 2001 to 2021; the main manifestation of this phenomenon is the decrease in the occurrence of severe drought, with severe drought covering less than 10% of the area in 2010 and subsequent years. The most severely affected regions in the province are the northern Loess Plateau region and Guanzhong Plain region. In terms of the overall trend, only 0.21% of the area shows an increase in drought, primarily concentrated in the Guanzhong Plain region and the outskirts of the Qinling–Bashan mountainous region. (3) Drought conditions are generally improving, with the droughts’ center of gravity moving northeastward at a rate of 3.31 km per year. The results of this paper can provide a theoretical basis and a practical reference for drought control and decision-making in Shaanxi Province.

MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.

Seasonally flooded landscape connectivity and implications for fish in the Napo Moist Forest: A high-resolution mapping approach

The Amazon River Basin's fish diversity is shaped by its dynamic flood-pulse system, critical for hydrological and ecological connectivity. This study examines the Napo Moist Forest (NMF) ecoregion, mapping permanent and seasonally flooded areas from 2018 to 2021 using remote sensing and deep learning models. We aimed to map these areas at a high spatial resolution (10-m pixels) and analyse their role in maintaining lateral connectivity essential for fish diversity. Using synthetic aperture radar data from Sentinel-1 combined with deep learning algorithms, we produced high-accuracy flood maps to assess landscape connectivity between rivers and floodplains. Our methodology included creating a ground truth dataset with the Normalized Difference Water Index and integrating high-resolution optical data for model training, overcoming challenges of cloud coverage and dense vegetation. Our predictive model achieved high accuracy (mean pixel accuracy = 97 %) and consistently predicted 4801 km² of surface water, with only 3 % (130 km²) being seasonally flooded areas over four years. The Caquetá, Bajo Marañón, Napo, and Pastaza watersheds accounted for nearly 60 % of the flooded areas, highlighting their ecological importance. Connectivity analysis in three areas of interest within the NMF ecoregion revealed important seasonal and interannual fluctuations in hydrological connectivity due to changes in flooded patch characteristics. Reductions in the number and flooded patch area during low water seasons increased the distance between patches, leading to a disconnection between flooded areas, channels and rivers. Despite hydrological fluctuations, certain patches maintained consistent flooding, critical for lateral connectivity and sustaining aquatic biodiversity. These seasonally flooded areas act as connectors, influencing patch dynamics and connectivity with tributaries. Seasonal and interannual variations in hydrological connectivity are crucial for sustaining fish diversity. Conserving dynamic floodplains supports migratory fish life cycles and biodiversity. This study underscores the importance of high-resolution temporal and spatial data in conservation planning for aquatic ecosystems.

Dynamic Changes and Driving Factors in the Surface Area of Ebinur Lake over the Past Three Decades

Dryland lakes are indispensable to regional water resource systems. Ebinur Lake, the largest saline lake in Xinjiang Uygur Autonomous Region, is vital for regional biodiversity and environmental stability but has been facing the predicament of gradual shrinkage in recent decades. In this study, we proposed a new dual-index method for Landsat (-5, -7, -8, and -9) data to extract water with the combinations of the normalized difference water index (NDWI) and the modified NDWI for turbid waters (NDWIturbid). The dual-index method showed a high overall accuracy of 96.36% for Ebinur Lake. Landsat series images from 1992 to 2023 were employed to acquire the water areas of Ebinur Lake. The results showed that, over the past three decades, the area of Ebinur Lake exhibited a fluctuating decreasing trend, with an average lake area of 568.74 ± 152.43 km². The northwest intermittent water areas showed significant changes, and there was a close connection between the northwest and core water areas. Seasonally, the lake area decreased from spring to autumn. River inflow, driven by rainfall and human activities, was the primary factor affecting the inter/inner annual changes in Ebinur Lake. Furthermore, due to the valley effects, wind was found to be a critical factor in the diurnal changes in the water areas. This study should deepen the understanding of the variations of Ebinur Lake and benefit local water resource management.

Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

Cutting-edge approaches for judging surface water dynamics in semi-arid environments: Integrating landsat 8 OLI/TIRS and HYDROSAM model

This research addresses the critical need to assess surface water dynamics in semi-arid regions of Andhra Pradesh, India, by using advanced Spectral Indices for hydrological applications and methodologies. It aims to answer how remote sensing data from Landsat 8 OLI/TIRS can be effectively used to monitor and classify surface water bodies. The study applies indices such as Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Thermal Index (NDTI), and Water Ratio Index (WRI). Additionally, the Weighted Composite Index (WCI) and Normalized Composite Index (NCI) are integrated with Principal Component Analysis (PCA) for multi-criteria decision making. The HYDROSAM model successfully classifies and maps various categories of surface water bodies, including non-water features (53.62%), urban water zones (37.89%), seasonal water bodies (4.32%), transitional zones (2.17%), permanent water bodies (1.05%), and river bodies (0.95%). The resultant map was validated using the AUC-ROC curve, achieving an AUC of 0.820, indicating a high level of accuracy. This methodology provides a nuanced understanding of water resource distribution and availability in the region. The findings demonstrate the robustness and reliability of the HYDROSAM model in accurately assessing surface water characteristics, thereby providing critical insights for informed water resource management, strategic land-use planning, and effective ecological conservation. This innovative methodology not only fosters sustainable water resource management in semi-arid regions but also sets a precedent for advancing research in similar ecosystems globally.