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Flash flooding is one of the most noteworthy natural disasters in arid regions, especially in urban areas. The Helwan-Atfih area is a heavily populated region and part of the Eastern Desert drylands of Egypt. It is characterized by ten drainage basins that dissect it and drain toward the Nile River (Wadies of Degla, Hof, Al-Gebbu, Garawy, Hera, Al-Hay, Al-Werg, Al-Nowya, Al-Reshrash, and AL-Atfehe). Landsat-8, STRM-DEM, and CFSR remote sensing satellite data of 15 m, 30 m, and 0.3-degree resolution, respectively, were prepared and utilized to evaluate flooding hazards within the study area using the GIS-weighted overlay technique. Weighted overlay analysis is a GIS-based multi-criteria decision-making technique. This technique was performed to delineate the most vulnerable areas for flooding, depending on 14 thematic layers representing the multi-class factors that influence flood hazard (nine morphometric parameters, slope, relief, lineament density, surface lithology, and surface runoff). According to the morphometric parameters, the basins of the study area are characterized by moderate drainage densities, and moderately permeable subsoil. Limestone occupies 83.41% of the total lithological units within the basins' area, which indicates a high flooding potential. Steep slopes are primarily observed in the southern basins, especially in the Al-Reshrash basin. Wadi Al-Atfehe and Wadi Al-Reshrash have the lowest lineament density areas, reflecting a higher flooding hazard. The total runoff volume ranges between 2.42 × 106 and 12.08 × 106 m3. Based on the results, Wadi Al-Reshrash receives the highest runoff volume (12.08 × 106 m3) and has the highest slope degree (57○-71○). 85.4% of its area is covered with limestone and it has a low to moderate lineament concentration. Accordingly, Wadi Al-Reshrash is the most prone basin to flooding within the study area, followed by Wadi Al-Werg, while the other basins show a moderate flood hazard degree. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Africa’s diverse climates and sparse hydro-meteorological networks create significant challenges in accurately estimating river discharge. Discharge data are crucial for managing water resources and predicting extremes. Our review assesses the data gap, existing methods, and technologies for river discharge estimation in Africa. Limited gauging networks on rivers, including in 63 transboundary basins, hinder accurate discharge modelling, affecting resource management and disaster response. Despite the potential of remote sensing, Geographic Information System (GIS), satellite imagery, and machine learning, their large-scale application for river discharge monitoring in Africa is limited. We propose the use of a monitoring system involving local communities in data collection and decision making, supported by global data centres, enhanced regional data sharing, and strengthened transboundary cooperation. For example, incorporating water data products, including discharge data, in data cubes, such as Digital Earth Africa, could improve monitoring. Strategic investments in hydro-meteorological instrumentation are crucial for strengthening climate resilience. [ABSTRACT FROM AUTHOR]

Abstract 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.

Overexploitation and climate change have threatened the availability and sustenance of groundwater resources. A proper understanding of the regional distribution of groundwater is crucial to ensure long-term water security. The present study aims to identify the groundwater potential zones in the Lakhimpur district of Assam using the Analytical Hierarchy Process (AHP) in combination with geospatial technologies. The occurrence of groundwater in the region was determined by several factors including geomorphology, lithology, slope, distance from the river, drainage density, lineament density, rainfall, curvature, soil, land use, land cover, Normalized difference vegetation index (NDVI), and topographic wetness index (TWI). These factors organized as thematic layers were utilized to generate a groundwater potential zones (GWPZ) map in the GIS environment. The AHP, an effective decision-making technique, was adopted to assign weights to each thematic layer corresponding to their relative importance in influencing groundwater availability. The GWPZ map prepared using the weighted overlay techniques was categorized into three classes: good, moderate, and poor. The result revealed that the good potential zone comprises 1909.41 km2 (65.12%), moderate 1018.25 km2 (34.72%) and the poor zone comprises 4.22 km2 (0.14%) of the total geographical area. The obtained results of 73.33% (Overall accuracy), 0.708 (ROC-AUC), and 0.50 mbgl (groundwater level fluctuation) between pre-monsoon and post-monsoon prove that the model has performed satisfactorily in identifying groundwater potential zones. The findings provide a framework for the effective exploration and management of groundwater resources, ensuring their future availability in the region.

Global life-threatening weather conditions and sea level rise (SLR) increasingly impact coastal landforms and increase shoreline change. The island ecosystem is affected by many natural hazards, including flooding, saltwater intrusion, vegetation degradation, shoreline change and population growth. Simultaneously, urbanization and ecological diversification are also trigger the environmental degradation in island ecosystem. Remote sensing (RS) and GIS-based research are carried out at Djerba Island using decadal spatiotemporal Landsat datasets to identify and manage such problems. Landsat imageries are widely applied for landforms monitoring along with the ocean dynamics, therefore decadal landforms and environmental issues of Djerba Island are examined through 30 m medium resolution satellite imageries. The selected study years are 1984 to 2021 applying Landsat datasets. Between 37 years of analysis, build-up areas have increased by 105.18 km2, while sediment areas have decreased by 3.2 km2 due to the SLR in Borj El Kastil, Aghir, Plage, and Trou Deniaud locations. Vegetation increased around 17.59 km2 while bare lands decreased 47.28 km2 due to the palm, olive, and tree plantations over the Island. The entire area of Djerba Island observed 513.20 km2 (1984), 513.79 km2 (1991), 515.22 km2 (2001), 516.26 km2 (2011) and 513.42 km2 (2021) respectively. The Djerba Island needs more attention, awareness, and protection to reduce the environmental degradation. This study information is supportive and aimed at future policymaking, policy implementation, disaster-related management, coastal management, and coastal erosion reduction. [ABSTRACT FROM AUTHOR]

Abstract The management of groundwater systems is essential for nations that rely on groundwater as the principal source of communal water supply (e.g., Mohmand District of Pakistan). The work employed Remote Sensing and GIS datasets to ascertain the groundwater recharge zones (GWRZ) in the Mohmand District of Pakistan. Subsequently, a sensitivity analysis was conducted to examine the impact of geology and hydrologic factors on the variability of the GWRZ. The GWRZ was determined by employing weighted overlay analysis on thematic maps derived from datasets about drainage density, slope, geology, rainfall, lineament density, land use/land cover, and soil types. The use of multi-criteria decision analysis (MCDA) involves the utilization of the multi-influencing factor (MIF) and analytical hierarchy procedure (AHP) to allocate weights to the selected influencing factors. The MIF data found that very high groundwater recharge spanned 1.20%, high zones covered 40.44%, moderate zones covered 50.81%, and low zones covered 7.54%. In comparison, the AHP technique results suggest that 1.81% of the whole area is very high, 33.26 is high, 55.01% is moderate, and 9.92% has low groundwater potential. The geospatial-assisted multi-influencing factor approach helps increase conceptual knowledge of groundwater resources and evaluate possible groundwater zones.

The management of groundwater systems is essential for nations that rely on groundwater as the principal source of communal water supply (e.g., Mohmand District of Pakistan). The work employed Remote Sensing and GIS datasets to ascertain the groundwater recharge zones (GWRZ) in the Mohmand District of Pakistan. Subsequently, a sensitivity analysis was conducted to examine the impact of geology and hydrologic factors on the variability of the GWRZ. The GWRZ was determined by employing weighted overlay analysis on thematic maps derived from datasets about drainage density, slope, geology, rainfall, lineament density, land use/land cover, and soil types. The use of multi-criteria decision analysis (MCDA) involves the utilization of the multi-influencing factor (MIF) and analytical hierarchy procedure (AHP) to allocate weights to the selected influencing factors. The MIF data found that very high groundwater recharge spanned 1.20%, high zones covered 40.44%, moderate zones covered 50.81%, and low zones covered 7.54%. In comparison, the AHP technique results suggest that 1.81% of the whole area is very high, 33.26 is high, 55.01% is moderate, and 9.92% has low groundwater potential. The geospatial-assisted multi-influencing factor approach helps increase conceptual knowledge of groundwater resources and evaluate possible groundwater zones. [ABSTRACT FROM AUTHOR]

Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations. [ABSTRACT FROM AUTHOR]

Climate change is one of the main factors that caused scarcity of fresh water phenomenon all over the world. The lack of water in major parts of Iraq affected all sectors that use water and cause obvious damages to ecosystems. Karbala province suffers from frequent water scarcity due to water scarcity and abnormally high temperatures. In the present study, remote sensing and GIS were applied to quantify water scarcity and evaluate its effects on vegetation in this fragile semiarid ecosystem. Analysis of hydrological data of the study area was carried out during 2013 to 2022 to compute water availability and shortage based on the criteria and requirements of water sector and environmental management in Iraq. Remotely sensed Landsat 8 images data were applied to measure changes on vegetation and the effects of water scarcity. Soil Adjusted Vegetation Index (SAVI) was employed to identify vegetation and detect its change. Results showed that the area witnessed decreasing in water availability compared to the reference year. Maximum available water reached 1977.535 million m 3 in 2013, while the minimum of 859.227 million m 3 was observed in 2022. The maximum and minimum vegetation area reached 535.610 km 2 and 430.605 km 2 in 2013 and 2022, respectively. Results indicated that all the years posterior to the reference year experienced water scarcity and vegetation damage, where the maximum and minimum water scarcity rates were 56% and 8% in 2022 and 2016, respectively. The maximum impact of water scarcity rate on vegetation was ca. 20% in both years 2015 and 2022. Water scarcity is constantly increasing over time, thus evaluating its impacts and forecasting its future specification will support decision-makers to take the necessary measures to mitigate its effects. [ABSTRACT FROM AUTHOR]

This work looks at developing an object-driven decision support system (DSS) model with the goal of improving the prediction accuracy of the present expert-driven DSS model in assessing groundwater potentiality. The database of remote sensing, geological, and geophysical information was constructed using the technological efficiency of GIS, data mining, and programming tools. Groundwater potential conditioning factors (GPCF) extracted from the datasets include lithology (Li), hydraulic conductivity (K), lineament density (Ld), transmissivity (T), and transverse resistance (TR) for groundwater potentiality mapping in a typical hard rock multifaceted geologic setting in south-western Nigeria. A Python-based entropy approach was used to objectively weight these factors. The weightage findings determined that the greatest and lowest given values for Ld and K were 0.6 and 0.03, respectively. The produced Python-based PROMETHEE-Entropy model algorithm was born through combining the weight findings with the Python-based PROMETHEE-II method. The groundwater potentiality model (GPM) map of the area was created using the model algorithm's outputs on the gridded raster of GPCF themes. Based on the suggested approach, the validated results of the created GPM maps using the Receiver Operating Characteristic (ROC) curve technique yielded an accuracy of 86%. An object-driven DSS model was created using the approaches that were used. The created object-driven model is a viable alternative to existing approaches in groundwater hydrology and aids in the automation of groundwater resource management in the research region. [ABSTRACT FROM AUTHOR]

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Sikkim Himalaya, a part of the North-Eastern Himalayan region, is affected by the landslides and it causes the loss of life, property, and other human infrastructure, etc. The objective of study is identification of landslides susceptibility zones of the Sikkim Himalaya, using various factors/thematic layers, such as absolute relief, relative relief, relief ratio, dissection index, hypsometric integral, slope index, drainage density, drainage frequency, drainage intensity, drainage texture, infiltration number, junction frequency, length of overland flow, ruggedness index, stream transport index, topographic wetness index, stream power index, and rainfall and all these layers are integrated in Arc GIS software using FR model. These spatial factors are generated using Alos Palsar DEM and rainfall data with the help of the Arc GIS. The FR model was utilised for the purpose of determining the weights of such all-thematic layers for the possibility of landslides occurring in regions that are susceptible to the effects of landslides. These weight of such all thematic layers are combined using the Arc GIS to create the map of landslide susceptibility zones. The map of the landslide susceptibility zones of the region has been split into five distinct categories, including 'very high' (13.20%), 'high' (19.75%), 'moderate' (30.81%), 'low' (27.14%), 'very low' (9.09%). For accuracy analysis of the model the area under the curve is used and is estimated as 84.6% with the help of the FR model and occurrence of previous landslides in the region. [ABSTRACT FROM AUTHOR]

Hydrogeomorphological features such as paleochannels, oxbow-lakes, meander scars, etc., are the remnants of ancient flowing rivers which scrolled and shifted to successively new courses due to various geomorphological, tectonic and climatological factors and are presently buried with younger fluvial sediments. A series of aligned hydrogeomorphological features in the interfluvial zone of the Ganga and Sai Rivers exist in the upper reaches of the currently existing Bakulahi River. Their continuity and correlation with the Bakulahi River remained unexplored as yet. The present paper aims to identify, delineate, and hydrogeomorphologically reconstruct these features in the Ganga and Sai Rivers' interfluve and find out their association with the currently existing Bakulahi River. Remote sensing data (Landsat-8, Sentinel, and Shuttle Radar Topography Mission- Digital Elevation Models) were used to identify and delineate these paleochannels. Spatial cross-sectional profiles were generated through the Digital Elevation Model data to know the paleochannel's longitudinal and transverse forms and dimensions. The delineated paleochannels in the satellite image were cross-verified with the electrical resistivity survey and the sedimentological analysis, which confirmed the existence of paleochannels of the Bakulahi River. The study reveals that all the hydrogeomorphological features in the interfluvial zone of the Ganga and Sai Rivers are an integral part of the currently existing Bakulahi River and concludes that the Bakulahi River was flowing from near Gouria Kalan village, Unnao district, and finally met with the Sai River at Khajurni village in Pratapgarh district. The river dynamics changed with time, possibly owing to multiple factors such as changes in energy state, sediment load, extensive deforestation, neo-tectonism, etc., which caused the loss of the continuity of the Bakulahi River. Eventually, the relics of the Bakulahi River remained as disconnected riverine wetlands at present. This study is a matter of wide concern of global interest in the diminishing riverine wetlands of flood plains. [ABSTRACT FROM AUTHOR]

Rapid urbanization and changing climatic procedures can activate the present surface urban heat island (SUHI) effect. An SUHI was considered by temperature alterations among urban and rural surroundings. The urban zones were frequently warmer than the rural regions because of population pressure, urbanization, vegetation insufficiency, industrialization, and transportation systems. This investigation analyses the Surface-UHI (SUHI) influence in Kolkata Municipal Corporation (KMC), India. Growing land surface temperature (LST) may cause an SUHI and impact ecological conditions in urban regions. The urban thermal field variation index (UTFVI) served as a qualitative and quantitative barrier to the SUHI susceptibility. The maximum likelihood approach was used in conjunction with supervised classification techniques to identify variations in land use and land cover (LULC) over a chosen year. The outcomes designated a reduction of around 1354.86 Ha, 653.31 Ha, 2286.9 Ha, and 434.16 Ha for vegetation, bare land, grassland, and water bodies, correspondingly. Temporarily, from the years 1991–2021, the built-up area increased by 4729.23 Ha. The highest LST increased by around 7.72 °C, while the lowest LST increased by around 5.81 °C from 1991 to 2021. The vegetation index and LST showed a negative link, according to the correlation analyses; however, the built-up index showed an experimentally measured positive correlation. This inquiry will compel the administration, urban planners, and stakeholders to observe humanistic activities and thus confirm sustainable urban expansion. [ABSTRACT FROM AUTHOR]

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district’s land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation. [ABSTRACT FROM AUTHOR]

Global croplands, pastures, and human settlements have expanded in recent decades. This is accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. In sub-Saharan Africa, policies are implemented without critical consideration; e.g., agricultural expansions impair ecosystem services. We studied land use/cover and the associated rate of change for four time epochs, i.e., 1991, 2001, 2011, and 2021. This employed remote sensing and GIS techniques for analysis, while future projections were modeled using cellular automata and the Markov chain. The kappa coefficient statistics were used to assess the accuracy of the final classified image, while reference images for accuracy assessment were developed based on ground truthing. Overall change between 1991 and 2021 showed that major percentage losses were experienced by water, forest, woodland, and wetland, which decreased by 8222 Ha (44.11%), 426,161 Ha (35.72%), 399,584 Ha (35.01%), and 105,186 Ha (34.82%), respectively. On the other hand, a percentage increase during the same period was experienced in cultivated land, built-up areas, and grasslands, which increased by 659,346 Ha (205.28%), 11,894 Ha (159.93%), and 33,547 Ha (98.47%), respectively. However, this expansion of thirsty sectors has not reversed the increasing amount of water discharged out of the Kilombero River catchment. We recommend the promotion of agroforests along with participatory law enforcement and capacity building of local communities' institutions. [ABSTRACT FROM AUTHOR]

Flooding is a common and catastrophic natural event that has serious consequences for both human and nature. The assessment of flood susceptibility within watersheds and the implementation of measures to mitigate flood-related impacts and damages are integral aspects of environmental, water resources and disaster management. Assam is significantly impacted by recurring flood events consisting of multiple waves. Consequently, it is imperative to precisely identify the areas susceptible to flooding. Therefore, this research aims to assess the capability of the Frequency Ratio (FR) and Shannon's Entropy (SE) model for mapping flood susceptibility; and to identify extremely flood prone locations in Assam, India. The study was conducted in four main stages. Firstly, a flood inventory map was developed by using historic records (1998 to 2017) from the Assam Water Resources Department (AWRD), Assam State Disaster Management Authority (ASDMA), and extensive field surveys. A total of 1630 flood occurrence points were extracted; and of these 75% were randomly selected for model training and 25% for validation. Secondly, nine flood conditioning factors were considered, including elevation, aspect, distance from river, drainage density, Land-use and Land-cover (LULC), rainfall, geomorphology, Topographic Wetness Index (TWI), and Normalised Difference Vegetative Index (NDVI). The thematic layers were prepared in a Geographic Information System (GIS). Thirdly, flood susceptibility maps (FSMs) were generated by applying FR and SE models, using the flood conditioning factors and the occurrence locations. The FSMs were classified into five classes. Finally, the predictive capability of FR and SE models and the validation was done by using Receiver Operating Characteristic (ROC) curves and computing the Area Under Curves (AUC). The AUC for the FR model was 0.748, and the SE model, it was 0.761. The validation results suggests that the SE model is more suitable for predicting flood susceptibility in the study area. The study makes a unique contribution by identifying the flood susceptible zones in Assam, a state afflicted by recurrent floods. The derived FSMs of the region can be used for effective decision making in flood management, as well as for strategic planning and mitigation measures toward flood events in the future. [ABSTRACT FROM AUTHOR]

The changes in land use and land cover (LULC) are one of the primary forces for the worldwide climate, ecosystem, and environmental changes. A detail understanding of the dynamics of LULC changes is requisite for natural resource management and sustainable development. The present study region is the Mand catchment situated in Chhattisgarh, which has witnessed changes in LULC due to a rise in anthropogenic activities, such as an increase in population, urbanization, mining activity, and agriculture, as well as natural causes, such as climate change. The present study focused on spatiotemporal LULC change analysis and the subsequent prediction of future LULC patterns. A supervised image classification method in Geographic Information System (GIS) was used to analyze decadal LULC changes from 2001 to 2021. The Cellular Automata–Artificial Neural Network (CA–ANN) model, incorporated in the MOLUSCE (Modules of Land Use Change Evaluation) plugin of QGIS, was used for the future prediction of LULC from 2030 to 2040. The overall accuracy of LULC for 2001, 2010, and 2021 was obtained as 82, 86, and 90%, respectively, and the overall kappa coefficient was obtained as 0.79, 0.84, and 0.88, respectively. The decadal study of LULC change concludes that the agricultural land counts highest area of 29.21% as compared to other LULCs in 2001 which was further increased 31.76% in 2021. Increasing trend were also noticed for the open forest, shallow waterbody, fallow land, and settlement for the decadal years 2001 to 2021 by 1.7, 7.41, 7.57, and 2.55%, respectively. A decreasing trend was observed in LULC changes during the decadal years 2001 to 2021 for dense forest, deep water body, and barren land by 10.28, 0.66, and 10.24%, respectively. The LULC predictions for 2030 and 2040 indicate a similar trend to the prior years, with an increase in settlement, fallow land, agricultural land, open forests, shallow waterbodies, scrubland, and a decrease in dense forests, deep waterbodies, and barren land. This comprehensive analysis of the changes in LULC over an extended period will prove to be a valuable resource for policymakers and planners seeking to achieve sustainable development and effective management of the ecosystem within the study area. [ABSTRACT FROM AUTHOR]

Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations.

Basins formed on active strike-slip faults are important prospect areas for geothermal energy exploration since the crust gets thinner in these areas and tectonic structures provide favorable conditions for heat-fluid circulation and transport. The Edremit (Balıkesir) Basin holds a great promise for the discovery of new geothermal energy sources. The objective of the recent study is to evaluate the geothermal capacity of the Edremit Basin utilizing tectonic, geological, and hydrogeological studies, combining remote sensing (land surface temperature-LST, hydrothermal alteration, and multi-temporal InSAR (MT-InSAR) assessments). We present structural literature data and the results of field mapping, which revealed the geometry, kinematics, and dynamics of structural features, geological units as geothermal system components, thermal and cold water chemistry, and thermal infrared remote sensing analysis. For the purpose of assessing new targets and recent geothermal capacity, these data are combined and evaluated. According to the outcomes of the study, the fault pattern in the Edremit Basin is generated by N-S extension, which produced E-W dominant striking normal faults with a heritage of paleostructures oriented in various directions. According to remote sensing analyses, the primary LST regions in the basin are defined by the active faults. Therefore, a high sodium sulfate ratio recorded in the chemical analyses of the water samples indicates a deep circulation and high possibility for the presence of thermal water. Consequently, our findings are consistent with the work to include thorough field geology surveys, structural patterns, LST, and water chemistry to refined exploration process.

The integration of remote sensing data, machine learning and geographic information system in managing and analysing spatial data helps in generating maps showing groundwater potential. These maps are important tools for aiding stakeholders and decision-makers in groundwater resources to make informed decisions during groundwater development and management; to ensure the reliability of these maps, validation with the field data is conducted. This study analysed 125 scientific articles spanning the period from 2002 to 2023. The results show that around 85% of articles contain validated maps, indicating a significant number of researchers adhere to validate the remote sensing and GIS-based maps with field data, which is crucial in scientific research. However, 15% of articles contain non-validated maps. This is an alarming figure; therefore, journals should be strict in ensuring that validation is adhered to. In the reviewed articles, a total of 10 methods were used to validate groundwater potential maps using various parameters such as well yield, well/spring discharge rate, aquifer transmissivity, well specific capacity, and presence of wells/springs. This study will also add to the knowledge of selecting appropriate methods for validating remote sensing and GIS-based groundwater potential maps. The use of field data reflecting aquifer productivity is more appropriate for validation of groundwater potential maps.

The mapping of water potential in arid regions holds crucial significance for the sustainable management of water resources in these vulnerable environments. The Anzi sub-basin in Morocco is confronted with water scarcity, presenting a significant challenge. The region's arid climate and limited accessibility to groundwater resources further exacerbate this scarcity, ultimately constraining the region's socioeconomic development, heavily reliant on agriculture. To address this issue, the current study employs mathematical modeling, specifically the frequency ratio (FR) method, and geomatics tools such as remote sensing (RS) and geographic information systems (GIS) to map and assess potential groundwater zones. The study considers multiple factors influencing water availability, including the density of drainage nodes, lineament density, permeability, slope, NDVI, curvature, distance to drainage, topographic wetness index (TWI), sediment transport index (STI), distance to lineament, and rainfall. The investigation identifies 45 wells, of which 70% are used for model application and the remaining 30% for validation. The model application results in the identification of five potential zones: very high, high, moderate, low, and very low, encompassing 3.97%, 10.11%, 23.38%, 35.02%, and 27.53% of the total study area, respectively. Furthermore, the model's validity is confirmed through the ROC (receiver operating characteristic) curve analysis, indicating a reliability value of 82.5% for the FR method in mapping groundwater potential. These findings align with the results obtained from geophysical prospecting in the region. Notably, the locations of high-flow wells generally correspond to moderately to highly conductive anomalies of resistivity, primarily attributed to the presence of quaternary formations, geological faults, and geophysically derived faults. These results are further validated by proton magnetic resonance surveys. [ABSTRACT FROM AUTHOR]