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The escalating rates of deforestation, compounded by land degradation arising from intensified mining operations, forest fires, encroachments, and road infrastructure, among other factors, are severely disrupting the botanical and soil ecology of tropical ecosystems. This research focused on the upper Narmada River catchment area in central India, employing geospatial methodologies to assess land use and land cover (LULC) changes. Landsat 5, 7, and 8 satellite data for 2000, 2010, and 2022 were digitally classified using the maximum likelihood algorithm within the ERDAS IMAGINE and ArcGIS platforms. LULC was delineated into five categories (i.e., water bodies, built-up land, agricultural areas, forested regions, and fallow land). A spatio-temporal analysis revealed substantial declines of approximately 156 km2 in fallow land and 148 km2 in forested areas, accounting for 3.21% of the total area, while built-up land, water bodies, and agriculture land expanded between 2000 and 2022. There was a notable negative correlation observed between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) across all LULC categories, except water bodies. The Land Degradation Vulnerability Index indicated that fallow lands, followed by forests and agriculture areas, exhibited a high vulnerability, with 43.16% of the landscape being categorized as vulnerable over the past 22 years. This study underscores the imperative of effective ecological restoration to mitigate land degradation processes and foster resilient ecosystems. The findings emphasize the importance of integrating scientific data into policy-making frameworks to ensure the comprehensive and timely management of the Narmada River landscape. [ABSTRACT FROM AUTHOR]

Tropical cyclone causes large-scale devastation and destruction in the coastal plains of India, particularly in Odisha, which is the most cyclone-affected state in the country. Tropical cyclones are projected to be more powerful and widespread due to changing climate. Hence, the risk assessment of tropical cyclone is necessary to identify cyclone-risk areas in coastal Odisha which may help in the mitigation of the damages caused by cyclones. Therefore, this study utilizes geospatial techniques to produce a comprehensive risk map posed by tropical cyclones and to estimate the degree of risk for coastal districts of Odisha. For this, we evaluated the district-level cyclone risk for coastal Odisha using multi-criteria decision-making (MCDM) technique by considering 21 parameters for each of the four components of risk, i.e., exposure, hazard, vulnerability, as well as mitigation capacity. For each criterion, thematic raster map layers were created and weighted using a fuzzy analytical hierarchy process (FAHP). We prepared individual risk component maps using weighted overlay techniques and finally integrated all indices to create the risk map. The study shows that 13% area of the study area comes under a very high-risk zone whereas, 25% area comes under a high-risk zone. The central (Cuttack, northern parts of Khordha, and south-western parts of Jajpur district) and the eastern part (most of the parts of Jagatsinghpur, Kendrapara, and northern parts of Puri district) of the study area come under high to very high tropical cyclone impact zone. Almost 67% of the total area is highly vulnerable to tropical cyclones and mainly concentrated near the shoreline. The applied approach and results can assist the local authorities in identifying vulnerable and hazardous locations and developing workable solutions for the mitigation of revised cyclone threats in the coastal districts of Odisha. [ABSTRACT FROM AUTHOR]

Imphal city and its surrounding regions have been experiencing a wide range of changes in land use and land cover (LULC) over recent years. However, there is a shortage of comprehensive studies that specifically examine the long-term dynamics in general and driving factors behind these changes in particular. This study provides a detailed analysis of LULC changes in Imphal city and its surrounding regions from 1991 to 2021, using advanced geospatial techniques to investigate underlying dynamics and driving factors behind urbanization. Multi-spectral datasets from Landsat 7 and 8 were utilized for four distinct time periods (1991, 2001, 2011, and 2021). The supervised classification method, employing the maximum likelihood classifier method, was used to generate the classified LULC maps in the ERDAS 2014. The classified images produced by this technique were evaluated for accuracy through matrix union using the statistical kappa coefficient and overall accuracy measures. Change detection for the periods 1991–2001, 2001–2011, and 2011–2021 was conducted using matrix union (intersection) to identify apparent changes in various LULC classes. The findings reveal a significant increase in built-up areas, expanding from 2440.35 hectares in 1991 to 8205.98 hectares in 2021, at an annual rate of 192.19 hectares/year. In contrast, agricultural lands decreased from 7484.13 hectares in 1991 to 3585.42 hectares in 2021, declining annually by 129.96 hectares/year. The study utilizes classified supervised images from various years, alongside seven independent spatial variables, to analyze urban growth and expansion using the method of random forests regression technique in Python, validated through receiver operating characteristic curve. Analysis of these seven variables, including distance to roads, proximity to the city centre, and infrastructure development, identifies them as primary drivers of urban expansion. The study concludes that rapid urbanization is significantly reshaping LULC dynamics, underscoring the urgent need for sustainable urban planning and management strategies. [ABSTRACT FROM AUTHOR]

Abstract The primary priority of bridge construction over a waterway is to support and confirm constant communication. Conversely, it may locally disrupt the health of the river which will have an adverse influence on the hydrological as well as morphological behavior of nearby regions by way of the river’s narrowing. The current study evaluated the effect of bridge structure on river morphological characteristics for Teesta River. Hence to do, this work robbed two approaches. Firstly, the Landsat images from a pre-road bridge (2001 and 2006) and post-road bridge construction (2011, 2016, and 2021) are processed and used for bar formation and bank line shifting. Secondly, the bar properties, differences in channel width, and changes in river bank were evaluated using geospatial technology. The outcomes revealed a recurrent change of bar area and channel width at the upstream side of the bridges and likewise dominating at the downstream side. Throughout the post-road bridge period, the average bar area has increased noticeably by 32.45 sq.km which is 7.75% of the total river area and the downstream bar area has also increased considerably. Besides, both bank lines of the river were dominated by erosion in the post-road bridge construction era. Accordingly, the Teesta Road bridge’s existence has had a significant impact on the morphological modification in recent years. The study also reveals that the bars and islands of Teesta River undergo a drastic change and the river can be treated as a braided one, and also showed the minor trend of meandering. The findings of this research may be supportive for sustainable and long-term planning and development of the rivers and neighboring floodplains in Bangladesh.

The primary priority of bridge construction over a waterway is to support and confirm constant communication. Conversely, it may locally disrupt the health of the river which will have an adverse influence on the hydrological as well as morphological behavior of nearby regions by way of the river's narrowing. The current study evaluated the effect of bridge structure on river morphological characteristics for Teesta River. Hence to do, this work robbed two approaches. Firstly, the Landsat images from a pre-road bridge (2001 and 2006) and post-road bridge construction (2011, 2016, and 2021) are processed and used for bar formation and bank line shifting. Secondly, the bar properties, differences in channel width, and changes in river bank were evaluated using geospatial technology. The outcomes revealed a recurrent change of bar area and channel width at the upstream side of the bridges and likewise dominating at the downstream side. Throughout the post-road bridge period, the average bar area has increased noticeably by 32.45 sq.km which is 7.75% of the total river area and the downstream bar area has also increased considerably. Besides, both bank lines of the river were dominated by erosion in the post-road bridge construction era. Accordingly, the Teesta Road bridge's existence has had a significant impact on the morphological modification in recent years. The study also reveals that the bars and islands of Teesta River undergo a drastic change and the river can be treated as a braided one, and also showed the minor trend of meandering. The findings of this research may be supportive for sustainable and long-term planning and development of the rivers and neighboring floodplains in Bangladesh. [ABSTRACT FROM AUTHOR]

Introduction: Anthropogenic disturbances resulting from extensive mining activities in tropical regions pose significant threats to native land use, leading to deforestation, biodiversity loss, climate change impacts, environmental degradation, health risks, landscape fragmentation, compromised ecological security, and societal wellbeing. Monitoring Land Use and Land Cover Change (LULCC) becomes imperative for evaluating the extent and nature of land degradation in mined areas. Methods: This study examined and compared land cover change patterns across three coalmined sites: Sohagpur (Site-I), Jamuna & Kotma (Site-II), Bishrampur (Site-III) in Central India over 3 decades using Landsat satellite imagery from 1994, 2007, and 2022. The Maximum Likelihood (ML) algorithm, within a supervised classification framework, was applied to discern mining impacts on decadal land use shifts. Results and discussion: The analysis revealed that 7.32%-17.61% of forest cover, 5.0%-10% of water bodies, and 3%-5% of agricultural lands were lost due to mining activities, with Site three and Site two experiencing greater losses compared to Site 1. Overall, native land cover diminished by 35% between 1994 and 2022. Indices including Soil Index, Climate Index, Terrain Index, Land Utilization Index, and Vegetation Index were derived to assess land degradation patterns. These indices were integrated using a weighted index model in ArcGIS to generate the Land Degradation Vulnerability Index (LDVI). Vulnerability notably escalated with mining expansion, particularly pronounced at Site 3 (Bishrampur) and lower at Site 1 (Sohagpur). The "extremely vulnerable" class encompassed a substantial area (25%-40%), while the "low vulnerable" class was less than 5% across all sites. This study's comprehensive analysis aids policymakers, planners, and managers in prioritizing targeted interventions and implementing sustainable land management 175 practices for ecorestoration, aligning with the goal of Zero Net Land Degradation 176 (ZNLD) in coal-mined landscapes. [ABSTRACT FROM AUTHOR]

The Indian Himalayan region is frequently experiencing climate change-induced landslides. Thus, landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard. This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region. It examined the effectiveness of random forest (RF), multilayer perceptron (MLP), sequential minimal optimization regression (SMOreg) and bagging ensemble (B-RF, B-SMOreg, B-MLP) models. A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training (70%) and testing (30%) datasets. The site-specific influencing factors were selected by employing a multicollinearity test. The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method. The effectiveness of machine learning models was verified through performance assessors. The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves (ROC-AUC), accuracy, precision, recall and F1-score. The key performance metrics and map validation demonstrated that the B-RF model (correlation coefficient: 0.988, mean absolute error: 0.010, root mean square error: 0.058, relative absolute error: 2.964, ROC-AUC: 0.947, accuracy: 0.778, precision: 0.819, recall: 0.917 and F-1 score: 0.865) outperformed the single classifiers and other bagging ensemble models for landslide susceptibility. The results show that the largest area was found under the very high susceptibility zone (33.87%), followed by the low (27.30%), high (20.68%) and moderate (18.16%) susceptibility zones. The factors, namely average annual rainfall, slope, lithology, soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility. Soil texture, lineament density and elevation have been attributed to high and moderate susceptibility. Thus, the study calls for devising suitable landslide mitigation measures in the study area. Structural measures, an immediate response system, community participation and coordination among stakeholders may help lessen the detrimental impact of landslides. The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics. [ABSTRACT FROM AUTHOR]

Historically, and in recent times, efforts have been to understand, predict, analyze, and quantify floods and their impacts in various countries of the globe. Although recent scientific advances have introduced approaches to assessing the risks presented by flooding, little studies have been carried out in the Sunyani Municipality of Ghana for generating a pluvial flood-risk and vulnerability map for risk identification, resilience, emergency preparedness, and urban spatial planning. In this study, five parameters that influence both pluvial and fluvial flooding were assessed to map flood-prone areas within the Sunyani Municipality. These are precipitation, drainage density, LULC, elevation, and slope, which were integrated in GIS. Using an AHP, weights were assigned to each parameter based on its level of influence on flooding. The findings reveal that 21.32 % of the Sunyani Municipality lies within a highly flood-prone area, 39.65 % in a flood-prone area, while 28.06 % and 10.97 % in slightly flood-prone and not flood-prone areas respectively. Built-up areas close to watersheds with lower elevations and larger drainage density are the places that are highly flood-prone. Some towns within the highly flood-prone and flood-prone areas are Abesim, Newtown, Nkwarbeng, Baakoniaba, Kootokrom, and Penkwase. Highly valued infrastructure such as schools, churches, and hospitals have also been found within these highly flood-prone areas. These findings can aid the government and relevant stakeholders in disaster risk management to be better informed, and to effectively plan and prevent flood challenges in the Sunyani Municipality. Moreover, urban spatial planners in the study setting can consider incorporating the flood hazard maps generated from this study into their spatial plans for proactive physical developments.

This study investigates the major environmental and socio-economic impacts of an increase in the area of rubber plantations and the changing patterns of drivers of land use changes. Using a combination of geospatial techniques and socio-economic methods, we mainly analyzed the rate of increase in area under rubber plantations, the major impacts of land use changes, and the changing drivers of land use changes. Our results show that the area under rubber plantations has increased significantly within the study area, with the area under rubber plantations increasing from 30 to 74% of the total area within five decades. Impact assessment of land use changes based on household surveys showed significant improvement in the socio-economic conditions of the farmers, however, at the expense of severe environmental degradation. Our results also indicate that while areas under rubber plantations continue to increase, the drivers of land use changes have changed over time. Furthermore, it has been observed that in the past, many interventions prioritized social and economic development and placed less emphasis on the ecological stability of the region. Perceptions of farmers revealed that the effects of ecological fragility already affected the economic robustness of the whole area. Therefore, we conclude that government interventions to support additional rubber cultivation should also focus on ecosystem stabilization in order to minimize the risk of an ecological catastrophe that would significantly affect the economic prosperity of the region. [ABSTRACT FROM AUTHOR]

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

THE POPULATION surge in Egypt has led to the conversion of agricultural land into urban areas, necessitating urgent agricultural expansion into new areas, particularly in the interference zone between the Nile valley and desert regions. To address this issue, this research study aims at assess the temporal and spatial land cover changes in West Samlout area, El-Minia government, Egypt using remote sensing data and to evaluate land capability and suitability for some agricultural crops. The study area, located in the Western Desert of Egypt, was analyzed using multi-temporal Landsat imagery (2011, 2016 and 2021) and Digital Elevation Models. The Soil Adjusted Vegetation Index (SAVI) was utilized to determine the spatial distribution and temporal changes of vegetation. Soil samples were collected and analyzed to determine soil attributes like depth, salinity, pH, calcium carbonate, and texture. Results showed that the vegetation areas were increased with 18.96 % during the period from 2011 to 2021. The annual rate of vegetation change was increased by 910.42 ha year1 through the same period. Land capability evaluation results showed that the study area was grouped into three grades; Grade 3 (1105.96 ha), grade 4 (20682.83 ha) and grade 5 (967.38 ha) represented 4.61%, 86.14% and 4.03 % of the examined area, respectively. The results of land suitability analysis illustrated that mango, citrus, and olives were the most appropriate fruit crops for the investigated area, where 83.8 5 %, 74.7 %, and 71.8 % of the total area were moderately suitable (S2), respectively. Moreover, barley and wheat were found to be highly suitable field crops. The highly suitable (S1) for barley and wheat covered 77.6% and 55% of the total area were, respectively. While, onion was the most suitable vegetable crop in the study area occupied an area of 2768.4 ha (11.5 %) and 19987.7 ha (83.2 %) as highly suitable (S1) and moderately suitable (S2), respectively. With regarding to the oil crops, the finding conducted that sunflower was the highest suitable crop in investigated area occupied 1914.2 ha and 20842.0 ha as highly suitable (S1) and moderately suitable (S2), respectively. Finally, sorghum and millets, as feed crops, were the most suitable crops occupied 82.1 % and 9.2 % of the total area as high suitable (S1), respectively. Furthermore, results indicated that the soil salinity, slope, depth, and coarse texture were the most limiting criteria for suitability. Overall, this research study provides valuable information for land use planning, agricultural development, and sustainable resource management in the West Samlout area, Egypt. The results emphasize the importance of remote sensing and spatial analysis techniques in understanding land cover changes and making informed decisions for agricultural expansion and development projects. [ABSTRACT FROM AUTHOR]

The upsurge in economic activities and urban dwellers has spurred sprawl-like growth in the Sekondi-Takoradi metropolis. This study used Landsat imageries and Shannon entropy to assess sprawl in the Sekondi-Takoradi metropolis. Analysis of the study revealed an increase in built-up by 63.07 km2 while Shannon entropy result (which ranged from 2.17 to 2.47), revealed that the metropolis has been sprawling from 1991 to 2023. Hence, causing significant changes in the land cover composition of the metropolis. This study provides valuable insights into the spatial dynamics of the metropolis, which will guide policymakers in addressing the environmental challenges associated with urban expansion. [ABSTRACT FROM AUTHOR]

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

The Western Ghats of India is rich in variety of flora and fauna and specially known for its endemism. The growing urbanization, expansion of agriculture and other anthropogenic interferences results in forest fragmentation and degradation of habitat. Spatio-temporal changes in forest cover and the variation in forest fragmentation in the Anjaneri, Panchgani, and Kas Plateau regions of the Western Ghats for the years between 1989 and 2015 were assessed. Thematic maps of the area were generated using multitemporal satellite images and geospatial techniques for the years of 1989, 1999, 2006, and 2015. Substantial decline in area under forest cover of the study area occurred during this period. Deforestation at Panchagani in comparison is higher than that at Aanjaneri and Kas. The causes of decline in area under forest are increasing area under agriculture, expansion of plantation agriculture, increasing built-up area and tourism infrastructure development. The forest fragmentation analysis revealed increase in the number of forest patches and decrease in patch size over the period. Rapid landscape transformation at the outer edges of the forest regions effects more habitat fragmentation and loss within the core forest area. The Anjaneri, Panchgani and Kas Plateau regions are ecologically and culturally significant. There is an urgent need of forest conservation measures and sustainable land-use planning which includes control of deforestation, promoting reforestation efforts, and improving landscape connectivity by establishing ecological corridors. The study also recommends policies based on principle of judicious balance in development activities with consideration preservation and restoration of the natural environment to preserving the environmental stability of this biodiversity hotspot region. [ABSTRACT FROM AUTHOR]

Introduction: Anthropogenic disturbances resulting from extensive mining activities in tropical regions pose significant threats to native land use, leading to deforestation, biodiversity loss, climate change impacts, environmental degradation, health risks, landscape fragmentation, compromised ecological security, and societal wellbeing. Monitoring Land Use and Land Cover Change (LULCC) becomes imperative for evaluating the extent and nature of land degradation in mined areas.Methods: This study examined and compared land cover change patterns across three coalmined sites: Sohagpur (Site-I), Jamuna & Kotma (Site-II), Bishrampur (Site-III) in Central India over 3 decades using Landsat satellite imagery from 1994, 2007, and 2022. The Maximum Likelihood (ML) algorithm, within a supervised classification framework, was applied to discern mining impacts on decadal land use shifts.Results and discussion: The analysis revealed that 7.32%–17.61% of forest cover, 5.0%–10% of water bodies, and 3%–5% of agricultural lands were lost due to mining activities, with Site three and Site two experiencing greater losses compared to Site 1. Overall, native land cover diminished by 35% between 1994 and 2022. Indices including Soil Index, Climate Index, Terrain Index, Land Utilization Index, and Vegetation Index were derived to assess land degradation patterns. These indices were integrated using a weighted index model in ArcGIS to generate the Land Degradation Vulnerability Index (LDVI). Vulnerability notably escalated with mining expansion, particularly pronounced at Site 3 (Bishrampur) and lower at Site 1 (Sohagpur). The “extremely vulnerable” class encompassed a substantial area (25%–40%), while the “low vulnerable” class was less than 5% across all sites. This study’s comprehensive analysis aids policymakers, planners, and managers in prioritizing targeted interventions and implementing sustainable land management 175 practices for ecorestoration, aligning with the goal of Zero Net Land Degradation 176 (ZNLD) in coal-mined landscapes.

Road Traffic Accidents (RTAs) provide a substantial risk to both public safety and infrastructure within severe earthquake zones. This research aims to provide a thorough analysis of the many elements that contribute to RTAs in the area. Although research on RTAs has been conducted in numerous settings, there is a lack of localized research specifically focused on developing countries. Therefore, this study is necessary to address this gap in literature. Our study focuses on examining the distinct issues and elements that are particular to this specific location. The objective is to provide customized solutions that effectively reduce the occurrence of accidents. The objective of this study is to examine the status of RTAs, ascertain the main factors contributing to RTAs, and provide viable approaches to improve road safety. A comprehensive study of data was undertaken, which included the examination of key highways, accident-prone areas, and causes contributing to accidents. Insights were derived by the use of statistical analysis, hotspot mapping, and the categorization of incidents. The results revealed a distressing roadway are afflicted by a significant frequency of accidents, even those occurring on prominent thoroughfares. Excessive speed has been revealed as the primary contributing factor, closely followed by negligence and recklessness. The common factor seen in these events was the insufficient enforcement of traffic regulations. The research highlights several practical consequences. Firstly, it is advised to implement urgent actions such as the upgrading of the traffic database, the adoption of contemporary traffic management software, and the enforcement of rigorous traffic laws. The establishment of a comprehensive centralized database system is crucial in order to enhance the documenting and management of accidents. Furthermore, the use of cost-benefit analysis provides justification for the adoption of traffic calming measures in locations with a high incidence of accidents. Furthermore, the implementation of continuous road safety awareness efforts and the establishment of clear legislative guidelines pertaining to fatal accidents are imperative measures in promoting enhanced road safety.

Sekondi-Takoradi is the most urbanized metropolis in the Western region of Ghana. Over the past two decades, it has experienced rapid shifts in both population density and land cover. Unfortunately, these transformations have significantly altered the natural landscape and expanded urban coverage. Previous studies have not established enough evidence on land use and land cover changes in the metropolis. For this reason, this study examined the existence, rate, and spatial distribution of land use and land cover as well as the future changes in land cover in the metropolis. Landsat imagery spanning from 1991, 2009, 2016, to 2023 provided the basis for evaluating the spatiotemporal dynamics of land use and cover within the metropolis. The Random Forest Classification algorithm was employed to categorize the images into three distinct classes: water, vegetation, and built-up areas. Analysis revealed a rapid growth in built-up areas by 63.08 km² (32.91 %), accompanied by a decrease in vegetation and water coverage by 60.99 km² (31.82 %) and 2.08 km² (1.09 %) respectively. This highlights the accelerating urbanization trend, emphasizing the critical need for vigilant monitoring and controlled urban expansion to mitigate potential adverse effects. Projections for 2030 indicate further changes with water areas decreasing to 1.21 km² (0.63 %), vegetation diminishing to 95.31 km² (49.73 %), and built-up areas expanding to 95.14 km² (49.64 %). This signifies a significant shift towards built-up areas, altering the metropolis' land cover composition and potentially impeding the achievement of Sustainable Development Goal 11. Understanding these changes in land use and land cover holds significant value for policymakers, enabling them to effectively monitor and safeguard natural resources.

The escalating rates of deforestation, compounded by land degradation arising from intensified mining operations, forest fires, encroachments, and road infrastructure, among other factors, are severely disrupting the botanical and soil ecology of tropical ecosystems. This research focused on the upper Narmada River catchment area in central India, employing geospatial methodologies to assess land use and land cover (LULC) changes. Landsat 5, 7, and 8 satellite data for 2000, 2010, and 2022 were digitally classified using the maximum likelihood algorithm within the ERDAS IMAGINE and ArcGIS platforms. LULC was delineated into five categories (i.e., water bodies, built-up land, agricultural areas, forested regions, and fallow land). A spatio-temporal analysis revealed substantial declines of approximately 156 km2 in fallow land and 148 km2 in forested areas, accounting for 3.21% of the total area, while built-up land, water bodies, and agriculture land expanded between 2000 and 2022. There was a notable negative correlation observed between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) across all LULC categories, except water bodies. The Land Degradation Vulnerability Index indicated that fallow lands, followed by forests and agriculture areas, exhibited a high vulnerability, with 43.16% of the landscape being categorized as vulnerable over the past 22 years. This study underscores the imperative of effective ecological restoration to mitigate land degradation processes and foster resilient ecosystems. The findings emphasize the importance of integrating scientific data into policy-making frameworks to ensure the comprehensive and timely management of the Narmada River landscape.

This study was conducted in the Northern transect of Bangalore, Karnataka, India, aiming to map the spatial variability of soil properties using geospatial techniques. Surface soil samples (0-15 cm) were collected from two villages Kachahalli and Karanalu using GPS. Laboratory measurements were performed to determine the physico-chemical properties of the soil. The accuracy of various ordinary kriging methods was executed and compared in this research. Semivariogram analysis was employed to quantify the spatial variability of soil properties and surface maps were generated using ordinary kriging. The exponential model demonstrated a good fit with the experimental semivariograms of pH, organic carbon (OC), available nitrogen (N), phosphorus (P2O5), potassium (K2O), sulfur (S) and zinc (Zn). The coefficient of variation for soil properties exhibited considerable variability, with the highest variation observed in K2O (60.57). OC, N, P2O5 and K2O displayed moderate spatial variation, while pH showed the smallest variation (13.43%). In the case of P2O5, the goodness of prediction (G) had a negative value, whereas N, P2O5, K2O, pH, Zn and S displayed positive values. Cross-validation of the krigged maps demonstrated that spatial prediction of soil nutrients using semivariogram parameters outperformed assuming the mean of observed values for unsampled locations. [ABSTRACT FROM AUTHOR]

Access to safe drinking water for the public is of utmost concern in developing nations to achieve sustainable development. The usage of groundwater for drinking purposes is well known on account of its less susceptibility to contamination; it is thus considered essential to examine the quality of water that is intended for public supply. This research assesses the groundwater quality of Dehradun Valley of outer Himalayas, Uttarakhand, India, for drinking purpose using geographical information system and multivariate statistical tools. The results from the research revealed groundwater of the study area dominated by calcium–magnesium–bicarbonate hydro-chemical facies, which show the occurrence of limestone/dolomite, magnesite, talc rock types in the geology of the region, controlling the hydrogeochemistry of the groundwater. Few water samples falling under the 'mixed type' zone of the plotted Piper diagram reflect influence of anthropogenic sources of pollution. The groundwater quality was also evaluated using the water quality index, that grouped 96% of tested water sources for having 'excellent' to 'good' water quality, whereas 4% fall under 'fair' category, which were not fit for consumption purposes. The physico-chemical investigations showed turbidity and iron concentration at few sources, exceeding the limits of Bureau of Indian Standards drinking water specifications. The multivariate statistical techniques (Pearson Correlation Analysis, Principal Component Analysis and Hierarchical cluster analysis) suggest the rock–water interactions and corrosion of underground pipelines and metal surfaces influence the groundwater quality in the study area. The study suggests to undertake regular maintenance and repair works of damaged underground water supply machinery, to consistently monitor and understand the groundwater characteristics of the region and identify the associated factors which can impact its quality, so as to ensure safe public water supply and to manage the groundwater sources sustainably in the study area. [ABSTRACT FROM AUTHOR]

Morphometric analysis provides an essential understanding of the geo-hydrological nature of a drainage basin. The advancements in remote sensing products like digital elevation model (DEM) and geographical information system (GIS) have made the assessment of morphometric indices more effective, easier, cheaper, and faster. Many studies have been carried out in different river basins of the country but there are meager works in context of the river basins of Arunachal Pradesh, India. Therefore, in this study, we assessed the morphometric parameters of the Bichom River basin for the first time using the DEM of Advanced Land Observing Satellite-Phased Array-Type L-Band Synthetic Aperture Radar (ALOS-PALSAR) with 12.5m spatial resolution in ArcGIS 10.3. The basin was divided into four sub-watersheds, namely Upper Bichom (SW-1), Dirang-Chu (SW-2), Tenga (SW-3), and Kaya (SW-4), and the linear, areal, and relief parameters have been analyzed. The Bichom River is of 8th order and exhibits dentritic drainage pattern. The results of linear aspects show that the basin is lithologically and geologically controlled with variations in slope and topography. The areal parameters indicate moderately permeable subsoil, moderate to high runoff with steep slope, rapid rainwater discharge, low to moderate permeability or infiltration, mature topography, and semi-circular basin. Finally, the relief attributes of the basin also exhibit steep slope, high runoff with low to moderate infiltration potential, and active erosional processes. The present baseline findings of the morphometric parameters could be effectively used by the decision-makers for prioritizing soil and water resource management at the basin and sub-watershed level. [ABSTRACT FROM AUTHOR]

The major goal of this research is to adopt analytical hierarchy process (AHP) based geospatial technique to select suitable zone for the solar photovoltaic (PV) power plants. Nine thematic layers altogether—slope, global horizontal irradiation (GHI), relative humidity, direct normal irradiation (DNI), elevation, distance from major roads, distance from protected areas, rainfall, and land use/land cover (LULC)—are combined through overlay analysis in ArcGIS to create the final map of suitability for the placement of solar photovoltaic (PV) power plants in Bangladesh. This map has been classified into five categories namely, restricted zone, less suitable zone, moderate suitable zone, good suitable zone, and excellent suitable zone. These categories are covered by 7.28%, 16.61%, 28.51%, 27.77%, 21.83% land of total area in Bangladesh respectively. The findings of this research have been presented that ‘the excellent suitable’ and ‘good suitable’ areas for the construction of solar power plants are in the western and northwestern part (Rajshahi, Pabna, Sirajganj, Natore, Naogaon, Chapainawabganj, Bogura, Faridpur, Jessore, Jehenaidha, Magura, Kushtia, Choudanga, Meherpur) of the study area which contain a high value of global horizontal irradiation, direct normal irradiation, elevation and low value of slope, rainfall, temperature, relative humidity. Besides the restricted and less suitable zone for installing solar photovoltaic (PV) power plants indicates a high value of rainfall, slope, temperature, relative humidity and low value of global horizontal irradiation, direct normal irradiation, and elevation. Bangladesh's currently operational solar plants were taken into consideration for this study's validation purposes. The proposed framework may potentially be used in different locales on a national and worldwide scale. This study offers a consistent GIS process for the accurate, inexpensive implementation of a solar energy plan to achieve environmentally friendly goals.

Neotectonics is a major factor for controlling landform development in tectonically active Chittagong Tripura Fold Belt (CTFB) region, along the western flank of the Indo-Burma ranges. Morphotectonic parameters are excellent indicators for landscape morphology evaluation to highlight past and ongoing tectonic processes in a region. To investigate the matter, different morphometric indices such as linear, areal, and relief parameters were quantified using the Digital Elevation Model (DEM) data and its attributes of Chengi River Basin (CRB) and Myinee River Basin (MRB) in CTBF. All the computations for morphometric characteristics and visualization were performed in the Geographical Information System (GIS) and statistical platforms. The CRB and MRB are identified as fifth and fourth-order basins with a total area of 503.53 km2 and 267.28 km2, respectively which have low drainage frequency. The Bifurcation ratio (Rb) value of both these basins is indicative of structural control. TSI controls the upper course of the rivers and the variations have been observed in year-wise Topographic Sinuosity Index (TSI) distribution. The Asymmetry Factor (AF) in both these basins indicates that CRB is tilting towards the western side, whereas the MRB is tilting towards the eastern side. Moreover, these two rivers are elongated in nature indicates tectonic activity along with Google earth (GE) seamless mosaic image-based longitudinal profiles indicates significant deformation along their courses. In addition, the moderate Hypsometric Integral (HI) value shows a convex shape in the lower portion, which might be related to the upliftment along a fault or perhaps an upliftment associated with recent folding. The overall results in both these basins indicate the presence of tectonic activity. Therefore, morphotectonic analyses using DEM, Landsat satellite data, and GE seamless data are found useful in this study and could be considered an ideal tool for any complex basin morphotectonic study.

Abstract In complex hard rock terrain where the population suffers from water scarcity, the use of site-specific single-method surface geophysical surveys to prospect for groundwater is a common practise. However, this has not completely solved the problem of drilling dry wells. To combat this failure, geospatial data and the analytical hierarchy process (AHP) are being used as supporting techniques to increase the chance of success. This study had the aim of generating a groundwater potential zones (GWPZ) map of the complex strato-volcanic terrain of Buea by integrating geospatial techniques, AHP, and the seismoelectric method. Five factors influencing groundwater recharge were used to define the groundwater potential zones. Via the AHP and weighted overlay methods, five classes of groundwater potential zones were delineated: very poor, poor, moderate, good, and very good. More than half of the study area has good to very good groundwater potential. Lastly, the GWPZ model is validated with an iso-conductivity map from a seismoelectric survey and existing static water level data. The iso-conductivity map revealed four main conductivity zones that correlate positively with the GWPZ map. The northwestern part of the study area is characterised by moderate groundwater potential, poor formation conductivity, and dry boreholes. There is an uneven distribution of groundwater and variable water table depth in the area. The results of this study are very encouraging, and the integrated approach used has proven to be efficient in determining groundwater potential zones in complex volcanic terrain.

Drought continues to be the worst natural hazard in the world affecting ecosystems, economies and overall human welfare with severe consequences in developing countries. While drought is an unavoidable climatic phenomenon, actions can be made to improve preparedness and mitigate the impacts upon proper forecast on susceptibility. Drought susceptibility modeling plays a key role in determining how best to mitigate and adapt to drought occurrences. This research explores the application of geospatial techniques and Analytic Hierarchy Process (AHP) in drought susceptibility modeling for the drought-prone Bilate River watershed, located in the central Rift Valley drylands of Ethiopia. A total of 15 parameters were used including rainfall, temperature, evapotranspiration, soil moisture, normalized difference vegetation index, land surface temperature, soil texture, land use-land cover, topographic wetness index, modified normalized difference water index, drainage density, slope, elevation, population density and aspect to model drought susceptibility. Findings showed that nearly 70.2% of the area falls under the moderate drought category, followed by the severe (23.2%), mild drought (6.6%) and extreme (0.02%) drought categories in the watershed. Based on zonal administration, Wolayta Zone has a high spatial coverage of severe drought with 54.1% (659.3 km2) while Hadiya has a high spatial coverage of moderate drought with 24.6% (908.9 km2). The drought susceptibility model (DSM) receiver operating characteristic (ROC) curve was then created, and the area under curve (AUC) was calculated. The results of the analysis showed that the AUC is 0.701 (70.1%) indicating that the model is reasonably good model. Hence, geospatial approaches in conjunction with the AHP model improved the drought susceptibility modeling’s reliability, which has significant implications for drought adaptive management and preparedness planning.

Conceptual Framework: Coastal decrepitude is the loss of topographical resources along the coast as a result of silt, temperature fluctuations, tidal shifts, and wave breaking. Hydraulic activity, scraping, abrasion, and erosion are the major causes of erosion; other factors that affect it include the condition of the shore, rocks, joints, fissures, marine chemical reactions, and wave force. Objectives: The primary objectives of this study are to analyze the coastline changes between 1972 and 2022 utilizing remote sensing and GIS data and forecast the changes in the shoreline in 2032. Methods: The study examined coastal areas from 1972 to 2022 using multi-temporal satellite data from Landsat TM and Landsat OLI/TIRS. Shoreline change and the calculations related to it were examined using the Digital Shoreline Analysis System using near-infrared bands and tasseled cap transformation, to determine the rate of change in the shoreline, the study also employed Shoreline Change Envelope and End Point Rate techniques. Results: Coastal erosion caused substantial damage to the island between 1972 and 2022. The northern, southeastern, and western regions of the coast are seeing the highest levels of coastal erosion. The south coast experiences considerable erosion, whereas the west and southeast coasts experience the least. The EPR of Ghoramara Island has mean, minimum, and maximum changes of −10.59, −4.13, and −35.93, respectively. A notable inshore shift has occurred in the northern portion of Ghoramara, extending from 676 m to 855 m. The study also uses tidal gauge records and Revised Local Reference data from the Haldia Gauge Station to track long-term sea level variations. According to the study, Ghoramara Island's shoreline will change by 2032, with the greatest negative changes expected to occur around the island's western and southern borders. Conclusion: The study shows a significant decrease in Ghoramara's shoreline regions between 1972 and 2022, with a 3,000-population shifted from the place in 2016 due to Lohachara's submergence. Changes in the island's morphology and human activities have reduced cultivation. The study suggests increasing mangrove vegetation to combat coastal erosion and prevent further displacement of people to neighboring islands.

The present study envisions the influences of land use dynamics on the spatial trend of groundwater depth levels over a period of two decades in the south region of NCT Delhi, India. The findings have inferred that among five major LULC categories, built-up has shown a sprawling trend (+2.17 km2/year) from the north, northeastern, and central portions to the confined patches observed in the south and southwest fringes of South Delhi from 2001 to 2021. Likewise, vegetation class has also witnessed significant increment (+1.91 km2/year) to the peripheral boundary, i.e., southern, southwest/eastern fringes, and central portions under recent initiatives of city forests, plantation drives, and urban green spaces programs. On the contrary, a negative rate of change has been observed in fallow land (−2.78 km2/year), agriculture (−1.22 km2/year), and water bodies (−0.07 km2/year). LULC transition matrix has also showed prominent conversion of fallow land into vegetation and built-up class, and change of vegetation into built-up, and fallow land category. Subsequently, corresponding LULC maps have been superimposed with long-term trends of groundwater depth levels (as spatial contours). For entire South Delhi region, depth to water levels ranged from 2.02 to 66.45 meters below ground level (mbgl) where shallower depths followed a negative trend and remain persistent throughout the time period in north and northeastern fringes. Higher fluctuations in groundwater depletion with positive trends directly get influenced with allied land use transitions such as a steady increase in built-up area and steeper depth levels (> 40 mbgl) as observed in central, southwestern, and southern parts. Moreover, buffer peripheries in the proximity of groundwater monitoring stations viz., Hauz Khas, Pushp Vihar, Jamali, Gadaipur, and Bhatti Kalan have observed deeper groundwater levels allied to built-up expansion. Thus, groundwater depletion trends can be ascribed to the incessant conversion of recharging areas into impervious zones along with uneven distribution of groundwater usage and supply. Conversely, expanding vegetative land has also shown improved groundwater depth levels. Therefore, land use influences must be managed in the long run for ensuring sustainable management of groundwater resources. [ABSTRACT FROM AUTHOR]