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Severe socio-environmental pressures and land degradation are substantially impacting Ethiopia, eventually leading to low agricultural productivity, with a consequent very high rate of poverty and food insecurity. The current study investigates the future effect of four management practices on reducing sediment yield in the Fincha sub-watershed, Ethiopia, by developing a soil and water assessment tool (SWAT) model over the next three decades (2019–2050). Four best management practices (BMPs) largely applied in the region were considered here. It was found that filter strips can decrease the sediment yield by 65.64 and 58.77, soil or stone bund by 76.37 and 73.07, contour farming by 79.79 and 75.86, and terracing by 84.9% and 76.32% for the years 2019 and 2050, respectively. The impact of these BMPs on various hydrological processes also was evaluated using SWAT. It was found that BMPs are effective in reducing surface runoff and water yield and in increasing groundwater and lateral flows, while they have a reduced effect on evapotranspiration, lateral flow and water yield. The findings presented here point out that all the simulated management practices significantly lower surface runoff and consequently sediment yield across the watershed, but they are not effective enough to reduce soil erosion below a critical threshold that assures crop production. Therefore, to achieve tolerable soil loss, additional soil and land management strategies, such as biological measures and a combination of BMPs are needed and should be considered in future investigations. In summary, the current study offers evidence for managing river basins in semi-arid regions, and can help in ensuring sustainable management of natural resources. [Display omitted] [ABSTRACT FROM AUTHOR]

Flood hazard assessment of cities gained significance globally due to rise in frequency of flood events and rapid urbanisation. Uncertainties in flood inundation models largely depend on the quality of input datasets, among which topography plays a vital role. This study demonstrates the effectiveness of global terrain models in simulating accurate flooding and generating hazard maps while considering the influence of land-use dynamics focussing on data-scarce regions. Open-source forest and building removed digital elevation model (FABDEM) and a terrain model TDX-12 DTM derived from TanDEM-X 12 DSM using a simple-morphological-filtering technique are considered for comparing their performance in simulating a flood event occurred in Surat city during the year 2006. Spatially varying short-term urban growth scenario for the year 2035 is developed by utilizing historical land-use maps of the study area and urban growth indicators. These are combined using multi-criteria-decision-making techniques and Cellular-Automata-Markov-Chain model. The FABDEM based hydrodynamic model performed better (Root-Mean-Squared-Error RMSE of 1.59 m) than TDX-12 DTM based model (RMSE: 1.88 m). Intercomparison of hazard maps of the FABDEM and TDX-12 DTM with ground-surveyed TopoDEM based-model showed an overall accuracy of 71.8% and 72.8%; for the future scenario 71.8% and 75.5% respectively. In a span of 29 years, a notable increase in hazard magnitude of 7.5% is solely attributed to change in land dynamics. In this study, though the FABDEM based-model showed better RMSE than TDX-12 DTM, the model is relatively less successful in capturing high-hazard regions. The DEMs processed for removal of non-ground objects yield accurate models than globally trained models. [ABSTRACT FROM AUTHOR]

River systems act as conveyor belts carrying sediments in huge quantities from upstream to downstream regions of the basins. Suspended sediment (SS) is a vital element in a river system due to its importance in the planning, design, and management of hydraulic structures. Abrupt variations in sediment dynamics have been observed recently, attributing these to both changes in climate and different anthropogenic actions. A proper understanding of the dominance and impact of these anthropogenic and natural factors on sediment dynamics will help to maintain the sediment equilibrium of any basin. In this study, we present a comprehensive analysis of different changed regimes of suspended-sediment concentration (SSC) and quantify the dominant factors during such changed regimes in Indian river basins. Declining trends are evident in the SSC profiles of most of the gauge stations (∼80%) and can be attributed to increasing anthropogenic activities, especially the construction of water retaining structures throughout India. On average, more than a 50% reduction in sediment quantity was recorded at most gauge stations over the past three decades. It is apparent that natural factors such as discharge and rainfall still are the most dominant factors; but quite interestingly, their relative importance is decreasing after each change point, whereas the influence from anthropogenic factors such as changes in the cropland and grassland are increasing. Large river basins show more influence of anthropogenic activities on sediment dynamics when compared with other smaller basins. Also, changes in land use land cover (LULC) have increasing impact on upstream/midstream parts of the river basins. It is evident that the completion/commissioning of water retaining structures brings abrupt changes in the sediment dynamics, but once the system stabilizes, other natural and anthropogenic factors take over as dominant factors until the next structure comes up and changes the system dynamics again. [ABSTRACT FROM AUTHOR]

Urban flooding problem has been exacerbated in recent times, especially in developing nations, due to haphazard changes in land use and land cover (LULC) resulting from rapid urban expansion, coupled with river encroachments and inadequately engineered river management structures. Kathmandu Valley Watershed (KVW), encompassing Kathmandu, Bhaktapur and Lalitur districts, the fastest growing cities in South Asia, is constantly growing, with a significant increase in urban areas. Due to urbanization, the watershed's water storage capacity is diminishing, while surface runoff volume and rate are accelerating. We evaluated the isolated as well as the integrated impact of multiple scenarios of LULC change and river encroachment on flood inundation characteristics in KVW. LULC prediction revealed an increase in built-up areas by 113% between 1990 and 2020, which are further projected to increase by 29% by 2050. Inundation modeling using Rainfall-Runoff Inundation (RRI) model showed that rather than the increase in inundation extent, the depth of inundation is projected to increase in future as a result of increasing urban areas. Furthermore, our research highlighted that the impact of river width encroachment had a more substantial effect on flooding compared to changes in LULC alone. Similarly, integrated impact of LULC change and river encroachment was more pronounced than the impact of change in LULC alone. The aggregate of observations leads to the conclusion that the encroachment of rivers is the predominant factor contributing to the flooding issue within the KVW. The findings of the study is anticipated to assist policymakers in effective land use planning and in proposing appropriate development initiatives concerning the river environment. [ABSTRACT FROM AUTHOR]

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation. [ABSTRACT FROM AUTHOR]

In developing countries, examining land use land cover (LULC) change pattern is crucial to understanding the land surface temperature (LST) effect as urban development lacks coherent policy planning. The variability in LST is often determined by continuously changing LULC patterns. In this study, LULC change effect analysis on LST has been carried out using geometric and radiometric corrected thermal bands of multi-spectral Landsat 7 ETM + and 8 TIRS/OLI satellite imagery over Gandhinagar, Gujarat, in the years 2001 and 2022, respectively. Maximum likelihood classification (MLC) was applied to assess LULC change while an NDVI-based single-channel algorithm was used to retrieve LST using Google Earth Engine (GEE). Results showed a substantial change in built-up (+ 347.08%), barren land (− 50.74%), and vegetation (− 31.66%). With the change in LULC and impervious surfaces, the mean LST has increased by 5.47 ℃. The impact of sparse built-up was seen on vegetation and agriculture as a maximum temperature of > 47 ℃ was noticed in all LULC classes except agriculture, where the temperature reached as high as > 49 ℃ in 2022. Since Gandhinagar is developing a twin-city plan with Ahmedabad, this study could be used as a scientific basis for sustainable urban planning to overcome dynamic LULC change and LST impacts. [ABSTRACT FROM AUTHOR]

According to the Times of India, Baripada city recorded the highest temperature in the world on April 14, 2023, at 43.5°C. It is located in the south‐west direction of the Similipal Biosphere Reserve. Observing the third highest temperature in the world, we are interested in finding out the reasons affecting climate change. This paper investigates the reasons for the temperature change in Baripada city and its surroundings by considering factors like Normalized Difference Vegetation Index (NDVI), Normalized Multi‐band Drought Index (NMDI), Normalized Difference Water Index (NDWI), Normalized Difference Built‐up Index (NDBI), forest fires, rainfall patterns, wind patterns, and registration of motor vehicles. For this study, data has been collected from USGS (United States Geological Survey), NASA POWER, Forest Survey of India, ArcGIS Online, etc. The study of NDVI shows a significant relationship with the LST. Similarly, NMDI shows an abnormal change in the drought index from 0.092 in 1990 to 0.276 in 2022, and NDWI shows a continuous reduction of the water index of −0.382 in 1990 and −0.176 in 2010, followed by −0.212 in 2022. NDBI values show that the urban build‐up index is increased to an extent due to rapid urban settlements removing natural vegetation. The overall forest fire count found an abnormal rise in the Baripada region during 2011 and 2022 to 143 times. Focusing on all the above factors, an experimental study is finally carried out to confirm the rise in temperature using time‐series analysis and a multi‐layer locally tuned hidden layer perceptron (MLTHP) model to ensure the Times of India's report. [ABSTRACT FROM AUTHOR]

Wetlands, which serve as transitional zones between terrestrial and aquatic environments, play a crucial role in providing diverse ecosystem services for the sustenance of wildlife and mankind. Unfortunately, these delicate systems have experienced illicit human interventions in recent times, resulting in degradation and deterioration. Despite being designated as protected areas, wetlands are being exploited beyond their ecological capacity, posing a threat to their sustainability and potential existence in the near future. The present study aims to analyse the historical spatial changes in three wetlands located in Kerala, India, both before and after their declaration as Ramsar sites. Ashtamudi Lake, Sasthamkotta Lake, and Vembanad Lake were the focus areas, and the study employed multi-temporal satellite data sets from 1988 to 2023 to generate land use and land cover maps. The objective was to assess the ecological transitions, particularly the expansion of urbanization and subsequent reclamation of wetlands. By analysing the changes in aerial extent of each land cover class within the study area, the study identified spatio-temporal transitions and modifications occurring in and around the Ramsar sites. A comparison of the thematic maps from 1988 to 2023 revealed a significant increase in built-up areas and a noticeable reduction in vegetation, agriculture, and water bodies, both in terms of extent and quality. The study identified various socio-economic drivers that are responsible for these changes and emphasized the urgent need for regular monitoring and management of these wetland ecosystems. It stresses the importance of treating wetland conservation as a priority. [ABSTRACT FROM AUTHOR]

Abstract The morphometric analysis of the watersheds is essential for the conservation of natural resources, including soil, water, and vegetation. The Morphometric analysis defines the linear, areal and relief aspects of the watershed. It involves the comprehensive analysis of various factors such as drainage network, surface water flow, and other topographical features. The aim of this study is to develop a sustainable watershed management strategy for the Nayar watershed based on morphometry and Land Use Land Cover (LULC) change dynamics. The Nayar watershed has a total area of 1956.33 km2. The multispectral satellite imagery, Digital Elevation Model (DEM) data, and Survey of India (SOI) Toposheets were used for understanding the topographical and morphological characteristics along with the LULC change dynamics. The findings of this research conclude that the Nayar watershed has parallel and dendritic drainage patterns with high relief. According to the LULC change dynamics, the Nayar watershed's area change comprising 57.60 km2 and 57.15 km2, are from Agricultural Land to Wasteland and from Forest Cover to Wasteland, respectively. This shift in the hilly region will increase the risks of landslides and erosion. Furthermore, the change of 110.03 km2 area of Forest Cover to Agricultural Land raises further challenges due to loss of natural vegetation in long run. In summary the change in LULC of Nayar watershed is vulnerable to risk of natural calamities like landslide, erosion. This work would be helpful to many experts and decision-makers for sustainable watershed management and natural resource management.

Abstract This paper presents a comprehensive analysis of the spatial patterns and temporal dynamics of land use and land cover changes in South Kashmir from 2000 to 2022 leveraging remote sensing technologies revealing significant transformations in various land cover classes. The study used maximum likelihood classification, a supervised classification method, to analyze Landsat satellite imagery and identify ten major land use categories. The findings demonstrate notable increases in, barren land by 18.78 km2 (0.35%), built-up areas by 72.28 km2 (1.33%), forests by 274.76 km2 (5.05%), grasslands by 68.06 km2 (1.25%), scrubland by 307.82 km2 (5.66%). horticulture experienced a significant rise of 419.17 km2 (7.70%), Conversely, several land use classes reported decline, agriculture by 757.21 km2 (13.91%), exposed rockmass by 258.58 km2 (4.75%), glaciers and snow by 136.83 km2 (2.51%), and water bodies contracted by 8.23 km2 (0.15%). The primary drivers of land use change in the region are identified as climate change, population growth, and economic factors. Climate change has altered precipitation patterns affecting agricultural productivity and leading to the retreat of glaciers. Population growth and economic reasons, including the rise of horticulture and changes in land use policies, have also played a significant role in shaping the landscape dynamics of South Kashmir. These changes underscore the dynamic nature of land use in South Kashmir, with significant implications for regional planning and environmental management. The study underscores the cost-effectiveness and efficacy of geospatial technologies in conducting spatiotemporal analyses and formulating evidence-based policies for the sustainable management of natural resources.

Changes in land use and land cover (LULC) have an impact on our comprehension of regional, local, and global environmental change, and cities are dynamic because change is constant and can be attributed to a variety of factors depending on the socioeconomic, political, and environmental circumstances of a particular location. Socioeconomic changes have a direct impact on the local environment because of the significant changes in the natural world. The study aims to classify the many forms of land use and land cover that existed in the Killa Abdullah region between 1992 and 2020, including the built-up, barren terrain, vegetated, and rangeland sectors. to evaluate the impact of the changes on LULC. For this study, both primary and secondary data were gathered. Secondary data was gathered using satellite images from the corresponding years 1990 and 2020, while primary data was gathered using GPS coordinates. Both satellite images were analyzed using the ERDAS Imagine 9.8v and ArcGIS 10.1v software. The findings of the study illustrated the intricate dynamics of land cover in the area. These nuances highlight potential avenues for expanding the field of research, notably in the area of customized training approaches. The accuracy for the years 1990, 2000, 2010, and 2020 was 92%, 88%, 87%, and 93%, respectively. The overall precision, which took into account all classes, reached a respectable 90%. The vegetation appears to have changed over time, peaking around 2010. While it was lower in 2020 than it was in 2010, it was still higher than it had been in the earlier years. When compared to 1990, water areas substantially declined in 2000, increased in 2010, and then dramatically decreased once more in 2020. Built- up areas have grown steadily over time, with a notable increase in 2020 compared to earlier years. As built-up regions have grown over time, especially in 2020, there is a clear pattern of urban expansion. Although there is considerable variety in bare places, it is not as noticeable as it is in other land cover types. Changes in land cover are a result of adjustments made to the human surface. Although the output's accuracy was only modest, a visual analysis revealed multiple misclassifications and overstatements of built-up and water.

The Bangkok Metropolitan Administration's expansion, driven by economic growth, includes extending Metro systems and altering land use. This transformation, especially noticeable near these routes, has led to shifts in land cover – reduced green spaces, increased impervious surfaces, and decreased water storage. These changes, coupled with denser population and higher waste-generation, have compromised drainage efficiency, amplifying flood vulnerability. This study focuses on Pink Line's 34.5-kilometre stretch from Khae Rai intersection along Tiwanon Road to Min Buri, aiming to evaluate Flood Vulnerability Index (FVI) near the Metro line, identify critical influencing factors and propose mitigation strategies. The analysis divided the study area into five zones based on characteristics, evaluating FVI using population density (PO), drainage efficiency (DE), impervious ratio (IR), garbage management (GB), and pond area ratio (PA). FVI values ranged from 0.41 to 0.55. Sensitivity analysis showed minor FVI impacts at Lat Pla Khao and Ram Intra km.4 stations due to DE and IR, with reduced FVI at Khu Bon and Eastern Ring Road stations from PA and GB improvements. PA and IR enhancements consistently lowered FVI, while fluctuations were observed with GB and DE changes, with notable impacts at Min Buri Market Station. Understanding factor sensitivity aids in planning local mitigation-strategies. HIGHLIGHTS Flooding is the number one risk in Thailand.; Water disaster risk statistics show that the proportion of the population in Thailand that is at a point of concern that they will be at risk from disasters is as high as 34%.;

The main objective of protecting ecosystems and enhancing the supply of ecosystem services (ESs) is to quantify the value of ecological services. This article calculates the ecological service value (ESV) of the Tarim Basin over the past 40 years using the improved benefits transfer method of satellite remote sensing data, such as Landsat, analyzes the spatiotemporal evolution characteristics of ESV, and studies the driving mechanism of ESV changes using GeoDetector. Finally, the FLUS model was selected to predict the ecosystem service value until 2030, setting up three scenarios: the Baseline Scenario (BLS), the Cultivated Land Protection Scenario (CPS), and the Ecological Protection Scenario (EPS). The results indicate that (1) the ESV in the Tarim Basin decreased by USD 1248.21 million (−2.29%) from 1980 to 2020. The top three contributors are water bodies, wetlands, and grassland. (2) Waste treatment and water supply functions had the highest service value, accounting for 44.53% of the total contribution. The rank order of ecosystem functions in terms of their contribution to the total value of ESV was as follows, refining from high to low importance: water supply, waste treatment, biodiversity protection, climate regulation, soil formation, recreation and culture, gas regulation, food production, raw material. (3) The spatial differentiation driving factors of ESV were detected, with the following Q-values in descending order: net primary productivity (NPP) > normalized difference vegetation index (NDVI) > precipitation > aspect > temperature > slope > soil erosion > GDP > land use intensity > per capita GDP > population > human activity index. (4) The ESVs simulated under the three scenarios (BLS, CPS, and EPS) for 2030 were USD 51,133.9 million, USD 53,624.99 million, and USD 54,561.26 million, respectively. Compared with 2020, the ESVs of the three scenarios decreased as follows: BLS (USD 4209.33 million), CPS (USD 1718.24 million), and EPS USD (−781.97 million). These findings are significant for maintaining the integrity and sustainability of the large-scale ecosystem, where socioeconomic development and the fragile features of the natural ecosystem interact. Additionally, the study results provide a crucial foundation for governmental decision-makers, local residents, and environmental researchers in northwest China to promote sustainable development. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels. [ABSTRACT FROM AUTHOR]

For proper planning of urban infrastructures such as road networks, pipelines, and other linear engineering structures, it is necessary to construct precise land use and land cover maps. Multiple attempts to develop land use land cover classification techniques have been made using various methods ranging from surveying to date image interpretation using remote sensing techniques. Land use land cover classification remains an intricate and challenging task due to the spectral and spatial complexity of the imagery. This work generates a labelled dataset called Sen-2 LULC () using QGIS for land use land cover classification to train the state-of-the-art available convolutional neural networks with the backbone for pixel-wise classification of seven classes. This work deals with five models: UNet with ResNet50 backbone, UNet with ResNet152 backbone, UNet with DenseNet169, FPN with VGG16, and LinkNet with MobileNetv2. UNet-ResNet50 gives the best overall accuracy of 94.10% and Matthews correlation coefficient value of 0.69. [ABSTRACT FROM AUTHOR]

This study explores the relationship between Land Use Land Cover (LULC), Land Surface Temperature (LST), and Urban Heat Island (UHI) in Dakahlia Government using Landsat 8 images from 2014 to 2020. Support Vector Machine (SVM) and Mono-Window Algorithm were used to generate LULC and estimate LST. Results reveal an increase in built-up areas, rising LST, and variable UHI thresholds. The study highlights the impact of COVID-19 on LST in 2020. Positive correlations between LST and Normalized difference build-up index (NDBI) and negative correlations with Normalized difference vegetation index (NDVI) were observed. Projections for 2030 suggest an increase in high-temperature areas. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of − 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy and class specificity. However, deploying deep learning algorithms to characterize present-day, modern land cover based on state-of-the-art data is insufficient for understanding trends in land cover change and identifying changes in and drivers of ecological and social variables of interest. These identifications require characterizing past land cover, for which imagery is often lower-quality. We applied a deep learning pipeline to classify land cover from historical, low-quality RGB aerial imagery, using a case study of Vancouver, Canada. We deployed an atrous convolutional neural network from DeepLabv3+ (which has previously shown to outperform other networks) and trained it on modern Maxar satellite imagery using a modern land cover classification. We fine-tuned the resultant model using a small dataset of manually annotated and augmented historical imagery. This final model accurately predicted historical land cover classification at rates similar to other studies that used high-quality imagery. These predictions indicate that Vancouver has lost vegetative cover from 1995–2021, including a decrease in conifer cover, an increase in pavement cover, and an overall decrease in tree and grass cover. Our workflow may be harnessed to understand historical land cover and identify land cover change in other regions and at other times. [ABSTRACT FROM AUTHOR]

Land use changes and urban activities cause environmental degradation and climate change. This study aims to assess the impacts of Land Use Land Cover (LULC) changes on land surface temperature (LST) in Kigali, Rwanda over the past three decades. The combined techniques of remote sensing and GIS were applied to generate good quality Landsat images, categorize land use classes and retrieve urban indices. The modified Mann-Kendall test and Sen’s slope estimator were used to analyze trends in LST. The results indicate that between 1990 and 2020, the extent of builtup and forest areas have increased at an average rate of 3.39 km2/year and 2.42 km2/year respectively, while open land has decreased at an average rate of 5.81 km2/year. In the same period, water bodies and wetlands showed minimal changes with an increase of 1.56 km2 and a decrease of 1.66 km2 for water bodies and wetlands respectively. The slope magnitudes of LST are predominantly positive (p ≤ 0.05) with a high increase observed in dry seasons (0.51 °C per decade for Tmin in JJA and 0.49 °C per decade for Tmax in JF). The positive correlations between LST and urban indices were observed in all studied subregions (all values are above 0.61, p ≤ 0.05). The findings of this study are useful for the development of future urban land use schemes and the adoption of mitigation and adaptation strategies in response to climate change.

The urban climate has undergone significant changes due to rapid population growth, leading to a decline in vegetation cover and an increase in land surface temperature (LST). This study aims to assess the influence of land use land cover (LULC) changes on LST in four major urban areas in southwestern Ethiopia, namely Jimma, Bonga, Mattu and Nekemte, during the period from 2002 to 2024. To investigate the impact of LULC dynamics on LST, 30 m spatial resolution images from Landsat were utilized, including Thematic Mapper (TM) for the year 2002 and Landsat Operational Land Imager (OLI) and Thermal Infrared (TIRS) for the years 2014 and 2024. Over the past 22 years, the mean LST has increased by 2.81 °C, 2.94 °C, 3.37 °C, and 3.96 °C for Bonga, Nekemte, Mattu, and Jimma, respectively. The increase in LST can be attributed to various factors, but one of the primary reasons is linked to the rapid urbanization and decrease in forest cover. Changes in LULC triggered by rapid urbanization significantly influences LST in major cities. The results highlight the increment of impervious surface and the decline in vegetation cover as key factors contributing to the upward trend in LST. The results indicate that urban centers with less vegetation cover experienced higher LST compared to their surroundings. The results of this study indicate the necessity of effective urban planning to increase vegetation cover through urban greenery and parks to mitigate the increasing trends of LST, which can improve urban thermal comfort levels.

Abstract Human activities are altering the existing patterns of Land Use Land Cover (LULC) and Land Surface Temperature (LST) on a global scale. However, long-term trends of LULC and LST are largely unknown in many remote mountain areas such as the Karakorum. . The objective of our study therefore was to evaluate the historical changes in land use and land cover (LULC) in an alpine environment located in Islamabad Capital Territory, Pakistan. We used Landsat satellite pictures (namely Landsat 5 TM and Landsat 8 OLI) from the years 1988, 2002, and 2016 and applied the Maximum Likelihood Classification (MLC) approach to categorize land use classes. Land Surface Temperatures (LST) were calculated using the thermal bands (6, 10, and 11) of Landsat series data. The correlation between the Human Modification Index (HMI) and LULC as well as LST was evaluated by utilizing data from Google Earth Engine (GEE). Over the study period, the urbanized area increased by 9.94%, whilst the agricultural and bare soil areas decreased by 3.81% and 3.94%, respectively. The findings revealed a significant change in the LULC with a decrease of 1.99% in vegetation. The highest LST class exhibited a progressive trend, with an increase from 12.27% to 48.48%. Based on the LST analysis, the built-up area shows the highest temperature, followed by the barren, agricultural, and vegetation categories. Similarly, the HMI for different LST categories indicates that higher LST categories have higher levels of human alteration compared to lower LST categories, with a strong correlation (R-value = 0.61) between HMI and LST. The findings can be utilized to promote sustainable urban management and for biodiversity conservation efforts. The work also has the potential of utilizing it to protect delicate ecosystems from human interference and to formulate strategies and regulations for sustainable urban growth, including aspects of land utilization and zoning, reduction of urban heat stress, and urban infrastructure.

In order to create sustainable conservation policies for biodiversity, it is imperative that participatory forest management (PFM) be assessed. Forests contribute to the sustainability of the planet by controlling soil erosion in agricultural areas and by moderating the effects of climate change. However, Ethiopia's forest resources have been under intense pressure because of the increased demand for wood products and agricultural conversion. As one of the potential solutions, the PFM programme was implemented in 1990. This study set out to investigate the effects of the PFM programme on land use and land cover (LULC) in the Alle district of southwest Ethiopia, as well as the variables influencing community involvement and the obstacles to PFM implementation and community involvement. Changes in forest cover were detected using Landsat images from 1992, 2012, and 2022 obtained from Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI). Images were obtained during the dry season and were cloud-free. A total of 240 respondents were chosen by means of a straightforward random sampling technique, and survey data were collected using questionnaires, interviews, and field observations. Data were analyzed using ArcGIS 10.5, ERDAS Imagine 2015, SPSS version 20, and Excel 2010. The change in forest cover shows an increasing trend from 2012 to 2022. Again, grassland and wetland coverage in this study decreased rapidly. In the years 2012–2022, forest land increased from 462.7ha (74.8 %), to 569.8ha (92.1 %), while, the agricultural land, grassland, and wetland were reduced from 109.5ha (17.7 %) to 37.8ha (6.1 %), 31.9ha (5.2 %) to 0.0ha (0.0 %); 14.1 ha (2.3 %), to 10.8 ha (1.7 %) respectively. There have been beneficial developments in the forests over the last 30 years. The binary logistic regression model disclose that, land ownership had a negative impact on forest management participation, while other factors such as gender, education level, family size, TLU, access to credit, training, and law enforcement had a positive and significant (p

Tropical highland regions are susceptible to climate change and natural disasters due to their geographical location and hilly terrain. The objectives of this study are to determine land use land cover (LULC) changes in the Cameron Highlands and analyse the climatic parameters of the Cameron Highlands. This study integrates LULC analysis using remote sensing techniques and 10-year climatic parameters data to evaluate the impact of climate variability on the sustainability of the Cameron Highlands. Based on the validation results, the overall accuracy of LULC was 95.42% in 2016, 96.60% in 2018, and 97.40% in 2020. The results show an 18% rise in agriculture, a 16% increase in urban growth, and an 8.14% decline in forest coverage in Tanah Rata and Ringlet, Cameron Highlands, from 2016 to 2020. The Mann -Kendall and Sen slope indicated a statistically significant increasing trend in rainfall (Kendall's Tau, z = 0.102, p < 0.0001 and Sen value= 0.131, p < 0.001, respectively) and temperature (Kendall's Tau, z = 0.151, p < 0.001 and Sen value = 0.294, p < 0.001, respectively) from 2012 to 2021, increasing the area's susceptibility towards climate change impact and natural disasters. This study highlights the vulnerability of the Cameron Highlands to natural disasters, emphasizing the crucial need for efficient land management in slope areas. HIGHLIGHTS The increase in rainfall in Cameron Highlands suggests a potential rise in surface runoff and greater vulnerability to flooding and landslides.; The increase in temperature can lead to prolonged drought conditions, adversely affecting agricultural yields and productivity in Cameron Highlands.; Changes in land use land cover show the decline in forest and an increase in agriculture, increasing the risk of landslides and floods;

The present study focused on evaluating the separate and combined response of land use land cover and climate change (CC) on future water balance components of a Subarnarekha River basin, spanning between the latitudes 21°33′N–23°18′N and longitudes 85°11′E–87°23′E, situated in the eastern India. The Soil and Water Assessment Tool is used for single-site calibration and multi-site calibration (MSC) of the model to characterize the future water balance components of the basin using the Cellular Automata-Markov model and climate projections under two representative concentration pathway (RCP) scenarios (4.5 and 8.5). The findings indicate that the model parameters obtained through MSC better represent spatial heterogeneity, making it the preferred calibration approach for model simulations. In the middle region of the basin, future annual water yield, groundwater recharge (GWR), and streamflow showed a reduction, respectively, by 46–47%, 29–30%, and 13–15%, while evapotranspiration showed an increase by 5–7% following projected CC under both RCP scenarios. The findings are relevant for policy-makers to mitigate the adverse effects of reduced GWR for sustainable water resources management. Future research may integrate reservoir operation frameworks to effectively address the water management issues of the basin. HIGHLIGHTS Single-site and multi-site calibrations of the hydrological model are compared.; A multi-site calibration approach is used to assess the response of land use land cover (LU/LC) and climate change (CC) on water balance components using the Soil and Water Assessment Tool model.; Groundwater recharge (GWR) reduced rapidly under LU/LC and CC scenarios.; CC governs the streamflow and water yield components.; CC and LU/LC change contribute equally to GWR and evapotranspiration.;

Most anthropogenic activities have a profound effect on land cover that affects the water cycle and, ultimately, the availability of water in the watershed. The idea of this study was to evaluate the hydrological response to land use and land cover change in the Bilata watershed. To achieve this objective, supervised land use land cover classification was applied for the years 2000, 2010, and 2020 using ERDAS IMAGINE 2015. The watershed underwent significant land use and land cover changes from 2000 to 2020. There was an abrupt expansion of agricultural land and a reduction of forest. The SWAT model was applied to evaluate the impact of land use and land cover change on hydrological processes. The calibration and validation of the SWAT model showed that the model has performed well in simulating the hydrology of the watershed with a coefficient of determination, Nash–Sutcliffe efficiency, and per cent of bias greater than 0.6 for both calibration and validation. Due to intensive agricultural expansion and settlement, the surface runoff increased from 172 to 259.5 and 265.0 mm in 2000, 2010, and 2020, respectively. The average streamflow increased from 132 to 134 and 150 m3/s between 2000, 2010, and 2020. HIGHLIGHTS Using ERDAS IMAGINE 2015 classify Landsat 7 and 8 images for land use land cover change assessment.; Change detection analysis.; Modeling the Bilata watershed using the SWAT model, through static land use (SLU), sensitivity, and model calibration was done.; Evaluating the response of the hydrological process in the watershed under land use land cover change.;

ABSTRACTChange in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classification technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.

Groundwater is crucial for agriculture, domestic use, and industry. This study represents groundwater potential zones in the Amravati district, Maharashtra, India, using the analytical hierarchy process (AHP) and multi-influencing factor (MIF) techniques. These techniques are employed for a detailed spatial analysis, which is essential for sustainable groundwater management. The study integrates data layers including lithology, geomorphology, land use/land cover, drainage density, lineament density, rainfall, soil, elevation, and slope to evaluate the groundwater potential. The resulting groundwater potential map classifies the area into five categories: poor, fair, moderate, good, and excellent based on groundwater availability. The study reveals that 9% of the area has poor groundwater potential, 37% fair, 28% moderate, 13% good, and 13% excellent. This map is instrumental for stakeholders and policymakers, as it aids in resource allocation and the formulation of sustainable groundwater management strategies. Through the application of AHP and MIF techniques, this study effectively maps the groundwater potential, providing an essential tool for evidence-based decision-making for water resource management in the Amravati district. HIGHLIGHTS The analytical hierarchy process and multi-influencing factor techniques are used for detailed groundwater mapping.; Factors like lithology and rainfall are integrated for comprehensive evaluation.; Key spatial data are provided for sustainable water management in the Amravati district.; High-potential areas are identified for efficient resource allocation.; The need for targeted conservation is highlighted in low-potential areas.;

Nepal, known for its challenging topography and fragile geology is confronted with the constant threat of floods leading to substantial socio-economic losses annually. However, the country's efforts in planning and managing flood risks remain insufficient, especially in the vulnerable Mohana-Khutiya River. Therefore, this study focused on the Mohana-Khutiya River and utilizes the Maximum Entropy (MaxEnt) model to comprehensively map flood susceptibility and fill crucial gaps in flood risk assessments. This study employed a combination of 10 geospatial environmental layers and field-based past flood inventory to implement the MaxEnt machine learning model for flood susceptibility modeling. The available past flood data were divided into two sets, with 75% allocated for model construction and the remaining 25% for model validation. This study demonstrated that the proximity of the river had a significant impact (33.1%) on the occurrence of the flood. Surprisingly, the amount of annual precipitation throughout the year exhibited no detectable contribution to the flood event in the study site. About 4.9% area came under the high flood susceptible zone followed by 12.75 % in the moderate zone and 82.34% in the low-risk zone. The model exhibited excellent performance with an Area Under Curve (AUC) value of 0.935 and a low standard deviation of 0.018, indicating accurate predictions and consistent precision. These results highlight the model's reliability and its significance for developing disaster management policy by local government in the study site. Future research should refine the MaxEnt model by including more variables, validating against observed flood events, and exploring integration with other flood modeling approaches.

Anthropogenic activities have significantly changed global land use land cover (LULC), especially in areas with extreme population expansion and climate change. Remote sensing and the geographic information system are popular methods for keeping track of LULC changes. This research analyzed historical changes and predicted future patterns of LULC in Chittagong Metropolitan City, Bangladesh. LULC maps were created using the semi-automated classification plug-in in QGIS, which was used to classify the satellite images from 1990 to 2020. Eight distinct LULC groups, including (i) agricultural land, (ii) fallow land, (iii) trees outside of forest, (iv) hill vegetation, (v) mangrove, (vi) built-up, (vii) pond/lake, and (viii) river, were used to classify the images. The generated LULC maps showed the changes in the area from 1990 to 2020 in several classifications, showing increases in built-up, pond/lake, and river waterbodies of 278.5, 407.69, and 10.07%, respectively. However, a significant decline was observed in agricultural (61.64%), hill vegetation (43.26%), mangrove forest (30.7%), fallow (22.02%), and Tree Outside of Forest (TOF) land (8.81%). The LULC changes between 2020 and 2035 were predicted using the cellular automata-artificial neural network (CA-ANN) model. The prediction maps from 2020–2035 illustrated increasing trends of built-up land (+ 15.65%) and decreasing trends of all other LULC types, predominantly, agricultural (−3.53%), fallow (−3.13%), trees outside of forest (−6.53%), and pond/lake (−1.44%). The findings of this research may be helpful to design future strategies for sustainable landscape management and help decision-makers in government make better choices for the environment and the ecosystem. [ABSTRACT FROM AUTHOR]

The water availability concerns have been increasing due to significant impacts of land use land cover change, and climate variability. In terms of developing countries, it is one of the biggest challenges to overcome and manage sustainability in the present and future. This study aims to evaluate the change in hydrological components and simulation of sediment yield and water yield on the large-scale basin of Kotri barrage with a change in runoff due to a change in land use land cover. This study has been done on the watershed as well as the sub-watershed level to have an accurate estimation and simulation by finding the response of hydrological components toward its natural and human-induced factors using the Soil and Water Assessment tool with high-resolution geospatial-temporal inputs over the Kotri catchment. The sediment and water yield were quantified using 42 years of simulation (1981–2022) on the subbasin level, projected to land use land cover 1990, 2000, 2010, and 2022. The increase in deforestation, agriculture, and settlement areas resulted increase in sediment load in the catchment. The sub-basins 14, 11, 12, and 13, with a high elevation and slope and with less vegetation showed higher sediment load and water yield than the sub-basins with gentle slope and with high natural vegetation cover. The sub-basins 10, 4, and 1 showed high water yield availability compared to basins 2, 3, 5, 6, 7, 8, 9. This may be the result of vegetation differences. However, contained less sediment load than basins 14, 11, 12, and 13. The main objective was to quantify the significant changes affecting catchment and sub-catchment areas, to have a better understanding of the management plan regarding land use land cover. The simulated data was further projected to prediction using machine algorithms (autoregressive integrated moving average) model for precipitation prediction, and (seasonal autoregressive integrated moving average with exogenous factors) model to predict the sediment yield and water yield in the catchment to 2060. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Micro-hydropower plants are significant contributors of electricity and clean source of renewable energy. A nationwide or large watershed inventory of potential micro hydropower dam sites is lacking, hindering micro-hydropower development. Traditional ground survey approaches for locating micro-hydropower dam sites are expensive, time-consuming, laborious, and vulnerable to inconsistency. Geographic information system frameworks are commonly used, and they provide significant value to hydropower evaluation. A suitability approach for dam site identification is important in supporting the optimization of hydropower utilization in the context of watershed management and in eliminating the inconsistency of conventional approaches. The objective of this study was to identify potential sites for micro-hydropower dams on the basis of various parameters by using a suitability modeling approach based on geographic information system. METHODS: The Saddang Watershed was chosen as the study area, it is located in the South Sulawesi and West Sulawesi Provinces of Indonesia, and it is an example of a large watershed. The analytical hierarchy process was used for criterion weighting and to create a dam suitability index map based on the following criteria: geomorphometry, geology, rainfall, soil texture, and land use land cover. The developed dam suitability index map was validated by comparing it with existing dams by using the receiver operating characteristic curve. The identification of potential micro-hydropower dam sites involved overlay and query methods. It considers dam suitability index, proximity from road and settlement, existence of conservation forest, and the potential hydraulic head. FINDINGS: The dam suitability index map with five suitability classes was obtained, with the high and very high suitability indexes extending to 8.7 percent of the study area. These classes were typified by high drainage density, topographic wetness index, stream power index, low vegetation cover, moderate slope, situated on second or higher stream orders, normal temporal distribution of rainfall, and sandy clay loam soil texture with igneous and sedimentary complex rocks. The developed suitability model was sufficiently effective in determining dam suitability index, as indicated by a value greater than 0.9 of the area under the curve. A total of 635 potential dam locations were identified with high and very high suitability indexes, located on first or second stream orders, within a 4,000 m radius of roads and settlements, outside conservation forest areas, and with a potential hydraulic head greater than 20 meter. CONCLUSION: Integrating a dam suitability index map and restriction factors into a geographic information system framework, enabled a robust analysis for identifying potential sites of microhydropower dams. The proposed approach is expected to contribute to the advancement of renewable energy initiatives and water resource management within large watersheds. It is also expected to serve as a valuable resource for policymakers involved in the implementation of microhydropower projects and watershed management to support the achievement of renewable energy development targets. [ABSTRACT FROM AUTHOR]

Temperate broadleaf forests in the Western Himalaya support a large number of local agro-pastoral communities and provide numerous ecosystem services. Several authors have raised serious concerns about the sustainability of current extractive pressures in these forests. We conducted a detailed ecological study on different forests in Askot Wildlife Sanctuary area to assess their current status in terms of species composition and regeneration of dominant tree species. We quantified structure and composition, population structure and regeneration pattern in six major forest types and compared the findings of a previous study conducted by other authors in the same area about 20 years ago. A total of 180 plots in 18 sites were sampled following the standard phytosociological methods. Canonical component analysis was used to predict the impact of environmental factors on forest communities, and land use land cover changes detected influence of ongoing anthropogenic pressures in the area. The study revealed that extractive pressures in the study area have increased since then, which resulted in the decline of the forested area. However, there has been an expansion of Macaranga pustulata, Quercus leucotrichophora and Quercus lanuginosa since the year 2000 and there is a decline in the regeneration of Quercus semecarpifolia. Considering the significance of high-altitude forests and the ecological sensitivity of Q. semecarpifolia, it is recommended to initiate urgent conservation measures in the region. [ABSTRACT FROM AUTHOR]