Analysis of land use/ land cover changes and landscape fragmentation in the Baro-Akobo Basin, Southwestern Ethiopia.

This study investigated the relationship between land use/land cover (LULC) changes and forested landscape fragmentation in the southwestern region of Ethiopia. Satellite images from 1986, 2002 and 2019 were collected and analyzed using standard procedures in ERDAS 2015 software. Fragstat 4.2.1 software was utilized to assess landscape fragmentation by examining a raster datasets derived from the classified LULC map over the research period. The study identified seven LULC classes in the study area. Findings revealed a substantial reduction in shrubland by 46.3%, dense forest by 23.75%, open forest by 17.3%, and wetland by 32.63%, while cropland increased by 38.06%, agroforestry by 20.29%, and settlements by 163.8% during the study period. These changes varied across different agroecological zones and slope gradients. Landscape metrics results indicated an increase in the number of patches and patch density for all LULC classes, demonstrating significant fragmentation of the landscape. The largest patch index, mean patch areas, and the percentage of landscape occupied by open forest, dense forest, shrubland, and wetland declined as a result of conversion to cropland, agroforestry, and settlement. Conversely, the largest patch index, the mean patch area and the percentage of the landscape occupied by agroforestry, cropland and settlement increased, indicating their increasing dominance in the landscape over the study periods. The findings highlighted the potential deleterious impacts of ongoing land use change and fragmentation on the environment, ecosystem function and local livelihoods. Therefore, it is crucial to implement appropriate conservation efforts and sustainable land management practices to mitigate the rapid change and fragmentation of land use and its negative impacts on sub-watershed ecosystems.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

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.

Human intervention caused massive destruction of the second largest mangrove forest, Chakaria Sundarbans, Bangladesh.

Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering area a total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest area degradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach.

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

A spatio-temporal analysis of the magnitude and trend of land use/land cover changes in Gilgel Gibe Catchment, Southwest Ethiopia.

Analyzing alterations in land use/land cover is crucial for water Scientists, planners, and decision-makers in watershed management. This examination enables the development of effective solutions to mitigate the adverse impacts resulting from such changes. The focus of this research was analyzing alterations in land use/land cover within the Gilgel Gibe Catchment in 1991 - 2021. LULC data of 1991-2021 were derived from multispectral Landsat images. Data were also gathered using field observations and key informant interview. Data of LULC classes (1991-2021) were generated utilizing supervised classification with maximum likelihood algorithm of ENVI 5.1 and ArcGIS 10.5. Change detection analysis and accuracy assessment were done where accuracy levels all the study periods were > 85 %, and the overall Kappa statistics of the periods were > 0.89. Built-up area and cultivated land of the catchment are increasing with increasing magnitude of change; whereas, while forest cover and grazing land of the catchment are shrinking with declining magnitudes of change, shrubland covers and water body are declining with increasing magnitude of change in the catchment. The net increase in degraded land is a reflection of the increasing degradation of natural resources in the catchment. Swift escalation of population and the subsequent raising demand for farmland and forest and shrub (e.g. fuel-wood and construction) products, decline yield, unemployment and lack of alternative income source, and open access and limited conservation of resources are the principal factors for the dramatic shrinkages of grazing, forest, water body and shrubland resources. Thus, concerned bodies should take rehabilitation measures to restore degraded lands, improve production and yield of farmland by increasing improved farm-inputs and irrigation, and create employment and alternative income sources for the youth, women and the poor so as to ensure sustainable rural livelihoods and to curb the impacts on forest, shrubland and other resources.

Dynamics of carbon storage driven by land use/land cover transformation in coal mining areas with a high groundwater table: A case study of Yanzhou Coal Mine, China.

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985-1995, 1995-2005, 2005-2015, and 2015-2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management.

Neighbouring effect of land use changes and fire emissions on atmospheric CO2 and CH4 over suburban region of India (Shadnagar).

The present study investigated the effects of land use/land cover (LU/LC) changes on atmospheric carbon dioxide (CO2) and methane (CH4) concentrations over the sub-urban region of India (Shadnagar) using continuous decadal CO2 and CH4in-situ data measured by the greenhouse gas analyser (GGA). Data was collected from 2013 to 2022 at a 1 Hz frequency. Analysis of the current study indicates that during pre-monsoon, the seasonal maximum of CO2 was 409.91 ± 9.26 ppm (µ ± 1σ), while the minimum during monsoon was about 401.64 ± 7.13 ppm. Post-monsoon has a high seasonal mean CH4 concentration of 2.08 ± 0.06 ppm, while monsoon has a low seasonal mean CH4 concentration of 1.88 ± 0.03 ppm. The primary classes, such as forest, crop, and built-up, were considered to estimate the effect of LU/LC changes on atmospheric CO2 and CH4 concentrations. Between 2005 and 2021, the study's results show that the built-up area at radii of 10 km, 20 km, and 50 km increased by 0.17 %, 0.10 %, and 0.4 %, respectively. While other LU/LC categories declined by 30 %, agriculture areas increased by 30 % on average. As a result, the CO2 and CH4 concentrations at the study site are increased by 6 % (26 ppm) and 6.5 % (140 ppb), respectively. The present study utilised the fire-based carbon emissions data from the Global Fire Emissions Database (GFED) to understand the impact on atmospheric CO2 and CH4. Analysis of the present work investigated the influence of transported airmass on CO2 and CH4 during the pre-monsoon and post-monsoon seasons using the HYSPLIT trajectories and found emissions were from the northwest, southeast, and northeast of the study site. Further, in-situ CO2 and CH4 records are compared against the MIROC4-ACTM simulation, and strong agreement was found with bias of 1.80 ppm and 0.98 ppb for CO2 and CH4, respectively.

Application of geospatial techniques and binary logistic regression model for analyzing driving factors of urban growth in Bahir Dar city, Ethiopia.

Understanding the drivers of urban growth and spatiotemporal land use change is important for rational land use and sustainable urban development. Based on the land use data, GIS data of explanatory variables, experts' knowledge and field observation, the study used a binary logistic regression model (BLRM) to analyze factors that drive rapid urban growth in Bahir Dar city, Ethiopia, using the LOGISTICREG module in IDRISI Selva software. Nine factors were used to reflect the influence of proximity and physical factors on urban growth from 1984 to 2019. This model helped in quantifying and identifying the factors of urban growth, which includes topography (slope, elevation and aspect) and accessibility (Dis. to the main road, Dis. to international airport, Dis. to CBD, Dis. to existing built-up area, Dis. to forest land and Dis. to water body). Furthermore, urban growth probability maps were created based on LRM results, revealing that the biggest urban growth would occur around existing built-up areas along the main roads and near Bahir Dar international airport. The Relative Operating Characteristic (ROC) values of 0.85, 0.90 and 0.93 and PCP values of 96.72 %, 98.46 % and 98.51 % indicate the urban growth probability maps are valid and BLRM had an ideal ability to predict urban growth. So, the study highlighted the relation between urban growth and its drivers in Bahir Dar, giving a decision making framework for better land use management and resource allocation.

Integrated assessment and mapping of provisioning services for sustainable management of natural resources, the case of Lake Hawassa Basin, Ethiopia.

Lake Hawassa Basin (LHB)-the study area is known for its rich and diverse aquatic and terrestrial natural resource base. However, the prevailing environmental and social problems, such as land degradation, deforestation, pollution, resource exploitation, etc. impacted the existing provisioning services (PS), and the effect becomes remarkable unless sound management is in place. The study aimed at the assessment and mapping of PS to suggest development options for decision-makers. The study employed various methods including primary and secondary data collection, including existing Land Use Land Cover (LULC), desk review, stakeholder consultations, site visits, expert judgment matrix, and ArcGIS v10.1. The study results include 6 PS identified and prioritized from the existing 14 PS, mapping of the spatial pattern of the selected 6 PS at the basin scale, and alternative development options recommended for the decision-making process conducted by decision-makers and development partners to ensure efficient management of ecosystem services in LHB. The importance of this study, as well as the simplicity and user-friendly nature of the methods and approach adopted, enables interested parties to replicate while conducting similar studies in different places within the country or globally. The intervention of adopting this study approach helps also to avoid or minimize the aforesaid biophysical and socioeconomic environmental problems and ensure development activities planned or implemented in the respective study area are environmentally friendly, and socially acceptable, through sustainable management of natural resources. In this regard, decision-makers and development partners shall provide adequate consideration for this study approach and the result of demonstrating basin scale spatial variability of PS. This plays a vital role in the sustainable management of natural resources as well as provisioning services existing in the study area to benefit the community members, ensure human well-being, and secure the livelihood of the people residing within or around the Lake Hawassa Basin.

Reference evapotranspiration estimation using reanalysis and WaPOR products in dryland Croplands.

Accurate estimation of the reference evapotranspiration (ETo) is crucial for determining crop water requirements. However, the lack of appropriate weather stations representing croplands, particularly in drylands, may adversely influence the accuracy of ETo estimates. To overcome this issue, a promising approach is to use meteorological stations in cropland areas to collect weather data that are representative of actual conditions. However, the number of agrometeorological stations in these areas is limited. Therefore, this study aims to assess the effectiveness of three datasets, including ERA5 and ERA5-Land, and WaPOR (Water Productivity Open-access portal), for estimating ETo in cropland areas on a basin scale. The land use/land cover (LULC) of the European Space Agency (ESA) was used to identify the sites resembling agrometeorological stations. Data were collected from 2009 to 2022, and the FAO-Penman-Monteith method was used to estimate daily and monthly ETo. The accuracy and reliability of ETo estimates with the three datasets were evaluated by comparing them with ETo estimated by ground measurements. Statistical analysis metrics, normalized root mean squared error (nRMSE), and relative mean bias error (rMBE) were used to assess the performance of the datasets. This study highlights that ERA5 exhibited superior overall performance compared to other datasets in estimating ETo. However, WaPOR performed better at high-altitude stations with inhomogeneous topography than ECMWF reanalysis (i.e., ERA5 and ERA5-L). Thus, none of the datasets could provide accurate ETo estimates for all the stations within the basin. Therefore, applying the best-performing data source yielded better results than using a single dataset. These findings are valuable for improving irrigation scheduling and water management practices on a large scale, particularly in regions facing data scarcity challenges.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Identifying broken linkages coupling water availability and dryland urbanization for sustainability: The case of the Phoenix metropolitan region, USA.

One third of the world's largest cities are located in drylands, where much of future urbanization is projected to occur. This is paradoxical and unsustainable considering water scarcity in drylands, which is exacerbated by climate change. Thus, it is critical to better understand why and how dryland urbanization and water scarcity are decoupled so that sustainable measures can be designed. Focusing on the Phoenix Metropolitan Area (PMA) of the United States, we addressed the following questions: 1) What are the relative influences of water and economic factors on urbanization in recent decades? 2) Which linkages connecting water storage to urban development have been decoupled? and 3) How can water availability and development be better coupled to improve regional sustainability? We tested the relationships between economic factors, water availability, and urbanization, with Pearson Correlation Analysis and Structural Equation Modeling. We found that, from 1986 to 2019, urban population growth and urban land expansion in the PMA were driven by economic factors, and not influenced by fluctuations in water supply. We identified specific broken linkages among water storage, water deliveries, municipal water supply, and urbanization, which must be coupled to enforce water availability constraints on urban expansion in the context of climate change. Our study has important implications for dryland urban sustainability as urbanization on borrowed water is, by definition, unsustainable.

Insight into land cover dynamics and water challenges under anthropogenic and climatic changes in the eastern Nile Delta: Inference from remote sensing and GIS data.

The destabilization of delta's worldwide due to climate change and human activities presents challenges in meeting the growing demands for freshwater and food. The Nile Delta in Egypt is a prime example of a vulnerable region facing various stressors. In order to preserve land and water resources, it is crucial to monitor the spatial and temporal changes in Land Use/Land Cover (LULC), shoreline, and Terrestrial Water Storage (TWS) in these vulnerable regions This study comprehensively investigates the dynamic changes in LULC and their associated water and soil responses in the Eastern Nile Delta under these combined impacts. To achieve this goal, a combination of remote sensing techniques utilizing Landsat (5, 8, and 9), and GRACE datasets, along with field observations and Geographic Information System (GIS) tools, was employed. Accordingly, shoreline changes show coastal erosion rates ranging from 5.28 to 34.92 m/year due to climate change-induced SLR, with continued inland movement predicted for the next 20 years. Moreover, the dynamic changes in urbanization and alterations in agricultural cover have considerable penalties for water demand. Analysis of GRACE data indicates a notable reduction in average TWS by 77.89 mm between 2002 and 2017, with an annual rate, estimated at -5.821 mm/year. Soil sampling in highly vulnerable areas confirms agricultural degradation attributed to elevated salinity levels, with EC values ranging from 3.60 to 190 ds/m. These finds provide valuable insights for stakeholders and policymakers, to make reliable strategies regarding water allocation, land use regulations, and climate change adaptation in the worldwide vulnerable deltas.

Examining the informal urban growth trends in a Port city.

Rapid urban developmental growth is a heated debate worldwide due to environmental challenges. This research has examined the spatiotemporal trend of informal built-up growth in Karachi city. Using a geo-information system, the past twenty years (2000-2020) trends of informal built-up growth are examined. For attaining the research objectives, geo-referenced high-resolution maps and satellite images are used for accuracy based spatial data. Karachi is divided into five different land use and land cover (LULC): formal built-up, informal built-up, vacant, water bodies, and green spaces. Spatial data of informal built-up growth change of five different years, 2000, 2005, 2010, 2015, and 2020 are generated through acquired maps digitization using ArcMap. Subsequently, the gains and transfers of Karachi's informal built-up growth based on five years 2000-2005, 2005-2010, 2010-2015, and 2015-2020 are analyzed using the Land Change Modeler (LCM) in IDRISI software. Also, land use land cover changes (LULCC) are predicted for the next 40 years (2020-2060) using the integrated Cellular Automata Markov (CA-Markov) simulation model in IDRISI. The results revealed that Karachi's built-up is expanding rapidly. Land conversion into the informal built-up area is alarming, as it has changed from 144.31 km2 to 217.19 km2 with 72.88 km2 in the past twenty years (2000-2020) and has occupied green and agricultural land. Most informal built-up areas have transitioned from vacant (71.01 km2) land use land cover (LULC). The informal built-up area could expand from 217.19 km2 to 317.63 km2, with about 100.44 km2 up to 2060. The planned and unplanned development will be towards the city's East (E) direction and will convert and ruin agriculture and vacant land. The present study provides suggestions to urban planners, administrative authorities, and policymakers to control informal growth and achieve sustainable development goals in developing countries.

Deforestation inhibits malaria transmission in Lao PDR: a spatial epidemiology using Earth observation satellites.

BACKGROUND: The present study aimed to analyze the impact of deforestation on the malaria distribution in the Lao People's Democratic Republic (Lao PDR), with consideration of climate change. METHODS: Malaria distribution data from 2002 to 2015 were obtained from the Ministry of Health of Lao PDR and each indicator was calculated. Earth observation satellite data (forested area, land surface temperature, and precipitation) were obtained from the Japan Aerospace Exploration Agency (JAXA). Structured equation modeling (SEM) was conducted to clarify the relationship between the malaria incidence and Earth observation satellite data. RESULTS: As a result, SEM identified two factors that were independently associated with the malaria incidence: area and proportion of forest. Specifically, malaria was found to be more prevalent in the southern region, with the malaria incidence increasing as the percentage of forested land increased (both p < 0.01). With global warming steadily progressing, forested areas are expected to play an important role in the incidence of malaria in Lao PDR. This is believed because malaria in Lao PDR is mainly forest malaria transmitted by Anopheles dirus. CONCLUSION: To accelerate the elimination of malaria in Lao PDR, it is important to identify, prevent, and intervene in places with increased forest coverage (e.g., plantations) and in low-temperature areas adjacent to malaria-endemic areas, where the vegetation is similar to that in malaria-endemic areas.

Impacts of LULC and climate changes on hydropower generation and development: A systematic review.

There is a growing concern on a global scale that the world should transition towards the utilisation of energy-efficient technologies. Hydropower plays a very significant part in the fight against climate change, and as a result, it lessens the impact that climate changewill have on our ability to achieve the Sustainable Development Goals (SDGs). Both the effectiveness of hydropower generation and the amount of streamflow are impacted by climate change as well as land use and land cover (LULC). Accordingly, the purpose of this study is to conduct a literature review on the topic of the past and future effects of climate, land use, and land cover changes on hydropower generation. This review will be based on the entries found in a number of reliable databases. A systematic literature review was carried out to analyse how LULC and climate change will affect hydropower generation and development. The research was based on 158 pieces of relevant literature that had been reviewed by experts and indexed in Scopus, Google Scholar, and ScienceDirect. The review was carried out to determine three goals in mind: the impact of climate change on hydropower generation and development; the impact of climate change on streamflow; and the combined impact of changes in climate and changes in LULC on hydropower. The findings bring to light the primary factors contributing to climate change as well as shifts in LULC which are essential to the generation of hydropower on all scales. The study identifies factors such as precipitation, temperature, floods, and droughts as examples of climate change. Deforestation, afforestation, and urbanisation are identified as the primary causes of changes in LULC over the past several decades. These changes have a negative impact on the generation and development of hydropower.

Sen-2 LULC: Land use land cover dataset for deep learning approaches.

Land Use Land Cover (LULC) classification is pivotal to sustainable environment and natural resource management. It is critical in planning, monitoring, and management programs at various local and national levels. Monitoring changes in LULC patterns over time is crucial for understanding evolving landscapes. Traditionally, LULC classification has been achieved through satellite data by remote sensing, geographic information system (GIS) techniques, machine learning classifiers, and deep learning models. Semantic segmentation, a technique for assigning land cover classes to individual pixels in an image, is commonly employed for LULC mapping. In recent years, the deep learning revolution, particularly Convolutional Neural Networks (CNNs), has reshaped the field of computer vision and LULC classification. Deep architectures have consistently outperformed traditional methods, offering greater accuracy and efficiency. However, the availability of high-quality datasets has been a limiting factor. Bridging the gap between modern computer vision and remote sensing data analysis can revolutionize our understanding of the environment and drive breakthroughs in urban planning and ecosystem change research. The "Sen-2 LULC Dataset" has been created to facilitate this convergence. This dataset comprises of 213,761 pre-processed 10 m resolution images representing seven LULC classes. These classes encompass water bodies, dense forests, sparse forests, barren land, built-up areas, agricultural land, and fallow land. Importantly, each image may contain multiple coexisting land use and land cover classes, mirroring the real-world complexity of landscapes. The dataset is derived from Sentinel-2 satellite imagery sourced from the Copernicus Open Access Hub (https://scihub.copernicus.eu/) platform. It includes spectral bands B4, B3, and B2, corresponding to red, green, and blue (RGB) channels, and offers a spectral resolution of 10 m. The dataset also provides an equal number of mask images. Structured into six folders, the dataset offers training, testing, and validation sets for images and masks. Researchers across various domains can leverage this resource to advance LULC classification in the context of the Indian region. Additionally, it catalyzes fostering collaboration between remote sensing and computer vision communities, enabling novel insights into environmental dynamics and urban planning challenges.

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (-3.27%), bare surface (-33.47%), and water bodies (-0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes.

Predicting the future land use and land cover changes for Saroor Nagar Watershed, Telangana, India, using open-source GIS.

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management.

Comparative analysis of soil organic matter fractions, lability, stability ratios, and carbon management index in various land use types within bharatpur catchment, Chitwan District, Nepal.

BACKGROUND: Land use and land cover changes have a significant impact on the dynamics of soil organic matter (SOM) and its fractions, as well as on overall soil health. This study conducted in Bharatpur Catchment, Chitwan District, Nepal, aimed to assess and quantify variations in total soil organic matter (TSOMC), labile organic matter fraction (CL), stable organic matter fraction (CS), stability ratio (SR), and carbon management index (CMI) across seven land use types: pastureland, forestland, fruit orchards, small-scale conventional agricultural land, large-scale conventional agricultural land, large-scale alternative fallow and conventional agricultural land, and organic farming agricultural land. The study also explored the potential use of the Carbon Management Index (CMI) and stability ratio (SR) as indicators of soil degradation or improvement in response to land use changes. RESULTS: The findings revealed significant differences in mean values of TSOMC, CL, and CS among the different land use types. Forestland and organic farming exhibited significantly higher TSOMC (3.24%, 3.12%) compared to fruit orchard lands (2.62%), small scale conventional farming (2.22%), alternative fallow and conventional farming (2.06%), large scale conventional farming (1.84%) and pastureland (1.20%). Organic farming and Forestland also had significantly higher CL (1.85%, 1.84%) and CS (1.27%, 1.39%) compared to all other land use types. Forest and organic farming lands showed higher CMI values, while pastures and forests exhibited higher SR values compared to the rest of the land use types. CONCLUSIONS: This study highlights the influence of various land use types on soil organic matter pools and demonstrates the potential of CMI and SR as indicators for assessing soil degradation or improvement in response to land use and land cover changes.