Land Use And Land Cover Research Articles

Assessing Environmental Sustainability in the Transnational Basin of the Tumen River Based on Remote Sensing Data and a Geographical Detector

Evaluating environmental sustainability in the transnational basin of the Tumen River (TBTR) is of great significance for promoting sustainable development in Northeast Asia. However, past research has mostly concentrated on a particular environmental element, making it impossible to thoroughly and effectively show the environmental sustainability dynamics in this transnational area. In this study, we attempted to reveal environmental sustainability trends in the TBTR from 2000 to 2021 using the Environmental Degradation Index (EDI) and analyze the driving forces using a geographical detector. It was found that the TBTR’s environmental sustainability decreased significantly, with a degraded region (13,174.75 km2) accounting for 31.01% of the whole area from 2000 to 2021. The dynamics of environmental sustainability on the three sides of China, the Democratic People’s Republic of Korea (DPRK), and Russia have shown significant differences, with the most significantly improved in environmental sustainability being the subregion of China. On the Chinese side, the area that significantly improved in environmental sustainability accounted for 26.19% of the area on the Chinese side, which was 1.17 times higher than that of the DPRK’s side and 1.24 times higher than that of the Russian side. Land use intensity (LUI), land use and land cover (LULC), and population density (PD) were the most dominant driving forces for environmental sustainability dynamics on the three sides of China, the DPRK, and Russia. China, the DPRK, and Russia can improve international environmental cooperation to promote sustainable development in the TBTR and Northeast Asia.

Site suitability analysis of urban green parks in Ranchi city using GIS–AHP based multi-criteria decision analysis

Purpose The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand. Design/methodology/approach It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites. Findings The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect. Originality/value This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

Assessing sediment dynamics and retention services in the vulnerable mountain ecosystem of the Indian Himalayas.

Sediment loss and export pose significant global environmental issues, profoundly affecting water quality, soil fertility, and ecosystem stability, particularly in vulnerable mountain ecosystems like the Indian Himalayas. The present study used remote sensing data and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) sediment delivery ratio (SDR) model to analyze spatial-temporal variations in soil loss (SL), sediment export (SE), and sediment retention (SR) capabilities in the South Shimla watershed, Himachal Pradesh, India, from 1993 to 2023. The findings showed significant changes in land use and land cover (LULC): evergreen forest and scrub land decreased sharply by 11.53% and 36.43%, respectively, while agricultural areas and built-up areas increased notably by 71.16% and 215.76%, respectively. Despite a decline of 19.18% in SL and 24.43% in SE, sediment loss and export varied across the study area, highlighting the heterogeneous nature of sediment dynamics. The overall retention capacity increased by 2.59%, with scrub forests playing a critical role in SR, while built-up areas showed the lowest retention. Northern and central sub-watersheds (SWs) experienced a significant decrease in retention capacity (from - 1.92 to - 11.6%), whereas those in the southern and eastern regions saw an increase in SR (from 3.69 to 28.24%). These results underscore the complex interactions between LULC changes, sediment dynamics, and retention services, highlighting the importance of preserving natural ecosystems and informing policy for landscape-based conservation and development planning in the vulnerable Himalayan region.

Spatio-temporal analysis of land use and land cover dynamics in a part of Southern Nigeria

Agriculture, industrialization, transportation, urbanization and other anthropogenic activities have impacted on the natural cover of the earth surface, hence the dual concepts of land use and land cover (LULC). This study analyses spatio-temporal dynamics in LULC in part of southern Nigeria (path 189 and row 056) between 2002 and 2023using satellite imageries: namely, Landsat Enhanced Thematic Mapper Plus ETM+ of 2023, 2014 and 2002. The classification of land coverwas done with the aid of ArcGIS.The study revealed spatial dynamics in LULC over the study period. The percentage of the study area under cultivation increased from 7.96% in 2002, 17.79% in 2014 to 30.67% in 2023. Also, built-up area increased from 4.36% in 2002, 8.29% in 2014 but decreased to 7.06% in 2023. While areas of dense forest were 34.59% in 2002, 21.42% in 2014 and 11.57% in 2023, bare surface was 4.94% in 2014, 1.19% in 2002 and 25.34% in 2023.Oil spill was 6.13% in 2002, 6.42% in 2014 and 2.07% in 2023. Secondary regrowth/Plantation was 0.19%, 4.22% and 5.53% in 2002, 2014 and 2023 respectively. Water bodieswere 3.37% (2002), 7.06% (2002) and 11.27% (2023). The study recommends enactment of an enabling law or enforcement of existing laws governing forest protection and raising awareness and education of inhabitants on the need to preserve the natural land cover.

Panzara River Watershed Prioritization Based on Geomorphometric and LULC Change Analysis using Geo-Spatial Techniques

Human activities can significantly influence the quality of water flowing from a watershed, either positively or negatively. As water moves through the system, these impacts accumulate, with all land-based activities having the potential to affect the water quality and quantity experienced by downstream stakeholders. Similarly, the actions of upstream landowners impact the water that flows across others' properties. Geospatial techniques like remote sensing and geographic information systems (GIS) are invaluable tools for analysing drainage patterns within a watershed and the associated changes in land use and cover. This study focuses on the Panzara river basin, a principal tributary of the larger Tapi river basin, situated in central India between the westward-flowing Godavari and Narmada river systems, which both ultimately discharge into the Arabian Sea. The study area spans latitudes from 20°42'0" N to 21°18'0" N and longitudes from 74°06'0" E to 75°00'0" E, covering a geographical area of 2,986.05 square kilometers with a perimeter of 570.51 kilometers. The watershed delineation was carried out using Shuttle Radar Terrain Mapper (SRTM) data with a 30-meter resolution. For land use and land cover (LULC) analysis, Landsat 5 TM C2L1 and Landsat 8 OLI/TIRS C2L1 datasets, both with 30-meter resolution, were utilized. The present study conducts a morphometric analysis and assesses LULC changes within the Panzara river basin between 2000 and 2021. Morphometric parameters such as linear parameters [Drainage density (Dd), Stream frequency (Fs), Mean bifurcation ratio (Rbm), Drainage texture ratio (Dt), Length of overland flow (Lo)] and areal parameters [Elongation ratio (Re), Circulatory ratio (Cr), Form factor (Rf), Compactness coefficient (Cc)] were used to prioritize sub-watersheds. Furthermore, the study classifies the observed LULC changes between satellite imagery datasets from 2000 and 2021, quantifying the percentage changes in the respective LULC classes across the sub-watersheds over the two decades. The overall accuracy of the LULC classification was 81.82% for 2000 and 88.88% for 2021, with Kappa coefficients of 0.772 and 0.85, respectively. In terms of prioritizing sub-watersheds, common sub- watersheds such as SW-1, SW-10, and SW-15 were classified under moderate priority, while SW-5, SW-8, and SW-14 were classified under the lowest priority. The results of this study, particularly the prioritization of sub-watersheds, can be instrumental for hydraulic engineers in planning and managing water resources in the Panzara river basin.

Exploring Stormwater Best Management Practices for Urban Flood Reduction in Khulna City of Bangladesh Using PCSWMM

Khulna City Corporation (KCC), located in a low-lying area in southwestern Bangladesh, has been suffering from frequent water logging in the rainy season almost every year. This situation is intensified due to rapid urbanization and the impact of climate change, which have consequently created numerous problems in KCC related to stormwater management issues that require immediate consideration. Therefore, the current study aims to explore stormwater management problems in KCC and find possible countermeasures through different best management practices. Stormwater management must be incorporated into urban design and development, particularly in densely populated urban areas like KCC. In Ward No. 30 of KCC, controlling stormwater is a vital concern due to heavy rainfall, a lack of proper drainage infrastructure, unplanned growth, extensive land cover areas, etc. In the current study, the most widely used Personnel Computer Storm Water Management Model (PCSWMM) software was adopted to evaluate the city's current stormwater management practices and develop a long-term stormwater management strategy. A reconnaissance investigation of the existing states of the soil, drainage, and outlet infrastructures was conducted. The study area (KCC Ward No. 30) was then divided into many small catchments in the ArcGIS platform, and areas of each sub-catchment were identified. To determine the percentage of impervious area and the elevation of the study area, the land use and land cover (LULC) map and the digital elevation model (DEM) were generated using the ArcGIS software. All the aforementioned parameters were then entered into the PCSWMM software, and simulations were performed for various rainfall durations using different best management practices. The PCSWMM simulation provided the results of runoff, infiltration, peak runoff, floods, or surcharges at every drainage node or discharge point in the existing and altered drainage system of the study area due to the occurrence of extreme rainfall. The results indicate that various best management practices, increasing the slope, reducing the roughness of the drainage systems, reducing imperviousness, permitting more infiltrations, and promoting green infrastructure, are highly effective in controlling urban flooding or surcharges in the study area caused by the occurrence of extreme rainfall in the future. The findings of this study are expected to be supportive to policymakers, urban planners, and water managers in implementing and promoting sustainable and climate-resilient urban drainage infrastructures to tackle stormwater management problems in the KCC area. Journal of Engineering Science 15(1), 2024, 107-118

Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO 2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO 2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density(DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Patterns of change, driving forces and future simulation of LULC in the Fuxian Lake Basin based on the IM-RF-Markov-PLUS framework

Studying land use and land cover (LULC) patterns, identifying driving forces, and simulating future scenarios are vital for grasping the complex connection between human actions and the environment. This helps in shaping sustainable land management strategies and preparing for the impacts of climate change. However, there is a necessity for a comprehensive system modeling framework that can accurately capture spatial and temporal changes in LULC, analyze driving mechanisms, and provide an integrated analysis of future simulations. In this paper, a comprehensive IM-RF-Markov-PLUS analysis framework is developed, focusing on the Fuxian Lake Basin (FLB) as a study area. The study aims to achieve accurate prediction of LULC by combining the microscopic LULC change trend and the contribution of macroscopic driving forces. The results show that: (1) The IM-RF-Markov-PLUS framework can explore the change patterns and driving mechanisms of LULC in the FLB, and accurately predict the LULC in the FLB. Compared with the PLUS model, its accuracy is improved by 2 %. (2) IM analysis reveals that LULC transformation in the FLB is both general and specific. Although the area of grassland, buildings, roads and structures converted to desert and bare land is minimal, it shows relatively tendentious and specific change characteristics. (3) Different land types are significantly affected by driving factors, with the expansion of LULCs is constrained by major factors. The distance to the lake has the most significant impact on the distribution of garden land, while the primary road has the greatest impact on the distribution of forestland. (4) Under different scenarios, the spatial heterogeneity of LULC patterns is obvious. In 2035, under baseline and economic development scenarios, cultivated land will decrease, while other LULC types will increase. Under the cultivated land protection scenario, cultivated land is protected, with an increase of 5.85 %. Under the ecological protection scenario, there is an increase in ecological land. The largest increase is in forestland, which increases by a total of 3.46 %. The ecological protection scenario presents a viable approach for ensuring the sustainable development of the FLB. The results of this paper may serve as a reliable foundation for implementing LULC strategies in the FLB and offer guidance for crafting sustainable development regulations at the regional level.

Estimation of land surface temperature and LULC changes impact on groundwater resources in the semi-arid region of Madhya Pradesh, India

The land surface temperature (LST) changes influence on many factors such as land use and land cover (LULC), vegetation and water resources etc. Now a day’s urban population growth, industrial development and human activity are increased due to human migration and urban expansion. The surface temperature is very harmful to vegetation, groundwater level, and ecosystem health. Urban pollution growth and air pollution factor is also playing important role for increase and decrease the LST with climate change impact. Demand of drinking water are increases particular in the urban city due to the population growth and urban expansion, which all of this direct effect on surface and groundwater sources and resources. Hence we have been chosen the latest technologies such as remote sensing (RS), Geographic information System (GIS) and Google Earth Engine (GEE) are very effective for mapping, monitoring and assessment of spatial maps of LST, LULC, and NDVI (Normalised differential vegetation index) estimation. In this study, main focus on the LULC of 2011 and 2021 were classified using random forest (RF) algorithm, ML and remote sensing datasets. LULC were divided into four categories i. e. agricultural land, built up land, waterbody and waste land. For better understanding about the growth of LST, this study examines the association between LST and NDVI change, what vegetation condition impact on the LST. The main and important results have been found the built-up land and LST positive correlation due to increases both the factors in the urban area. LST increases gradually and its bad effect on the green land, water level and expansion of barren land. The range of LST is 43.60 °C to 45.30 °C. Less vegetation cover showing highest LST and vice versa. All of the LST, NDVI, and LULC direct effect on the groundwater availability in the area. This paper important result found the built-up land and LST both are increased in 2021, NDVI and LST having negative correlation. The results of study can very much useful for understanding the LST, NDVI and LULC, these factors what impact on groundwater availability in the study area.

Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China

The drylands of China cover approximately 6.6×106 km2 and are home to approximately 5.8×108 people, providing important ecosystem services for human survival and development. However, dryland ecosystems are extremely fragile and sensitive to external environmental changes. Land use and land cover (LULC) changes significantly impact soil structure and function, thus affecting the soil multifunctionality (SMF). However, the effect of LULC changes on the SMF in the drylands of China has rarely been reported. In this study, we investigated the characteristics of the SMF changes based on soil data in the 1980s from the National Tibetan Plateau Data Center. We explored the drivers of the SMF changes under different LULC types (including forest, grassland, shrubland, and desert) and used structural equation modeling to explore the main driver of the SMF changes. The results showed that the SMF under the four LULC types decreased in the following descending order: forest, grassland, shrubland, and desert. The main driver of the SMF changes under different LULC types was mean annual temperature (MAT). In addition to MAT, pH in forest, soil moisture (SM) and soil biodiversity index in grassland, SM in shrubland, and aridity index in desert are crucial factors for the SMF changes. Therefore, the SMF in the drylands of China is regulated mainly by MAT and pH, and comprehensive assessments of the SMF in drylands need to be performed regarding LULC changes. The results are beneficial for evaluating the SMF among different LULC types and predicting the SMF under global climate change.

Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate: A case study of the Erbil City, Iraq

Urban expansion of cities has caused changes in land use and land cover (LULC) in addition to transformations in the spatial characteristics of landscape structure. These alterations have generated heat islands and rise of land surface temperature (LST), which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas. Greenbelts are employed as an urban planning containment policy to regulate urban expansion, safeguard natural open spaces, and serve adaptation and mitigation functions. And they are regarded as a powerful measure for enhancing urban environmental sustainability. Despite the fact that, the relation between landscape structure change and variation of LST has been examined thoroughly in many studies, but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well, with the lacking of comprehensive research combing both aspects. Accordingly, this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq. The study utilized remote sensing data to retrieve LST, classified LULC, and calculated landscape metrics for analyzing spatial changes during the study period. The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area. The Pearson’s correlation showed the significant effect of Vegetation 1 type (VH), cultivated land (CU), and bare soil (BS) on LST, as increase of LST was related to the decrease of VH and the increases of CU and BS, while, neither Vegetation 2 type (VL) nor built-up (BU) had any effects. Additionally, the spatial distribution of LULC also exhibited significant effects on LST, as LST was strongly correlated with landscape indices for VH, CU, and BS. However, for BU, only aggregation index metric affected LST, while none of VL metrics had a relation. The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment, and further promote sustainable development and enhance well-being in the cities with semi-arid climate.

Spatio-temporal assessment of land use and land cover dynamics in Urmia lake basin of Iran: A bi-directional approach using optical and radar data on the Google Earth Engine platform

Sustainable development, a cornerstone in responsible land resource management, occupies a critical role in global efforts to balance economic progress with ecological preservation. This research explores the intricate dynamics of sustainable development and land resource management in the Lake Urmia watershed, northwestern Iran, with a specific focus on Land Use and Land Cover (LULC) alterations. Leveraging cutting-edge methodologies within the Google Earth Engine (GEE) framework, the study employs the Random Forest (RF) model, and multi-sensor satellite data to achieve meticulous LULC classification from the years 2016 to 2020.This strategy incorporates satellite images from Sentinel-1 (S1), Sentinel-2 (S2), coupled with ancillary data, and the implementation of the RF algorithm. It highlighted the intricate synergy between optical and Synthetic Aperture Radar (SAR) data for LULC classification in the Lake Urmia watershed. The Overall Accuracy (OA) values ranging from 88.28 % to 92.00 % affirmed the robustness of the RF model, demonstrating its potential for research objectives. In addition, Dynamic World, a near real-time global dataset, was applied to the study area, provides a convenient view to detect changes from the years 2016 to 2023, while enabling comparison with the widely used RF algorithm in past studies. Notably, this study goes beyond methodological developments to examine the concrete consequences of the observed changes. The decline in water bodies within the Urmia Lake basin and the parallel expansion of cultivated lands underscore the profound impact of human activities, particularly agricultural practices, on the ecological equilibrium. As we navigate the intricate terrain of sustainable development, this research serves as a clarion call for remote sensing approaches to monitor and manage land resources, emphasizing the delicate balance between economic growth and ecological preservation.

Evaluating the Impact of Mini Dams on Agricultural Production and Socio-economic Conditions: A Case Study of Latamber’s Dam District Karak (Pakistan)

Water scarcity is an escalating global concern, and Pakistan is no exception, facing increasing water crises and growing demand. Effective water supply is crucial for the development of any area. Mini dams not only provide water for irrigation but also support domestic uses. This study evaluates the impact of mini dams on agricultural productivity in Latamber village, focusing on crop yield, land use and land cover (LULC) changes, and socio-economic conditions. A multifaceted methodology, including field surveys, departmental data collection, questionnaires, and remote sensing, was employed. Data collected through questionnaires and farmer interviews highlighted the economic and environmental benefits of mini dams. Statistical analysis and image processing revealed significant expansions in cultivation areas for crops like maize and rice, alongside increases in wheat yield and the adoption of new vegetable crops during the Rabi season. Additionally, socioeconomic benefits, such as enhanced agricultural incomes and employment opportunities, were observed due to improved water availability and irrigation technologies. The study emphasizes the importance of evaluating the impact of mini dams on agricultural production and socio-economic conditions to inform sustainable water resource management strategies, which are vital for the long-term development of similar regions.

Application of GIS-Based Multi-Criteria Decision Analysis of Hydro-Geomorphological Factors for Flash Flood Susceptibility Mapping in Bangladesh

Flash floods are one of the most prevalent natural disasters, triggering deadly damage to homesteads, crops, infrastructure, road networks, communications, and the natural environment in the Haor (Wetland) region of Bangladesh. The purpose of the study aims to identify eleven (11) hydro-geomorphological driving factors, namely elevation, slope, aspect, rainfall, land use and land cover (LULC), lithology, soil type, topographic wetness index (TWI), NDVI, distance from the river, and drainage density, which are being explored for mapping flood-prone areas. This research has produced a flash flood susceptibility map using the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP), which are interactive decision-making approaches under multi-criteria decision analysis (MCDA) in ArcGIS 10.8. The findings of this study showed that the susceptibility to flood hazards differs significantly among the seven Haor districts. As a result of the ANP and AHP, a more significant proportion of the Haor region is moderately susceptible to flooding (8685.09 to 9275.15 sq. km.), whereas 35.34% to 38.32% (7069.70 to 7668.67 sq. km.) accounts for high susceptible to flooding. Furthermore, 200 flood locations were identified in the northeast Haor region, where 140 (70%) randomly selected floods were used for training, and the remaining 60 (30%) were employed for validation purposes. The validation results showed that the AHP model had greater prediction accuracy (AUROC = 92.1%) than the ANP (AUROC = 88.5%) model. Therefore, the study findings can be helpful for researchers, academics, policymakers, and planners for sustainable flood mitigation strategies, particularly in Haor areas.

GIS based Soil Erosion Susceptibility Mapping in Middle Himalayas using MCDM: A Case Study of Rajouri District.

The Pirpanjal range, nestled within the Himalayan region, confronts substantial soil erosion challenges owing to its diverse topography and the inherent instability of geological formations. This study focuses on the Rajouri region, to assess soil erosion susceptibility and spatially prioritize vulnerable zones. Utilizing a Geographical Information System (GIS)-based approach, we employed the Analytic Hierarchy Process (AHP) and the Weighted Sum Method (WSM). Various datasets, including precipitation records, geological maps, soil maps, and satellite imagery, were incorporated to derive eleven critical factors. These factors encompassed topographical derivatives; land use and land cover (LULC), soil properties, drainage patterns, rainfall data, lithological characteristics, wetness index, and the vegetative health of the area. The methodology employed yielded unbiased and reliable ratings and weightages, as substantiated by a Consistency Ratio (CR) of 0.095. The results reveal that 41% of the total area in the study region is exceedingly vulnerable to soil erosion. Slope variations range from 0 to 67.14 degrees, delineating high and very high susceptibility zones spanning 531.79 km² (19.82%) and 316.20 km² (11.78%) of the area, respectively. Additionally, assessments based on the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) underscore the severity of soil erosion, covering 40% and 25% of the highly susceptible zones, respectively. High drainage density and curvature zones were identified in 13% and 31% of the study area, respectively. This comprehensive study contributes valuable insights for the planning and implementation of effective soil conservation measures in the Rajouri region.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

Assessment of urbanization impacts on soil erosion and groundwater recharge in Enugu, Southeastern Nigeria

The study of Land Use and Land Cover (LULC) in Enugu, southeastern Nigeria, is crucial for understanding its effects on erosion and groundwater recharge. Different LULC types, coupled with varying slope gradients and soil types, create a complex interaction that influences soil stability and water infiltration in the region. This research aims to assess the distribution of LULC types, slope gradients, and soil types in Enugu, and to evaluate their impacts on erosion rates and groundwater recharge potential. Data for LULC, slope gradients, and soil types were collected and analyzed to determine their spatial distribution and characteristics. The study employed GIS techniques to map these variables and assess their interactions. Erosion potential and groundwater recharge capacity were evaluated based on the characteristics of the LULC types, slopes, and soils. The analysis revealed that trees cover the largest area (3709.74 km²), playing a significant role in stabilizing the soil and reducing erosion through their extensive root systems and canopy cover. Rangeland (2631.81 km²) also contributes to soil stability, although less effectively than forested areas. Crops (192.40 km²) have mixed impacts on erosion depending on agricultural practices. Built areas (1162.69 km²) present challenges due to impervious surfaces, which increase surface runoff and reduce groundwater recharge. Slope gradients were found to correlate with erosion processes and groundwater dynamics. Gentle slopes (0 - 1.81 degrees) cover 1870.15 km² and facilitate infiltration, enhancing groundwater recharge. Moderate slopes (1.81 - 4.06 degrees), covering 3275.46 km², are more prone to erosion, while steeper slopes (4.06 - 11.73 degrees) covering 2176.54 km² experience accelerated runoff and increased erosion rates. The steepest slopes (11.73 - 44.06 degrees) are the most erosion-prone areas, requiring significant intervention. The soil analysis showed that Dystric Nitosols (4052.80 km²) with the lowest K-factor (0.0178) are least prone to erosion and have high infiltration capacity, making them beneficial for groundwater recharge. Plinthic Acrisols (2732.53 km²) and Ferric Acrisols (102.36 km²) exhibit moderate erosion susceptibility. Dystric Fluvisols (793.92 km²) with the highest K-factor (0.0223) are highly erosion-prone. Gleysols (20.69 km²) have low to moderate erosion susceptibility. The interplay between LULC types, slope gradients, and soil types significantly influences erosion and groundwater recharge in Enugu. The study highlights the need for targeted land management practices, such as afforestation, contour farming, terracing, and the use of cover crops to mitigate erosion and enhance groundwater recharge. This research provides a comprehensive analysis of the relationships between LULC, slope gradients, and soil types in Enugu, offering valuable insights for developing effective land management strategies to address erosion and groundwater recharge challenges.

Application of a Multi-Layer Perceptron and Markov Chain Analysis-Based Hybrid Approach for Predicting and Monitoring LULCC Patterns Using Random Forest Classification in Jhelum District, Punjab, Pakistan.

Land-use and land-cover change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the land-use and land-cover (LULC) spatiotemporal dynamics across a three-decade period (1998-2023) in a district area. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the random forest method with multi-layer perceptron (MLP) and Markov chain analysis. To predict the dynamics of LULC changes for the year 2035, a hybrid technique based on multi-layer perceptron and Markov chain model analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. This study also discovered that between 1998 and 2023, the built-up area increased by 468 km2 as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will increase by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. The model provides valuable insights for policymakers, land managers, and researchers to support sustainable land-use planning, conservation efforts, and climate change mitigation strategies.

Long-term assessment of fire-induced carbon loss in Southeast Atlantic Forest

Fires threaten tropical forests such as Atlantic Forest in Brazil, compromising the ecosystem service of carbon stock. However, there is a literature gap regarding these studies in these ecosystems. Therefore, we conducted this analysis in different land use and land cover (LULC) classes, considering seasonality and topographic, hydrological, anthropogenic and fire variables correlations, during 2000-2020. The InVEST Carbon model was used, applied to carbon biomass pre-fire and pos-fire, based on field work and linear regression, weighted by pre- and post-fire NBR spectral index. The results, in 21 years, revealed a total loss after fire of 55.7GgC (43%), and of these, 79% is in old-growth Ombrophilous dense. In general, fire negatively impacts the carbon stock of native forests by an average of 38% (ranging from 19.9% to 69.1%, depending on phytophysiognomy and seasonality), Eucalyptus plantations by 87.1%, high-altitude grasslands by 79.5% and pasture in 90.4%. Burn frequency and severity as well as distance from rivers and roads were significantly correlated with carbon loss. A small portion of this biome has shown a high potential for fire-induced carbon loss, indicating a danger for the whole Atlantic Forest conservation and to international agreements commitments.