Land use and land cover (LULC) change is a major anthropogenic factor influencing flood behavior and hydrological processes. This systematic review synthesizes two decades (2005-2025) of research on hydrological modeling approaches used to assess flood responses under LULC transitions. A total of 114 publications were retrieved from the Scopus database, and after applying PRISMA-based screening, 78 peer-reviewed studies were analyzed using bibliometric and content mapping. The review categorizes hydrological models by spatial scale, process representation, and sensitivity to LULC dynamics. Findings consistently indicate that urban expansion, deforestation, and vegetation loss intensify surface runoff, peak flow, and flood frequency. Despite advancements, significant challenges remain particularly related to data scarcity, model calibration, and the limited integration of socio-economic variables. Emerging tools such as Remote Sensing (RS), Geographic Information Systems (GIS), and machine learning especially within platforms like Google Earth Engine (GEE) enhance LULC detection accuracy and flood prediction capability. The study proposes an integrated decision framework linking bibliometric trends with model selection strategies, enabling researchers to align model choice with data availability and landscape characteristics. Overall, this review emphasizes the importance of interdisciplinary, data-driven modeling to strengthen flood resilience in rapidly transforming land systems.

The impact of land use change awareness on the psychological adaptation of migrant communities in Khulna city

A novel approach to optimizing predictive models for land use and land cover changes by utilizing a triple-memristor hopfield neural network for improved environmental change detection and forecasting

Geospatial analysis of land use and land cover change and environmental impacts in Uttarakhand, India: A review

Integrating conservation zoning, land-use and land-cover change, and habitat integrity to support protected area management: Evidence from Golestan National Park, Iran

Extractive activities drive land transformation in many mineralised river basins. However, linking these changes to observable and attributable water-quality outcomes remains methodologically challenging. This study applies an integrated monitoring framework to examine how multi-decadal Land-use and land-cover change (LULC) translates into spatially differentiated river water quality in the Ankobra River basin, Ghana. Using harmonised Landsat and Sentinel imagery, LULC dynamics was reconstructed for 1986, 2002, 2016, and 2025. Field-based measurements of key physico-chemical water-quality parameters were collected to support the analysis. Spatial interpolation using Ordinary Kriging and redundancy analysis was then applied to assess the extent to which land-use composition explains the observed variation in water quality. The results showed a shift from forest-dominated land cover towards agriculture, settlement, and mining-related disturbance during the study period. Bareland/Mining expanded from less than 1% of the basin in 1986 to approximately 3.7% by 2025 (>100 km2), while combined forest cover declined overall throughout the study period. Water-quality patterns exhibited strong spatial gradients, with turbidity ranging from approximately 114 to more than 1000 NTU and total suspended solids (TSS) from around 100 to nearly 3000 mg L-1. Redundancy analysis indicated that land-use composition explained approximately 47.5% of the variance in water quality, with the mining-related land cover exerting the strongest influence (F=13.66, p<0.001) and showing robust positive associations with turbidity and TSS. Closed forest cover displayed a significant buffering effect, while agricultural land use did not show significant association on the spatial scale examined. These findings demonstrate how integrated Earth observation and field data can move sustainability assessment beyond descriptive convergence towards diagnostic clarity. The analytical framework offers a transparent and scalable approach for prioritising regulatory attention and monitoring in extractive landscapes where environmental pressures are spatially uneven and governance capacity is constrained.

Land-use change and surface warming in Uganda’s oil-rich Albertine region (1995–2025): A geodetector analysis

Spatial land transformation and the psychosocial exposure among climate migrants in southwestern Bangladesh

Telecoupled landscapes: Spatial effects of external financial inflows on Africa’s biodiversity

Hurricanes have significant consequences for ecosystems, potentially disrupting the carbon cycle at both local and regional scales and releasing carbon back into the atmosphere through storm-associated impacts on vegetation and agricultural areas. The present work analyzes the interactions amongst terrestrial carbon fluxes, rainfall, and land cover for three significant hurricanes: Harvey (Texas), Irma (Florida), and Maria (Puerto Rico). This study utilized net ecosystem exchange (NEE) data derived from the Soil Moisture Active Passive (SMAP) NASA satellite mission, which provides global estimates of soil moisture and carbon flux, and analyzed these data for coastal climate zones during the hurricane season. The results were validated using eddy covariance tower-based in-situ CO 2 flux observations during hurricane landfall. Results showed that southern Texas (Harvey) experienced the highest amount of carbon release (0.33 megatons), followed by Florida (Irma) (0.03 megatons) and Puerto Rico (Maria) (0.02 megatons). The land cover products, such as the National Land Cover Dataset (NLCD) and the Copernicus Global Land Service (CGLS), showed overall reductions in land cover in Florida (-1.02%), Texas (-0.97%), and Puerto Rico (-0.46%). Furthermore, vegetation cover changes were estimated using MODIS-derived enhanced vegetation index (EVI), showing major changes over Puerto Rico (-3.81%) and southeast Texas (-2.94%), while normalized difference vegetation index (NDVI) showed more moderate reductions over Puerto Rico (-3.06%), southeast Texas (-1.12%), and Florida (-0.16%). These reductions indicate short-term vegetation stress and decreased photosynthetic activity, which may temporarily reduce carbon uptake, leading affected regions to transition from carbon sinks to temporary carbon sources. These findings highlight hurricanes as significant drivers of short-term carbon emissions and vegetation change. This study enhances understanding of hurricane-associated disturbances in the carbon cycle by examining spatial and temporal variations in carbon fluxes during extreme weather events. • The impacts of hurricanes Harvey, Irma, and Maria (2017) on terrestrial carbon fluxes were assessed. • Carbon release during and after landfall was measured using SMAP-derived NEE and eddy covariance CO 2 flux data. • Hurricane Harvey resulted in the largest carbon emission (0.33 megatons), followed by Irma (0.03 megatons) and Maria (0.02 megatons). • Vegetation loss, derived from MODIS NDVI and land cover change products, was greatest in Texas (6,199.3 km 2 ), then Florida (492.92 km 2 ), and Puerto Rico (14.53 km 2 ). • Vegetation declines led to reduced photosynthetic activity, temporarily turning affected areas from carbon sinks into carbon sources. • Findings highlight hurricanes as significant short-term drivers of carbon emissions and ecosystem disturbance across coastal regions.

Land cover change dynamics: Leveraging time series geospatial data for informed restoration in Tanzania

Analyzing urban sprawl in response to land use land cover change dynamics in Areka town and surrounding area: Wolaita Zone, Ethiopia

• Industrialization in Ennore is unauthorized, unsustainable, infringed and unjust • Industrialization and urbanization have decreased the area of wetlands by 89.34% • Subnational government had manipulated 1996-CZMP Map to favour political elites • Manipulation had served political elites’ interests, to favor that of global elites • Peri-urban environmental injustice is caused by regulatory capture by the elites Industrialization, urbanization and population growth are the major drivers behind abominable ‘Land-Use Land-Cover Change (LULCC)’, and the loss of local ecosystem services and environmental quality, at peri-urban interfaces. Such dynamics indicate the need to analyse the LULCC pattern, and explore the political drivers behind unsustainable LULCC. This paper, taking ‘Ennore Peri-Urban Region’ as the study area, has adopted a ‘Geospatial Mixed-Methods Case-Study Approach’ that synergises ‘Quantitative LULCC Analysis’ and ‘Qualitative Political Discourse Analysis’. The quantitative LULCC analysis was performed by utilizing ‘Supervised Image Classification’ and ‘Change Detection Analysis’. Quantitative results have revealed that total area of wetland, waterbody and cropland/shrubland has decreased by 89.34%, 14.43% and 10.61% respectively, in the period 1988-2023. Especially, cropland/shrubland has been severely affected in the core industrial region. Such unsustainable LULCC has occurred due to an intensive peri-urban industrialization, and a gradual peri-urbanization. The area under settlement and dense-vegetation have increased by 507.84% and 3.42%, respectively. Qualitative-political discourse analysis has revealed that such an unsustainable peri-urban LULCC has occurred due to the five-year delay in preparing the ‘Coastal Zone Management Plan (CZMP)’ and its map, and the unauthorized manipulation of 1996-CZMP Map by the subnational ‘Government of Tamil Nadu’, without the approval of the national ‘Government of India’. Such delay and manipulation had initially favoured the vested interests of political elites, and eventually that of global urban business elites, through regulatory capture by the latter. These indicate an inefficient, unfair, unequitable, unjust, incoherent and non-transparent intergovernmental environmental governance, and a weak public participation in decision-making.

Aim: Charcoal production remains a significant source of energy in sub-Saharan Africa and is increasingly linked to deforestation and land degradation in peri-urban areas. This study aimed to analyze the temporal trends, spatial land cover changes, and socio-environmental impacts of charcoal production in Bamenda I Sub-Division between 2000 and 2023. Methods: Primary data were collected through structured questionnaires administered to 198 respondents across 13 quarters, in addition to key informant interviews and GPS-based field observations. Secondary data included Landsat 5, 7, and 8 satellite imagery from the United States Geological Survey (USGS) for land cover change detection, as well as production records from the Northwest Regional Delegation of Forestry and Wildlife (MINFOF). Analytical methods comprised descriptive statistics, cross-tabulation, and GIS-based spatial analysis. Results: Results indicate that charcoal production in Bamenda I Sub-Division increased from 28,024 bags in 2000 to a peak of approximately 69,000 bags between 2012 and 2015, before declining sharply to 21,563 bags in 2022, likely due to the progressive depletion of accessible wood resources. Over the study period, natural woodland declined from 12.35% to 8.09% of the sub-divisional area, representing a net loss of 34.5%, while built-up areas expanded from 3.58% to 9.77%. Deforestation was rated as high by 50.56% of respondents across 13 quarters. Income generation was identified as the primary motivation for charcoal production (70%), which sustains livelihoods in the 50,000 - 100,000 FCFA/month range for 48% of producers. Conclusion: Charcoal production in Bamenda I Sub-Division has yielded both material livelihood benefits and cumulative geographical damage, manifesting as forest loss, land cover transition, and the erosion of ecosystem services. Recommendation: Policy responses should prioritize afforestation, adoption of improved kiln technologies, promotion of alternative energy sources, and the establishment of regulated forest governance frameworks to mitigate environmental impacts and enhance the sustainability of peri-urban energy systems.

ABSTRACT The Time-Series Data Downloader and Preprocessor (TSDP) is an open-source Python tool designed to streamline the acquisition and preprocessing of multispectral satellite imagery for time-series analysis. Built on Google Earth Engine (GEE), TSDP supports Landsat 1–9—from Landsat MSS to Landsat OLI—as well as Sentinel-2 imagery, GEDI canopy height and biomass products, and the recently released Google Satellite Embedding dataset. TSDP applies a consistent set of preprocessing routines across all optical sensors—including automatic image filtering, cloud, snow, and smoke masking, and topographic correction—generating analysis-ready time series spanning 1972 to the present. The tool integrates recent advances such as the Cloud Score+ algorithm for improved cloud detection in Sentinel-2 imagery and implements the SCS+C method for topographic correction using user-selectable digital elevation models (DEMs). TSDP prioritizes data quality by downloading imagery at its native spatial resolution and, in most cases, avoiding spatial resampling. This approach makes it particularly well suited for developing multi-resolution and multisource methods—an emerging direction in remote sensing for long-term environmental monitoring, land-cover change detection, and vegetation dynamics research. This technical note describes the tool’s functionalities, input parameters, processing workflow, and outputs, providing guidance for users who wish to generate high-quality time series datasets from publicly available satellite archives.

An explainable AI approach to deciphering groundwater depth responses to climate variability and human activities in Western U.S.

Moths (Lepidoptera) are sensitive to anthropogenic threats and serve as valuable bioindicators. Despite the remarkable diversity and abundance of Lepidoptera globally, there is a lack of information on how moth species are impacted by urbanization. Notably, very little is known about moths in the most populus city of the United States, New York City, where pervasive urban pollutants, artificial light at night, land cover change, and habitat fragmentation are severe. We examined the effects of urbanization on moth biodiversity in New York City, with a focus on green spaces. We used citizen science records from iNaturalist and complemented these data with ground sampling at twelve locations across six parks at night. While the iNaturalist dataset is comprehensive both spatially and temporally, it failed to detect some species we observed on the ground. However, the scope of the field survey dataset is limited in geographical breadth and seasonal coverage. Overall, we found a negative relationship between greater urbanization and moth diversity, with community similarity related to environmental similarity. Our results found greater biodiversity with less light at night and less urban development, and more deciduous tree cover and more open land. Our structural equation model reveals additional insight: although we detected a strong direct negative effect of developed land on moth diversity, urbanization also negatively impacts diversity via indirect effects of reducing open space and deciduous tree cover. Developed open space alone does not directly affect diversity but may positively impact diversity through its covariance with vegetation cover. These findings support the importance of mitigating artificial light at night in urban green spaces and maintaining urban vegetation to ensure nocturnal Lepidoptera can persist in rapidly urbanizing landscapes.

Changes in land use and land cover (LULC) have a substantial impact on environmental sustainability, urban planning, and agricultural management, necessitating accurate classification using high-resolution satellite imagery. Conventional methods struggle with challenges, such as noise, edge preservation, and misclassification, due to the complexity of multispectral and hyperspectral data. To overcome these issues, this paper presents a Finite-Element Diffusion Banyan Tree Growth Kernel-Driven Attention Network (FED-BTG-KDAN) for LULC mapping. High-resolution satellite images from Sentinel-2, Google Earth Engine (GEE), and Landsat-8 are very useful for multispectral and hyperspectral image classification. Image pre-processing is done by Robust Double-Weighted Guided Image Filtering (RDWGIF) to improve the image quality and spatial resolution. Feature extraction is done by a modified ResNet-152 with Multi-Axis Vision Transformer (MAViT) to extract spatial, spectral, and contextual information. The classification process uses a Finite-Element-Integrated Neural Network (FEINN) with a Diffusion Kernel Attention Network (DKAN) to reduce misclassification and enhance spatial representation. Optimisation is done by Banyan Tree Growth Optimisation (BTGO) to ensure fast convergence and minimise classification errors. The proposed method achieves 99.9% accuracy, demonstrating superior performance in precision, robustness, and computational efficiency compared to conventional approaches. Advantages include enhanced feature extraction through multi-scale attention and optimised classification using bio-inspired learning.

ABSTRACT This study analyzes long-term forest cover dynamics and associated socio-ecological drivers in the Okwangwo Division of Cross River National Park (CRNP), Nigeria, between 1987 and 2022, contributing to SDGs 15, 13, and 11 on sustainable ecosystems and community-centered environmental governance. Using a mixed-methods framework, we combined remote-sensing analysis with community-based data collection to assess land-cover change, its drivers, and implications for local livelihoods and conservation governance. Landsat imagery for four time periods was classified into Dense Forest, Sparse Forest, Bare Land, and Water Bodies using a CART–Random Forest model, achieving 99.5% overall accuracy (Kappa = 0.98). Structured surveys, participatory mapping, and focus group discussions were conducted with 200 respondents across four forest-adjacent communities to capture socio-economic characteristics, forest-use practices, and local ecological knowledge. Results show that Dense Forest expanded by 52.4% from 1987 to 2013 but declined slightly (1.8%) from 2013 to 2022, indicating renewed pressure after earlier conservation gains. Sparse Forest decreased by 44.1% overall, while Bare Land declined until 2013 but increased sharply thereafter (+60.4%). Water Bodies experienced the most severe decline (–82.7%). Community data reveal high dependence on farming and forest resources, low educational attainment, and strong cultural attachment to forest landscapes. Respondents demonstrated strong awareness of vegetation change, consistently attributing degradation to agricultural expansion, logging, fuelwood extraction, and persistent governance challenges. By integrating geospatial analysis with indigenous knowledge, the study highlights the importance of adaptive, community-inclusive conservation strategies aligned with local livelihoods and monitoring capacities. Findings underscore the need for strengthened protected area governance, improved resource management, and culturally grounded approaches to sustainable forest conservation in CRNP.

ABSTRACT This study examines the changes in land-use land-cover (LULC) across India over seven years, with a particular focus on the impacts of the COVID-19 lockdown in 2020. Using high-resolution Sentinel-2 10-m data, a short-term analysis of LULC dynamics from 2017 to 2023 was conducted. The unprecedented lockdown measures in 2020 provided a unique opportunity to observe the environmental effects of reduced human activity. Key findings include significant reductions in urban expansion and industrial activity during the lockdown, leading to temporary increases in water areas, green cover, flooded vegetation, and improved air quality in many regions. Agricultural areas such as crops and rangeland experienced varying impacts, with some shifts in crop patterns due to labour shortages and supply chain disruptions. The study highlights the resilience of natural systems in the face of reduced anthropogenic pressure and underscores the importance of continuous monitoring for sustainable land management. Our analysis leverages advanced remote sensing techniques to provide detailed spatial and temporal insights into LULC changes, contributing valuable data for policymakers and environmental planners aiming to balance development and conservation in post-pandemic recovery efforts.