Search Results (1,141)

LULCC is a major driver of habitat fragmentation, biodiversity loss, and declining landscape connectivity, particularly in rapidly urbanizing regions. Although Islamabad has experienced substantial urban expansion and forest-cover change, long-term city-scale assessments linking land-cover dynamics with landscape connectivity remain limited. This study examined spatiotemporal LULCC in Islamabad from 1991 to 2021 and assessed whether recent forest recovery improved landscape structural connectivity. Landsat images acquired in 1991, 2001, 2011, and 2021 were classified into five land-cover categories: water, forest, built-up area, bare land, and agricultural land. Classification was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE). Landscape composition and spatial configuration were quantified using FRAGSTATS 4.3, while forest fragmentation was evaluated using the Landscape Fragmentation Tool v2.0 (LFT) with a 100 m edge threshold. The classifications achieved overall accuracies above 90%, with Kappa coefficients (K) greater than 0.85. Built-up area increased from 76.31 km², representing 7.55% of the study area, in 1991, to 258.62 km², or 25.60%, in 2021, demonstrating rapid urban expansion and associated habitat conversion. Forest cover increased to 340.86 km² in 2001, declined to 271.96 km² in 2011, and subsequently recovered to 409.22 km² in 2021. Despite this increase in forest extent, fragmentation metrics indicated persistent spatial subdivision and limited structural connectivity. High patch density (PD), reduced landscape aggregation, and changes in the largest patch index (LPI) indicated persistent spatial subdivision and limited habitat continuity. These findings highlight the value of integrating RF-based land-cover classification, multitemporal remote sensing, and landscape metrics for urban environmental monitoring. The findings suggest that future land-use planning should consider landscape connectivity, protection of existing forest patches, and spatially coordinated restoration alongside continued reforestation. Full article

Mega-events can accelerate land–use/land cover change (LULCC) through infrastructure development and ecological interventions, but the carbon consequences of different land transition pathways remain unclear. Using Yanqing District, a host region of the Beijing 2022 Winter Olympics, as a case study, this study investigates LULC transitions, carbon storage dynamics, and future land management. A coupled PLUS–InVEST framework integrating transition attribution and trajectory analysis was applied to 10-m LULC datasets (2018, 2021, and 2024) to reconstruct historical transitions and simulate four land management scenarios for 2035. Results indicated that total carbon storage increased continuously from 41.19 × 10⁶ t C in 2018 to 42.14 × 10⁶ t C in 2024 despite concurrent urban expansion. This increase resulted from distinct transition processes across the two periods: cropland-to-rangeland conversion contributed most to carbon gains during 2018–2021, whereas rangeland-to-trees conversion dominated gains during 2021–2024. Pixel-level trajectory analysis further revealed that these dominant transitions rarely formed a continuous restoration sequence at the same locations, with rangeland–rangeland–trees trajectories accounting for 76.2% of trajectories leading to trees-class gains. Future simulations showed that the Urban Development Scenario would reduce carbon storage by 5.6%, whereas the Ecological Protection Scenario would increase carbon storage by 2.3% relative to the 2024 baseline. These results indicate that regional carbon dynamics depend not only on LULC composition but also on transition pathways, providing insights into how transition pathways can inform ecological restoration and sustainable land management. Full article

The role of land use/land cover (LULC) dynamics in predisposing slopes to shallow landsliding is widely acknowledged but seldom translated into operational susceptibility modelling. Most data-driven approaches still treat LULC as a static factor, neglecting the legacy effects of recent transitions. This study presents a methodological framework to quantify the influence of multi-temporal LULC changes on shallow landslide initiation and to incorporate this information into susceptibility mapping. The procedure was tested in the Metropolitan City of Messina (formerly known as the Province of Messina), Southern Italy, a representative Mediterranean area repeatedly affected by rainfall-triggered slope failures. Freely available LULC maps from 1990 to 2006 were processed through post-classification change detection to identify dominant land cover trajectories. Preliminary analyses within buffer areas showed higher landslide indices (LI, LAI) and Frequency Ratios for some transition classes, suggesting a potential role of LULC changes. These findings motivated the comparison between a static LULC configuration and a dynamic one incorporating the detected transitions within a Frequency Ratio susceptibility model. The dynamic model did not improve the mean Area Under the Curve (AUC) compared to the static model (0.7774 vs. 0.7742), and the observed reduction in variability across five independent random splits (standard deviation 0.012 vs. 0.064) should be considered preliminary. The proposed workflow, based entirely on open data and GIS-based processing, offers a transparent and reproducible methodology for integrating LULC transitions into dynamic susceptibility maps. The use of higher-resolution input data could potentially reduce the scale mismatch and improve the detection of fine-scale transitions, supporting more effective landslide risk mitigation and evidence-based land planning. Full article

Drought is a complex and periodic issue that has a significant impact on agriculture and water resources, particularly in semi-arid areas. This study evaluated meteorological and agricultural drought conditions in the Thanjavur district by combining rainfall data with remote-sensing methods. Meteorological drought was analyzed using 33 years of rainfall data and the Standardized Precipitation Index (SPI) (1992–2024) using 20 rainfall stations for the data available between 1992 and 2024. The spatial variation in rainfall was analyzed using Kriging interpolation in GIS. Agricultural droughts were analyzed using the Normalized Difference Vegetation Index (NDVI) and Vegetation Con0dition Index (VCI) using multi-temporal Landsat satellite images (Landsat 5 and Landsat 8). Land Use and Land Cover (LULC) classification was included to determine drought vulnerability across different land types. The NDVI and VCI indices showed an intensification of agricultural drought in 2010. The results demonstrated temporal and spatial differences in drought conditions for the years 1992, 1997, 2004, 2009, 2014, 2019, and 2024 and indicated that the region experienced periodic severe drought conditions of 3%, 3%, 3%, 8%, 19%, 9%, and 11% in the study area, respectively. During the drought period, the vegetation indices showed a strong sensitivity of agricultural areas to changes in rainfall, and low NDVI and VCI values indicated increased vegetation stress. Meteorological and agricultural droughts were integrated using the Analytical Hierarchy Process (AHP) method by combining various indicators to analyze the drought condition across the Thanjavur district. The multiple criteria decision-making (MCDM) method uses pairwise comparisons of various factors, such as giving high importance to SPI and rainfall, followed by vegetation indices and LULC. The consistency ratio validated the reliability of the weighting term. This method shows that combining meteorological and remote sensing indicators advances a robust framework for monitoring and assessing droughts. Conceptual droughts illustrate how meteorological droughts are associated with the development of agricultural droughts. The results of this study can be adopted for effective drought management, irrigation planning, and sustainable agricultural practices in this region. Full article

The increasing tourism pressure on the UNESCO World Heritage Altai Mountains calls for efficient environmental monitoring tools. This study presents a Google Earth Engine framework that couples the Remote Sensing Ecological Index (RSEI) with land use/land cover (LULC) mapping to assess ecological changes in the Lake Manzherok area between 2020 and 2025. RSEI was derived from Sentinel-2 and Landsat imagery by combining four indicators (NDVI, MNDWI, NDBSI, LST) through principal component analysis. LULC classification was carried out using Random Forest trained exclusively on Sentinel-2 spectral bands. The results confirm that RSEI effectively captures ecological gradients in complex mountainous terrain, with the first principal component explaining 57–62% of the total variance. While 92% of the study area remained stable, 5.9% showed a decline in ecological status, spatially coinciding with a near doubling of built-up and bare surfaces from 9.89 km² to 18.17 km². The largest negative RSEI changes were associated with transitions from forestland (ΔRSEI = −0.29) and grassland (ΔRSEI = −0.20) to built-up/bare land, whereas reverse transitions displayed positive ΔRSEI values. These spatial patterns are consistent with the visible development related to tourism. However, because the built-up/bare land class also includes naturally bare surfaces, and because interannual climate variability may affect the RSEI components, it is important to interpret the ΔRSEI values as relative changes rather than absolute measurements of tourism impact. The proposed framework provides a reproducible and transferable tool for monitoring ecological quality in data-scarce mountain regions, delivering spatially explicit evidence that can support conservation and land-use planning. Full article

Population growth is increasing food demand, while watershed food systems face intense pressure from land-use/land-cover (LULC) change. Therefore, this study aimed to identify the critical threshold for food sufficiency in the Cimanuk Watershed. The methods used include dynamic analysis and prediction of LULC changes in line with four scenarios using Support Vector Machine (SVM)–Markov model, ecosystem service-based food production modeling through Crop Production module in Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, food demand estimation based on population projections, and calculation of Food Sufficiency Index (FSI) to identify the critical threshold at which the food system shifts from surplus to deficit. The results showed that paddy rice production is projected to continue meeting food demand across all scenarios through 2042. However, food demand is expected to increase as the population grows, specifically in the Accelerated Population Growth (APGS) scenario. FSI analysis indicated that the Cimanuk Watershed remained in food surplus (FSI > 1) up to 2042. The watershed may transition to a food-deficit condition after 2062 in the absence of intervention measures. Therefore, this study recommends protecting productive paddy fields, conserving forests, and managing population growth to maintain long-term food sufficiency. Full article

Land degradation threatens ecosystem productivity and food security across dryland regions worldwide. Currently, land degradation affects 3.2 billion people and approximately 25% of the world’s terrestrial land. This study assessed the spatiotemporal dynamics and potential drivers of land degradation in Punjab, Pakistan, from 2001 to 2024 using an integrated remote sensing framework combining Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Net Primary Productivity (NPP), and Soil Organic Carbon (SOC). A weighted overlay approach and Moisture-Adjusted Net Primary Productivity (MNPP) analysis were employed to evaluate degradation severity and vegetation productivity under moisture stress conditions. Land-cover transitions were analyzed using the Land Change Modeler (LCM), while sensitivity analysis was conducted to evaluate the robustness of the weighting scheme under alternative scenarios. Results indicate higher degradation severity in southern Punjab, where SOC levels are lower. Land cover analysis revealed notable changes, with grassland decreasing by 1.77%, while built-up areas increased from 1.89% in 2001 to 3.04% in 2024. Tree cover and sparse vegetation also exhibited declining trends over the study period. In addition, MNPP was used as an indicator of vegetation productivity under moisture constraints. The MNPP results indicate an overall decline in productivity, particularly in southern and central Punjab, despite localized improvements in some regions. These trends align with a net loss of 1790 km² of non-degraded land from 2001 to 2024 and a 163% increase in very high degradation. Northern districts maintained relatively higher productivity levels compared with other areas. Both the weighted overlay and MNPP approaches indicate that land degradation is associated with declining soil organic carbon and increasing moisture stress. The results highlight the importance of continuous monitoring to support policy interventions aimed at mitigating desertification and ensuring long-term food security. Full article

Land use and land cover (LULC) dynamics are critical indicators of environmental transformation and anthropogenic pressure on regional landscapes. Mirzapur, located in the transitional zone between the Indo-Gangetic Plain and the Vindhyan uplands in Northern India, represents a region characterized by ecological sensitivity, mineral-based industries, agricultural dependency, and rapid infrastructural growth. In recent decades, Northern India has experienced accelerated urbanization, population pressure, land fragmentation, and environmental stress, thus making systematic LULC monitoring crucial for sustainable resource management and policy planning. The present study examines the spatio-temporal changes in land use and land cover in Mirzapur for the years 2004, 2014, and 2024. The study employed a cloud-based platform and the Random Forest algorithm for supervised classification of multi-temporal satellite imagery. LULC maps were generated and post classification comparison was used to assess changes across the selected years. Accuracy assessment was conducted using standard validation metrics, including the Kappa coefficient, to evaluate classification. From 2004 to 2024, urban areas expanded by a relative increase of 169.36%, largely through the conversion of cropland, although the overall cropland area showed a slight increase due to agricultural expansion in other parts of the study area. A slight increase in forest cover was also observed during this period. Water bodies and barren lands declined, indicating ecological stress in the region. These changes reflect rapid urbanization, demographic pressure, and evolving socio-economic activities within the district. The LULC classification achieved overall accuracies of 96.50% (2004), 97.52% (2014), and 96.08% (2024), showing the reliability of the generated maps. The study demonstrates the effectiveness of cloud-based geospatial analysis combined with a machine learning algorithms for long-term LULC monitoring and provides valuable insights for sustainable land management and regional planning. Full article

To enhance regional carbon sequestration capacity and achieve carbon neutrality, it is crucial to accurately estimate the net primary productivity (NPP) of vegetation and its response to changes in land use/land cover (LULC). Based on remote sensing data, the study estimated the NPP of Qingdao, a typical coastal city in eastern China, from 1990 to 2020 using the CASA model and examined its spatiotemporal dynamics. The Geographic Detector was used in the study to measure the impact of anthropogenic and natural factors on variations in NPP. Additionally, the Markov–PLUS model was utilized to forecast future LULC patterns and NPP changes for 2030 under three development scenarios: natural development, cropland protection, and ecological protection. The results indicate considerable regional variation and an initial decrease followed by an increase in Qingdao’s NPP. It is distributed in a strip-like pattern from northwest to southeast, with the regions around Jiaozhou Bay showing the lowest levels. The changes in NPP are driven by multiple interacting factors; among these, LULC, DEM, and slope exhibit significant spatiotemporal correlations with NPP, with LULC playing a dominant role in this process. In addition, interaction analysis demonstrated that the synergistic effects between LULC and other factors constituted the principal driving force behind NPP dynamics, and the interactions between the two factors that contribute show bivariate or nonlinear enhancement effects. The ecological protection scenario produces the greatest NPP values among the three scenarios. Optimizing LULC may significantly increase carbon sequestration capability, as evident in the NPP forecast, which is higher than that of 2020 in all scenarios. This study demonstrates that the ability of vegetation to store carbon can be effectively enhanced through land-use optimization informed by remote sensing technologies. The results provide a scientific basis for adjusting LULC policies and conducting carbon-neutral spatial planning in coastal urban areas. Full article

Rapid urbanization in tropical Asia has fundamentally transformed land use–land cover while intensifying urban thermal stress, yet the relationship between land cover change and thermal conditions is frequently assumed to be spatially uniform. This study challenges that assumption by demonstrating that land cover–thermal relationships in Colombo District, Sri Lanka, are highly spatially and temporally heterogeneous, with statistically significant associations detected in only 17–47% of the study area in any given year, underscoring that context, not land cover type alone, governs thermal outcomes. Using multi-temporal Landsat satellite imagery, LULC maps were derived, and the urban heat island effect (UHIE) and urban thermal field variance index (UTFVI) were calculated for seven time periods (1989, 1996, 2002, 2009, 2014, 2019, 2024). Geographically weighted regression (GWR) was applied to model local relationships between LULC classes, namely wetland vegetation, water bodies, impervious surfaces, and other pervious surfaces, and thermal indices across a 500 m spatial grid, revealing a 74% loss in wetland vegetation and a 326% increase in impervious surfaces over the study period. Water bodies exhibited spatially variable cooling effects relative to wetland vegetation, most pronounced in eastern regions during earlier periods, while impervious surfaces showed consistent, spatially persistent warming effects concentrated in western and southern urban cores. By coupling GWR with a 35-year multi-sensor time series, this study provides a spatially explicit, longitudinal account of how land cover–thermal relationships evolve as tropical urbanization intensifies, offering an evidence base for spatially targeted rather than uniform climate adaptation planning in rapidly urbanizing tropical cities. Full article

The Ifrane National Park (INP), situated in the Middle Atlas Mountains in Morocco, is renowned for its unique bioecological attributes and rich biodiversity. The park provides key ecosystem services, notably climate regulation, with its Atlas cedar forests serving as an important carbon reservoir. Despite its importance, the park faces challenges associated with Land Use Land Cover Changes (LULCCs). These dynamics, driven by population settlement in mountain areas, intensify pressure on forest resources and accelerate the conversion of rangelands into croplands. Our study aims to examine the carbon sequestration service (CSS) in INP and its changes in response to land use dynamics before and after the park’s creation. We first use Google Earth Engine platform to map Land Use Land Cover (LULC) over three decades (1992/2022). Then, we quantify and map CSS using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.10.2) model. Finally, we assess the economic value of this service over time using the social cost of carbon, adjusted by an appropriate discount rate. Our findings show that CSS declined by −217,738.4 tC/year (1992–2002) and −177,903.2 tC/year (2002–2012) prior to the park’s establishment, before rising to +61,786.8 tC/year during 2012–2022, following the park’s establishment and subsequent forest restoration, increasing its economic value from a net loss to +12,340,544 USD/year. By addressing a significant knowledge gap regarding CSS dynamics and economic value within a protected area, this study highlights the effectiveness of conservation measures and offers a practical tool for managers to inform decision makers, promote conservation and restoration investments, and engage local communities in sustainable development initiatives. Full article

Understanding how urbanization modifies stream thermal regimes is essential for assessing freshwater ecosystem responses to climate variability and land-use and land-cover (LULC) change. This study investigated air temperature (AT)–water temperature (WT) relationships at annual, monthly, and diurnal timescales in two adjacent, relatively flat watersheds with contrasting urbanization levels in North Central Texas: the urbanized Doe Branch and less urbanized Little Elm Creek during 2012–2021. A single harmonic analysis was applied to characterize annual and diurnal thermal patterns, including mean temperature, amplitude, and phase, while statistical analyses were used to evaluate seasonal and daily thermal variability and peak timing. At the annual scale, AT and WT metrics were strongly correlated at both sites (r = 0.85–0.94, p < 0.01) indicating that atmospheric conditions were the dominant control of annual stream temperature variability. Annual mean WT increased with AT, suggesting strong air–water thermal coupling and the potential for warmer stream temperatures under future climate warming. However, Doe Branch exhibited higher annual mean WTs, delayed seasonal peak WTs, and reduced annual temperature ranges compared with Little Elm Creek, reflecting the influence of urban watershed characteristics on seasonal thermal responses. At the diurnal scale, daily mean WT remained strongly coupled with daily mean AT, whereas daily temperature range and peak timing showed weaker relationships with AT. The greater variability in peak WT timing at Doe Branch suggests that short-term stream thermal dynamics were influenced by additional watershed characteristics beyond atmospheric forcing alone. Full article

Land-use and land-cover (LULC) change is a major driver of ecosystem degradation and ecosystem service loss in tropical landscapes. This study assessed the impacts of LULC changes between 1985 and 2023 on the potential of different LULC classes to supply ecosystem services in the Upper Rio Pardo Basin (Rio Pardo and Rio São João do Paraíso watersheds), northern Minas Gerais, Brazil. LULC data from the MapBiomas Project were used to quantify transitions among LULC classes within the study area. The potential supply of ecosystem services was assessed using the Burkhard matrix, adapted to local environmental conditions and informed by expert knowledge, while the relative importance of ecosystem services was evaluated through a participatory assessment involving local communities. The expansion of pasturelands and commercial forest plantations was identified as the primary driver of ecosystem service loss in the study region. Native ecosystems showed the highest potential to provide regulating, supporting, provisioning, and cultural ecosystem services, particularly water regulation, soil protection, carbon sequestration, and biodiversity conservation. In contrast, anthropogenic land uses were primarily associated with provisioning services and showed limited capacity to sustain regulating and supporting services, indicating clear trade-offs between production and ecosystem functioning. Local communities identified water-related services as the highest priority, followed by climate regulation, soil fertility, and food provision. These findings demonstrate the value of integrating expert-based and participatory approaches to ecosystem service assessment and provide a basis for territorial planning strategies that prioritize the conservation and restoration of native vegetation, particularly in hydrologically sensitive areas. Full article

The Hetao Irrigation District in arid northwestern China presents a significant challenge in balancing agricultural intensification and water conservation, particularly in its terminal lake, Wuliangsu Lake. This study examined how changes in Land Use/Land Cover (LULC) and cropping structures influenced the lake’s water quality. By using remote sensing data for LULC classification and agricultural statistics for crop composition, we analyzed the spatio-temporal variation in LULC and cropping structure in the irrigation district and quantified the associated agricultural non-point source pollution loads (total nitrogen, total phosphorus, and chemical oxygen demand) entering the lake. A calibrated Environmental Fluid Dynamics Code model was applied to evaluate water quality responses to cropping structure optimization. Our findings revealed significant shifts in LULC and cropping structure during the study period, driven by agricultural intensification, ecological restoration policies, urbanization, market forces, and national food security strategies. Concurrently, agricultural non-point source pollution loads into the lake showed a steady declining trend from 2018 to 2023, with total nitrogen (TN) decreasing by 15%, total phosphorus (TP) by 16.9%, and chemical oxygen demand (COD) by 19.4%. Model simulations demonstrated that optimizing the cropping structure, specifically by reducing the area of high-fertilizer crops (sunflower) and expanding low-fertilizer crops (spring wheat) and forage crops for ecological purposes, could further improve lake water quality. Under the intensive adjustment scenario, the inflow loads of TN, TP, and COD decreased by 10%, 11.7%, and 10.9%, respectively, while the corresponding in-lake concentrations decreased by 22.1%, 19.8%, and 18.7%, respectively. TP exhibited the highest sensitivity to such adjustments. By linking cropping structure adjustments with hydrodynamic-water quality modeling, this study provides a quantitative framework for assessing water quality responses in arid irrigated systems, offering a scientific basis for balancing agricultural production and water ecosystem protection in the Hetao district and similar regions. Full article

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article