Search Results (5,860)

Land-use change affects regional carbon accounting through ecological conversion and the concentration of energy-intensive economic activity. We combined 30 m China Land Cover Dataset maps for 2010, 2015, 2020, and 2025 with provincial socioeconomic and energy statistics to quantify land-use transitions and carbon emissions across nine Yellow River Basin provinces. Construction-land-associated emissions were decomposed using the logarithmic mean Divisia index, factors associated with land expansion were examined using random-forest models, and three 2030 scenarios were evaluated. Construction land expanded by 38.87% from 2010 to 2025, with 71.33% of new construction land converted from cropland and 17.75% from grassland. Net land-use carbon emissions increased by 69.82%, from 1139.06 to 1934.33 million t C. Economic-output density contributed 1144.84 million t C to the increase in construction-land-associated emissions, compared with 576.11 million t C from land expansion, whereas declining energy intensity offset 922.72 million t C. Projected 2030 emissions ranged from 2124.72 million t C under ecological protection to 2866.55 million t C under urban expansion. Construction-land expansion was substantial, but economic-output density made the larger positive contribution to historical emission growth. The projected 2030 estimates depended on the combined trajectories of construction-land demand, economic growth, and energy intensity. These findings highlight the importance of coordinating land-use planning, economic development, and energy-efficiency improvement for sustainable low-carbon transitions. Full article

Reliable aquaculture monitoring requires spatial, administrative, and environmental evidence to be integrated without treating these sources as equivalent. This study characterized mapped aquaculture land-cover dynamics in six coastal provinces of Ecuador from 1985 to 2024, assessed their 2023 correspondence with authorized or concessioned shrimp-farm area, quantified 2023–2024 land-cover transitions, and described climate exposure over a fixed historical aquaculture footprint. Annual MapBiomas Ecuador Collection 3.0 classifications were aggregated by province, canton, and parish; MPCEIP records were harmonized with INEC administrative codes; and CHIRPS v3 precipitation and ERA5-Land temperature were summarized over the union of all pixels mapped as aquaculture at least once during the study period. Mapped class 31 area increased from 58,198.792 ha in 1985 to 138,200.970 ha in 2024, with a maximum of 153,516.642 ha in 2022. In 2023, mapped area represented 74.508% of 202,114.900 ha of authorized or concessioned area, with strong canton-level rank correspondence. Between 2023 and 2024, persistence reached 135,774.430 ha, gross loss 14,816.766 ha, gross gain 2426.130 ha, and net change −12,390.636 ha. Climate exposure was wettest in 1998 and warmest in 2023. Together, the results show that coastal aquaculture development cannot be inferred reliably from a single territorial measure: long-term mapped expansion, administrative extent, recent classification changes, and climate exposure describe complementary but non-equivalent dimensions of the system. This distinction provides a more defensible basis for interpreting aquaculture land-use change and for targeting territorial monitoring and administrative verification. Full article

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

Long-term monitoring of land cover dynamics is essential for evaluating conservation outcomes in World Heritage sites, yet remains challenging in conflict-affected regions with limited field observations. This study aims to reconstruct annual land cover dynamics and assess forest sustainability in the Okapi Wildlife Reserve (OWR), a World Heritage site in the Democratic Republic of Congo. Using Landsat dense time series observations (2000–2023), the Continuous Change Detection and Classification (CCDC) algorithm, landscape metrics, and the Sustainable Development Goal (SDG) 15.1.1 indicator, we developed an annual scale framework to quantify land cover changes and conservation status. The results showed that forest loss was the dominant land cover change process, with degradation accelerating after 2013. Forest disturbances were spatially heterogeneous, mainly concentrated in the boundary buffer zone and along National Road No. 4, while localized disturbances also occurred within the core protected area. Landscape fragmentation increased during the study period, and SDG 15.1.1 trends revealed differentiated conservation pressures among management zones. This study provides an annual-scale land cover history of the OWR and demonstrates the potential of Earth observation for monitoring endangered World Heritage sites. Full article

Protegira songi Chen and Zhang is a monophagous defoliating pest that feeds exclusively on Eucommia ulmoides Oliv., an economically and medicinally important tree species widely cultivated in China. Climate change may influence the potential suitable habitat of this pest, with implications for future monitoring and management. In this study, a Maximum Entropy (MaxEnt) model integrating occurrence records and environmental variables was used to evaluate current habitat suitability and project future changes under four Shared Socioeconomic Pathway (SSP) scenarios. The minimum temperature of the coldest month (Bio6) and precipitation of the driest month (Bio14) were identified as key climatic factors associated with potential habitat suitability. Currently, suitable habitats were estimated to cover approximately 20.58% of China’s land area, with highly suitable regions mainly distributed in central and southwestern China. Future projections indicated that potential suitable habitats would undergo spatial redistribution, with core suitable areas remaining relatively stable while peripheral regions showed more variation. Under SSP245, the suitable habitat area exhibited the largest increase by the 2090s, accompanied by an overall southward shift in the centroid of suitable habitat. These findings improve understanding of climate-driven changes in the potential suitable habitat of P. songi and provide a scientific basis for climate-informed monitoring and adaptive pest management. 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

Land use and land cover change represent major anthropogenic carbon emission sources, yet most existing studies on landscape patterns and carbon emissions predominantly focus on plain urban agglomerations, with limited empirical evidence from terrain-restricted hilly regions. Taking Xuancheng, a typical hilly city in the Yangtze River Delta, as the study area, this paper utilized seven time-series of Landsat remote-sensing datasets spanning 1990–2020. Integrating the land use dynamic degree, transfer matrix, landscape pattern indexes, calibrated carbon coefficients, Spearman correlation analysis and grey relational analysis, this study explored the associations between land use patterns and carbon emissions. Results show that built-up areas expanded 4.08-fold in the past three decades and emerged as the dominant carbon source, while forests maintained persistent carbon sequestration. The largest patch index of built-up area exhibited the strongest correlation with carbon emissions; cultivated land and forest displayed temporally synchronous fluctuations with emissions rather than exerting independent causal effects. Terrain constraints drive axial urban sprawl along transport corridors, elevating correlations of edge-related indexes and generating carbon response patterns distinct from those observed in plain cities. The two-step correlation analysis framework proved suitable for small-sample long-term datasets. These findings suggest that curbing contiguous built-up area expansion, optimizing urban morphology, and strengthening ecological connectivity should be prioritized in low-carbon spatial planning for hilly cities. Full article

Wetlands monitoring is essential for sustainable watershed biodiversity conservation, and climate resilience. This study performs a spatiotemporal wetland change analysis for the Sangamon River Watershed, Illinois between 2000 and 2025, using Landsat 5 Thematic Mapper (TM), Sentinel–2 Surface Reflectance (SR), Synthetic Aperture Radar (SAR), Gray–Level Co–occurrence Matrix (GLCM) and terrain data through cloud–based processing in Google Earth Engine (GEE), Google Colab and ArcGIS Pro 3.6.0. We conducted a comparative assessment of deep learning (Deep Neural Network [DNN], U-Net, Attention U-Net, and SegFormer), and machine learning models (Random Forest [RF], Gradient Tree Boosting [GTB], and Support Vector Machines [SVM]) through pixel–based and object–based methods. National Land Cover Database (NLCD) was used for training and validation using stratified random sampling for five categories namely wetlands, forest, agriculture/grassland/barren land, urban/developed and water. A proportion of 54.85% (860.68 km²) of wetlands extent was lost to other land uses, particularly agriculture, urban and forest, along with 46.51% (694.88 km²) forest and 36.80% (66.13 km²) water bodies loss. Agriculture/grassland/barren and urban/developed witnessed increases of 8.56% (820.62 km²) and 72.92% (799.52 km²), respectively. For Landsat–based and Sentinel–based classifications, SegFormer outperformed all ML and DL classifiers with (OA = 94%, Kappa = 0.89, mean F1 = 0.80, mean IoU = 0.70 and OA = 96%, Kappa = 0.92, mean F1 = 0.95, mean IoU = 0.73, respectively) with excellent wetland delineation (PA = 0.99, UA = 0.97, F1 = 0.98, IoU = 0.97 and PA = 0.99, UA = 0.99, F1 = 0.98, IoU = 0.99, respectively). Sentinel–based classifications had improved performance than Landsat, while object–based models consistently outperformed pixel–based methods. The Digital Elevation Model (DEM) and slope were the most influential predictors for RF models, while GLCM and SAR produced negligible influence. The integrated and comparative GeoAI framework provides a robust methodology for watershed–scale wetland monitoring and supports evidence–based conservation, restoration prioritization, climate resilience, and sustainable land–use planning, while offering strong potential for application in other agricultural watersheds following regional validation. Full article

Dominant factors of changes in natural habitat quality in arid municipal areas of western China over recent decades remain quantitatively unverified, due to limitations in traditional attribution methods that depend on linear causality or low-dimensional interactions. We generated 30 m resolution habitat quality indexes (HQI) of Yinchuan Municipality of western China from 2000 to 2020 using the InVEST model, and then quantified the relative importance of anthropogenic and climatic variables to HQI changes by Random Forest modeling after eliminating cross-variable collinearity, taking, a typical arid Results indicate that a relatively high-level (HQI > 0.6) of the habitat quality predominated in natural habitats covering forests, shrublands, grasslands, and wetlands within Yinchuan Municipality since 2000, accounting for 76.95% of total natural habitat area. Moreover, this relatively high-level remained stable, with 93.32% of all natural habitat types over the past two decades, though the 4.78% of total natural habitat area with improvement slightly outnumbered the 1.90% of area with decline. The municipality-wide mean HQI of the natural habitats fluctuated from 0.65 in 2000 to 0.66 in 2020, which was primarily shaped by anthropogenic factors without a statistically significant monotonic trend, with population density, gross domestic product, and land use dynamic degree accounting for 31.79%–43.46%, 18.95%–27.96%, and 10.66%–18.85% of the fluctuation, respectively. Climatic factors contributed far less to the fluctuation, with the relative importance of individual variables falling in the range of annual precipitation (9.37%–14.77%), mean annual temperature (4.10%–9.61%) and annual solar radiation (3.28%–9.60%). These findings confirm the dominant role of persistent influences from anthropogenic activities rather than their temporal intensification in maintaining the stable high-level habitat quality of Yinchuan Municipality since 2000, and provide direct targeted guidance for formulating habitat quality improvement strategies that coordinate urban construction and ecological protection in arid western China. Full article

Flood events in agricultural floodplains reflect not only rainfall intensity but the vulnerability of the affected area at the moment of the event. This study examines the January–February 2026 flood in Larache Province, Morocco, through an integrated remote sensing workflow combining Sentinel-1 SAR, CHIRPS precipitation, and Dynamic World land cover in Google Earth Engine, processed via the rgee R package. Flood extent was mapped using SAR backscatter change detection and cross-checked against rainfall dynamics, yielding about 6660 ha of inundation (2.4% of the province), concentrated along the main Loukkos river corridor and adjacent floodplain around Ksar El Kebir, with smaller scattered patches to the south. Land cover was assessed across four temporal windows (spring 2025, pre-event, post-event, and spring 2026) to evaluate how the landscape entered the event and how it recovered. A significant share of normally cultivated land was in a bare-soil state before the flood, a condition that the literature associates with increased runoff. Post-flood analysis across 25 sample areas grouped into three geomorphic zones shows spatially uneven recovery, with cropland still below seasonal norms in spring 2026. These patterns are consistent with structural land-use conditions (wetland loss, intensive seasonal agriculture, drought-degraded soils) that may have amplified an already severe event. The findings support cover cropping, updated flood-hazard zoning, and targeted wetland restoration to reduce vulnerability in the Loukkos floodplain and comparable Mediterranean alluvial floodplains. Full article

Surface mining significantly alters land cover and vegetation, making long-term monitoring essential for assessing its environmental impacts. The objective of this study was to analyze the long-term development of the active Dargov quarry (Slovakia) during the period 2009–2025 using Landsat image time series. Changes in vegetation cover and exposed surfaces were assessed using the NDVI, BSI, and NDBI spectral indices in combination with the Mann–Kendall test, Sen’s slope estimator, and the LandTrendr algorithm. The results revealed a gradual decline in NDVI values accompanied by a concurrent increase in BSI and NDBI values within the active quarry, reflecting the expansion of exposed rock and soil surfaces associated with ongoing mining. Statistically significant trends (p < 0.05) were identified in 76.36% of NDVI pixels, 65.45% of BSI pixels, and 63.64% of NDBI pixels within the deposit boundary. The relationships between spectral indices and quarry production were also evaluated using Pearson’s correlation analysis. The spectral indices showed a substantially stronger relationship with cumulative quarry production than with annual production. The LandTrendr algorithm identified the most pronounced vegetation-cover changes primarily during 2019–2024, with 70.91% of the pixels in the Time of Largest Change output within the deposit boundary falling within this period. The proposed methodology provided a comprehensive assessment of the spatial and temporal patterns of changes associated with surface mining at the Dargov quarry and demonstrated its potential to support environmental monitoring, environmental impact assessment (EIA), and mineral resource management. Full article

Ecosystem carbon storage is highly sensitive to land-use/land-cover change (LUCC). However, retrospective carbon assessment, spatial factor analysis, and future land-use optimization are often conducted separately, limiting their ability to support territorial planning involving competing economic, ecological, and carbon-storage objectives. Taking Jiangsu Province, China, as a rapidly urbanizing region, this study developed an integrated framework combining multi-temporal remote-sensing-derived land-use data with the InVEST carbon storage model, Geodetector, multi-objective programming (MOP), and the intPLUS model. Land-use data from 2000, 2010, and 2020 were used to quantify changes in land use and ecosystem carbon storage and to examine how natural, socioeconomic, and accessibility conditions corresponded spatially to the observed land-use configuration. Pareto optimization was then applied to identify non-dominated land-use allocations, which were spatialized to simulate five land-use scenarios for 2030. From 2000 to 2020, cropland decreased by 7475 km², while construction land increased by 6974 km². Correspondingly, ecosystem carbon storage declined from 1431.85 Mt to 1402.55 Mt, representing a cumulative loss of 29.30 Mt. This decline was primarily associated with cropland loss and construction land expansion, particularly the conversion of cropland into construction land. Although the overall spatial pattern of carbon storage remained relatively stable, high-value areas gradually contracted, whereas low-value areas expanded in intensively urbanized regions. Geodetector results showed that pairwise factor combinations generally corresponded more closely to the spatial differentiation of land-use patterns than individual topographic factors, and that associations involving socioeconomic activity, transport accessibility, and vegetation cover became more pronounced over time. The 2030 scenario analysis revealed clear trade-offs: the carbon storage maximization scenario produced the highest carbon storage (1497.47 Mt), the economic development scenario generated the highest economic benefit but the lowest ecosystem service value, and the natural development scenario produced the lowest carbon storage (1384.46 Mt). The Pareto-based comprehensive optimization scenario maintained 1487.12 Mt of carbon while achieving a comparatively balanced combination of economic benefit and ecosystem service value. These findings demonstrate that integrating multi-temporal remote sensing, ecosystem-service modeling, and Pareto optimization can translate historical land-use information into flexible spatial planning alternatives for rapidly urbanizing regions. Full article

Wind-energy expansion is indispensable to decarbonization, but a strong wind resource does not by itself establish that development is spatially suitable, socially legitimate or institutionally deliverable. This article develops an empirical spatial–legal screening framework that converts technical wind opportunity into Justice-adjusted Wind Development Potential (JAWDP). The analysis covers Australia, Brazil, Canada, Chile, China, Denmark, Germany, India, Kenya, Morocco, South Africa and the United Kingdom. A 25 km grid is used for the primary analysis and a 10 km grid for resolution sensitivity. The Wind-Resource Potential Index (WRPI) combines 100 m wind power density with grid remoteness; the Landscape-Conflict Index (LCI) combines protected-area coverage, ecological sensitivity, agricultural land, settlement exposure and cultural-heritage proximity; and the Legal and Justice Friction Index (LJFI) codes nine functional safeguards on a 0–2 scale. Equal component weights and a preregistered symmetric baseline of $\alpha =\beta =0.25$ are used in the multiplicative JAWDP model. The United Kingdom and Denmark rank first and second (0.702 and 0.697), followed by Morocco under a disclosed midpoint legal scenario (0.539). Grid-resolution changes leave ten country ranks unchanged and shift Chile and Germany by one position. Across the full preregistered sensitivity set, eleven countries are classified as stable and Germany as moderately stable. As an external plausibility check, JAWDP correlates with 2024 installed wind-capacity density (Spearman $\rho =0.692$, $p=0.013$) and 2015–2024 capacity-addition density ($\rho =0.699$, $p=0.011$). An additive equal-weight multicriteria benchmark produces only moderate rank correspondence ($\rho =0.559$), illustrating the non-compensatory purpose of the justice adjustment. The framework is intended for comparative screening, not as a prediction of generation, a grid-feasibility model or a substitute for project-level legal review. Additional robustness checks show high stability to sample composition and transmission-vintage perturbation, but the deployment association weakens under wind-suitable-area denominators; the external comparison is therefore interpreted as a denominator-sensitive plausibility check rather than empirical validation. Full article

Pyracantha koidzumii (Hayata) Rehder is native to Taiwan, where it has an endangered IUCN conservation status, although it is invasive in other parts of the world. It was introduced to South Africa as an ornamental species. In South Africa, P. koidzumii is classified as having density-based negative impacts, although its distribution is sparse and no impact assessments have been conducted to date, compared with its established congener, Pyracantha angustifolia. We asked the following questions: (1) does the current distribution of P. koidzumii and P. angustifolia indicate any relationship with anthropogenic activities as the main driver of plant invasions; (2) what are the seed viability and germination capacity of P. koidzumii?; and (3) what is the current distribution and habitat suitability of P. koidzumii relative to P. angustifolia, and does P. koidzumii show any potential to be invasive in different climate change scenarios (i.e., SSP1 and SSP5). We found a significant relationship between land use and cover and species occurrence records, with disturbed areas having the highest occurrence records (43.5%; N = 4297), followed by grassland (23.4%). The seed viability and germination capacity were high (78.6–87.2%; n = 50), confirming that P. koidzumii can successfully recruit in southern Africa. Climate suitability projections across scenarios for both species yielded a strong data fit (i.e., training AUCs above 0.9), with precipitation as the most important climate predictor. The climate-based habitat suitability projections showed greater net expansion for both species in SSP1, with P. angustifolia projected to invade 254,974 km², while P. koidzumii could invade 49,829 km². Alternatively, there was contraction (i.e., positive net habitat loss) of suitable habitat in SSP5, characterised by significant area loss for P. angustifolia (12,0139 km²), while P. koidzumii displayed a moderate loss (13,174 km²). For the first time, this study showed that P. koidzumii has high potential to invade the southern African grassland biome, driven by high germination combined with biophysical disturbance, and that its future habitat occupancy will be moderately affected by extreme environmental temperature conditions. Full article

Myanmar, an agriculture-based economy, is one of the most important agricultural countries in mainland Southeast Asia. Although previous studies have documented changes in agricultural land-use and cropping patterns, trends in farmland area, cropping patterns, and food sufficiency across Myanmar’s 14 states/regions remain limited. This study addresses these gaps by using land-cover data from the global 30 m dynamic dataset and statistical data from 2000 to 2025, and household surveys from selected areas. GIS-based spatial analysis, trend analysis, and multivariate analysis of variance were applied to analyze both the direction and magnitude of farmland and cropping pattern changes over time. The results showed that Myanmar’s farmland area increased significantly (τ = +0.52, p < 0.01), expanding by 23.6% over the study period. Double cropping remained an important system, increasing by 55.9%. In the central dry zone, sufficient water availability showed the strongest effect on crop production and farmland area (F = 11.191, p < 0.001, η²p = 0.037), followed by soil fertility loss (F = 10.263, p < 0.001, η²p = 0.034). The analysis estimated that domestic consumption requirements were approximately 7.97 million tons of rice, 3.12 million tons of wheat, and 0.42 million tons of maize. These findings can support sustainable farmland use planning and inform food security policy decisions in Myanmar. Full article