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Glacier retreat is creating extensive areas of newly exposed terrain where primary succession drives the transition from abiotic substrates to developing ecosystems. Monitoring these early successional stages remains challenging because pioneer communities, including biological soil crusts (BSCs), often exhibit weak and heterogeneous spectral signatures. This study investigates the potential of Sentinel-2 multispectral imagery for detecting and mapping primary succession within the Forni Glacier forefield (Italian Alps), using terrain exposed by glacier retreat between 1954 and 2015 as a natural chronosequence. A hierarchical classification framework integrating vegetation-sensitive (NDVI-Narrow), moisture-sensitive (NDMI-Narrow), and water-sensitive (NDWI) indices was developed to identify four major land-cover classes: Water/Snow/Ice, Intermediate Pioneer Stage, Initial Moisture-Sensitive Stage, and Barren Rock/Debris. The classification revealed that Barren Rock/Debris was the dominant class (73.4%), followed by Initial Moisture-Sensitive Stage (13.9%), Water/Snow/Ice (10.7%), and Intermediate Pioneer Stage (2.0%). Independent validation yielded Overall Accuracy values ranging from 72.5% to 80%, supporting the robustness of the proposed approach despite the intrinsic heterogeneity of proglacial environments. Analysis of seven exposure-age intervals revealed significant age-related changes in land-cover distribution. Spearman’s rank correlation showed a strong positive association between the Initial Moisture-Sensitive Stage and terrain exposure age (rs = 0.93), whereas Water/Snow/Ice exhibited a strong negative correlation (rs = −0.78), supporting the existence of a directional successional trajectory following glacier retreat. The results also suggest the presence of a possible ecological transition occurring approximately 18–25 years after deglaciation, potentially associated with increasing ecosystem stabilization. The proposed framework provides a scalable and transferable approach for monitoring ecosystem development in glacier forefields and demonstrates that moisture-sensitive spectral information can complement vegetation-based indicators by identifying distinct moisture-sensitive transitional surfaces within recently deglaciated terrain. Full article

This study investigated the long-term relationship between land-use change and land-cover-based ecological quality patterns in Ordos City, a typical coal resource-based city in northern China. To explicitly capture land-use transitions driven by coal exploitation, mining areas were classified as an independent land-cover type. An improved U-Net semantic segmentation model integrating multispectral information and land surface temperature was subsequently employed to generate multi-temporal land-cover maps. In the internal semantic segmentation validation, the proposed model achieved a mean Intersection over Union (mIoU) of 69.51%, a mean accuracy (mAcc) of 81.65%, and a pixel-level overall accuracy (aAcc) of 83.16%. An independent point-based accuracy assessment of the final land-cover map yielded an overall accuracy (OA) of 66.00% and a Kappa coefficient of 0.6033, indicating acceptable classification reliability in complex mining areas. Based on the classification results, three indicators, namely the land-use transition matrix, ecological environmental quality index (EQI), and ecological contribution index, were adopted to analyze spatiotemporal land-use dynamics and associated land-cover-based ecological quality patterns in Ordos City over the past 25 years. The results indicate that: (1) Marked land-use changes occurred in the land-use pattern of the study area during 2000–2025, with grassland and unused land consistently remaining the dominant land-use types. The area classified as Mining increased from 105.83 km² to 1184.17 km² in 2020, exhibiting distinct characteristics of phased expansion and subsequent adjustment. Built-up land continued to expand, whereas the water area decreased by 47.55%. (2) The land-cover-based EQI exhibited a pattern of decline, recovery, and stabilization, decreasing from 0.529 in 2000 to 0.489 in 2015 before recovering to 0.522 in 2025. This pattern was associated with changes in land-cover composition over the study period, including mining expansion and restoration-related transitions. The findings provide a scientific basis for ecological restoration planning and high-quality transformation in Ordos and other coal resource-based cities with similar arid and semi-arid environmental conditions. Full article

This study used 30 m Landsat imagery from 1995 to 2020 to investigate vegetation dynamics and their driving factors in Yengisar County, an arid oasis region in northwestern China. The Normalized Difference Vegetation Index (NDVI) was calculated, and the Dimidiate Pixel Model was used to estimate fractional vegetation cover (FVC). Trend analysis and a center-of-gravity migration model were employed to examine the spatiotemporal evolution of vegetation and its responses to climatic and human factors. The results showed that: (1) FVC exhibited clear spatial heterogeneity, with high vegetation coverage mainly distributed along river systems in the southwest and concentric expansion occurring around urban areas in the northeast. (2) From 1995 to 2020, the total vegetated area increased by 409.38 km², representing a 76.4% increase relative to 1995, while the average FVC increased by 15.4%. Except for the extremely low-coverage class, all vegetation classes expanded, with the medium-coverage class showing the largest increase (142.9%). (3) The vegetation center of gravity gradually shifted toward the southeast, and the period from 2015 to 2020 represented the most rapid stage of vegetation improvement. (4) Human activities, particularly agricultural reclamation and grassland expansion, exhibited a strong association with recent vegetation changes and played an important role in the expansion of the artificial oasis. The results indicate that recent oasis expansion in Yengisar County has been closely associated with land-use change and irrigation activities. These findings provide useful information for oasis management, land-use planning, and water resource allocation in arid regions. Full article

Differences in land use and land management significantly alter the physicochemical and microbial properties of soil, especially in the long term. These properties in turn determine the land’s ability to sustain plant growth and support agricultural productivity. Studies of soil properties, mainly microbial and enzymatic ones, have mainly focused on surface horizons, although substantial biological activity and transformation of soil organic matter are also known to occur in deeper horizons. An important role in shaping the biological activity of soil, including in its deeper horizons, is attributed to plant cover, and more precisely to variations in root mass and structure. That is why it is essential to assess the influence of differences in land use and plant species diversity by determining how plants with contrasting root system morphologies affect soil enzymes and other properties at different depths of the soil profile. We therefore aimed to study the differences in a set of soil properties among various soil depths (horizons) in eight soil profiles sampled from land under four different soil uses and management practices. The four land use systems represented were: arable lands (A), orchard (O), hop plantations (H), and grasslands (G). The potential hydrolase activities involved in the cycling of C, N and P were determined, as well as fluorescein diacetate hydrolysis (FDAH). In addition, the activity of selected oxidoreductases and the content of microbial biomass C and N were determined. We also evaluated the content of total and dissolved forms of C and N, pH in CaCl₂, CEC, available K and P, and clay content. The agricultural land uses differed in terms of their influence on both the microbial biomass content and enzyme activities. Considering the mean values for all five depths, six of the studied enzymes exhibited the highest activity in the G profiles, while five other enzymes were most active in the A profiles. A similar pattern of changes in the potential enzymatic activity in relation to the land use system was also noted for the top horizons of the studied profiles. Regardless of the cultivated plant species, soil variables exhibited a significant decline with increasing depth, and the most pronounced changes occurred between the surface and the second soil horizon (I–II) in the A and G profiles. The enzymatic activity throughout the O and H profiles was less variable, with relatively high activity in deep soil compared to the topsoils. The activity of two extracellular oxidases (phenol oxidase and peroxidase) differed in the patterns in their activity throughout the studied profiles as compared to the other enzymes and did not decrease progressively with depth. Sometimes, their activity was higher in deep horizons than in the surface ones. The soil C and N content had a stronger influence on the studied enzyme activities than other soil properties. This was confirmed by the significant and positive correlations calculated between various forms of C and N and enzyme activity. The highest correlation coefficients were noted between TOC and the FDAH rate and between TN and the FDAH rate (r = 0.818 and 0.888). No significant correlation coefficients were found between enzymatic activity and either pH in CaCl₂ or clay content. Because enzyme activities respond variously to different land use systems, they serve as suitable indicators for soil health assessment. Analysis of the variation in microbial and enzymatic properties throughout the soil profiles fills gaps in the existing knowledge about the biogeochemical processes and nutrient cycling that affect soil fertility and the health of agroecosystems. Full article

Vegetation change is regarded as a key indicator of environmental change and ecosystem functional evolution. In mountainous regions, vegetation dynamics arise from complex and non-linear interactions among climate, topography, land use, and human activities, yet the mechanisms governing these interactions remain poorly understood. Clarifying long-term vegetation trajectories and their interacting controls is essential for understanding ecosystem structure and function. In this study, we integrated machine learning and causal modeling by combining random forest (RF) and structural equation modeling (SEM) to quantify both the relative importance and the direct and indirect pathways of natural and anthropogenic drivers of vegetation change in the Longnan region from 2000 to 2020. The RF results showed that the selected driving factors explained 82.62% and 72.39% of the spatial variation in vegetation cover in 2000 and 2020, respectively, with climatic and anthropogenic factors ranking as the most important drivers. Although ecological restoration activities contributed to an overall improvement in vegetation conditions, land-use heterogeneity and ecological constraints imposed by high elevation jointly produced contrasting local responses, resulting in vegetation degradation in surrounding areas. SEM revealed that the net influence of anthropogenic activities shifted from positive to negative over time, mainly due to land-use change, indicating a reorganization of human–vegetation interactions. Climate effects remained positive, with precipitation having a stronger influence than temperature. Topography moderated vegetation responses, as slopes below 40° favored vegetation growth. Soil effects shifted from positive to negative, likely associated with changes in soil organic matter. By jointly applying RF and SEM, this study captures both non-linear responses and causal pathways, providing a system-level perspective on the complex mechanisms underlying vegetation change. Full article

Climate-induced floods increasingly disrupt agricultural production systems, posing escalating threats to food security and economic stability in climate-exposed and trade-integrated regions. This study examines the relationship between flood shocks and agricultural total factor productivity (TFP) change across China and ten ASEAN economies and investigates whether public service capacity and structural conditions attenuate this negative association. A country–year panel dataset covering 1993–2024 is constructed by aggregating event-level flood records from EM-DAT and matching them with World Bank indicators of agricultural production, population, public services, and industrial structure. Agricultural TFP change is measured using the DEA-Malmquist index, with arable land, agricultural land, agricultural employment, fertilizer use, agricultural freshwater use, and rural electricity access as inputs and crop production and agricultural value added as outputs. A two-way fixed-effects model is then applied to examine the conditional association between flood mortality severity and agricultural TFP change. The empirical results show that greater flood mortality severity is significantly associated with lower agricultural TFP change. This negative contemporaneous association remains evident across the reported alternative specifications and sensitivity analyses. Current health expenditure per capita, energy use per capita, access to basic drinking-water services, and medium- and high-tech manufacturing value added are positively associated with an attenuation of the negative association between flood mortality severity and agricultural TFP change. These findings suggest that agricultural productivity performance under climate-related disturbances is shaped not only by exposure to flood shocks but also by policy-relevant capacities embedded in public service provision and structural conditions. A notable finding is that the marginal association becomes positive and statistically significant at high observed levels of health expenditure per capita and energy use per capita, whereas the corresponding estimates for drinking-water services and medium- and high-technology manufacturing remain statistically indistinguishable from zero. Full article

Rapid urbanization in the Huaihe River Basin has reshaped land use and intensified the tension between ecological protection and food security. A key gap is that most basin-scale ecosystem service value (ESV) assessments describe changes in land-cover composition but do not identify how landscape configuration–ESV associations vary across space. Using land use remote sensing images from 2000 to 2020, we quantitatively characterized the spatiotemporal evolution of land use via transfer matrices and landscape pattern indices. By integrating the equivalent value factor method, contribution analysis, spatial correlation analysis, and the geographically weighted regression (GWR) model, we examined the spatiotemporal dynamics and driving mechanisms of ecosystem service value (ESV). Key findings are as follows: (1) From 2000 to 2020, cropland area steadily decreased, while construction and forest land expanded markedly, with a synthetic land-use dynamic degree reaching 9.66%. (2) ESV first rose then fell, showing an overall decline. Forest land (18.882 billion CNY) and water areas (20.331 billion CNY) were the primary contributors. The ESV response to land use change manifested as a trade-off between short-term gains and long-term degradation. (3) Landscape patterns exhibited significant spatial heterogeneity, and ESV correlated strongly with patch number (NP) and landscape shape index (LSI). Class area (CA) emerged as the dominant factor, with CA of forest land, cropland, and water areas exerting the most pronounced effects. Based on the differential driving effects of landscape patterns and identified land use change hotspots, we propose enhancing ecosystem service values and mitigating conflicts between ecological conservation and food security through differentiated ecological engineering and ecological compensation strategies. Full article

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