Search Results (2,491)

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions. Full article

The Mayanghe National Nature Reserve, a key habitat for the endangered François’ langur (Trachypithecus francoisi), faces significant anthropogenic disturbances, including extensive distribution of croplands, roads, and settlements. These human-modified features are predominantly concentrated at elevations between 500 and 800 m and on slopes of 10–20°, which notably overlap with the core elevation range utilized by François’ langur. Spatial analysis revealed that langurs primarily occupy areas within the 500–800 m elevation band, which comprises only 33% of the reserve but hosts a high density of human infrastructure—including approximately 4468 residential buildings and the majority of cropland and road networks. Despite slopes >60° representing just 18.52% of the area, langur habitat utilization peaked in these steep regions (exceeding 85.71%), indicating a strong preference for rugged karst terrain, likely due to reduced human interference. Habitat type analysis showed a clear preference for evergreen broadleaf forests (covering 37.19% of utilized areas), followed by shrublands. Landscape pattern metrics revealed high habitat fragmentation, with 457 discrete habitat patches and broadleaf forests displaying the highest edge density and total edge length. Connectivity analyses indicated that distribution areas exhibit a more continuous and aggregated habitat configuration than control areas. These results underscore François’ langur’s reliance on steep, forested karst habitats and highlight the urgent need to mitigate human-induced fragmentation in key elevation and slope zones to ensure the species’ long-term survival. Full article

Against the backdrop of rapid urbanization in western China, which has triggered remarkable land-use changes and habitat degradation, Chengdu, as a developed city in China, plays a demonstrative and leading role in the economic and social development of China during the transition period. Therefore, integrated modeling approaches are required to balance development and conservation. This study responds to this need by conducting a scenario-based assessment of urbanization-induced land-use changes and regional habitat quality dynamics in Chengdu (1990–2030), using the FLUS-InVEST model. By integrating remote sensing-derived land-use data from 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we simulate future regional habitat quality under three policy scenarios: natural development, ecological priority, and cropland protection. Key findings include the following: (1) From 1990 to 2020, cropland decreased by 1917.78 km², while forestland and built-up areas increased by 509.91 km² and 1436.52 km², respectively. Under the 2030 natural development scenario, built-up expansion and cropland reduction are projected. Ecological priority policies would enhance forestland (+4.2%) but slightly reduce cropland. (2) Regional habitat quality declined overall (1990–2020), with the sharpest drop (ΔHQ = −0.063) occurring between 2000 and 2010 due to accelerated urbanization. (3) Scenario analysis reveals that the ecological priority strategy yields the highest regional habitat quality (HQ_mean = 0.499), while natural development results in the lowest (HQ_mean = 0.444). This study demonstrates how the FLUS-InVEST model can quantify the trade-offs between urbanization and regional habitat quality, offering a scientific framework for balancing development and ecological conservation in rapidly urbanizing regions. The findings highlight the effectiveness of ecological priority policies in mitigating habitat degradation, with implications for similar cities seeking sustainable land-use strategies that integrate farmland protection and forest restoration. Full article

Eco-enzymatic stoichiometry provides a basis for understanding soil ecosystem functions, with implications for land management and ecological protection. Long-term climatic factors and human interferences have caused significant land-use transformations in the Qinghai–Tibet Plateau region, affecting various ecological functions, such as soil nutrient cycling and chemical element balance. It is currently unclear how large-scale land-use conversion affects soil ecological stoichiometry. In this study, 763 soil samples were collected across three land-use types: farmland, grassland, and forest land. In addition, changes in soil physicochemical properties and enzyme activity and stoichiometry were determined. The soil available phosphorus (SAP) and total phosphorus (TP) concentrations were the highest in farmland soil. Bulk density, pH, SAP, TP, and NO₃⁻-N were lower in forest soil, whereas NH₄⁺-N, available nitrogen, soil organic carbon (SOC), available potassium, and the soil nutrient ratio increased. Land-use conversion promoted soil β-1,4-glucosidase, N-acetyl-β-glucosaminidase, and alkaline phosphatase activities, mostly in forest soil. The eco-enzymatic C:N ratio was higher in farmland soils but grassland soils had a higher enzymatic C:P and N:P. Soil microorganisms were limited by P nutrients in all land-use patterns. C limitation was the highest in farmland soil. The redundancy analysis indicated that the ecological stoichiometry in farmland was influenced by TN, whereas grass and forest soils were influenced by SOC. Overall, the conversion of cropland or grassland to complex land-use types can effectively enhance soil nutrients, enzyme activities, and ecosystem functions, providing valuable insights for ecological restoration and sustainable land management in alpine regions. Full article

Since the outbreak of the Russia–Ukraine conflict in 2022, Ukraine’s agricultural production has faced significant disruption, leading to widespread cropland abandonment. These croplands were abandoned at different stages, primarily due to war-related destruction and displacement of people. Existing methods for detecting abandoned cropland fail to account for crop type differences and distinguish abandonment stages, leading to inaccuracies. Therefore, this study proposes a novel framework combining crop-type classification with the Bias-weighted Time-Weighted Dynamic Time Warping (BTWDTW) method, distinguishing between sowing and harvest abandonment. Additionally, the proposed framework improves accuracy by integrating a more nuanced analysis of crop-specific patterns, thus offering more precise insights into abandonment dynamics. The overall accuracy of the proposed method reached 88.9%. The results reveal a V-shaped trajectory of cropland abandonment, with abandoned areas increasing from 28,184 km² in 2022 to 33,278 km² in 2024, with 2023 showing an abandoned area of 24,007.65 km². Spatially, about 70% of sowing abandonment occurred in high-conflict areas, with hotspots of unplanted abandonment shifting from southern Ukraine to the northeast, while unharvested abandonment was observed across the entire country. Significant variations were found across crop types, with maize experiencing the highest rate of unharvested abandonment, while wheat exhibited a more balanced pattern of sowing and harvest losses. The proposed method and results provide valuable insights for post-conflict agricultural recovery and decision-making in recovery planning. Full article

Odonata constitute an important invertebrate group that is strongly dependent on water conditions and sensitive to habitat disturbances, rendering them reliable indicators of habitat quality of both aquatic and terrestrial habitats. We studied the compositional and diversity patterns of Odonates in total, and separately for the two suborders (Zygoptera, Anisoptera) in relation to geographic and ecological parameters at the riparian zone of four rivers and one canal within the Axios Delta National Park and the Natura 2000 SAC GR1220002 in northern Greece, using the line transect technique. In total, 6252 individuals belonging to 28 species were identified. The compositional and diversity patterns were significantly different between agricultural and natural sites. Odonata assemblages at croplands were comparatively poorer, dominated by a few, widely distributed, taxonomically proximal species, tolerant to environmental changes, as a result of modifications and consequent alterations of abiotic conditions at croplands, which also led to higher local contribution to β-diversity and species turnover. The absence of several percher, endophytic, and threatened species from agricultural sites led to significantly lower diversity, as a result of environmental filtering due to ecophysiological restrictions. Taxonomic and functional diversity, uniqueness, and Dragonfly Biotic Index (DBI) were significantly higher in riparian forests, due to the sensitivity of damselflies to dehydration, and the avoidance of habitat loss and extreme temperatures by dragonflies, which prefer natural shelters near the ecotone. The newly introduced Conservation Value Index (CVI) revealed 21 conservation hotspots of Odonata (14 at canopy cover sites), widely distributed within the borders of NATURA 2000 SAC GR1220002. Full article

Recent progress in geospatial foundation models (GFMs) has demonstrated strong generalization capabilities for remote sensing downstream tasks. However, existing GFMs still struggle with fine-grained cropland classification due to ambiguous field boundaries, insufficient and low-efficient temporal modeling, and limited cross-regional adaptability. In this paper, we propose CropSTS, a remote sensing foundation model designed with a decoupled temporal–spatial attention architecture, specifically tailored for the temporal dynamics of cropland remote sensing data. To efficiently pre-train the model under limited labeled data, we employ a hybrid framework combining joint-embedding predictive architecture with knowledge distillation from web-scale foundation models. Despite being trained on a small dataset and using a compact model, CropSTS achieves state-of-the-art performance on the PASTIS-R benchmark in terms of mIoU and F1-score. Our results validate that structural optimization for temporal encoding and cross-modal knowledge transfer constitute effective strategies for advancing GFM design in agricultural remote sensing. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

Rapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land cover (LULC) changes and temperature increases; however, the directional and distance-based patterns of these changes remain unquantified. Therefore, this study is conducted to examine spatiotemporal changes in LULC and variations in the Urban Thermal Field Variation Index (UTFVI) between 2001 and 2021 and to project future scenarios for 2031 and 2041 using (1) Earth Observation Indices (EOIs) with machine learning (ML) classifiers (Random Forest) for precise LULC mapping through the Google Earth Engine (GEE) platform, (2) Cellular Automata–Artificial Neural Networks (CA-ANNs) for future scenario projection, and (3) Gradient Directional Analysis (GDA) to quantify directional (16-axis) and distance-based (concentric zones) patterns of urban expansion and thermal variation from 2001–2021. The study revealed significant LULC changes, with built-up areas expanding by 7.5% from 2001 to 2021, especially in the east, northeast, and southeast directions within a 20 km radius. Due to urban encroachment, vegetation and cropland decreased by 1.47% and 1.83%, respectively. The urban thermal environment worsened, with the highest land surface temperature (LST) rising from 41 °C in 2001 to 55 °C in 2021. Additionally, the UTFVI showed expanding areas under the ‘strong’ and ‘strongest’ categories, increasing from 30.58% in 2001 to 33.42% in 2041. Directional analysis highlighted severe thermal stress in the southern and southwestern areas linked to industrial activities and urban sprawl. This integrated approach provides a template for analyzing urban thermal environments in developing cities, supporting targeted mitigation strategies through direction- and distance-specific planning interventions to mitigate UHI impacts. Full article

Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10⁸ years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments. Full article

With the acceleration of global urbanization, cropland loss and fragmentation due to construction land expansion have become critical threats to food security and ecological sustainability, particularly in rapidly developing coastal regions. Understanding urban–rural differences in these processes is essential as divergent governance policies, socioeconomic pressures, and land use transition pathways may lead to uneven impacts on agricultural systems. However, past comparisons of urban–rural differences regarding this issue have been insufficient. Therefore, this study takes Jiangsu Province, China, as an example. Based on 30 m-resolution land use data, Geographic Information System (GIS) spatial analysis, and landscape pattern indices, it delves into the urban–rural differences in cropland loss and fragmentation caused by construction land expansion from 1990 to 2020. The results show that cropland in urban and rural areas decreased by 44.14% and 5.97%, respectively, while the area of construction land increased by 2.61 times and 90.14%, respectively. 94.36% of the newly added construction land originated from cropland, with the conversion of rural cropland to construction land being particularly prominent in northern Jiangsu, while the conversion of urban cropland to construction land is more pronounced in southern Jiangsu. The expansion of construction land has led to the continuous fragmentation of cropland, which is more severe in urban areas than in rural areas, while construction land is becoming increasingly agglomerated. There are significant differences in the degree of land use change between urban and rural areas, necessitating the formulation of differentiated land management policies to balance economic development with agricultural sustainability. Full article

Cropland is a critical resource for safeguarding food security. Ensuring both the quantity and quality of cropland is essential for achieving zero hunger and promoting sustainable agriculture. However, whether urbanization-induced cropland loss poses a substantial threat to regional food security remains a key concern. This study examines the central region of the Yangtze River Delta (YRD) in China, integrating CLCD (China Land Cover Dataset) land use/cover data (2001–2023), MOD17A2H net primary productivity (NPP) data, and statistical records to evaluate the impacts of urban expansion on grain yield. The analysis focuses on three components: (1) grain yield loss due to cropland conversion, (2) compensatory yield from newly added cropland under the requisition–compensation policy, (3) yield increases from stable cropland driven by agricultural enhancement strategies. Using Sen’s slope analysis, the Mann–Kendall trend test, and hot/coldspot analysis, we revealed that urban expansion converted approximately 14,598 km² of cropland, leading to a grain production loss of around 3.49 million tons, primarily in the economically developed cities of Yancheng, Nantong, Suzhou, and Shanghai. Meanwhile, 8278 km² of new cropland was added through land reclamation, contributing only 1.43 million tons of grain—offsetting just 41% of the loss. In contrast, stable cropland (102,188 km²) contributed an increase of approximately 9.84 million tons, largely attributed to policy-driven productivity gains in areas such as Chuzhou, Hefei, and Ma’anshan. These findings suggest that while compensatory cropland alone is insufficient to mitigate the food security risks from urbanization, the combined strategy of “Safeguarding Grain in the Land and in Technology” can more than compensate for production losses. This study underscores the importance of optimizing land use policy, strengthening technological interventions, and promoting high-efficiency land management. It provides both theoretical insight and policy guidance for balancing urban development with regional food security and sustainable land use governance. Full article

Agriculture accounts for 29% of the Gross Domestic Product of the Nusa Tenggara Islands (NTIs). However, recurring agricultural droughts pose a major threat to the sustainability of agriculture in this region. The interplay between precipitation, solar radiation, and surface temperature as meteorological factors plays a key role in affecting vegetation (Soil-Adjusted Vegetation Index) and agricultural drought (Temperature Vegetation Dryness Index) in the NTIs. Based on the analyses of interplay with temporal lag, this study investigates the effect of each factor on agricultural drought and attempts to provide early warnings regarding drought in the NTIs. We collected surface information data from Moderate-Resolution Imaging Spectroradiometer (MODIS). Meanwhile, rainfall was estimated from Himawari-8 based on the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The results showed reliable performance for 8-day and monthly scales against gauges. The drought analysis results reveal that the NTIs suffer from mild-to-moderate droughts, where cropland is the most vulnerable, causing shifts in the rice cropping season. The driving factors could also explain >60% of the vegetation and surface-dryness conditions. Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. The low error rates and the ability to capture the spatial distribution of droughts, especially in open-land vegetation, highlight the potential of these models to provide an estimation of agricultural drought. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article