As the largest urban agglomeration and a critical grain production base in northern China, the Beijing–Tianjin–Hebei (BTH) region faces a sharp conflict between rapid urbanization and cropland conservation. Urban expansion inevitably leads to the loss of high-quality agricultural land, posing dual threats to food security and the terrestrial carbon cycle. To accurately assess the ecological costs of this process, this study integrates the CASA model with a time-weighted cumulative model to quantify the spatiotemporal impacts of urban expansion on cropland NPP in the BTH region from 2001 to 2020. Furthermore, a Geographically Weighted Regression (GWR) model was employed to examine the spatially varying effects of key driving factors on cropland NPP loss. The results indicate that urban land in the BTH region expanded by 45.2% over the past two decades, with 91.04% originating from cropland. Despite an overall upward trend in regional cropland NPP driven by climate change and agricultural intensification, the time-weighted cumulative cropland NPP loss attributable to urban encroachment over 2001–2020 reached 29.24 Tg C, which is equivalent to 0.751× the annual total cropland NPP in 2020 (used as a reference benchmark). Crucially, this expansion exhibits distinct ecological selectivity toward high-quality cropland, meaning that urban development has disproportionately encroached upon highly productive land with productivity levels exceeding the regional average. This selective occupation has led to a structural decline in the region’s potential agricultural production capacity. Additionally, GWR results reveal significant spatial non-stationarity in the relationships between cropland NPP loss and its drivers, revealing differentiated response patterns between plains and mountainous areas in terms of socio-economic drivers and physical constraints. These findings expose the hidden threats of urban expansion to food security, providing a crucial scientific basis for formulating differentiated land management policies and coordinating regional urbanization with cropland protection.

In response to negative impacts of human disturbances, governments and international organizations have launched large-scale environmental restoration programs to mitigate ecological degradation. However, few studies have assessed these projects from a cost-efficiency perspective. In the context of increasing global macroeconomic uncertainty, such projects face mounting challenges to their financial sustainability, underscoring the need for systematic evaluations of their cost efficiency. This study takes the forest-steppe ecotone project within China's Three-North Shelter Forest Program as a case study. Using 18 years of project implementation data, we establish an evaluation framework that integrates ecological returns and cost efficiency to assess project effectiveness. The results show that from 2002 to 2019, the forest area in the project region increased by 5.32 %, while sand-fixing capacity rose from 2.00 x 1011 tons in 2005 to 4.39 x 1011 tons in 2019. In terms of cost efficiency, the total investment in afforestation from 2002 to 2019 ranged from CNY 341.134-391.537 billion, with an average annual investment of CNY 18.952-21.752 billion. Sub-regional analyses revealed that the Liao River Basin and Taihang Mountains zones exhibited relatively lower ecological returns. Correlation analysis indicated strong relationships between afforestation cost and sand-fixing capacity in certain regions (|cor| = 0.89-0.91), moderate correlations in others (|cor| approximate to 0.59), and weak correlations in some areas (|cor| approximate to 0.47), accounting for 1.58 %, 6.19 %, and 7.62 % of the total area, respectively. These findings establish a preliminary cost-effectiveness evaluation framework, highlighting the linkages between financial investments and ecological benefits. Regions showing positive correlations demonstrate high ecological payoffs from investment, while negatively correlated areas suggest issues of diminishing returns and resource misallocation.

Tropical rainforests play a vital role in maintaining global ecological balance, carbon cycling, and biodiversity conservation, making research on their biomass dynamics scientifically significant. This study integrates multi-source remote sensing data, including canopy height derived from GEDI and ICESat-2 satellite-borne lidar, Landsat imagery, and environmental variables, to estimate forest biomass dynamics in Hainan's tropical rainforests at a 30 m spatial resolution, involving a correlation analysis of factors influencing spatiotemporal changes in Hainan Tropical Rainforest biomass. The research aims to investigate the spatiotemporal variations in forest biomass and identify key environmental drivers influencing biomass accumulation. Four machine learning algorithms-Backpropagation Neural Network (BP), Convolutional Neural Network (CNN), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)-were applied to estimate biomass across five forest types from 2003 to 2023. Results indicate the Random Forest model achieved the highest accuracy (R2 = 0.82). Forest biomass and carbon stocks in Hainan Tropical Rainforest National Park increased significantly, with total carbon stocks rising from 29.03 million tons of carbon to 42.47 million tons of carbon-a 46.36% increase over 20 years. These findings demonstrate that integrating multimodal remote sensing data with advanced machine learning provides an effective approach for accurately assessing biomass dynamics, supporting forest management and carbon sink evaluations in tropical rainforest ecosystems.

Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Global warming is driving large-scale shifts in the climatically suitable habitats of many species. However, climate-only species distribution assessments may overestimate the spatial availability of future suitable habitats when dynamic land-use change is not considered. To assess potential spatial overlaps between climate-driven habitat suitability shifts and human land-use patterns, this study focuses on Quercus L. as a widely distributed keystone forest taxon in China. The genus-level assessment was designed to identify broad-scale habitat-land-use conflict patterns under multiple climate pathways and territorial spatial planning scenarios, rather than to predict species-specific distribution responses. We developed a soft-coupled framework integrating the Maximum Entropy (MaxEnt) model and the Patch-generating Land-Use Simulation (PLUS) model, and applied the Habitat-Land-Use Conflict Index (HLCI) as a categorical spatial overlay framework to classify potential overlaps between projected suitable habitats and future land-use categories across 16 exploratory scenario combinations integrating Shared Socioeconomic Pathway (SSP)-based climate projections and land-use/land-cover (LULC) scenarios for the 2040s at the grid scale. The results indicate that: (1) climate warming may reshape Quercus habitat suitability, characterized by northward/westward expansion and southward contraction in some low-latitude regions; (2) future land-use patterns may reduce the spatial availability of projected suitable habitats by increasing their overlap with built-up land and cultivated land. Under high-emission scenarios, potential newly suitable habitats overlapped with built-up land by up to 5.90 & times; 104 km2 and with cultivated land by up to 36.42 & times; 104 km2; and (3) the Ecological Protection scenario showed lower overlap with non-ecological land-use categories and a larger area of potentially realizable habitat expansion. This study provides a scenario-based spatial assessment of where future Quercus habitat suitability may overlap with human land-use patterns, offering broad-scale support for adaptive forest conservation and territorial spatial planning.

Pine wilt disease (PWD) is one of Europe's most acute transboundary forest health threats, with Portugal and the Portugal-Spain buffer zone forming its primary defense line. Although mixed-forest conversion is widely viewed as a promising strategy for reducing host continuity and enhancing resistance, its real-world implementation has remained unclear due to the complexity of coordinating climate-driven habitat shifts, large-area planning, and long-term costs. Here we move from scientific conception to engineering practice by developing a spatial-temporal-economic assessment framework for nationwide PWD-resistant mixed-forest establishment in mainland Portugal. Using optimized MaxEnt models under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the 2030 s and 2050 s, we show that maritime pine (Pinus pinaster Ait.) suitability contracts sharply, with a northwestward centroid shift of up to similar to 101 km by 2050, while a four-species mixed forest retains markedly higher stability, maintaining similar to 43.3 % suitability even under SSP5-8.5. Integrating suitability with slope, accessibility, and afforestation mechanisms, we design a three-phase conversion blueprint (2025-2050) targeting 3.61-3.62 x 10(4) km(2). Implementation relies on mechanical afforestation and UAV seeding, with mechanical operations accounting for > 83 % of total area. Cost modeling indicates a required investment of 3.186-4.404 B (128-176 M/yr), with Phase II comprising similar to 74 % of the total effort. This framework demonstrates that large-scale mixed-forest establishment is not only ecologically advantageous but also operationally and economically feasible. It provides a transferable model for converting ecological concepts into engineered forest-resilience programs under accelerating climate and biosecurity pressures.

The Daxing’anling Mountains, as a climate-sensitive region, are experiencing forest fires that threaten the area’s ecological security. Nevertheless, most of the existing fire prediction models are stationary. They do not have an all-embracing scheme for simultaneously managing fire ignition causes, dynamic fire scenarios and spatial targeting. Hence, the development of an accurate and efficient forest fire forecasting system is vital. This study establishes a prediction framework that integrates long-term survey data with multi-source remote sensing, incorporating spatiotemporal clustering, spatial autocorrelation and an optimised ensemble of LR–RF–SVM–GBDT algorithms. Among the 3368 recorded fire incidents, lightning-ignited fires accounted for 51.19%, making lightning storms the predominant cause of ignition. While the frequency of lightning-induced fires increased significantly (1.24 per year, p < 0.05), the total burned area remained relatively stable. The proposed framework outperformed individual models by achieving higher predictive metrics (accuracy = 0.89, AUC = 0.94, F1 = 0.89) and providing robust support for operational early warning and real-time management. The projections for future climate, based on the SSP126 and SSP585 scenarios, depict a notable geographical shift in fire-prone areas. Besides the traditionally known eastern areas of Xiaogenhe and Chabanhe, which are expected to see an increase in fire occurrences, new high-fire-risk areas are expected to emerge in the central–western regions, such as Huzhong and Wuyuan. Quantitative findings reveal that the divergence in forest fire probabilities between the high-emission SSP585 and SSP126 scenarios will increase over time. The expected increase ranges from 0.29% in the 2030s to 0.92% in the 2050s, then rises to 4.48% in the 2070s and reaches 6.48% by the 2090s. These figures highlight the urgency of implementing fire management practices that are not only adaptive but also specific to particular areas. The scenario-based forecasts represent a proactive approach to assisting forest fire governance under climate change, providing a basis for future decisions as quantitative evidence.

As an essential blue carbon ecosystem, mangroves play a vital role in coastal protection, biodiversity conservation, and climate regulation. However, their complex and variable growth environments pose challenges for precise monitoring. Hainan Island represents a region within China where mangrove forests are the most concentrated and diverse in type. In recent years, ecological restoration efforts have led to the recovery of their coverage areas. This study analyzed the spatial distribution, canopy height, and aboveground carbon storage variations in Hainan mangrove forests. Deep-learning and multiple machine-learning algorithms were used to integrate multitemporal Sentinel-2 remote sensing imagery from 2019 to 2023 with unmanned aerial vehicle observations and field survey data. Multi-rule image fusion and deep-learning techniques effectively enhanced mangrove identification accuracy. The mangrove classification achieved an overall accuracy exceeding 90%. The mangrove area in Hainan increased from 3948.83 ha in 2019 to 4304.29 ha in 2023. Gradient-boosted decision tree (GBDT) models estimated average canopy height with a high coefficient of determination (R2 = 0.89), and Random Forest (RF) models yielded the best estimations of total above-ground carbon stock with strong agreement to field observations. Integrating multisource remote sensing data with artificial intelligence algorithms enabled high-precision dynamic monitoring of mangrove distribution, structure, and carbon storage to provide scientific support for the assessment, management, and carbon sink accounting of Hainan mangrove ecosystems.

Although LiDAR is widely used for tree measurement, its high cost and operational complexity remain significant barriers to widespread adoption. With advances in photogrammetry and deep learning, efficient and accurate alternatives have become increasingly important for forest resource surveys. Accordingly, we propose an automated trunk-parameter measurement framework that maps image pixels to physical units. The framework integrates the SegFormer deep-learning model, trunk-skeleton extraction, an adaptive curvature-segmentation algorithm, and segment-wise 3-D reconstruction, thereby enabling image segmentation, curvature analysis, three-dimensional reconstruction, and measurement. To validate its practical value, we collected images of 3013 trees across four species in the Beijing region. Additionally, we acquired point-cloud data and conducted destructive measurements on 141 trees of various species for comparative evaluation. Experimental results indicate that the stem segmentation algorithm effectively extracts trunk regions in images, and the adaptive segmentation method substantially improves trunk volume estimation accuracy. The approach achieves only 2.01 %-7.68 % error in single-tree volume and height measurements-primarily due to segment-height inaccuracies-and offers an approximately 6.9-fold improvement in efficiency compared with the existing HMLS method. In summary, this method provides an efficient, low-cost solution for forestry surveys and shows great potential for monitoring tasks that require high accuracy under resource constraints. This innovative method is expected to further advance forest resource assessment.

A reliable forest fire probability map is vital for disaster management and an essential resource in land use planning. This study evaluates the efficacy of the multi-layer stacking ensemble Machine Learning (ML) method for forest fire susceptibility mapping, presenting a comparative case study within the Malakand division of Pakistan. Our extensive literature review shows that the present ML model has never been used in Pakistan’s forest fire scenarios. We employed several benchmark models for comparative evaluation, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN). A comprehensive fire inventory database was constructed, including satellite and ground hotspot data and relevant influencing factors. The fire probability indices from the six models were analyzed and validated using accuracy, area under the curve (AUC), precision, recall, and F1 score evaluation metrics. According to the Performance Evaluation Outcomes, the multi-layer stacking ensemble model provides the best outcomes in terms of accuracy 96.24

Climate change may induce regional climate shifts, profoundly affecting plant growth, distribution, and ecosystems. This study collected 37 Sabina chinensis (Sabina chinensis (L.) Ant. cv. Kaizuca) tree cores (74 samples) from a site in the Yangtze River Delta (YRD) coastal region. Utilizing traditional dendrochronological principles and methods, a standardized tree-ring width chronology was developed to detect climate shift points and explore the differences in Sabina chinensis radial growth responses to climate factors, before and after these shifts. The findings are as follows: (1) Between 1967 and 2020, temperature emerged as the main climate factor influencing the radial growth of Sabina chinensis in the study area. (2) There are differences in the correlations between the tree radial growth of Sabina chinensis and climate factors in different months and seasons, before and after climate change. (3) Moving correlation analysis indicated that the relationships between radial growth and precipitation and temperature gradually altered. The study reveals the intricate influencing mechanisms of different climate factors on Sabina chinensis radial growth, before and after climate shifts, offering valuable references for other similar dendrochronological studies.

Understanding the driving mechanisms and spatial heterogeneity of regional-scale ecosystem carbon storage (CS) is crucial for regional carbon cycle research and formulating climate change mitigation strategies. This study develops an integrated framework utilizing Optimal Parameters-based Geographical Detector (OPGD) for key driver identification and interaction analysis, Multi-scale Geographically Weighted Regression (MGWR) for quantifying spatial heterogeneity of driver influences, and the patch-generating land use simulation (PLUS) model to simulate future land use and land cover (LULC) scenarios, enabling CS assessment via InVEST. Applying this framework, spatio-temporal CS change (2000-2020) in the Beijing-Tianjin-Hebei (BTH) region was analyzed, key drivers and their spatially differentiated mechanisms were explored, and CS was projected for 2030. The results show that the total carbon storage in the BTH urban agglomeration decreased by 4.700 × 107 t C from 2000 to 2020. Slope and population density were identified as the main driving factors affecting carbon storage, with Q-values of 0.303 and 0.187, respectively, and their interaction was the strongest, with a Q-value of 0.343. The influence scales of driving factors exhibited spatial heterogeneity, with DEM and slope having relatively smaller influence scales of 70 and 254, respectively, while distance to water bodies and distance to secondary roads had broader influence scales, both a 24,029. Under different future land use scenarios, the predicted carbon storage for 2030 was highest under the ecological protection scenario, at 2.023 × 109 t C. In summary, this integrated framework offers an effective and reusable methodological tool for elucidating driving mechanisms, conducting spatial heterogeneity analyses, and accurately simulating future scenarios in regional carbon cycle research. Crucially, it systematically integrates multi-scale driving mechanism analysis with carbon storage spatial heterogeneity quantification, thus advancing a more comprehensive and refined understanding of regional carbon dynamics.

While LiDAR is widely used in tree measurement, its high cost and complexity remain limiting factors. With advances in photogrammetry and deep learning, efficient and accurate alternatives are gaining importance in forest resource surveys. Therefore, this study proposes a fully automated framework for tree stem volume measurement based on virtual measurement from a single photograph. This framework integrates the Segformer architecture, generative adversarial network inverse mapping technology, and Sobel operator (SGS-3DRecon), enabling multilevel analysis from image segmentation to 3-D reconstruction and measurement. To validate its potential application, we collected images of 5192 individual trees across multiple scenarios (natural forests, afforested area, and urban parks) in Beijing. Additionally, point cloud data of 250 trees were acquired using handheld mobile laser scanning (HMLS) technology for comparative evaluation. Results indicate that SGS-3DRecon achieved intersection over union (IoU) and recall accuracies of 86.40% and 92.61%, respectively, in the combined scenarios. The average accuracy fluctuation between different scenarios was 3.41%, with the highest accuracy observed in the park scenario. Compared to the complex processing required by HMLS, the SGS-3DRecon method achieved an 85.5% improvement in efficiency for volume measurement. While ensuring high efficiency, the average relative root-mean-square error (rRMSE) was 17.91%, and the average rBias was -0.40%. In summary, this framework provides an efficient and low-cost solution for forest resource surveys. It shows significant potential, especially in monitoring tasks with limited resources and large-scale requirements. This innovative approach has the potential to further advance forest resource survey technologies.

Spaceborne LiDAR satellites, including GEDI and ICESat-2, have shown significant potential in estimating aboveground biomass (AGB) using machine learning (ML) methods. In contrast to advances focused on the refinement of ML algorithms, this study aims to enhance AGB estimation accuracy by integrating an additional Canopy Height (CH) information. To obtain CH data, this study utilized three spaceborne LiDAR datasets: ICESat-2 ATL08, ICESat-2 ATL03/ATL08 fusion data, and GEDI-L2A. Random Forest (RF) and Monte Carlo-based uncertainty analysis were employed to evaluate the most suitable spaceborne LiDAR dataset for CH estimation. The accuracy of CH features in AGB estimation was then compared using both Linear Regression (LR) and RF models. The spectral saturation point was computed using a semi-variance function, and the contribution of CH features to AGB estimates was quantified across different gradients, especially when AGB neared or surpassed the saturation point. The findings demonstrate that the ATL03/08 fusion dataset surpasses the other datasets in terms of CH estimation accuracy and uncertainty, delivering enhanced precision and stability. Incorporating CH features notably improved AGB model performance, as evidenced by R2 increases of 13.89 % and 10.34 % in the LR and RF models, respectively. The correction of AGB estimates across various gradients with CH features demonstrated a nonlinear pattern, initially increasing, then decreasing, and subsequently rebounding. Notable inflection points were identified at 26 Mg/ha and 123 Mg/ha, marking significant transitions in the correction trend. Both positive and negative bias corrections were observed during the correction process, with their proportions varying according to AGB values. When AGB approached or exceeded the spectral saturation point, the ability of CH features to improve positive bias correction was markedly enhanced, resulting in a greater proportion of positively corrected pixels and more significant correction values. The results of this study provide new insights into the role of CH features in AGB estimation, offering important implications for enhancing biomass mapping accuracy in forest ecosystems.

Forests play a crucial role in the global carbon cycle, climate regulation, and biodiversity conservation, making them essential for understanding ecosystem responses to environmental change. However, the spatiotemporal dynamics of forest vegetation and their responses to climate change have yet to be fully explored. This study assessed the spatiotemporal dynamics and adaptation of forest vegetation from Northern China by extracting changes in forest vegetation and phenological characteristics from 2001 to 2023 with the time-series MODIS Normalized Difference Vegetation Index (NDVI) data and analyzing the impact of climate variables on these changes. The linear regression analysis method and the four-parameter double logistic model were employed to assess forest vegetation changes and identify forest vegetation phenological phases, respectively. Partial correlation analysis was used to assess the relationship between forest vegetation and climate variables. The results of this study indicate that over the past two decades, the annual mean NDVI of forest vegetation has exhibited a slow increasing trend of approximately 0.002 yr−1, with a spatial distribution pattern that gradually decreases from south to north, showing a significant correlation with latitude. The magnitude of annual mean NDVI changes varies considerably among different forest vegetation types. However, except for evergreen broadleaf forests, the NDVI of all other forest types has shown a significant increasing trend. Additionally, central North China and southeastern Tibet exhibit higher NDVI values in both spring (>0.55) and autumn (>0.65) than other areas, while the NDVI values in Northeast China and North China are higher in summer (>0.8) compared to other areas. The study reveals substantial spatial heterogeneity in the average phenological phases and NDVI values of forest vegetation across different regions, influenced by latitude, altitude, and regional climatic conditions. The spatial distribution patterns of NDVI during the green-up and senescence phases remain relatively consistent, yet significant regional differences exist within the same phenological phase. Partial correlation analysis indicates that forest vegetation in different regions responds distinctly to meteorological factors. These findings contribute to a deeper understanding of the spatiotemporal dynamics of vegetation change and its complex interactions with climate change, offering valuable insights for forest ecosystem management and climate adaptation of forest vegetation.

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R2 = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations.

Biomass carbon sequestration and sink capacities of tropical rainforests are vital for addressing climate change. However, canopy height must be accurately estimated to determine carbon sink potential and implement effective forest management. Four advanced machine-learning algorithms—random forest (RF), gradient boosting decision tree, convolutional neural network, and backpropagation neural network—were compared in terms of forest canopy height in the Hainan Tropical Rainforest National Park. A total of 140 field survey plots and 315 unmanned aerial vehicle photogrammetry plots, along with multi-modal remote sensing datasets (including GEDI and ICESat-2 satellite-carried LiDAR data, Landsat images, and environmental information) were used to validate forest canopy height from 2003 to 2023. The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R2 values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. The RH80 percentile model using the RF algorithm was employed to estimate the forest canopy height distribution in the Hainan Tropical Rainforest National Park from 2003 to 2023, and the canopy heights of five forest types (tropical lowland rainforests, tropical montane cloud forests, tropical seasonal rainforests, tropical montane rainforests, and tropical coniferous forests) were calculated. The study found that from 2003 to 2023, the canopy height in the Hainan Tropical Rainforest National Park showed an overall increasing trend, ranging from 2.95 to 22.02 m. The tropical montane cloud forest had the highest average canopy height, while the tropical seasonal forest exhibited the fastest growth. The findings provide valuable insights for a deeper understanding of the growth dynamics of tropical rainforests.

To enhance the monitoring accuracy of agglomerate fog on expressways, this paper takes the frequently occurring agglomerate fog data on Shandong's expressways as an example. Based on the analysis of the spatiotemporal distribution characteristics of agglomerate fog, from the spatial perspective, it employs Geographic Weighted Regression (GWR) and Multi-scale Geographic Weighted Regression (MGWR) models to analyze the influence and scale of factors including Digital Elevation Model (DEM), DEM difference, water system density, Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) difference, and precipitation on agglomerate fog. The main research conclusions are as follows: agglomerate fog frequently occurred in the early morning during autumn and winter when the temperature difference is large. Three concentration centers of agglomerate fog-prone road segments were identified along Shandong's expressways, located near Jiaozhou Bay, within intermountain basins of the central region, and across the northern plain of Mount Tai (where the Yellow River traverses the concentration center). The impacts of various influencing factors on agglomerate fog are ranked as follows: DEM > DEM difference > LST difference > water system density > NDVI > precipitation, among which DEM difference and LST difference mainly promote fog formation, whereas other factors generally exhibit inhibitory effect. The influence range (adaptive scale) of precipitation is the largest, at 673 meters, followed by the water system with an influence range of 599 meters, and NDVI shows the smallest influence range at only 44 meters. It holds significant importance for reducing the accident rate on expressways.

Improving tree species classification accuracy often involves complex workflows, constrained by high computational costs, extensive data requirements, and sensitivity to spatiotemporal variations. This study introduces the Change Resistance Filter (CR-Filter), inspired by the stable growth patterns of the Climax Community. The CR-Filter, applied as a post-processing tool, integrates Change Resistance on Timelines and Change Resistance on Spatial Neighboring into a unified framework, enhancing classification precision by mitigating spatiotemporal fluctuations. Liupan Mountain Nature Reserve was selected as the study area for its ecological stability. Multi-temporal Sentinel-2 data spanning several years were used to extract and correct phenological indices, which were combined with Sentinel-1 and terrain data to generate interannual tree species classification maps. These maps were subsequently refined using the CR-Filter. Compared to traditional methods, robustness in highly heterogeneous regions was improved by leveraging interannual map integration, yielding species distribution maps with greater spatial consistency and temporal stability. Overall accuracy increased by 8.44%, from 85.85% to 93.10%, effectively reducing misclassification from noise or transient changes. This approach highlights the CR-Filter’s efficacy with limited samples and medium-to-low resolution, providing strong technical support for remote sensing-based species mapping and ecological research.

Understanding vegetation phenology responses to climate change is essential for predicting ecosystem dynamics, especially in mountainous transition zones, such as the Qinling Mountains, where climatic and ecological gradients are pronounced. To quantify these complex interactions, we combined high spatiotemporal resolution remote sensing data (30 m, 8-day) with CMFD climate datasets from 2010 to 2020. We leveraged a rigorous analysis of covariance (ANCOVA) framework to simultaneously test the spatial heterogeneity of phenological baselines and the temporal convergence of trends across vegetation types. Results revealed that the spatial pattern of the start of the growing season (SOS) exhibited highly significant heterogeneity (p < 0.001), primarily governed by vegetation composition and altitudinal gradients—a phenomenon we define as a spatial baseline constraint effect. In contrast, the interannual SOS trends (slopes) showed no significant differences among vegetation types (p = 0.685), indicating a temporal convergence effect. This regional synchrony, characterized by a consistent shift toward earlier SOS of approximately −0.8 to −0.9 days yr−1 at low and mid-elevations, was largely driven by rising spring temperatures (R2 ≈ 0.20). Crucially, the end of the growing season (EOS) displayed weak climatic sensitivity, revealing an asymmetric phenological response to temperature changes. Our findings demonstrate that vegetation phenology in the Qinling Mountains is jointly controlled by spatial baseline constraint and temporal trend convergence. This dual-mechanism framework provides new insights into the highly structured stability and resilience of mountainous ecosystems under regional warming.

In the context of climate change, understanding the carbon cycle of terrestrial ecosystem is important for projecting the future climate. Toward this end, it is essential to determine the roles of different terrestrial ecosystems as either carbon sources or sinks, as well as the associated influencing factors. This study employed the Carnegie-Ames-Stanford approach model to simulate the net primary productivity (NPP) and net ecosystem productivity (NEP) of the Amur River Basin, a high-latitude cold region. The simulated NPP values were validated against eddy tower data and then analyzed for different ecological zones. The results indicated a high accuracy of the simulated NPP and NEP, and both exhibited a spatial-temporal pattern. The carbon sequestration capacity was highest in the boreal coniferous forest zone, followed by the boreal mountain system zone, and temperate mountain system zone, with the lowest values present in temperate continental forest and temperate steppe zones. Changes in the vegetation productivity of temperate continental forest and temperate steppe zones occurred in 2001 and 2006, and the temperate continental forest zone has been presenting the highest annual NEP values since 2006. Our results suggest the ecological zone type should be considered when evaluating the carbon budget of a regional or even the global terrestrial ecosystem.