Search Results (392)

Accurate long-term and high-resolution forest disturbance monitoring are pivotal for forest carbon modeling and forest management. Many algorithms have been developed for this purpose based on the Landsat time series, but their nationwide performance across different regions and disturbance types remains unexplored. Here, we conducted a comprehensive comparison and validation of six widely used forest disturbance- monitoring algorithms using 12,328 reference samples in China. The algorithms included three annual-scale (VCT, LandTrendr, mLandTrendr) and three daily-scale (BFAST, CCDC, COLD) algorithms. Results indicated that COLD achieved the highest accuracy, with F1 and F2 scores of 81.81% and 81.25%, respectively. Among annual-scale algorithms, mLandTrendr exhibited the best performance, with F1 and F2 scores of 73.04% and 72.71%, and even outperformed the daily-scale BFAST algorithm. Across China’s six regions, COLD consistently achieved the highest F1 and F2 scores, showcasing its robustness and adaptability. However, regional variations in accuracy were observed, with the northern region exhibiting the highest accuracy and the southwestern region the lowest. When considering different forest disturbance types, COLD achieved the highest accuracies for Fire, Harvest, and Other disturbances, while CCDC was most accurate for Forestation. These findings highlight the necessity of region-specific calibration and parameter optimization tailored to specific disturbance types to improve forest disturbance monitoring accuracy, and also provide a solid foundation for future studies on algorithm modifications and ensembles. Full article

Weather conditions significantly influence urban cycling, shaping both its frequency and intensity. This study develops a predictive model to evaluate the impact of five key meteorological factors, namely temperature, humidity, precipitation, wind speed, and daylight duration, on urban cycling trends. Using non-linear regression analysis, the research examines cycling data from 2017 to 2019 in Hamburg, Germany, comparing predicted values for 2019 with actual data to assess model accuracy. The statistical analyses reveal strong correlations between weather parameters and cycling activity, highlighting each factor’s unique influence. The model achieved high accuracy, with R² values of 0.942 and 0.924 for 2017 and 2019, respectively. To further validate its robustness, the model is applied to data from 2021 and 2023—years not included in its initial development—yielding R² values of 0.893 and 0.919. These results underscore the model’s reliability and adaptability across different timeframes. This study not only confirms the critical influence of weather on urban cycling patterns, but also provides a scalable framework for broader urban planning applications. Beyond the immediate findings, this research proposes expanding the model to incorporate urban factors, such as land use, population density, and socioeconomic conditions, offering a comprehensive tool for urban planners and policymakers to enhance sustainable transportation systems. Full article

Coastal zones, as transition areas for sea/land interaction, have substantial carbon sequestration potential while also being particularly vulnerable to extreme climate. Consequently, it has become essential to evaluate the vegetation carbon sinks in coastal zone areas under extreme climate conditions. In this study, we evaluated the vegetation net ecosystem productivity (NEP) in typical regions within the Yangtze River Delta coastal zone from 2000 to 2020. We studied the regional and chronological properties of NEP and its response to extreme climate. The results revealed the following: (1) Vegetation NEP demonstrated a fluctuating rising trend over the past 21 years, with an interannual change rate of 1.96 gC·m⁻²·a⁻¹, and the 21-year average was 249.22 gC·m⁻²·a⁻¹. Spatially, the southern part of the region had a higher NEP than the northern part, and the northern part had a higher NEP than the central part. (2) The overall area showed characteristics of a vegetation carbon sink, with carbon sink areas accounting for 82.41%. Among the ecosystems, forest ecosystems exhibited the strongest carbon sink capacity, followed by cropland ecosystems, while wetland ecosystems, urban ecosystems, and grassland ecosystems had relatively weaker carbon sink capacities. (3) The overall spatial change trend showed an upward trend, consistent with the temporal trend. There is also a high risk of vegetation NEP degradation in the future. (4) The NEP’s response to extreme temperature was more pronounced. The largest explanatory power was observed with SU25 and TMAX during single-factor analysis. The strongest explanatory power in the interaction analysis was found in the following three factor groups: R99p∩TMAX, SU25∩TNx, and TXx∩LST. The results highlight a complex synergistic interplay among these influences on NEP. The findings offer a scientific basis for ecological protection and the attainment of dual-carbon goals in the coastal zone of the Yangtze River Delta. Full article

Vegetation indices are important representatives of plant growth. Climate change and human activities seriously affect vegetation. This study focuses on the Huojitu mining area in the Shendong region, utilizing the kNDVI index calculated via the Google Earth Engine (GEE) cloud platform. The Mann–Kendall mutation test and linear regression analysis were employed to examine the spatiotemporal changes in vegetation growth over a 25-year period from 1999 to 2023. Through correlation analysis, geographic detector models, and land use map fusion, combined with climate, topography, soil, mining, and land use data, this study investigates the influencing factors of vegetation growth evolution. The key findings are as follows: (1) kNDVI is more suitable for analyzing vegetation growth in this study compared to NDVI. (2) Over the past 25 years, vegetation growth has exhibited an overall fluctuating upward trend, with an annual growth rate of 0.0041/a. The annual average kNDVI value in the mining area is 0.121. Specifically, kNDVI initially increased gradually, then rapidly increased, and subsequently declined rapidly. (3) Vegetation growth in the study area has significantly improved, with areas of improved vegetation accounting for 89.08% of the total mining area, while degraded areas account for 11.02%. (4) Precipitation and air temperature are the primary natural factors influencing vegetation growth fluctuations in the mining area, with precipitation being the dominant factor (r = 0.81, p < 0.01). The spatial heterogeneity of vegetation growth is influenced by land use, topography, soil nutrients, and mining activities, with land use having the greatest impact (q = 0.43). Major land use changes contribute 46.45% to vegetation improvement and 13.43% to vegetation degradation. The findings of this study provide a scientific basis for ecological planning and the development of the Huojitu mining area. Full article

Accurate monitoring and assessment of forest disturbance and recovery dynamics are essential for sustainable forest management, particularly in ecological transition zones. This study analyzed forest disturbance and recovery patterns in China’s Funiu Mountains from 1991 to 2020 by integrating the LandTrendr algorithm with space-time cube analysis. Using Landsat time series data and the Geodetector method, we examined both the spatiotemporal characteristics and driving factors of forest change across three periods. The results showed that (1) between 1991 and 2020, the study area experienced 131.19 km² of forest disturbance and 495.88 km² of recovery, with both processes most active during the 1990s; (2) spatiotemporal analysis revealed that both disturbance and recovery patterns were predominantly characterized by cold spots, suggesting relatively stable forest conditions despite localized changes; (3) human activities were the primary drivers of forest disturbance in the early period, while forest recovery was consistently influenced by the combined effects of topographic conditions and precipitation. Additionally, forest fires emerged as an important factor affecting both disturbance and recovery patterns after 2010. These findings enhance our understanding of forest dynamics in transition zones and provide empirical support for regional forest management strategies. The results also highlight the importance of considering both spatial and temporal dimensions when monitoring long-term forest changes. Full article

The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region’s ecological restoration and protection. This study employs the Geographical Detector (Geodetector) model to quantitatively assess the impact of natural and human factors, such as temperature, precipitation, soil type, and land use, on vegetation growth. It aims to reveal the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau over the past 26 years. The results indicate that from 1995 to 2020, the vegetation coverage on the Loess Plateau shows an increasing trend, with a fitted slope of 0.01021 and an R² of 0.96466. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use. The highest average vegetation coverage is achieved when the temperature is between −4.8 and 2 °C or 12 and 16 °C, precipitation is between 630.64 and 935.51 mm, the soil type is leaching soil, and the land use type is forest. And the interaction between all factors has a greater effect on the vegetation cover than any single factor alone. This study reveals the factors influencing vegetation growth on the Loess Plateau, as well as their types and ranges, providing a scientific basis and guidance for improving vegetation coverage in this region. Full article

As the largest mountain range in Southern China, the natural vegetation of Nanling plays an irreplaceable role in maintaining the stability of the ecosystem and exerting its functions. The forested area of the Nanling Corridor encompasses 168,633 km², with a forest coverage rate exceeding 60% of all cities together. Long-term analysis of the temporal and spatial evolution of this forest and the disturbance factors in this region is of great importance for realizing the “dual carbon” goals, sustainable forest management, and protecting biodiversity. In this study, remote sensing images from a Landsat time series with a resolution of 30 m were obtained from the GEE (Google Earth Engine) cloud processing platform, and forest disturbance data were obtained using the LandTrendr algorithm. Using a machine learning random forest algorithm, the forest disturbance status and disturbance factors were explored from 2001 to 2020. The results show that the estimated disturbed forest area from 2001 to 2020 was 11,904.3 km², accounting for 7.06% of the total area of the 11 cities in the Nanling Corridor, and the average annual disturbed area was 595.22 km². From 2001 to 2016, the overall disturbed area increased, reaching a peak value of 1553.36 km² in 2008, with a low value of 37.71 km² in 2002. After 2016, the disturbed area showed a downward trend. In this study, an attribution analysis of forest disturbance factors was carried out. The results showed that the overall accuracy of forest disturbance factor attribution was as high as 82.48%, and the Kappa coefficient was 0.70. Among the disturbance factors, deforestation factors accounted for 58.45% of the total area of forest disturbance, followed by fire factors (28.69%) and building or road factors (12.85%). The regional distribution of each factor also had significant characteristics, and the Cutdown factors were mostly distributed in the lower elevations of the mountain margin, with most of them distributed in sheets. The fire factors were spatially distributed in the center of the mountains, and their distribution was loose. Building or road factors were mostly distributed in clusters or lines. These research results are expected to provide technical and data support for the study of the large-scale spatiotemporal evolution of forests and its driving mechanisms. Full article

Research on monitoring forest disturbances and analyzing its driving factors is crucial for the sustainable management of forest ecosystems. To quantitatively identify the spatial distribution and dynamic changes of forest disturbance and its driving factors in Guangdong Province from 1990 to 2019, the long-term Landsat time series imagery and the LandTrendr change detection algorithm were utilized. The impact of forest disturbances on four types of landscape fragmentation (attrition, perforation, shrinkage, and subdivision) was analyzed using the Forman index. The Geodetector model was used to analyze the driving factors of forest disturbance from human activity and the natural environment. The results showed that the LandTrendr algorithm achieved a Kappa coefficient of 0.79, with an overall accuracy of approximately 82.59%. The findings indicate a consistent increase in shrinkage patches, both in quantity and area. Spatially, the centroids of forest fragmentation processes exhibited a clear inland migration trend, reflecting the growing ecological pressures faced by inland forest ecosystems. Furthermore, interactions among driving factors, particularly between population density and economic factors, significantly amplified their combined impacts. The correlation between forest disturbances and socio-economic factors revealed distinct regional variations, highlighting significant differences in forest disturbance dynamics across cities with varying levels of economic development. This study provides critical insights into the spatiotemporal dynamics of forest disturbances under rapid urbanization and economic development. It lays the groundwork for sustainable forest management strategies in Guangdong Province and may contribute to global discussions on managing forest ecosystems during periods of rapid socio-economic transformation. Full article

The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹ (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg⁻¹·H₂O) > Cropland WUE (0.972 gC·kg⁻¹·H₂O) > Grassland WUE (0.805 gC·kg⁻¹·H₂O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg⁻¹·H₂O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation. Full article

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R² > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions. Full article

Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R² (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture. Full article

As one of the most well-preserved areas in the vertical band spectrum of vegetation in central China and even in the northern hemisphere at the same latitude, the vegetation in Shennongjia Forest District is vital to global ecological balance. In order to fully understand the vegetation change in the study area, remotely sensed data since 1990, combined with the Sen-MK test, Geo detector, and LandTrendr algorithm, were used to analyze the vegetation distribution characteristics and change trends. The results showed that: (1) the overall NDVI in the study area displayed an upward trend. (2) Vegetation disturbance occurred frequently before 2000 and decreased significantly after 2000. The most severely disturbed year was 1991 when the disturbed area amounted to 4.0851 km², accounting for 16.76% of the total disturbed area. The analysis of the topographic environment reveals that most of the vegetation disturbances occur in areas with slopes of 15–25° and elevations of 1500–2000 m, which indicates that these areas have frequent human activity. (3) The explanatory power of different influences on vegetation changes varied, with altitude having the most significant effect and the superposition of two influences increasing the effect on vegetation change. Over the past 30 years, vegetation in the Shennongjia Forest District has shown a general trend of recovery, with natural forest protection initiatives playing a critical role in mitigating disturbance. This comprehensive study of vegetation changes in Shennongjia offers a valuable research paradigm for forest conservation and sustainable development in temperate forests at similar latitudes, providing significant insights into the protection and management of similar ecosystems. Full article

The ridge–furrow plastic mulching technique has been widely applied due to its benefits of increasing temperature, conserving moisture, reducing evaporation, and boosting yields. Hydrus-2D is a computer model designed to simulate the two-dimensional movement of water in soil characterized by a low cost and high flexibility compared to field experiments. This study, based on field experiment data from Jianping County, Liaoning Province, China, during 2017–2018, developed Hydrus-2D models for two distinct field management practices: non-mulched flat cultivation (NM-FC) and mulched ridge tillage (M-RT). Furthermore, it simulated the dynamic changes in farmland water variations during the summer maize growth period (2021–2100) under climate change scenarios, specifically medium and high emission pathways (SSP2-4.5 and SSP5-8.5), based on the FGOALS-g3 model, which exhibits the highest similarity to the climate pattern of Jianping County in the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and the Shared Socioeconomic Pathways (SSPs). The results showed that in the future FGOALS-g3 model, net radiation exhibited a significant upward trend under the SSP2-4.5 scenario (Z = 2.38), while the average air temperature showed a highly significant increase under both SSP2-4.5 and SSP5-8.5 scenarios, with Z-values of 6.48 and 8.90, respectively. The Hydrus-2D model demonstrated high simulation accuracy in both NM-FC and M-RT treatments (R² ranging from 0.86 to 0.96, with RMSE not exceeding 0.011), accurately simulating the dynamic changes in soil water content (SWC) under future climate change. Compared to NM-FC, M-RT reduced evaporation, increased transpiration, and effectively decreased the leakage caused by increased future precipitation, resulting in a 0.04 and 0.01 cm³/cm³ increase in surface and deep soil SWC, respectively, during the summer maize growing season, significantly improving water use efficiency. Moreover, M-RT treatment reduced the impact coefficients of climate change on various water balance parameters, stabilizing changes in these parameters and SWC under future climate conditions. This study demonstrates the significant advantages of M-RT in coping with climate change, providing key scientific evidence for future agricultural water resource management. These findings offer valuable insights for policymakers and farmers, particularly in developing adaptive land management and irrigation strategies, helping to improve water use efficiency and promote sustainable agricultural practices. Full article

Soilborne plant pathogens significantly impact agroecosystem productivity, emphasizing the need for effective control methods to ensure sustainable agriculture. Soil fungistasis, the soil’s ability to inhibit fungal spore germination under optimal conditions, is pivotal for biological control. This study explores soil fungistasis variability across land-use intensities, spanning deciduous and evergreen forests, grasslands, shrublands, and horticultural cultivations in both open fields and greenhouses. Soil characterization encompassed organic matter, pH, total nitrogen, C/N ratio, key cations (Ca²⁺, Mg²⁺, K⁺, Na⁺), enzymatic activities, microbial biomass, and soil microbiota analyzed through high-throughput sequencing of 16s rRNA genes. Fungistasis was evaluated against the pathogenic fungi Botrytis cinerea and the beneficial microbe Trichoderma harzianum. Fungistasis exhibited similar trends across the two fungi. Specifically, the application of glucose to soil temporarily annulled soil fungistasis for both B. cinerea and T. harzianum. In fact, a substantial fungal growth, i.e., fungistasis relief, was observed immediately (48 h) after the pulse application with glucose. In all cases, the fungistasis relief was proportional to the glucose application rate, i.e., fungal growth was higher when the concentration of glucose was higher. However, the intensity of fungistasis relief largely varied across soil types. Our principal component analysis (PCA) demonstrated that the growth of both Trichoderma and Botrytis fungi was positively and significantly correlated with organic carbon content, total nitrogen, iron, magnesium, calcium, and sodium while negatively correlated with fluorescein diacetate (FDA) hydrolysis. Additionally, bacterial diversity and composition across different ecosystems exhibited a positive correlation with FDA hydrolysis and a negative correlation with phosphoric anhydride and soil pH. Analysis of bacterial microbiomes revealed significant differences along the land use intensity gradient, with higher fungistasis in soils dominated by Pseudoarthrobacter. Soils under intensive horticultural cultivation exhibited a prevalence of Acidobacteria and Cyanobacteria, along with reduced fungistasis. This study sheds light on soil fungistasis variability in diverse ecosystems, underscoring the roles of soil texture rather than soil organic matter and microbial biomass to explain the variability of fungistasis across landscapes. Full article

Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with few focusing on sub-daily urban Tair at high spatial resolution. In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. Four model schemes were constructed and compared by combining different auxiliary data (time-related and space-related) with LST. Cross-validation results were found to show that space-related and time-related variables significantly affected the model performance. When all auxiliary data were used, the model performed best, with an average RMSE of 1.6 °C (R² = 0.96). The best performance was observed on warm nights with an RMSE of 1.47 °C (R² = 0.97). The importance assessment indicated that LST was the most important variable across all conditions, followed by specific humidity, and convective available potential energy. Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research. Full article