Search Results (2,137)

Accurately identifying ecological compensation areas and scientifically determining appropriate compensation amounts are crucial for establishing a robust ecological compensation mechanism, which in turn is key to promoting the coordinated development of ecological protection and high-quality economic growth. This study innovatively proposes a framework for ecological compensation termed “Accounting of Ecosystem Services Value–Identification of Priorities for Payers and Recipients–Calculation of Ecological Compensation Amount (ESV–PPR–ECA)”. It utilizes the InVEST model and the emergy method to assess the value of ecosystem services, constructs the Ecosystem Payment and Recipient Priority Sequence (EPRPS) Model to identify the payers, recipients, and their priorities for ecological compensation, and employs the conversion factor method to calculate the Ecological Compensation Amount (ECA). This framework aims to address the questions of “How should compensation be provided?”, “Who should compensate whom?”, and “How much compensation is necessary?”, ensuring the optimal use of ecological compensation funds and providing a scientific basis for inter-regional ecological compensation. The study’s findings indicate that the total Ecological Compensation Amount for the Yellow River Delta in 2020 was 3.848 billion RMB, with the total amount receivable being 4.032 billion RMB and the total amount payable being 184 million RMB. The compensation funds should be prioritized for tideland and the Yellow River, and venture, cropland and industrial land should be the first to contribute compensation. Additionally, the Ecosystem Service Value of the Yellow River Delta showed a declining trend from 2015 to 2020, underscoring the urgent need to establish a horizontal compensation mechanism for the region. Such a mechanism would incentivize environmental protection and the construction of ecological civilization, ultimately enhancing ecosystem service functions. Therefore, we recommend the implementation of horizontal fiscal transfers, where financial assistance is provided from paying areas to recipient areas, offering a scientific reference for the establishment of a horizontal compensation mechanism within the Yellow River Delta. Full article

Land cover changes in mountainous areas due to silvo-pastoral abandonment can affect soil stability, especially on steep slopes. In addition, the increase in rainfall intensity in recent decades requires re-assessing landslide susceptibility and vegetation management for soil protection. This study was carried out using the software SlideforMAP in the Mt. Nerone massif (central Italy) to assess (i) the effects of land cover changes on slope stability over the past 70 years (1954–2021) and (ii) the role of actual vegetation cover during intense rainfall events. The study area has undergone a significant change in vegetation cover over the years, with a reduction in mainly pastures (−80%) and croplands (−22%) land cover classes in favor of broadleaf forests (+64%). We simulated twelve scenarios, combining land cover conditions and rainfall intensities, and analyzed the landslide failure probability results. Vegetation cover significantly increased the slope stability, up to three to four times compared to the unvegetated areas (29%, 68%, and 89%, respectively, in the no cover, 1954, and 2021 scenarios). The current land cover provided protection against landslide susceptibility, even during extreme rainfall events, for different return periods. The 30-year return period was a critical condition for a significant stability reduction. In addition, forest species provide different mitigation effects due to their root system features. The results showed that species with deep root systems, such as oaks, provide more effective slope stability than other species, such as pines. This study helps to quantify the mitigation effects of vegetation cover and suggests that physically based probabilistic models can be used at the regional scale to detect the areas prone to failure and the triggering of rainfall-induced shallow landslides. This approach can be important in land planning and management to mitigate risks in mountainous regions. Full article

Urban vitality, which indicates the development level of a city and the quality of life of its residents, is a complex subject in urban research due to its diverse assessment methods and intricate impact mechanisms. This study uses multisource data to evaluate the urban vitality of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) across social, economic, cultural, and environmental dimensions. It analyzes the spatial distribution characteristics of urban vitality and examines the relationships between urban vitality and land use at both regional and city scales. The results indicate that the urban vitality in the GBA generally exhibits a spatial distribution pattern of a high central density and a low peripheral spread, where built-up areas and cropland emerge as key influencing factors. Cities with different developmental backgrounds have unique relationships between land use and urban vitality. In high-vitality cities, the role of the built-up area diminishes, and natural ecosystems, such as wetlands, enhance vitality. In contrast, in low-vitality cities, built-up areas boost urban vitality, and agriculture-related land types exert a lower negative or even positive effect. This research contributes to the understanding of the spatial structures of urban vitality related to land use at different scales and offers insights for urban planners, builders, and development managers in formulating targeted urban vitality enhancement strategies at the regional collaborative and city levels. Full article

Accurate cropland distribution data are essential for efficiently planning production layouts, optimizing farmland use, and improving crop planting efficiency and yield. Although reliable cropland data are crucial for supporting modern regional agricultural monitoring and management, cropland data extracted directly from existing global land use/cover products present uncertainties in local regions. This study evaluated the area consistency, spatial pattern overlap, and positional accuracy of cropland distribution data from six high-resolution land use/cover products from approximately 2020 in the alpine agricultural regions of the Hehuang Valley and middle basin of the Yarlung Zangbo River (YZR) and its tributaries (Lhasa and Nianchu Rivers) area on the Qinghai-Tibet Plateau. The results indicated that (1) in terms of area consistency analysis, European Space Agency (ESA) WorldCover cropland distribution data exhibited the best performance among the 10 m resolution products, while GlobeLand30 cropland distribution data performed the best among the 30 m resolution products, despite a significant overestimation of the cropland area. (2) In terms of spatial pattern overlap analysis, AI Earth 10-Meter Land Cover Classification Dataset (AIEC) cropland distribution data performed the best among the 10 m resolution products, followed closely by ESA WorldCover, while the China Land Cover Dataset (CLCD) performed the best for the Hehuang Valley and GlobeLand30 performed the best for the YZR area among the 30 m resolution products. (3) In terms of positional accuracy analysis, the ESA WorldCover cropland distribution data performed the best among the 10 m resolution products, while GlobeLand30 data performed the best among the 30 m resolution products. Considering the area consistency, spatial pattern overlap, and positional accuracy, GlobeLand30 and ESA WorldCover cropland distribution data performed best at 30 m and 10 m resolutions, respectively. These findings provide a valuable reference for selecting cropland products and can promote refined cropland mapping of the Hehuang Valley and YZR area. Full article

The Ebinur Lake Basin (ELB), which is a typical watershed in an arid region, has an extremely delicate natural ecosystem. Rapid urbanisation and economic growth have triggered substantial ecological and environmental transformations in this key economic hub of Xinjiang. However, a comprehensive and systematic knowledge of the evolving ecological conditions in the ELB remains limited. Therefore, this study modelled the landscape ecological risk index (LERI) using land use/land cover (LULC) data from 1985 to 2022 and assessed the drivers of landscape ecological risk (LER) using a geographical detector model (GDM). The findings revealed that (1) from 1985 to 2022, the construction land, cropland, and forestland areas in the ELB increased, whereas those of water bodies, grasslands, and barren land decreased. (2) Between 1985 and 2022, LER in the ELB showed a downward trend. Spatially, LER was predominantly characterised by lower and lowest risk levels. The higher and highest risk status has been around Ebinur lake and has continued to improve each year. (3) Climatic factors, particularly temperature and precipitation, were identified as the most significant drivers of the LER change from 1985 to 2022. The findings provide crucial scientific knowledge for advancing sustainable development and maintaining ecological security in the ELB. Full article

Vegetation cover can regulate regional climate and associated dry–wet variations. However, the effects of the quantitative structure and landscape pattern of vegetation cover on climatic drought remain unclear. Yunnan Province in China, with its abundant vegetation resources, provides a good setting for addressing this research gap. Our objective is to provide guiding recommendations for climate-warming mitigation through the study of the topic. This study adopted four periods of vegetation cover data, from 1992 to 2020, and explored their dynamics. Monthly average precipitation and temperature data from 125 meteorological stations in Yunnan were used to calculate standardized precipitation–evapotranspiration index (SPEI) for 1992–2020 to understand the responses of climatic drought to vegetation cover dynamics. The correlations between quantitative structure, landscape pattern, and climatic drought were investigated by Pearson’s correlation coefficient in 10 km, 20 km, 30 km, and 40 km grid cells, respectively. The results indicate that changes in the quantitative structure of vegetation could influence regional climates, with the contributions to climatic drought mitigation ranked in the following order: broad-leaved forest > shrubland > needle-leaved forest > cropland > grassland. Landscape patterns significantly affected local climates, where broad-leaved and needle-leaved forests had the strongest and most stable correlations with climatic drought, whereas shrubland and grassland showed weaker correlations. The correlations between landscape patterns and climatic drought were stronger during the dry season than the rainy season. Factors such as the landscape dominance index, fragmentation index, and aggregation index had a significant impact on climatic drought. The dominant and aggregated-distribution broad-leaved forests were conducive to climatic drought mitigation, while needle-leaved forests, croplands, and grasslands might exacerbate climatic drought. Full article

The Rapid expansion of the Lanzhou–Xining (Lanxi) urban cluster in China during recent decades poses a threat to the fragile arid environment. Quantitatively assessing the impact of urban expansion on vegetation in the Lanxi urban cluster has profound implications for future sustainable urban planning. This study investigated the urban expansion dynamics of the Lanxi urban cluster and its impacts on regional vegetation between 2001 and 2021 based on time series land cover data and auxiliary remote sensing data, such as digital elevation model (DEM) data, nighttime light data, and administrative boundary data. Thereinto, urban expansion dynamics were evaluated using the annual China Land Cover Dataset (CLCD, 2001–2021). Urban expansion impacts on regional vegetation were assessed via the Vegetation Disturbance Index (VDI), an index capable of quantitatively assessing the positive and negative impacts of urban expansion at the pixel level, which can be obtained by overlaying the Enhanced Vegetation Index (EVI) and rainfall data. The major findings indicate that: (1) Over the past two decades, the Lanxi region has experienced rapid urban expansion, with the built-up area expanding from 183.50 km² to 294.30 km², which is an average annual expansion rate of 2.39%. Notably, Lanzhou, Baiyin, and Xining dominated the expansion. (2) Urban expansion negatively affected approximately 53.50 km² of vegetation, while about 39.56 km² saw positive impacts. The negative effects were mainly due to the loss of cropland and grassland. Therefore, cities in drylands should balance urban development and vegetation conservation by strictly controlling cropland and grassland occupancy and promoting intelligent urban growth. Full article

Land use/cover change (LUCC) can either enhance the areal carbon reserve capacity or exacerbate carbon emission issues, thereby significantly influencing global climate change. Comprehending the impact of LUCC on regional carbon reserve variation holds great significance for regional ecosystem preservation and socioeconomic sustainable development. This study focuses on Liaoning Province, leveraging land use remote sensing data from three periods from 2000 to 2020, natural environmental data and socioeconomic data in conjunction with the Integrated Valuation of Environmental Services and Trade-offs (InVEST) model, and patch-generating land use simulation (PLUS) models. It analyzes the interactive relationship between LUCC and carbon reserves in Liaoning Province between 2000 and 2020 and forecasts the trajectory of carbon reserve changes in Liaoning Province under various scenarios: business as usual, urban development, cropland protection, and ecological protection, all based on LUCC simulations. The findings indicate the following: (1) Over the study period, Liaoning Province experienced significant LUCC characterized primarily by the transformation of farmland to built-up land. Carbon reserves initially declined and later increased due to LUCC changes, resulting in a cumulative increase of 30.52 Tg C. The spatial distribution of carbon reserves was influenced by LUCC, displaying a pattern of spatial aggregation, with higher values in the east and lower values in the west. (2) Across the four simulation scenarios, the spatial pattern of carbon reserves in Liaoning Province continued to exhibit the characteristic spatial aggregation of higher values in the east and lower values in the west. Under the urban development scenario, carbon reserves decreased by 34.56 Tg C tons, representing a 2.45% decrease compared to 2020. Conversely, under the business-as-usual, cultivated land protection, and ecological protection scenarios, carbon reserves displayed a growing tendency, reaching 1449.35 Tg C, 1450.39 Tg C, and 1471.80 Tg C, respectively, with changes of 0.09%, 0.16% and 1.63% compared to 2020. The substantial increase in carbon reserves under the ecological protection scenario primarily stemmed from the significant expansion of woodland and other ecological land areas. In light of these findings, Liaoning Province may consider laying down and strictly executing spatial policies for ecological protection in future land projecting. The PLUS model and InVEST model can help curb the uncontrolled expansion of built-up land, facilitate the increment of ecological land areas, and with effect augment carbon reserves, thereby ensuring the achievement of the “double carbon” target of carbon peak and carbon neutralization. Full article

Returning livestock manure to the cropland as organic fertilizer is a sustainable and environmentally friendly treatment method, but its application also alters the soil microenvironment. However, the impact of soil microbial community disturbance and pollutant accumulation from different types of organic fertilizers remains largely unknown in South China. To fill this gap, we investigated the effects of organic fertilizers, including chicken manure, pig manure and vermicompost on the soil bacterial and fungal communities and environmental risks. The results show that applying organic fertilizer effectively increases the soil nutrient content. High-throughput sequencing of bacteria and fungi showed that the application of different organic fertilizers had differential effects on microbial community structure, with the highest number of microbe-specific OTUs in the vermicomposting treatment. Additionally, this study found no risk of heavy metal (Cu, Zn, Pb, Cr and Cd) contamination from short-term organic fertilizer application, but there was a risk of antibiotic (ENR and CHL) contamination. Functional microorganisms regulating heavy metals and antibiotics were identified by RDA analysis. This study facilitates the screening of types of organic fertilizers that can be safely returned to the field as well as developing strategies to regulate functional microbes. Full article

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery. Full article

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea. Full article

Soil erosion (SE) is a critical threat to the sustainable development of ecosystem stability, agricultural productivity, and human society in the context of global environmental and climate change. Particularly in tropical island regions, due to the expansion of human activities and land use/cover changes (LUCCs), the risk of SE has been exacerbated. Combining the RUSLE with machine learning methods, SE spatial patterns, their driving forces and the mechanisms of how LUCCs affect SE, were illustrated. Additionally, the potential impacts of future LUCCs on SE were simulated by using the PLUS model. The main results are as follows: (1) Due to LUCCs, the average soil erosion modulus (SEM) decreased significantly from 108.09 t/(km²·a) in 2000 to 106.75 t/(km²·a) in 2020, a reduction of 1.34 t/(km²·a), mainly due to the transformation of cropland to forest and urban land. (2) The dominant factor affecting the spatial pattern of SE is the LS factor (with relative contributions of 43.9% and 45.17%), followed by land use/cover (LUC) (the relative contribution is 28.46% and 34.89%) in 2000 and 2020, respectively. (3) Three kinds of future scenarios simulation results indicate that the average SEM will decrease by 2.40 t/(km²·a) under the natural development scenario and by 1.86 t/(km²·a) under the ecological protection scenario by 2060. However, under the cropland protection scenario, there is a slight increase in SEM, with an increase of 0.08 t/(km²·a). Sloping cropland erosion control remains a primary issue for Hainan Island in the future. Full article

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland. Full article

Net Primary Productivity (NPP) is a crucial indicator of ecological environment quality. To better understand the carbon absorption and carbon cycling capabilities of Henan Province, this study investigates the trends and driving factors of NPP across different climatic zones and land use types. The Theil–Sen Median trend analysis method and the Mann–Kendall trend test are employed to monitor NPP changes from 2001 to 2020. The average annual NPP in Henan Province during this period was 414.61 gC·m⁻²·year⁻¹, showing a significant increasing trend with a growth rate of 3.73 gC·m⁻²·year⁻¹. Spatially, both the annual average NPP and its increase rate were higher in the western part of Henan compared to the eastern part, and NPP variability was more stable in the southern region than in the northern region. By classifying climatic zones and using the Geodetector method to assess NPP sensitivity to natural factors, the results show that climate and vegetation factors jointly influence NPP variations, with annual precipitation being the primary natural factor affecting NPP trends in Henan Province from 2001 to 2020. By analyzing the NPP gain and loss matrix, the impact of land use changes on NPP was evaluated. Forests had the highest average annual NPP at 483.52 gC·m⁻²·year⁻¹, and the conversion of arable land to urban areas was identified as the primary land change type leading to NPP reductions. In the subtropical zone of Henan, forests, croplands, and grasslands exhibited higher NPP values and increase rates compared to those in the warm belt. This study provides new insights into the spatial variation of NPP caused by changes in climatic zones and land use types. Full article

In order to explore the effect of fishponds on soil water, salt transport and salinization in cropland wasteland, a study on soil water balance and salt distribution pattern in a cropland–wasteland–fishpond system was carried out in 2022–2023 in a typical study area selected from the Yichang Irrigation Area of the Hetao Irrigation District. A water balance model was established for the cropland–wasteland–fishpond system to analyze the effects of irrigation on soil salinity at the boundaries of the cropland, wasteland, and fishpond. The results showed that the lateral recharge from the cropland to the wasteland during spring irrigation in 2022 was 24 mm, the lateral recharge generated by fishponds to wasteland was 18 mm, and the lateral recharge from fishponds to fishpond boundaries was 34 mm. In the fertility period of 2023, the lateral recharge from cropland to wasteland was 15 mm, the lateral recharge from fishponds to wasteland was 9 mm, and the lateral recharge from fishponds to fishpond boundaries was 21 mm. Due to the low salinity content of fishpond water, it diluted the groundwater of the wasteland, and the soil salinity at the boundary between the wasteland and the fishpond was monitored. The data show that the soil salinity at the boundary of the fishpond was smaller than that of the wasteland, which indicates that the migration of fishpond water to the wasteland will not lead to an increase in the soil salinity of the wasteland, but rather to a decrease in the soil salinity of the wasteland. Fishpond regulation has a significant impact on soil and groundwater, and when the topographic conditions of the Hetao irrigation area allow, the model of cropland–wasteland–fishpond can be appropriately adopted to solve land degradation and increase the economic income of farmers; the results of the study provide a contribution for the improvement of the management of land use and soil salinization in the Hetao irrigation area. Full article