Grassland Areas Research Articles

Construction and assessment of a fire risk index system for typical grasslands in Xinjiang, China

BackgroundFire hazards have a substantial impact on grassland ecosystems, and they are becoming more frequent and widespread because of global changes and human activities. However, there is still a lack of a widely accepted or practical method to evaluate grassland fire risk. In our study of typical grasslands in northern Xinjiang, we selected 18 evaluation indicators for grassland fires from three aspects of hazard, exposure, and vulnerability. Employing the analytic hierarchy process, weighted comprehensive evaluation method, and standard deviation classification, we determined the fire risk level thresholds, aiming to develop efficient and precise methods for assessing grassland fire risks, and ultimately created a grid-based map of grassland fire risk levels.ResultsThe risk level of grassland fires is determined by the combined spatial heterogeneity of fire-causing factors’ hazard and fire hazard-bearing bodies’ vulnerability and exposure. The hazard of grassland fire and fire hazard-bearing bodies’ vulnerability and exposure are dominated by medium level and medium–low level. Most areas of grassland fire risk levels are medium–low, medium, or medium–high risk, with few areas being high risk or low risk. The grassland fire risk exhibits a spatial distribution characterized by higher risks in the western and lower in the eastern; high and medium–high risk areas are primarily distributed in the western and some northeastern regions of the study area. The simulate result effectively represents the spatial distribution of grassland fire in the research area.ConclusionWe established a grassland fire risk index system and model, creating a spatial distribution map of grassland fire risk levels based on grid. Few grassland areas have fire risks and show a patchy distribution. The results generally reflect the spatial distribution pattern of grassland fire risks in the study area. This research provides technical support for scientifically formulating local grassland fire disaster prevention and relief strategies.

Ecosystem service value and ecological compensation in Qilian Mountain National Park: Implications for ecological conservation strategies

Qilian Mountain National Park (QMNP) is a critical ecological barrier and water conservation area in northwestern China. Its diverse ecosystems provide significant ecosystem services, yet the valuation of these services and their dynamic responses to land use/cover (LULC) changes and ecological protection measures require detailed investigation. This study employs an improved equivalent factor method, tailored to the specific ecosystem types and LULC patterns of QMNP, to evaluate the ecosystem service value (ESV) across different LULC types. Using an elasticity analysis model, the sensitivity of ESV to LULC changes from 1990 to 2022 was quantitatively assessed. The results indicate that the ESV of QMNP increased from 46.13 billion RMB to 50.97 billion RMB between 1990 and 2022, mostly due to the growth of grassland and forest areas. The primary route for increasing the region’s ESV is the conversion of desert to grassland, and this should be a major area of emphasis for next ecological conservation initiatives. Regulating services form the core of the ESV in QMNP, with climate and hydrological regulation contributing significantly to the overall ESV. Maintaining the stability of forest, wetland, and water body areas is crucial for sustaining the total ESV of QMNP. Through the application of carbon emission coefficients and ESV methods, the average range for long-term ecological compensation standards in QMNP is expected to be between 3.78 billion RMB and 28.28 billion RMB annually. Additionally, we explore the impacts of ongoing ecological protection mechanisms on enhancing ESV. There is a pressing need to formulate integrated policy frameworks that balance ecological conservation with sustainable economic development. The study also highlights the necessity of strengthening the protection and foundational research of grasslands and water bodies to enhance the ESV of QMNP. These insights serve as a foundation for potential planning and management strategies in QMNP, aiming to maintain and enhance its ecosystem functions and services, thereby contributing to broader regional and global ecological stability.

Redefining the modified water benefit-based ecological index to evaluate the impact of cropland expansion on the ecological environment in an arid area

Global land-use changes have a significant impact on the environment and food security, and escalating human activity and uncontrolled agricultural land expansion pose a grave threat to regional ecosystems. This study aims to assess the challenges faced by the environment and food security, with a particular focus on the impact of land use change on ecosystems. The study selected the Ebinur Lake Basin, a typical arid region in China, as the object and analyzed the land type changes from 2013 to 2021 using the Google Earth Engine platform and the China Land Cover Dataset. We created an Improved Water-Benefit-Based Ecological Index (IWBEI) based on the poor air quality environment in the watershed, and then calculated the ecological index of the watershed. The results indicate that the grassland and bare land areas in the Ebinur Lake Basin have fluctuated and shown an overall downward trend. The trend of farmland expansion is to expand to surrounding areas on the basis of existing farmland. The overall center of gravity of cultivated land in the watershed is moving from northwest to southeast, mainly towards the water source area. The IWBEI index is more prominent in identifying cultivated land and wetlands in arid regions. According to IWBEI's calculations, the priority order of land types is: forest > cultivated land > wetland > water > snow > grassland > impermeable surface > bare land. The overall ecological environment quality of the watershed is showing a deteriorating trend. Therefore, relevant departments should pay attention to the impact of farmland expansion on the local ecological environment to avoid the continuous deterioration of the ecological environment.

Understanding scale effects and differentiation mechanisms of ecosystem services tradeoffs and synergies relationship: A case study of the Lishui River Basin, China

Understanding the scale effect of relationships among ecosystem services (ESs) and the factors influencing these relationships, is vital for sustainable management of ESs. Previous research has focused less on multiple scale units, and the driving mechanisms behind ESs interactions remain unclear. Therefore, in this study, we assessed the spatial distribution of water yield (WY), water purification (WP), and soil conservation (SC) across four spatial scales (3 km × 3 km grid, 5 km × 5 km grid, sub-watershed, and county scales) in the Lishui River Basin of China during 2020. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, geographical weighted regression, and Pearson correlation analysis, we examined tradeoffs and synergies among these ESs at various scales. Additionally, we employed a geographic detector to analyze the reactions and scale effects among these tradeoffs/synergies and social-ecological factors. Our findings revealed the following: (1) Significant spatial heterogeneity in ESs was observed in the basin. The WY decreased from west (2511.65 mm) to east (890.06 mm), with high WP values concentrated in the eastern part, and high SC values (8.30 × 105 t/hm2) found in densely forested and grassland areas surrounding water bodies. Forested and cultivated lands were key contributors to ESs in the basin. (2) Across the four investigated scales, tradeoffs or synergies between the same ESs becoming more pronounced as the scale increased. Notably, most ES relationships displayed their strongest synergies at the county scale, with certain spatial areas demonstrating change in interaction directions. (3) The explanatory power of single- or double-factor interactions increased with scale. ESs interactions were found to be closely linked to climatic factors, as well as topographic features (including slope gradient and elevation), with precipitation exhibiting relatively high explanatory power. The interaction of natural and social factors significantly outweighed the combined interaction of dual social factors. This indicated that socio-ecological factors, especially climate and topography, jointly influenced the intensity and direction of ES tradeoffs/synergies. Scale effects arose from the “peak cutting and valley filling” phenomenon during scale transitions. These insights hold significant value for guiding hierarchical management strategies for regional ESs and informing decisions regarding resource allocation.

Dynamic Monitoring and Analysis of Ecological Environment Quality in Arid and Semi-Arid Areas Based on a Modified Remote Sensing Ecological Index (MRSEI): A Case Study of the Qilian Mountain National Nature Reserve

The ecosystems within the Qilian Mountain National Nature Reserve (QMNNR) and its surrounding areas have been significantly affected by changes in climate and land use, which have, in turn, constrained the region’s socio-economic development. This study investigates the regional characteristics and application requirements of the ecological environment in the arid and semi-arid zones of the reserve. In view of the saturated characteristics of NDVI in the reserve and the high-altitude saline-alkali environmental conditions, this study proposed a Modified Remote Sensing Ecology Index (MRSEI) by introducing the kernel NDVI and comprehensive salinity index (CSI). This approach enhances the applicability of the remote sensing ecological index. The temporal and spatial dynamics of ecological and environmental quality within the QMNNR from 2000 to 2022 were quantitatively assessed using the MRSEI. The effect of land use on ecological quality was quantified by analyzing the MRSEI contribution rate. The findings in this paper indicate that (1) in arid and semi-arid regions, the MRSEI provides a more precise representation of surface ecological environmental quality compared to the remote sensing ecological index (RSEI). The high correlation (R2 = 0.908) and significant difference between MRSEI and RSEI demonstrate that MRSEI enhances the accuracy of evaluating ecological environmental quality. The impact of land use on ecological quality was quantitatively assessed by analyzing the contribution rate of the MRSEI. (2) The ecological quality of the QMNNR exhibited an upward trend from 2000 to 2022, with an increase rate of 1.3 × 10−3 y−1. The area characterized by improved ecological and environmental quality constitutes approximately 53.68% of the total area. Conversely, the ecological quality of the degraded areas accounts for roughly 28.77%. (3) Among the various land use types, the improvement in ecological environmental quality within the reserve is primarily attributed to the expansion of forest and grassland areas, along with a reduction in unused land. Forest and grassland types account for over 90% of the total area classified with “good” and “excellent” ecological grades, whereas unused land types represent more than 44% of the total area classified with “poor” ecological grades. Overall, this study provides a valuable framework for analyzing ecological and environmental changes in arid and semi-arid regions.

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

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.

Landscape pattern prediction method based on ANN-CA-Markov coupling model

The simulation and prediction of landscape patterns can provide a reference for the formulation of ecological space security policies and management and control measures. Therefore, it is necessary to use objective and accurate scientific methods to make accurate simulation predictions. In this study, the quantitative prediction of the Markov model is combined with the advantages of the space-time simulation of the CA model, and the artificial neural network (ANN) module is used for calculation and coupling correction. The Kappa coefficient and the FoM (figure of merit) index are used to test the simulation accuracy. Based on this process, the changes of the landscape pattern in Mudanjiang City at three time points in 2000, 2010 and 2020 were analyzed, and the simulation prediction was made based on this. The experimental results show that the CA-Markov model using the ANN module for coupling correction can be tested for accuracy in the study area, and its Kappa coefficient is 0.834 and the FoM index is 0.001. The model meets the requirements of the landscape pattern for the accuracy of simulation and prediction, and the prediction and analysis of the study area is carried out, and the accurate prediction results are obtained.The results of the study were: Looking at the change in landscape pattern over the 20 years from 2000 to 2020, significant changes occurred in the areas of arable land and artificial surface. The total area of arable land decreased by nearly 130 km2, while the area of artificial surface (i.e., built-up land) increased by about 167 km2. The forest area initially decreased and then increased, with an overall increase of approximately 56 km2, while the water and grassland areas first increased and then decreased. In terms of spatial distribution, the changes in agricultural and forest land are mainly concentrated in the central area where urban development is intensive and in the northern forest area. The results of this study are quite important for policy formulation, according to which it was found that human activities, especially urbanization, have led to a decrease in the area of forested land, and therefore the implementation of the natural forest protection policy can lead to a sustained increase in the area of forested land.

Land parcellation reduced spatial redundancy resulting in grassland degradation

Abstract Degradation is the most immediate threat to biodiversity and sustainable livelihoods of global pastoral grasslands. However, we are still uncertain why grassland degradation is enhanced by the land policy of grassland parcellation. We hypothesized that spatial redundancy of the grazing system, that is, parcellation accelerates land fragmentation and reduces spatial redundancy, leads to land degradation. This study aimed to clarify the relationship between households, livestock and grassland management under the grassland contract policy, and to determine the causes and consequences of grassland fragmentation and degradation. We interviewed households who received land under the grassland contract policy in typical pastoral areas of the Qinghai–Tibetan Plateau, and then, we employed binary logistic regressions, multinomial logistic regressions and fuzzy‐set qualitative comparative analyses to examine the relationship between spatial redundancy and grassland–livestock balance. A very significant (p < 0.001) negative asymptotic exponential trend emerged between grassland area and stocking rate, but with regional differences. Grassland spatial redundancy and stocking rate displayed a significant (r2 = 0.96; p < 0.05) negative asymptotic exponential trend, that is, households with a lower spatial redundancy had a higher stocking rate. Based on our results, spatial redundancy can be used as one of the key indices to evaluate landscape stability of grazing systems. In any terrestrial ecosystem, the size of spatial redundancy reflects the carrying capacity of the land and determines the delicate relationship between land, animals and population, that is, in equilibrium or non‐equilibrium. Therefore, taking the Qinghai–Tibetan Plateau as an example, we face the spread of grassland degradation caused by the gradual fragmentation of resources under the current grassland contract policy and parcellation in global pastoral areas. Determining the spatial redundancy size of grassland is an important prerequisite for understanding grassland degradation and formulating grassland management strategies. These novel findings have potential beneficial information for resource utilization, especially land management planning in pastoral areas worldwide. Read the free Plain Language Summary for this article on the Journal blog.

Dynamic land cover and ecosystem service changes in global coastal deltas under future climate scenarios

Coastal deltas, housing 4.5% of the global population, face profound shifts due to climate change and intensified human activities. This study utilizes the Patch-generating Land Use Simulation (PLUS) for detailed land cover dynamics and the Integrated Valuation of Ecosystem Services and Trade-Offs (InVEST) models for ecosystem services assessment, focusing on 46 global coastal deltas under future scenarios (SSP126, SSP245, SSP585). Our findings reveal a 145% increase in urban areas from 1995 to 2015, with expected continuations in urban sprawl. Grassland and barren areas are projected to diminish by up to 15% and 5%, respectively, with urban areas potentially expanding by up to 39% across scenarios. Significant decreases in surface water yield under SSP245 and SSP585 could challenge water sustainability. Moreover, a universal decline in carbon storage necessitates enhanced carbon sequestration strategies. Soil retention is anticipated to decline, leading to increased erosion risks, while habitat quality varies, improving in 29 deltas under SSP126 but worsening in 33 under SSP245 and SSP585. These insights underscore the urgency of strategic, adaptive land use planning to bolster ecosystem services, directly supporting the United Nations' Decade of Ocean Science for Sustainable Development and advancing global sustainable delta management.

Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions

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.

Impacts of Land Use Changes on Landscape Patterns and Ecosystem Service Values in Counties (Villages) in Ethnic Regions of China: A Case Study of Jianghua Yao Autonomous County, Hunan Province

This study, using Jianghua Yao Autonomous County in Hunan Province as a case, systematically analyzes the response of ecosystem service value (ESV) to land use and landscape pattern changes by employing landscape-level indices and landscape-type-level indices. The findings provide a reliable basis for scientifically formulating land use planning and ecological protection policies in ethnic regions, thereby promoting regional ecological security and sustainable development. This study reveals that (1) the land use structure in the county underwent significant changes between 2000 and 2020, with grassland and shrubland areas decreasing substantially by 71.66% and 78.41%, respectively, while urban and arable land areas increased significantly by 228.30% and 15.84%, respectively. Particularly under the scenario of prioritizing economic development, these changes led to increased landscape fragmentation and a decline in ecosystem service value (from CNY 296.571 billion in 2020 to CNY 287.959 billion in 2030). (2) In contrast, the scenarios of ecological protection and sustainable development significantly enhanced the region’s ecosystem service value by increasing forest and water area, effectively maintaining the stability of the landscape pattern. These findings provide important evidence for the formulation of scientifically sound land use planning and ecological protection policies, contributing to the dual goals of economic development, tourism growth and ecological protection in Jianghua Yao Autonomous County and similar regions.

Exploring the influencing factors of scrub typhus in Gannan region, China, based on spatial regression modelling and geographical detector

Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants. However, in order to effectively eradicate scrub typhus, it is crucial to identify the specific factors that contribute to its incidence at a detailed level. Therefore, the objective of our study is to identify these influencing factors, examine the spatial variations in incidence, and analyze the interplay of two factors on scrub typhus incidence, so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals. Additionally, spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021. The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation. Among the global spatial regression models, the spatial lag model was found to be the best fitting model (log likelihood ratio = −319.3029, AIC = 666.6059). The results from the SLM analysis indicated that DEM, mean temperature, and mean wind speed were the primary factors influencing the occurrence of scrub typhus. For the local spatial regression models, the multiscale geographically weighted regression was determined to be the best fitting model (adjusted R2 = 0.443, AICc = 726.489). Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan, while the southern region was found to be more susceptible to scrub typhus due to mean wind speed. The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index. Additionally, the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus (q = 0.357). This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus; and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships. The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.

Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO 2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO 2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems.

Long-term dynamics of grasslands and livestock in Norwegian cultural landscapes: implications for a sustainable transition of rural livelihoods

ContextAbandonment of cultural landscape practices has had a notable impact on grasslands and domestic livestock that depend on them. This affects the prerequisites for sustainable transitions of cultural landscapes, which combine traditional livelihoods and novel ones like tourism.ObjectivesThe aims of this study were to explore (1) the long-term temporal dynamics of grassland and livestock in a regional gradient from coastal to inland landscapes in Norway and (2) the temporal development of tourism types among regions.MethodsUsing three regions as case studies, with 62 municipalities, we analyzed (1) the temporal dynamics of grassland and livestock using agricultural census data from 1918 to 1999 and (2) two tourism types. Kruskal–Wallis and ANOVA were used to examine whether the relative changes in grassland areas and livestock units, respectively, differed significantly among regions. A PCA was conducted to explore relationships between grassland and livestock types. The proportions of tourist categories were compared.ResultsThe grassland area and thematic resolution of census data declined over time. Grassland areas correlated with domestic livestock units. Multivariate analysis explained 68% of the variation in grassland and livestock types among municipalities. There was a notable increase in the number of tourists, with summer tourism dominating where the cultural landscape was well conserved.ConclusionsOur study underscores the importance of integrating regional historical trajectories for the conservation and use of valuable cultural landscapes, thereby providing sustainable transitions that combine traditional livelihoods and new types as tourism. Additional in-depth studies are needed to understand the detailed drivers of these changes.

Ecological impacts and supply demand evolution of the Yangtze to Huaihe water transfer project in Anhui section

Human activities have profound impacts on land use and the supply–demand balance of ecosystem services (ESs). Various activities, such as urban construction, urban and rural planning, and inter-basin water transfer projects, continuously reshape land use patterns. This is a case study of the Anhui section of the Yangtze-Huaihe Water Diversion Project. Data from 2000, 2010, and 2020 is analyzed. Additionally, the patch-generating land use simulation (PLUS) model is utilized to quantify the specific impacts of the water diversion project construction on the supply and demand of ESs. The results indicate that the comprehensive dynamic attitude of land use during the project construction period significantly increased, rising from 0.16 to 13.79%, and mainly affected forest, water areas, construction land, and unused land. Specifically, the construction of the project led to significant changes in water purification, biodiversity, and, especially, hydrological regulation services. Additionally, the migration of residents significantly impacted the demand for ESs. The study also found a significant correlation between land use changes and the balance of ES supply and demand: the proportion of cultivated land and construction land is positively correlated with the balance, while the proportion of forest, grassland, and water areas is negatively correlated. This study provides empirical data for understanding the environmental and socio-economic impacts of large-scale water diversion projects and offers a scientific basis for local mitigation and control of adverse impacts. Through quantitative analysis and model prediction, this research effectively bridges the gap between theory and practice, providing important references for sustainable regional development.

Patterns of change, driving forces and future simulation of LULC in the Fuxian Lake Basin based on the IM-RF-Markov-PLUS framework

Studying land use and land cover (LULC) patterns, identifying driving forces, and simulating future scenarios are vital for grasping the complex connection between human actions and the environment. This helps in shaping sustainable land management strategies and preparing for the impacts of climate change. However, there is a necessity for a comprehensive system modeling framework that can accurately capture spatial and temporal changes in LULC, analyze driving mechanisms, and provide an integrated analysis of future simulations. In this paper, a comprehensive IM-RF-Markov-PLUS analysis framework is developed, focusing on the Fuxian Lake Basin (FLB) as a study area. The study aims to achieve accurate prediction of LULC by combining the microscopic LULC change trend and the contribution of macroscopic driving forces. The results show that: (1) The IM-RF-Markov-PLUS framework can explore the change patterns and driving mechanisms of LULC in the FLB, and accurately predict the LULC in the FLB. Compared with the PLUS model, its accuracy is improved by 2 %. (2) IM analysis reveals that LULC transformation in the FLB is both general and specific. Although the area of grassland, buildings, roads and structures converted to desert and bare land is minimal, it shows relatively tendentious and specific change characteristics. (3) Different land types are significantly affected by driving factors, with the expansion of LULCs is constrained by major factors. The distance to the lake has the most significant impact on the distribution of garden land, while the primary road has the greatest impact on the distribution of forestland. (4) Under different scenarios, the spatial heterogeneity of LULC patterns is obvious. In 2035, under baseline and economic development scenarios, cultivated land will decrease, while other LULC types will increase. Under the cultivated land protection scenario, cultivated land is protected, with an increase of 5.85 %. Under the ecological protection scenario, there is an increase in ecological land. The largest increase is in forestland, which increases by a total of 3.46 %. The ecological protection scenario presents a viable approach for ensuring the sustainable development of the FLB. The results of this paper may serve as a reliable foundation for implementing LULC strategies in the FLB and offer guidance for crafting sustainable development regulations at the regional level.

Study on the characteristics and scenario simulation of land use change in the Chaohu Lake Basin, China

Effectively evaluating the historical and future land use/cover change (LUCC) is significant for effective land use planning and management, ecological conservation, and restoration. Taking the Chaohu Lake Basin (CLB) as the study area, GIS technology and geographic detector were used to quantitatively analyze the change characteristics and driving factors of LUCC under the three periods in 2000, 2010, and 2020 of the CLB. This study aimed to comprehend the alterations that have transpired over the last two decades. In addition, the PLUS model was utilized to forecast LUCC trends under three scenarios: natural development, urban development, and ecological protection by 2030 in the CLB. The results suggest a significant decrease of the cultivated land area, while a considerable increase for the construction land area from 2000 to 2020 in the CLB. The expansion of the construction land area was mainly driven by the conversion of cultivated land area. Additionally, the slope was identified as the primary factor influencing LUCC, with q-values of 0.275, 0.266, and 0.258 in 2000, 2010, and 2020, respectively. The interaction between slope and soil type, distance to the trunk road and the secondary trunk road, and GDP was strong. The explanatory capacity of socioeconomic factors demonstrated a steady increase. The simulation results indicate that a decrease in cultivated land area and an increase in construction land area still occurred by 2030 in the CLB, particularly in the urban development scenario. Nonetheless, a notable deceleration of change was appeared in the ecological protection scenario. The alterations in forest and grassland areas were not significant. However, the water bodies area continued to enlarge, although the expansion was not substantial. The study results can provide policy references for the scientific management and long-term strategic planning of land resources in the CLB.

Drivers of vascular plant, bryophyte and lichen richness in grasslands along a precipitation gradient (central Apennines, Italy)

AbstractQuestionsSemi‐natural grasslands in Southern Europe are biodiversity hotspots, yet their patterns of plant species richness are less studied than in Central Europe. In the Central Apennines (Italy), there are large areas of dry calcareous grasslands, across a steep gradient of mean annual precipitation (from 650 to 1350 mm within c. 30 km). We asked: How do these grasslands compare to other Palaearctic grasslands in richness levels? How do the precipitation gradient and other environmental predictors influence species richness? Does this influence differ among taxonomic groups?LocationSubmontane and lower‐montane belt of the Central Apennines (Abruzzo and Lazio, Italy).MethodsWe recorded the species richness of vascular plants and (terricolous) bryophytes and lichens in 97 plots of 10 m2, aligning them with the precipitation gradient while maintaining geological substrate and elevation similar. Mean temperature and precipitation were estimated with a high‐resolution regional model. A wide array of environmental variables (including soil properties and grazing load) were measured for each plot. Multivariate relationships within and between response and predictor variables were studied with Canonical Correlation. The relative importance of predictors on response variables was modeled with Boosted Regression Trees.ResultsThe sampled grasslands were very species‐rich in the Palaearctic context. Vascular plant richness was negatively influenced by topographic heat load and soil sand content, but we did not detect a relationship with mean annual precipitation. Bryophyte richness was poorly modeled by the measured variables, although it was positively correlated with lichen richness. Lichen richness had a marked negative relationship with soil phosphorus and mean annual precipitation.ConclusionsIn Southern European semi‐natural mountain grasslands, vascular plant richness is driven more by fine‐scale edaphic factors than by precipitation gradients. In contrast, bryophyte and lichen species richness is predicted by a mixture of climatic and edaphic variables.

Ecological space management and control zoning of Giant Panda National Park from the perspective of ecosystem services and land use

Since China proposed building a national park system in 2017, the establishment of a planning system for nature reserves, with national parks as the main body, is being actively promoted around the country. Among them, scientific ecological space management and control zoning (ESMCZ) is an important link in maintaining the ecological stability of national parks. How to zone national parks and how to improve the precision of zoning has become a new task for national parks. Therefore, this study takes the Giant Panda National Park as the study area, takes ecosystem services and land use/cover change as the research perspective, integrates the InVEST model, PLUS model and bayes belief network (BBN) model, and builds a set of ecological space management and control zoning (ESMCZ) spatial zoning framework based on raster scale, dividing the study area into strictly protected zone, ecological buffer zone, ecological control zone and controlled development zone. The results showed that: (1) The study area showed an increasing trend in water conservation, soil conservation and carbon storage from 2005 to 2020, and the habitat quality index was generally high. The spatial heterogeneity of ecosystem services in the study area was significant, and the effect of a single factor on ecosystem services was most pronounced. (2) Large variation in area for different land uses under natural development scenarios and ecological protection scenarios. In both scenarios, the area of cultivated land, the area of grassland and the area of unused land decrease relative to 2020, and the area of forested land, the area of water and the area of constructed land increase relative to 2020. (3) The Giant Panda National Park is divided into strictly protected zone, ecological buffer zone, ecological control zone and control development zone, of which the strictly protected zone have the largest area and the best ecosystem background condition, and the control development zone have the smallest area and the worst ecosystem background condition. (4) The ecological space management and control zoning (ESMCZ) framework provides a more refined method for the secondary zoning of nature reserves such as the Giant Panda National Park, which is valuable for the implementation of zoning and categorization management for ecological conservation in the Giant Panda National Park.

Ecosystem health assessment of the largest lake wetland in the Yellow River basin using an improved vigor-organization-resilience-services model

Wuliangsu Lake is the largest lake wetland in the Yellow River basin, it is the Northern Sand Belt of China, which is essential to maintaining ecological security in northern China and the areas downstream. With the increasing environmental problems, the basin has become a sensitive and complex geographical unit, and a better understanding of the level of ecosystem health in the basin is helpful for policymakers in developing measures for ecological restoration and eco-space management. In this study, we proposed an improved VORS model containing ecosystem vigor, organization, resilience and services to assess the ecosystem health in the Wuliangsu Lake Basin in 2000, 2011, and 2022. To improve spatial precision, the study area was partitioned into grids measuring 2 km × 2 km each. The results indicated that: (1) the ecosystem health showed a slight increase trend, which indicated by the increase level grids reached 94 % in 2000–2022 change. (2) The reduction in vegetation cover decreased the ecosystem vigor, which was indicated by the weak level increased from 22 % in 2000 to 73 % in 2022. Ecosystem structure becomes more complex enhanced the ecosystem organization, the value increased from 0.3 to 0.6 in 2000 to 0.6–0.8 in 2022. Ecosystem resilience slightly declined in 2011 and 2022, ecosystem services were relatively stable but were dominated by weak levels. (3) The majority land use type in the study area is farmland, and the ecosystem vigor decreases as the area of grassland decreases to farmland in these 22 years. The ecosystem’s structure was enhanced and the land use structure was progressively brought into balance. Changes in all four parameters of ecosystem health assessment are related to land use, and changes in ecosystem health are likewise related to land use changes. The improved VORS model constructed for the Wuliangsu Lake Basin objectively reflect the basin’s state of ecosystem health, and provide a reference for ecological restoration and basin regional planning.