Ecological risk assessment models for simulating impacts of land use and landscape pattern on ecosystem services

Coupled InVEST-MGWR modeling to analyze the impacts of changing landscape patterns on habitat quality in the Fen River basin

The present study employed remote sensing images of the Fen River Basin from 2005, 2010, 2015, and 2020 as the primary data source. The software ENVI, ArcGIS, and Fragstats 4.2 were utilized to measure the landscape pattern index of the Fen River Basin. A collinearity test was conducted to remove any redundant landscape pattern indices. Based on the selected landscape indices, the landscape pattern index values were ascertained as follows. Using the shifting window method, the landscape pattern index of the Fen River Basin was obtained. Second, the habitat quality in the Fen River Basin was assessed using the InVEST model, and the spatial autocorrelation approach was employed to confirm that the habitat quality was spatially autocorrelated. Finally, the spatial impacts of landscape pattern indices on habitat quality were examined using the MGWR model. The results show that (1) the Fen River Basin's overall habitat quality declined between 2005 and 2020; however, the deterioration slowed with time and had a typical "poor in the middle and high around the margins" spatial distribution. The habitat quality of the low-value area continued to increase, the habitat quality of the lower-value area decreased annually, the habitat quality of the middle-value area decreased and then increased, the habitat quality of the higher-quality area tended to increase, decrease, and then increase again, and the habitat quality of the high-quality area decreased annually. (2) The fit of the MGWR model was greater than those of the OLS and traditional GWR models, and it was able to more clearly illustrate the various roles that landscape pattern indices and habitat quality play in one another. (3) Changes in landscape patterns had a major impact on habitat quality; habitat quality was positively impacted by PD and AI, negatively impacted by MESH, and had positive and negative bidirectional effects from CONTAG and AI.

Terrestrial vegetation carbon sink analysis and driving mechanism identification in the Qinghai-Tibet Plateau

The estimation of terrestrial carbon sinks in the Qinghai-Tibet Plateau (QTP) still faces significant uncertainties, and the spatiotemporal dynamics of terrestrial carbon sinks along altitudinal gradients remain unexplored. Moreover, the driving mechanisms of terrestrial carbon sinks at the watershed scale in the QTP continue to be lacking. To address these research gaps, based on multi-source remote sensing data and meteorological data, this study calculated the Net Ecosystem Productivity (NEP) in the QTP from 2000 to 2020 using the Modis NPP-soil respiration model. Through the coefficient of variation (CV), the Mann-Kendall test (MK), and the spatial autocorrelation methods, the spatial distribution pattern and spatiotemporal trends of NEP were investigated. Employing a pixel accumulation method, the variation of NEP along altitudinal gradients was explored. Grey relation analysis, Pearson correlation analysis, and Geographical detector (GD) were used to investigate the driving mechanisms of NEP at the watershed scale. Results showed that: (1) the terrestrial ecosystem in the QTP served as a carbon sink, which produced a total of 2.04 Pg C from 2000 to 2020, and the multi-year average of total carbon sinks was 96.92 Tg C; (2) the spatial distribution of NEP shows a decreasing change from southeast to northwest, and the clustering characteristic of NEP is significant at the watershed scale; (3) the elevation of 4507 m we proposed is likely to be a key threshold for biophysical processes of the terrestrial ecosystems in the QTP; (4) the fluctuation and change trend of carbon sources and carbon sinks show significant differences between the East and West; (5) at the watershed scale, precipitation and temperature play a dominant role in the variation of NEP, while the impact of human activities on NEP variation is weak. Our study aims to address the existing knowledge gaps and provide valuable insights into the management of terrestrial carbon sinks in QTP.

Urban river recovery: a systematic review on the effectiveness of water clean-up programs

Urban rivers are affected at different levels by the intensification of human activities, representing a serious threat to the maintenance of terrestrial life and sustainable urban development. Consequently, great efforts have been dedicated to the ecological restoration of urban rivers around the world, as a solution to recovering the environmental functionality of these environments. In this sense, the present work aimed to investigate the effectiveness of interventions carried out aimed at the recovery of urban rivers, through a systematic review of the literature between 2010 and 2022, using the search term "rivers recovery." The results showed that there have been notable advances in the implementation of river recovery programs in urban areas around the world between the years analyzed. The ecosystems studied were affected, for the most part, by the increase in the supply of nutrients from domestic and industrial effluents, in addition to having highly urbanized surroundings and with several changes in land use patterns. The preparation of this literature review made it possible to demonstrate that the effectiveness of river recovery is extremely complex, since river recovery projects are developed for different reasons, as well as being carried out in different ways according to the intended objective.

Future land use simulation model-based landscape ecological risk prediction under the localized shared socioeconomic pathways in the Xiangjiang River Basin, China

Landscape ecological risk (LER) is an effective index to identify regional ecological risk and measure regional ecological security. The localized shared socioeconomic pathways (LSSPs) can provide multi-scenario parameters of social and economic development for LER research. The research of LER under LSSPs is of scientific significance and practical value in curbing the breeding and spread of LER risk areas. In this study, land-cover raster files from 2010 to 2020 were used as the foundational data. Future land use simulation (FLUS), regression, and Markov chain models were used to predict the land cover patterns under the five LSSP scenarios in the Xiangjiang River Basin (XJRB) in 2030. Thus, an evaluation model was established, and the LER of the watershed was evaluated. We found that the rate of land cover change (LCC) in the XJRB between 2010 and 2020 had a higher intensity (increasing at an average of 18.89% per decade) than that projected under the LSSPs for 2020-2030 (averaging an increase of 8.58% per decade). Among the growth rates of all land use types in the XJRB, that of urban land was the highest (33.3%). From 2010 to 2030, the LER in the XJRB was classified as lower risk (33.73%), lowest risk (33.11%), and moderate risk (24.13%) for each decade. Finally, the LER exhibited significant heterogeneity among different scenarios. Specifically, the percentages of regions characterized by the highest (9.77%) and higher LER (9.75%) were notably higher than those in the remaining scenarios. The higher-level risk area under the localized SSP1 demonstrated a clear spatial reduction compared to those of the other four scenarios. In addition, in order to facilitate the differential management and control of LER by relevant departments, risk zoning was carried out at the county level according to the prediction results of LER. And we got three types of risk management regions for the XJRB under the LSSPs.

Projecting the response of ecological risk to land use/land cover change in ecologically fragile regions

Anthropogenic activities have dramatically altered land use/land cover (LULC), leading to ecosystem service (ES) degradation and further ecological risks. Ecological risks are particularly serious in ecologically fragile regions because trade-offs between economic development and ecological protection are prominent. Thus, ways in which to assess the response of ecological risks to LULC change under each development scenario in ecologically fragile regions remain challenging. In this study, future LUCC and its impact on ESs under four development scenarios in 2040 in western Jilin Province were predicted using a patch-generating land use simulation model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Ecological risk was assessed based on future LUCC possibilities, and potential ES degradation and potential drivers of ecological risks were explored using a geographic detector. The results showed that the cropland development scenario (CDS) would experience large-scale urbanization and cropland expansion. Carbon storage (CS), habitat quality (HQ), and water purification (WP) degraded the most under the CDS, and grain yield (GY) and water yield (WY) degraded the most under the ecological protection scenario (EPS). The LUCC probability under the CDS (14.37 %) was the highest, while the LUCC probability under the comprehensive development scenario (CPDS) (8.68 %) was the lowest. The risk of WP degradation was greatest under the CDS, but the risk of soil retention (SR) degradation was greatest under the natural development scenario (NDS), EPS, and CPDS. Ecological risk coverage was the largest (98.04 %), and ecological risks were the highest (0.21) under the CDS, while those under the EPS were the opposite. Distance to roads and population density had a higher impact on ecological risks than other drivers. Further attention should be given to the ecological networks and pattern establishment in urbanized regions. This study will contribute to risk prevention and sustainable urban and agricultural development.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.

Assessing the ecological risk of croplands in loess drylands by combining environmental disturbance with ecosystem vulnerability

Agroecosystems suffer various ecological risks due to the intensive production of crops. However, comprehensive assessments of cropland ecological risks remain limited. This study developed an assessment method for cropland ecological risks by combining environmental disturbance with ecosystem vulnerability. Environmental disturbance reflects stresses caused by risk sources in an environment, while ecosystem vulnerability is the susceptibility of an ecosystem to adverse disturbances and its capacity to cope and adapt. The proposed method is conducive to understanding the complex exposure-response relationship between croplands and environmental stresses. Cropland ecological risk was evaluated by conducting a case study on a loess dryland region in Shaanxi. The hot spots and driving factors of risk were explored using spatial autocorrelation and quantile regression methods, respectively. Results show that overall cropland ecological risk is at medium low level. Risk hot spots are concentrated in the north of the loess dryland. Ecosystem vulnerability exerts greater effect on the distribution of hot spots than environmental disturbance in the study area. Road density (RDD), river density, and soil organic matter exert the most important effects on cropland ecological risk. Moreover, the same driving factor exhibits various effects on cropland ecological risk in different risk level areas. RDD, slope, precipitation, elevation, fertilizer application rate, gross domestic product, and distance to town center have greater effects on risk in regions with high cropland ecological risk than in regions with low cropland ecological risk. The findings of this study must be considered in formulating targeted policies for controlling cropland ecological risk in loess drylands to realize sustainable crop production.

Exploring interactions in water-related ecosystem services nexus in Loess Plateau

Scientific understanding of the driving relationship between water-related ecosystem services (WESs) and influencing factors, as well as the trade-off and synergy relationship between WESs and WESs, is the premise of reasonably bringing them into management decisions. However, the existing research often separates the above-mentioned two relationships and conducts independent research, which leads to the conflict of research conclusions and cannot be well adopted by managers. Therefore, based on the panel data of Loess Plateau in 2000-2019, this paper uses the simultaneous equation model to combine the two kinds of relationships existing between WESs and influencing factors, establish a feedback loop, and reveal the interactions mechanism of WESs nexus. The results show that: (1) The fragmentation of land use leads to the uneven spatial-temporal distribution of WESs. (2) Vegetation factors and land factors are the main driving factors that affect WESs, and the impact of climate factors on WESs is decreasing year by year. (3) The increase of water yield ecosystem services will lead to the obvious increase in soil export ecosystem services, and there is a synergistic relationship between soil export ecosystem services and nitrogen export ecosystem services. The conclusion can provide an important reference for implementing the strategy of ecological protection and high-quality development.

Estimation of heavy metal soil contamination distribution, hazard probability, and population at risk by machine learning prediction modeling in Guangxi, China

Due to superposition of diverse pollution sources, soil heavy metal concentrations have been detected to exceed the recommended maximum permissible levels in many areas of Guangxi province, China. However, the heavy metal contamination distribution, hazard probability, and population at risk of heavy metals in the entire Guangxi province remain largely unclear. In this study, machine learning prediction models with different standard risk values determined according to land use types were used to identify high-risk areas and estimate populations at risk of Cr and Ni based on 658 topsoil samples from Guangxi province, China. Our results showed that soil Cr and Ni contamination derived from carbonate rocks was relatively serious in Guangxi province, and that their co-enrichment during soil formation was associated with Fe and Mn oxides and alkaline soil environment. Our established model exhibited excellent performance in predicting contamination distribution (R² > 0.85) and hazard probability (AUC>0.85). Pollution of Cr and Ni exhibited a pattern of decreasing gradually from the central-west areas to the surrounding areas with the polluted area (I_geo>0) of Cr and Ni accounting for approximately 24.46% and 29.24% of total area in Guangxi province, respectively, but only 10.4% and 8.51% of total area was classified as Cr and Ni high-risk regions. We estimated approximately 1.44 and 1.47 million people were potentially exposed to the risk of Cr and Ni contamination, which were mainly concentrated in the Nanning, Laibin, and Guigang. These regions are main heavily-populated agricultural regions in Guangxi, and thus heavy metal contamination localization and risk control in these regions are urgent and essential from the perspective of food safety.

Spatial pattern evolution and prediction scenario of habitat quality in typical fragile ecological region, China: A case study of the Yellow River floodplain area

The Yellow River basin is an important area for China to implement ecological protection policies. Studying the habitat quality of the Yellow River floodplain area is of great significance to the ecological security and sustainable development of the entire basin. This study primarily investigated the spatial pattern of habitat quality in the Yellow River floodplain area from 2000 to 2020, then, we also simulated changes of habitat quality in 2025-2035 and analyzed the influencing factors by coupling the PLUS (Patch-generating Land Use Simulation) model, InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model and RF (Random Forest) model. The results showed that:(1) From 2000 to 2020, cultivated land and build-up land constituted an important part of the Yellow River floodplain area, and the growth rate of build-up land was fast. (2) We also found that the ecological land (forest land, grassland, waterbody) had a higher contribution value to the habitat quality, while the build-up land had a lower contribution value to the habitat quality. (3) Overall, the habitat quality of the floodplain area showed a degradation trend from 2000 to 2020. In addition, the regions with low habitat quality accounted for the major proportion. (4) Based on the calculation results of the Random Forest (RF) model, we found that topographical relief (TR) and land use intensity (LUI) were the two most important factors affecting habitat quality of the floodplain area. (5) According to the four scenarios from 2025 to 2035, it is found that the habitat quality level would be the highest under the ecological protection scenario, while under the urban development scenario its level would be the lowest. This study attempts to combine the RF model with PLUS model to improve the objectivity and accuracy of the future prediction scenario of habitat quality, which can provide scientific reference for ecological governance and policy formulation in the Yellow River floodplain area.

Spatio-temporal characteristics and mechanism of ecological degradation in a hilly southern area-a case study of Dongting Lake Basin

Understanding the spatio-temporal characteristics of ecological degradation and its mechanism is the key to implementing national land space ecological restoration. Currently, there is a lack of knowledge about identifying ecologically degraded areas from a structure-function angle. This paper used the Dongting Lake Basin (DLB) as the research area, with the landscape pattern index and InVest model utilized to analyze the landscape distribution characteristics and ecosystem service functions in 2000 and 2018. Based on this, a fuzzy inference approach and geographic detectors were used to explore the characteristics and driving mechanism of ecological degradation in the DLB from 2000 to 2018. The results found are the following: (1) The overall landscape of the DLB was fragmented, the landscape shape tended to be complex, the degree of aggregation declined, and the landscape types were more discrete than before. In terms of the landscape-level index, the overall indicators of the landscape pattern in the DLB showed little change from 2000 to 2018, and the overall landscape pattern change was reasonably stable. (2) The three ecological services exhibited prominent spatial distribution features during the study period. In particular, food supply services showed a steady upward trend, while habitat quality and carbon storage services generally declined. (3) The ecological degradation in the DLB demonstrated striking spatial and temporal differences during the study period, and the ecological situation improved. The ecological degradation areas were mainly distributed in urban areas with denser populations and a higher level of urbanization, while the ecological restoration areas were mainly in the mountainous and hilly areas far away from the urban centers. (4) Among the influential factors, the production potential of urban land and farmland is the main factor that affects the ecological environment degradation and spatial distribution difference in the DLB. The interactive detection results indicate that the driving mechanism exhibits a two-factor enhancement or nonlinear increase.

The impact of land use and land cover changes on the landscape pattern and ecosystem service value in Sanjiangyuan region of the Qinghai-Tibet Plateau

Decades of intensifying human activities have caused dramatic changes in land use and land cover (LULC) in the ecologically fragile areas of the Qinghai-Tibet Plateau, which have led to significant changes in ecosystem service value (ESV). Taking the ecologically fragile Sanjiangyuan region of the Qinghai-Tibet Plateau as the research object, we focused on understanding the impact of LULC changes on the Sanjiangyuan's landscape pattern and its corresponding ESV, which was combined with a Markov-Plus model to predict LULC changes in 2030. The results showed: (1) from 2000 to 2020, the LULC of Sanjiangyuan has changed to varying degrees, respectively. In the central and southern regions where animal husbandry is the mainstay activity, the area of grass land converted to bareland had expanded; (2) from 2000 to 2010, the total regional ESV increased sharply. However, the total amount of ESV decreased from 2010 to 2020; (3) the overall ESV in the study area was observed to be trending down and is expected to decrease by approximately 4.25 billion CNY by 2030; (4) the fragmentation and complexity of regional landscape patterns will negatively affect local ecosystem stability and biodiversity. Overall, there is a strong temporal and spatial correlation between LULC and ESV. This study will provide a reference for the local government to provide targeted and sustainable land management policies, thereby promoting the improvement of the Qinghai-Tibet Plateau regional ecology value.

Spatio-Temporal Evolution and Influence Mechanism of Habitat Quality in Guilin City, China

Based on the models of ArcGIS10.5, Fragstats 4.2, and InVEST, this research describes the temporal and spatial evolution characteristics of habitat quality in Guilin from three aspects, which are land use change, landscape pattern change, and habitat quality evaluation, and further explores the main driving factors of Guilin's habitat quality change by using the method of geographic detector evaluation. The results indicate that from 2000 to 2020, the land use type in Guilin City is dominated by forest, accounting for the highest proportion of 77.87%. The forest has decreased significantly, the mutual transformation of forest and cropland is obvious, and the area of impervious has continued to increase. A large amount of cropland is occupied, indicating that human activities were the main factor in land use transformation. From 2000 to 2020, the irregularity of the patch shape of each land use type was deepened, the fragmentation degree was relatively stable, the landscape diversity was enhanced, and the spatial distribution of each patch showed a relatively obvious heterogeneity. From 2000 to 2020, the habitat quality of Guilin City was mainly high-grade and the habitat quality was good, but the overall trend showed a downward trend, and the spatial difference was obvious. From 2000 to 2020, elevation, normalized difference vegetation index (NDVI), splitting index (SPLIT), and slope were the main factors affecting the habitat quality of Guilin City, among which elevation and NDVI had the most significant effects.

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.

Multi-Scenario Simulation Analysis of Land Use Impacts on Habitat Quality in Tianjin Based on the PLUS Model Coupled with the InVEST Model

Land use change is an important cause of habitat quality change. In order to reveal the impact of urban land use change on habitat quality, and to explore sustainable development planning, this paper uses the city of Tianjin, China, as a case study. Based on land use data from 2000, 2010, and 2020, the PLUS model was first used to predict land use in 2030 under three scenarios, and the InVEST model was then used to assess habitat quality from 2000 to 2030. This study showed that habitat quality was highly correlated with land use change. The rapid expansion of construction land was the main reason for the year-by-year decline in habitat quality. From 2000 to 2030, habitat quality in Tianjin declined year-by-year according to the average habitat quality values for 2030 for the three scenarios: the Ecological Protection Scenario (EPS) > Natural Development Scenario (NPS) > Economic Construction Scenario (ECS). In the EPS, habitat quality will deteriorate and improve. It would be ecologically beneficial to continue to work on the revegetation of the Jizhou area. In the ECS, habitat quality will decline sharply. In Tianjin, urbanization will continue to accelerate. This is a threat to the sustainable development of the city.