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Introduction: The Kuye River Basin, a typical erosion area of the Loess Plateau region with the second largest tributary of the Yellow River Basin, faces significant environmental challenges and intense human activities. Balancing environmental sustainability and economic development in this region is urgent. Methods: Therefore, this study takes this basin as the object and evaluates the landscape ecological risk and human activity intensity from 2000 to 2022 based on land use/land cover, population distribution and nighttime lighting data. And a geographically weighted regression model was used to reveal the correlation between the two. Results and discussion: Results showed that the average landscape ecological risk index in 2000, 2015, and 2022 were 9.01×10-4, 5.61×10-4, and 7.40×10-4, respectively. This shows that the overall landscape ecological risk index is low in the first 15 years and then gradually increases over time. Cultivated land, grassland and construction land are the main landscapes, the expansion of 7.95 times construction land is a key factor in the intensification of human activities, mainly concentrated in the northwest, followed by a gradual expansion towards the south-east, and likewise the landscape ecological risks follow the same trend. Medium to high intensity human activities exacerbate landscape ecological risks, with a significant positive correlation, while low intensity human activities cause little damage to landscape ecology. To achieve sustainability, it is necessary to effectively control construction land and improve land use structure. Then, in the planning work, priority will be given to the northwest region with high human activity intensity and high landscape ecological risks, and the governance direction will gradually shift to the southeast region. These findings provide empirical evidence to support decision-making and underscore the need for comprehensive strategies to mitigate landscape ecological risks and promote sustainable development in the Kuye River Basin. [ABSTRACT FROM AUTHOR]

It has always been the focus of ecological geography research to analyze the disturbance of human activities to the habitats of endangered wild animals. This paper takes Hainan Island as the research area and the Easygo human footprints as the sample data, and it also selects the altitude, slope, land use type, average annual precipitation, average annual temperature, population density, distance from large settlements, distance from small settlements, distance from road network, and distance from water system as 10 environmental impact factors. The MaxEnt model is used to simulate and predict the spatial distribution pattern of human activity intensity in Hainan Island. The AUC is 0.745, which means the simulation result is pretty good. Furthermore, the result shows that the areas with high human activity intensity in Hainan Island are distributed along the road network in the surrounding flat coastal areas, while the high-altitude areas in the central and western regions have extremely low human activity intensity. Through the GIS spatial statistical analysis of both the distribution data of human activity intensity and the geographical distribution data of the endangered wild animals, it can be seen that the probability of wild animals being distributed in high inland areas with very low human activity is less than 20%, and the distribution probability of wild animals is over 60% in areas with low human activity intensity, while in surrounding coastal areas with high human activity intensity, the probability is also less than 20%. The results of this paper can provide support for the protection of endangered animals to better maintain biodiversity in Hainan Island. [ABSTRACT FROM AUTHOR]

IntroductionThe Kuye River Basin, a typical erosion area of the Loess Plateau region with the second largest tributary of the Yellow River Basin, faces significant environmental challenges and intense human activities. Balancing environmental sustainability and economic development in this region is urgent.MethodsTherefore, this study takes this basin as the object and evaluates the landscape ecological risk and human activity intensity from 2000 to 2022 based on land use/land cover, population distribution and nighttime lighting data. And a geographically weighted regression model was used to reveal the correlation between the two.Results and discussionResults showed that the average landscape ecological risk index in 2000, 2015, and 2022 were 9.01×10-4, 5.61×10-4, and 7.40×10-4, respectively. This shows that the overall landscape ecological risk index is low in the first 15 years and then gradually increases over time. Cultivated land, grassland and construction land are the main landscapes, the expansion of 7.95 times construction land is a key factor in the intensification of human activities, mainly concentrated in the northwest, followed by a gradual expansion towards the south-east, and likewise the landscape ecological risks follow the same trend. Medium to high intensity human activities exacerbate landscape ecological risks, with a significant positive correlation, while low intensity human activities cause little damage to landscape ecology. To achieve sustainability, it is necessary to effectively control construction land and improve land use structure. Then, in the planning work, priority will be given to the northwest region with high human activity intensity and high landscape ecological risks, and the governance direction will gradually shift to the southeast region. These findings provide empirical evidence to support decision-making and underscore the need for comprehensive strategies to mitigate landscape ecological risks and promote sustainable development in the Kuye River Basin.

ABSTRACTChina used to have the world’s most stringent prevention policies during the COVID-19 pandemic. The strict lockdown in Xinjiang region in 2022 was considered to be the longest in the process of COVID-19 pandemic prevention. Therefore, we examined the dynamic effects of the COVID-19 lockdown in Xinjiang region using night-time light data. The findings showed that before the outbreak of COVID-19, the night-time light in Xinjiang region still maintained a significant growth. However, after the lockdown of COVID-19 in August 2022, the night-time light showed a significant decrease, especially in August. Urumqi's night-time light decreased by at least 25% compared to the non-lockdown, while that in other major cities was between 5% and 15%. After the lockdown was lifted in November 2022, the night-time light in Urumqi increased by at least 50% compared to the lockdown, while that in other major cities was generally less than 30%. The regions where night-time light decreased in Urumqi were mainly within a radius of 40 km, whereas that in other cities were mainly concentrated within a radius of 6 km. The changes in statistical data and night-time light indicated that the economic activities in Xinjiang region were significantly affected by the COVID-19 lockdown.

Abstract Human activities profoundly impact the Earth system such as climate change, biodiversity, disease transmission. Accurately acquiring and assessing the human activity intensity (HAI) is crucial to exploring human‐nature relationships. However, the mismatch of geospatial data products between humans and natural environmental factors is a data bottleneck that restricts the innovation and development of regional human‐Earth systems. Nowadays, some HAI data products exist, such as the global human footprint map and the cumulative human modification map, but their spatial resolution is still too coarse (1 km) for regional research. Importantly, there are limitations to the method of mapping HAI: an incomplete indicator system that ignores the natural dimension makes the assessment of HAI less accurate and comprehensive; ignoring correlations among indicators, subjective weighting method and overlapping indicators lead to potential overestimation of HAI. Here, a new approach to improve the quantification of HAI at the regional scale was presented and the HAI of the Qinling‐Daba Mountains (QinBa) was mapped and analysed as a case study. First. an improved indicator system was constructed from two dimensions: natural environment and resources (including topography and river density), social and economics (including population density, degree of land modification, remoteness from roads/railways, remoteness from settlements and road density). The models for scoring the indicators were then improved. Additionally, principal component analysis was adopted to transform seven indicators into four independent principal components (PCs). The four PCs were combined based on their variance contribution to generate the HAI map, effectively eliminating redundancy and correlation among the indicators. The results showed that the improved method solved the problem of overestimation in previous studies and objectively mapped the HAI of QinBa. We found that although QinBa's HAI was moderate (MHAI = 0.48), places with low HAI were isolated as ‘islands’ by places with high HAI, indicating that the scope of human activities in this area is extensive. This study not only provides novel insights into quantifying HAI but also provides high‐resolution HAI data (100 m) and priority attention zones for human‐nature interaction studies in QinBa, which can help guide policy‐making for management and conservation efforts. Read the free Plain Language Summary for this article on the Journal blog.

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth, and severe anthropogenic-induced grassland ecosystem function declines have been reported recently. Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies. Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate (e.g., light, water, and wind), and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions. However, the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated. Here, we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China. Aboveground net primary productivity (ANPP) and resilience, two indicators of grassland ecosystem functions, were measured from field data and satellite remote sensing data. Terrestrial laser scanning data were collected to measure canopy structure (represented by mean height and canopy cover). Our results showed that plant species diversity was positively correlated to canopy structural traits, and negatively correlated to human activity intensity. Canopy structure was a significant indicator for ANPP and resilience, but their correlations were inconsistent under different human activity intensity levels. Compared to plant species diversity, canopy structural traits were better indicators for grassland ecosystem functions, especially for ANPP. Through structure equation modeling analyses, we found that plant species diversity did not have a direct influence on ANPP under human disturbances. Instead, it had a strong indirect effect on ANPP by altering canopy structural traits. As to resilience, plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover. This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances, which should be included in future grassland monitoring and management.

The interaction of human activity in national parks and their surrounding areas with natural landscapes is an important factor in the formulation of strategies for the protection and management of protected areas. From an integrated ecological and social perspective, this study aims to develop a human–land coupling model to reveal the ecological threats of human activities to national park and their surrounding areas under rapid urbanization. We first established a four-quadrant model based on the coupling relationship between human activity intensity (HAI) and the landscape pattern index (LPI). On this basis, we analyzed the spatial coupling characteristics of these indices from 2000 to 2020, preliminarily assessed the human–land relationship in Lushan National Park and its surrounding areas (LNPSA), and then explored the driving factors of the coupling relationship. The results show the following. (1) The proportion of regions with high and extremely high human activity intensity increased from 6.02% to 16.41% over the past two decades. These regions are mainly distributed in the surroundings of Lushan National Park, showing a gradually spreading trend to the core protected area. (2) The surroundings had higher landscape fragmentation, landscape diversity, and total variation in the landscape pattern compared with the core protected area. (3) Vegetation coverage and distance to Lushan have the most significant effect on the human–land coupling relationship in LNPSA, and human activity and natural evolution together shape the characteristics of this relationship in the study area. (4) Utilizing administrative divisions as the fundamental framework, the study area is delineated into four distinct zones based on the results of the human–land coupling analysis—harmonious development zones, stable transformation zones, environmental regulation zones, and risk prevention zones—with tailored optimization strategies proposed for each zone's characteristics. These findings can facilitate the optimized formulation of schemes for different regions and provide a comprehensive methodology to guide the planning and management of natural protected areas. [ABSTRACT FROM AUTHOR]

For the main grain-producing areas worldwide that balance multi-tasks of grain production, ecological protection, and economic development, quantitatively revealing the correlation between human activity intensity (HAI) and ecosystem service value (ESV) is conducive to formulating adapted ecological protection policies and promoting the coordinated development of the regional economy, society, and ecosystem. In this paper, we took the Songnen Plain of China as an example, employed a modified Equivalent Factor Method (integrating socio-economic data, the normalized difference vegetation index (NDVI), and land use data), and the HAI Assessment Model (based on the data of land use, night-time light, and population spatial distribution) to measure the ESV and HAI in the Songnen Plain of China for the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020. We further applied the standard deviational ellipse method, the coupled coordination degree model, and the bivariate spatial autocorrelation models to reveal the spatiotemporal dynamics and correlation characteristics of ESV and HAI. The results showed the following: (1) Temporally, the ESV declined from 950.96 billion yuan in 1990 to 836.31 billion yuan in 2015, and then increased to 864.60 billion yuan in 2020, with the total loss attributed to the significant decline in the ESV of the natural ecosystem. Spatially, the ESV in the western and northeastern regions was relatively high, with a significant increase in the northeast. (2) HAI showed an upward trend from 1990 to 2020, while the high HAI levels gradually shrank after reaching the peak in 2000. Low HAI levels were mainly distributed in the northeast and southwest, aligning with the ecological space, while high HAI levels were distributed in the middle and southeast. (3) The interaction between ESV and HAI was marked by a negative correlation, transitioning from conflict to coordination. The spatial pattern of HAI and ESV showed H (HAI)-L (ESV) and L-H clustering, with H-H and L-L scattered distributions. This study contributes to providing a framework, methods, and suggestions for the sustainable planning and utilization of land and ecological protection in order to offer scientific references for the Songnen Plain, other major grain-producing areas, and related studies. [ABSTRACT FROM AUTHOR]

Human activities profoundly impact the Earth system such as climate change, biodiversity, disease transmission. Accurately acquiring and assessing the human activity intensity (HAI) is crucial to exploring human‐nature relationships. However, the mismatch of geospatial data products between humans and natural environmental factors is a data bottleneck that restricts the innovation and development of regional human‐Earth systems. Nowadays, some HAI data products exist, such as the global human footprint map and the cumulative human modification map, but their spatial resolution is still too coarse (1 km) for regional research. Importantly, there are limitations to the method of mapping HAI: an incomplete indicator system that ignores the natural dimension makes the assessment of HAI less accurate and comprehensive; ignoring correlations among indicators, subjective weighting method and overlapping indicators lead to potential overestimation of HAI.Here, a new approach to improve the quantification of HAI at the regional scale was presented and the HAI of the Qinling‐Daba Mountains (QinBa) was mapped and analysed as a case study. First. an improved indicator system was constructed from two dimensions: natural environment and resources (including topography and river density), social and economics (including population density, degree of land modification, remoteness from roads/railways, remoteness from settlements and road density). The models for scoring the indicators were then improved. Additionally, principal component analysis was adopted to transform seven indicators into four independent principal components (PCs). The four PCs were combined based on their variance contribution to generate the HAI map, effectively eliminating redundancy and correlation among the indicators.The results showed that the improved method solved the problem of overestimation in previous studies and objectively mapped the HAI of QinBa. We found that although QinBa's HAI was moderate (MHAI = 0.48), places with low HAI were isolated as 'islands' by places with high HAI, indicating that the scope of human activities in this area is extensive.This study not only provides novel insights into quantifying HAI but also provides high‐resolution HAI data (100 m) and priority attention zones for human‐nature interaction studies in QinBa, which can help guide policy‐making for management and conservation efforts. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a displacement ventilation system, the human body heat source plays a significant role and should not be overlooked. The intensity of human activity is a crucial factor in determining the amount of heat generated by the human body. In this work, the impact of thermal plumes generated by different levels of human activity intensity on the temperature stratification and ventilation efficiency in displacement ventilation system have been explored with experimental and numerical methods. The airflow patterns in the displacement air supply system under various levels of human activity intensity were simulated. The results show that under the same air supply speed, as the human activity intensity increasing from 125 W to 402 W, the phenomenon of temperature stratification becomes more pronounced. With the promotion of thermal plumes, the air flow velocity on the human body surface increases, leading to an enhancement in ventilation efficiency. Therefore, to elimination of the indoor thermal load, the air supply speed can be reasonably reduced according to the human activity intensity in the room, further enhancing the energy-saving effect of displacement ventilation.

Human activities are the main factors threatening ecosystems, and ecological management should place a strong emphasis on guiding their positive development. More targeted and differentiated ecological preservation strategies can be proposed with research on the regional variability of the interaction between ecosystems and human activities. Utilizing the Vigor-Organization-Resilience-Services (VORS) model and the land surface human activity intensity model, the ecosystem health index (EHI) and human activity intensity (HAI) of the Poyang Lake Ecological Economic Zone (PLEEZ) were evaluated, respectively. Furthermore, their spatial association was investigated using spatial autocorrelation analysis, and the effect of HAI on EHI was analyzed using Geographic Detector and Geographic Weighted Regress. The results revealed that in the PLEEZ, (1) The zones of intensified human activity were highly consistent with the expansion of built-up land, and the EHI here has always been the lowest; (2) The HAI and EHI showed a significant and increasing spatial negative correlation, manifested as high HAI with low EHI clustering in the central plains and the adverse in the peripheral mountainous areas. (3) The HAI has the highest explanatory power on the spatial pattern of EHI, and its influence was greatest at the lower EHI. (4) Natural ecosystems with high EHI were highly sensitive to HAI, while areas with low EHI were less sensitive. The research can offer theoretical guidance for ecological construction in the future. The adverse effects of human activities in PLEEZ can be reduced through strict regulation of natural ecological sources, intensive utilization of human gathering areas, and promotion of green production and lifestyle.

Comprehensively and objectively revealing the spatial relationship between ecosystem services (ESs) and human activity intensity (HAI) is vital for achieving sustainable development goals. However, existing studies still have an incomplete understanding of how ESs change with HAI gradients. Therefore, taking Shenzhen City, China, as an example region experiencing rapid urbanization, the distribution of ESs and HAI in 2010 and 2020 were quantified using the InVEST model and the human footprint index method; at the same time, the gradient perspective was introduced and the spatial and temporal correlation characteristics of ESs along 10 HAI gradient bands, from weak to strong, were captured by applying multiscale geographically weighted regression (MGWR) and the bivariate spatial autocorrelation model. The findings showed that (1) the HAI demonstrated an increasing trend (20.63 (2010) and 23.36 (2020)), and the area with high values of HAI (the 10th gradient band) was distributed in the western part of the study area; meanwhile, the area with low HAI values (the 1st gradient band) was more distributed in the eastern part of the study area. (2) On the whole, the average levels of water conservation, soil conservation, carbon storage, and habitat quality decreased from 2010 to 2020; the spatial distribution characteristics of these parameters were similar. (3) In general, ESs were negatively correlated with HAI, and the negative correlation ratio was more than 65%. At the same time, the spatial and temporal correlations between ES patterns and HAI under different gradient bands were significant. These findings can effectively alleviate the pressure on the ecosystem caused by human activities, which is of great significance for the sustainable development of highly urbanized regions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantitatively investigating and assessing the spatiotemporal variation in the human activity intensity (HAI) and its influencing factors are crucial for coordinating regional human–land relationships and protecting the eco-environment. However, the scale, variation pattern, and driving factors of the human activity intensity in the Guangxi Beibu Gulf Zone have not yet been determined. To bridge this gap, this study focused on evaluating and analyzing the spatiotemporal variation characteristics of the HAI from 2000 to 2020 by a quantitative model based on multi-period land use data, field survey data, and statistical data. Moreover, the socioeconomic and geographical determinants were investigated using the PCA method and Geodetector model. The results showed that (1) the overall HAI value was relatively high and increased from 18.97% in 2000 to 20.06% in 2020. The HAI value in Haicheng District was always the highest and peaked at 60.78% in 2020. (2) Spatially, approximately 19.88% of the area exhibited high HAI values, and these areas were mainly distributed in urban districts, county seats, towns, and farming areas. In contrast, the areas with low HAI values were mainly concentrated in mountainous areas and natural reserves. (3) The HAI exhibited a significant spatial agglomeration effect, and HAI hot spots were concentrated mainly in areas with intense change, such as economic development zones, industrial parks, urban suburbs, and towns with added counties. (4) The HAI was affected by various influencing factors, and the explanatory power of multiple factors for the HAI change was greater than that of a single factor.

Investigating the spatiotemporal variation of human activity intensity and its determinants is a crucial basis for further revealing the mechanism of human-environment interaction and optimizing the human development mode. In this study, the human activity intensity on the Qinghai–Tibet Plateau (QTP) from 1990 to 2020 was measured based on the quantitative model of land use data and the actual regional background, and the underlying natural and socioeconomic determinants were investigated using spatial econometric methods. The results demonstrate that (1) the human activity intensity in QTP has increased by 11.96%, and there are differences in different spatial scales; the areas with high human activity intensity are distributed in the Hehuang Valley where Xining City and its surrounding areas are located, as well as the One-River and Two-River Area where Lhasa City and surrounding areas are located. (2) Human activity intensity has significant positive spatial spillover, suggesting that local changes will cause changes in the same direction in adjacent areas. (3) The human activity intensity in QTP is affected by various determinants. Concerning socioeconomic factors, the economic level has no significant impact on the human activity intensity in QTP, which differs from the general regional law. Both urbanization and traffic conditions have a significant positive effect, and the impact intensity continues to increase. Concerning natural factors, topographic relief has a significant positive effect; the impacts of temperature and vegetation coverage have changed from insignificant to a significant positive effect; the impacts of precipitation and river network density have not been verified; there is no linear relationship between altitude and human activity intensity in the entire QTP, while it exists in local regions. Finally, this study proposes three policy implications for the realization of a more harmonious human-environment relationship in QTP.

The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited. In this study, we used Tencent LBS data, Terra/Aqua MODIS land surface temperature (LST) data, and land use data to investigate the relationship between LBS and the urban thermal environment, specifically the LST and surface urban heat island intensity (SUHII) across China and its provinces. Our results showed that (1) in summer, the heat island effect was an issue in 94% of the urban areas in China, which was worse during the day. The high- and low-value periods of LBS data on a given day coincided with the acquisition times of MODIS LST products during the day and at night, respectively. (2) During both the day and at night, there was a significant connection between LBS data and the urban thermal environment in China. The highest correlation coefficient (r) between LBS data and the LST could reach 0.55 (p < 0.01) at the provincial level, and the highest correlation coefficient (r) between LBS data and the SUHII could reach 0.78 (p < 0.01) at the provincial level. (3) The urban thermal environment diurnal difference and LBS data exhibited a significant relationship. The ΔLBS diurnal differences were significantly positively related to the SUHII diurnal differences in China. The overall study findings revealed that LBS data constitute an important parameter to represent the human activity intensity when investigating the formation of the urban thermal environment in China. [ABSTRACT FROM AUTHOR]

Ecological resilience reflects the role of human activity intensity (HMI) on regional ecosystem services (ESs), and resilience improvement is crucial for the high-quality development of urban agglomeration areas. However, a theoretical framework for ecological resilience needs to be developed based on ES thresholds under human activities. Based on the threshold index, we used threshold regression model to determine of the nonlinear dominant factors affecting ESs and to identify the priority areas for ecological restoration. The results suggest that (1) CS, HQ, and TES declined, while FP increased. The spatial distribution of each ES showed higher values in the central region and lower values in the surrounding areas. HMI showed a significant upward trend, with expanding high-HMI areas. There is a threshold effect in the relationship between HMI and ESs, leading to variations in their positive, non-monotonic, or non-linear interactions. (2) HMI positively and significantly affects ESs, especially at low threshold levels. The effect of HMI on ESs is negative in counties with higher threshold levels rather than in lower-level grids. (3) The critical area of artificial potential ecological restoration was 712 km2, primarily concentrated around urban; the critical area of natural restoration was 490 km2. Therefore, the threshold in the relationship between HMI and ESs should be given more attention. This study serves as a guide for picking out key regions for territorial ecological restoration. [ABSTRACT FROM AUTHOR]

Restoration action is critical to ensure a safe environment for humans. Reasonable planning is essential to optimize the efficiency of ecological restoration inputs and outputs when implementing restoration measures. In this study, a method that combines human activity intensity assessment and multi-criteria decision analysis to determine ecological restoration priority (ERP) areas was developed to identify priority and feasible areas for ecological restoration in Shaanxi Province in 2020. The results showed that the total area involved in restoration feasibility assessment in Shaanxi is 10.89 × 104 km2. Among them, the percentage of regions with low feasibility (less than 0.2) is 68.86%, mainly located in Qinling area. High feasibility areas (more than 0.6) accounted for 2.47%, mainly located in the Loess Plateau area of northern Shaanxi. The spatial distribution of the human activity intensity is concentrated in urban areas and extended with the distribution of roads. In total, 10.69% of the regions showed high and very high intensity of human activity, including the Guanzhong urban agglomeration region. This study identified 6078 km2 and 671 km2 of medium and high ecological restoration priority areas, which are more concentrated in the north of the study area. The need for ecological restoration work is even more urgent in northern Shaanxi. In general, the framework in this study has spatially located the priority and feasible areas for restoration, and may provide a useful reference for landscape-scale spatial conservation planning. [ABSTRACT FROM AUTHOR]

The interaction of human activity in national parks and their surrounding areas with natural landscapes is an important factor in the formulation of strategies for the protection and management of protected areas. From an integrated ecological and social perspective, this study aims to develop a human–land coupling model to reveal the ecological threats of human activities to national park and their surrounding areas under rapid urbanization. We first established a four-quadrant model based on the coupling relationship between human activity intensity (HAI) and the landscape pattern index (LPI). On this basis, we analyzed the spatial coupling characteristics of these indices from 2000 to 2020, preliminarily assessed the human–land relationship in Lushan National Park and its surrounding areas (LNPSA), and then explored the driving factors of the coupling relationship. The results show the following. (1) The proportion of regions with high and extremely high human activity intensity increased from 6.02% to 16.41% over the past two decades. These regions are mainly distributed in the surroundings of Lushan National Park, showing a gradually spreading trend to the core protected area. (2) The surroundings had higher landscape fragmentation, landscape diversity, and total variation in the landscape pattern compared with the core protected area. (3) Vegetation coverage and distance to Lushan have the most significant effect on the human–land coupling relationship in LNPSA, and human activity and natural evolution together shape the characteristics of this relationship in the study area. (4) Utilizing administrative divisions as the fundamental framework, the study area is delineated into four distinct zones based on the results of the human–land coupling analysis—harmonious development zones, stable transformation zones, environmental regulation zones, and risk prevention zones—with tailored optimization strategies proposed for each zone’s characteristics. These findings can facilitate the optimized formulation of schemes for different regions and provide a comprehensive methodology to guide the planning and management of natural protected areas.

For the main grain-producing areas worldwide that balance multi-tasks of grain production, ecological protection, and economic development, quantitatively revealing the correlation between human activity intensity (HAI) and ecosystem service value (ESV) is conducive to formulating adapted ecological protection policies and promoting the coordinated development of the regional economy, society, and ecosystem. In this paper, we took the Songnen Plain of China as an example, employed a modified Equivalent Factor Method (integrating socio-economic data, the normalized difference vegetation index (NDVI), and land use data), and the HAI Assessment Model (based on the data of land use, night-time light, and population spatial distribution) to measure the ESV and HAI in the Songnen Plain of China for the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020. We further applied the standard deviational ellipse method, the coupled coordination degree model, and the bivariate spatial autocorrelation models to reveal the spatiotemporal dynamics and correlation characteristics of ESV and HAI. The results showed the following: (1) Temporally, the ESV declined from 950.96 billion yuan in 1990 to 836.31 billion yuan in 2015, and then increased to 864.60 billion yuan in 2020, with the total loss attributed to the significant decline in the ESV of the natural ecosystem. Spatially, the ESV in the western and northeastern regions was relatively high, with a significant increase in the northeast. (2) HAI showed an upward trend from 1990 to 2020, while the high HAI levels gradually shrank after reaching the peak in 2000. Low HAI levels were mainly distributed in the northeast and southwest, aligning with the ecological space, while high HAI levels were distributed in the middle and southeast. (3) The interaction between ESV and HAI was marked by a negative correlation, transitioning from conflict to coordination. The spatial pattern of HAI and ESV showed H (HAI)-L (ESV) and L-H clustering, with H-H and L-L scattered distributions. This study contributes to providing a framework, methods, and suggestions for the sustainable planning and utilization of land and ecological protection in order to offer scientific references for the Songnen Plain, other major grain-producing areas, and related studies.

The Three Gorges Dam project and other human activities, including regional urbanization and industrialization, have had a substantial influence on the biological environment of the Three Gorges Reservoir Area (TGRA). They have changed the surface land use pattern, disrupted ecosystem structure and function, and influenced changes in the value of ecosystem services. The human activity intensity (HAI) assessment model, the ecosystem services value (ESV) assessment model, and the bivariate spatial autocorrelation model were used based on the spatiotemporal evolution data of towns along the Yangtze River in the TGRA in 1995, 2000, 2005, 2010, 2015, and 2020. At the same time, the spatiotemporal impact of the HAI on land use patterns was evaluated and the magnitude of the spatiotemporal influences on the ESV was investigated. The findings demonstrate the following: (1) The TGRA's higher reaches are occupied by forested land, while the middle and lower portions are characterized by agricultural land. Land change in the reservoir region has mostly featured transitions between wooded land, agricultural land, grassland, and building land during the last 25 years. Because of differences in natural geography and administrative divisions, the intensity of human activity in the TGRA changes throughout the Yangtze River, with higher intensity in Chongqing and lower intensity in Hubei. By comparing the ESV and the HAI and validating with Moran scatter plots, it was determined that there is a negative relationship between the value of ecosystem services and the intensity of human activities. (2) The ESV rose from CNY 1017.16 × 108 in 1995 to CNY 1052.73 × 108 in 2020, suggesting that the policies of converting farmland back into forests, eliminating outdated production capacity, and developing green industries, among other ecological conservation measures, are effective. (3) In the research area, the effect coefficient of HAI on ESV ranges from −0.02 to −0.032 to −0.031. This coefficient represents the correlation between the HAI and ESV and can preliminarily judge the change in the degree of correlation between the HAI and ESV. The increase in HAI leads to a decrease in the value of ecosystem services, and there is a clear negative spatial correlation between the two. The low human activity area and low ecosystem service value area in the Chongqing section have been transformed into a high ecosystem service value area through years of returning farmland to forest and ecological management measures for sustainable development. [ABSTRACT FROM AUTHOR]

With the rapid development of society and the economy, human activities are increasing, which often brings potential threats such as a decline in forest resource quality and ecological function. In order to investigate the change in forest resource quality and human activity intensity, this study constructed a calculation model for a forest resource quality index and a human activity intensity index and conducted a quantitative analysis of the temporal and spatial changes in forest resource quality and human activity intensity in Yulin City based on sub-compartment data in 2017 and 2020. By combining spatial autocorrelation analysis, the changes in human activity intensity and spatial forest resource quality were explored, and key areas such as the prominent contradictions between humans and the land were superimposed and coupled as potential areas of concern. The results show the following: From 2017 to 2020, the forest resource quality in Yulin City improved as a whole, especially in Zizhou County, but there were increases and decreases in other regions. Human activity intensity increased as a whole, and the most obvious increase was in Hengshan District. Both the forest resource quality and human activity intensity indexes had spatial aggregation, the differences in forest resource quality between regions were reduced, and human activity intensity showed a trend towards aggregated development. The high–high cluster area for human activity intensity showed a decreasing trend, but it expanded outward in urban areas and other human-gathering areas, such as the surrounding area of Yulin City, Jingbian County, and Shenmu City. The high–high cluster area for forest resource quality showed a shrinking trend. Four specific regions were identified through a spatial coupled superposition analysis to reveal the dynamic relationship between forest resource quality and human activity intensity. The most obvious region was the Yuyang District, where the forest resource quality improved because of a reduction in the pressure of human activities on the natural environment. [ABSTRACT FROM AUTHOR]

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth, and severe anthropogenic-induced grassland ecosystem function declines have been reported recently. Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies. Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate (e.g., light, water, and wind), and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions. However, the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated. Here, we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China. Aboveground net primary productivity (ANPP) and resilience, two indicators of grassland ecosystem functions, were measured from field data and satellite remote sensing data. Terrestrial laser scanning data were collected to measure canopy structure (represented by mean height and canopy cover). Our results showed that plant species diversity was positively correlated to canopy structural traits, and negatively correlated to human activity intensity. Canopy structure was a significant indicator for ANPP and resilience, but their correlations were inconsistent under different human activity intensity levels. Compared to plant species diversity, canopy structural traits were better indicators for grassland ecosystem functions, especially for ANPP. Through structure equation modeling analyses, we found that plant species diversity did not have a direct influence on ANPP under human disturbances. Instead, it had a strong indirect effect on ANPP by altering canopy structural traits. As to resilience, plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover. This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances, which should be included in future grassland monitoring and management.

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The interactions between human activities and land cover have a significant impact on ecosystems. Therefore, studying human activity intensity based on land use or land cover is crucial for understanding the sustainable development of ecosystems. In this study, we selected Anhui Province as the study area and estimated the human activity intensity of land surface (HAILS) in 2015 and 2020 based on the ChinaCover datasets. We further analyzed the spatial, slope, and hydrological distribution characteristics of HAILS and explored the drivers of HAILS changes. The results show that the areas with higher HAILS were mainly located in the central part of Hefei, as well as along the Yangtze and the Huaihe rivers. The largest changes from 2015 to 2020 happened in the gentle slopes with the HAILS of 20–30%, and the percentage of HAILS > 20% decreased over the slope of 15°. In the riparian zone, the HAILS showed a clear decreasing trend after 2 km, while the HAILS in 2020 were higher than in 2015 in each flow-path distance belt, except for the Huaihe river. The HAILS index was strongly correlated with population density, rural population density, urban population density, average land GDP of primary industry, and nighttime light data. The rapid growth of the urban population and economy, as well as ecological protection policies, were identified as drivers of the above changes. Therefore, the HAILS in 2015 and 2020 of Anhui Province provide effective data support to address regional ecological conservation issues. [ABSTRACT FROM AUTHOR]

COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the heterogeneous impact of the COVID-19 lockdown on human activities and urban economy using monthly nighttime light data. We found that the impact of lockdown on human activities in the Yangtze River Delta is very obvious. The number of counties in Shanghai, Jiangsu, Zhejiang and Anhui showing a downward trend of MNLR (Mean of Nighttime Light Radiation) is 100%, 97%, 99% and 85%, respectively. Before the outbreak of COVID-19, the proportion of counties with a downward trend of MNLR was 19%, 67%, 22% and 33%, respectively. Although the MNLR of some counties also decreased in 2019, the scope and intensity was far less than 2022. Under regular containment (2020 and 2021), MNLR in the Yangtze River Delta also showed a significant increase (MNLR change > 0). According to NLRI (Nighttime Light Radiation Influence), the Shanghai lockdown has significantly affected the surrounding provinces (Average NLRI < 0). Jiangsu is the most affected province other than Shanghai. At the same time, Chengdu-Chongqing, Guangdong–Hong Kong–Macao and the Triangle of Central China have no obvious linkage effect. [ABSTRACT FROM AUTHOR]

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Colored dissolved organic matter (CDOM) is highly spatiotemporally varied due to the effects of complex environmental factors within a catchment or system. The seasonal nutritional status and potential risks of heavy metals in the coastal rivers of the Liaohe River basin were evaluated based on 40 water samples in January, April, May, and September. Meanwhile, the effects of environmental factors on CDOM, especially human activities, were quantitatively analyzed. The trophic state index (TSI) and the potential ecological risk index (RI) of heavy metals in the Liaohe River basin exhibited significant differences. The rivers were mesotrophic in January, lightly eutrophic in May, and highly eutrophic in April and September. An extremely high RI was shown in April and May, while a high RI was exhibited in September. CDOM exhibited great seasonal characteristics and showed significant seasonal correlations with environmental factors. Based on multiple general linear model analysis, total phosphorus (TP) was the most influential factor and significantly explained 62.1% of aCDOM(440) (p < 0.01) among the water parameters, followed by total alkalinity (38.3%). The percentages of built-up area exerted significantly positive effects on aCDOM(440) (R2 = 0.44), while distance from oil extraction sites significantly negatively affected aCDOM(440) (r = − 0.328, p < 0.05). Polluting enterprises showed non-significant correlation with CDOM (r = 0.314, p = 0.178). [ABSTRACT FROM AUTHOR]

Abstract As the ‘third pole’ of the world, the Qinghai‐Tibetan Plateau (QTP) is extremely ecologically sensitive and fragile, while also facing increasing levels of human activity, especially overgrazing. In this study, livestock data from the statistical yearbook were transformed to 1 km grazing intensity raster data by integrating the net primary productivity (NPP) data for 1990, 1995, 2000, 2005, 2010 and 2015. Furthermore, the human activity intensity (HAI) data at 1 km resolution were evaluated by the entropy weight method, which applied eight types of spatial data, including grazing intensity, Night‐Time Light, population density, Gross Domestic Product (GDP) density, the ratio of cultivated land, distance to road, distance to town and the slope of the Normalized Difference Vegetation Index (NDVI). Also, the five‐year interval human activity intensity data on the QTP from 1990 to 2015 were evaluated. By preparing the historical spatial data sets of grazing intensity and human activity intensity, our study will help to explore the influence of human disturbance on the alpine ecosystems on the QTP, as well as provide effective support for the decision‐making of the government aiming to achieve regional ecosystem management and sustainable development.

Vegetation changes and factors have a profound influence on the local ecology, the economy, and the long-term durability of human construction. This study focuses on the impacts of climate change and human activity on vegetation changes on the Qinghai-Tibet Plateau and aims to develop a dataset of human activity levels on the plateau. Sen and Mann-Kendall trend analysis was used to evaluate the spatial distribution of vegetation NDVI and its trends, as well as the lagged response of plant growth to climatic circumstances. Using a geodetector model, the effects of meteorological and anthropogenic intensity data were examined. The study's findings show that, although anthropogenic influences and ecosystem vulnerability caused a decline in the region's vegetation, a stable climate and a healthy ecosystem supported the growth of vegetation. From 2000 to 2017, the area where vegetation grass improved significantly accounted for the highest proportion, reaching 34.22%. Different anthropogenic intensities are distributed spatially, and this interplay of anthropogenic intensities and climatic factors affects the distribution of vegetation greenness more than each element acting alone. The study of how human activity and climate change affect vegetation greenness can offer practical recommendations for maintaining the Qinghai-Tibet Plateau's natural ecology. It is crucial to responsibly safeguard the Qinghai-Tibet Plateau's environment in response to the nation's ecological civilization. [ABSTRACT FROM AUTHOR]

Stream networks are the transportation channels of pollutants that can significantly influence water environment risk (WER). However, the influences of stream network structure and connectivity (SC) on WER at the national scale and its regional variability have been rarely investigated in China. In this study, the WER was assessed from the grey water footprint of nitrogen and phosphorus in 214 catchments in China. The relationship between WER and SC and its regional variability were analyzed using correlation and grey relational analysis. Results showed that the water environment risk index (RI) in some catchments located in the Hai and Liao River Basins was the highest in China (RI > 0.8). On national scale, longitudinal connectivity (Cl) and cyclical connectivity (Cc) had the strongest influences on WER with grey relational degree index (GRAI) of 0.68 and 0.67, respectively. The average slope (Sr) was the most important in humid zones, whereas Cl and water surface ratio (Rw) had a stronger influence in arid zones. In zones with intensive human activities, Cc, river density (Rd), and the node connection ratio (Rnc) mostly affected WER. The main influenced factors varied significantly among nationwide and different zones, which indicated that climate and human activities played an important role in the spatial variation of the relationship between WER and SC. This study highlights the important role of SC on WER and that the relationship between WER and SC varies with climate and human activities. [ABSTRACT FROM AUTHOR]

The coupling and coordinated change characteristics of land-use production, living, and ecological functions (PLEFs) and their relationship with human activity intensity (HAI) in ecologically fragile areas are important to study, especially in promoting the sustainable development of regional land-use and revealing the evolution of the human–land relationship. In this paper, the coupling coordination degree (CCD) model was used to analyze the coordinated development level of PLEF in Wanzhou District from 2000 to 2020. The HAI was measured by the equivalent of construction land. The synchronous development model was introduced to analyze the relationship between them. The results showed that, in Wanzhou District, the PLEFs showed significant spatial distribution differences and evident spatial complementarity. The PLEFs of Wanzhou District were at a good coordination level, but exhibited a downward trend. A spatial pattern of "high in the west and low in the southeast" was presented. The CCD of the production–living function was poor, which is the critical direction of future optimization. The value of HAI in Wanzhou District showed an increasing trend and exhibited a high concentration in the central town and its surrounding regions. According to the synchronous development state of the HAI and the CCD of the PLEFs, Wanzhou District was divided into three development types. The development type of most areas of Wanzhou District was positive, but the area decreased over the past 20 years. Therefore, it is crucial to propose other regulatory strategies for regions with different development types. This research will provide a decision-making reference for promoting the coordination of the PLEFs and alleviating human–land relations in the reservoir area of central and western China, mountainous regions, and similar areas in developing countries. [ABSTRACT FROM AUTHOR]

As the urbanization is being rapidly boosted, the urban habitat quality has been significantly disturbed by human activities through land use, which highly affects the urban ecological environment and sustainable development of social economy. However, the change characteristics of the habitat quality and human activities in different topographic gradients in rapidly urbanized mountainous cities remain unclear. Accordingly, Guiyang in China, is selected as the representative of typical mountain cities. The change characteristics of the habitat quality, the human activity intensity and their correlation in mountainous cities from 2000 to 2020, are analyzed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, as well as the ArcGIS software based on the remote sensing interpretation data in 2000, 2010 and 2020. The results demonstrate that the overall habitat quality in Guiyang decreased by 0.0304, while the human activity intensity increased by 0.0287 from 2000 to 2020. The amount of changes of the habitat quality and the human activity intensity in Guiyang from 2010 to 2020, are higher than those from 2000 to 2010. The amount of changes of the habitat quality and the human activity intensity in Guiyang decreases with the increase of the slope. The central and southern parts of Guiyang are the highlight areas with a significant decline of habitat quality and significant increase of human activity intensity. The areas with an increased habitat quality and decreased human activity intensity are sporadically distributed. A significant negative correlation is reported between the change of the habitat quality and human activity intensity in Guiyang. In addition, a prominent spatial heterogeneity is identified in the local indicators of the spatial association (LISA) map. The significant increase in the artificial land and the decrease in the natural land as affected by the rapid urbanization, act as crucial factors leading to the decline of the habitat quality and the increase in the human activity intensity in mountainous cities. This also facilitates the formation of the negative correlation between the habitat quality and the human activity intensity. [ABSTRACT FROM AUTHOR]

Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects, including fully understanding the causes and mechanisms of climate change, actively adapting to climate change, pursuing rational development, and protecting the ecological environment. Taking the north slope of Tianshan Mountains, located in the arid area of northwestern China and extremely sensitive to climate change, as the research area, this study retrieves the surface temperature of the mountain based on MODIS data, while characterizing the intensity of human activities thereby data on the night light, population distribution and land use. The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed, and the spatio-temporal correlation between them was further explored. It is found that: (1) The average human activity intensity (0.11) in the research area has kept relatively low since this century, and the overall trend has been slowly rising in a stepwise manner (0.0024·a−1); in addition, the increase in human activity intensity has lagged behind that in construction land and population by 1–2 years. (2) The annual average surface temperature in the area is 7.18 °C with a pronounced growth. The rate of change (0.02 °C·a−1) is about 2.33 times that of the world. The striking boost in spring (0.068 °C·a−1) contributes the most to the overall warming trend. Spatially, the surface temperature is low in the south and high in the north, due to the prominent influence of the underlying surface characteristics, such as elevation and vegetation coverage. (3) The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there, showing a strong distribution in the east section and a weak one in the west section. The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities, manifestations, and land-use changes. Vegetation-related human interventions, such as agriculture and forestry planting, urban greening, and afforestation, can effectively reduce the surface warming caused by human activities. This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development. [ABSTRACT FROM AUTHOR]

Human activities have significantly degraded ecosystems and their associated services. By understanding the spatio-temporal variability and drivers of human activity intensity (HAI), we can better evaluate the interactions between human and terrestrial ecosystems, which is essential for land-use related decision making and eco-environmental construction. As the "third pole," the Tibetan Plateau (TP) plays a strong role in shaping the global environment, and acts as an important ecological security barrier for China. Based on land-use/cover change data, environmental geographic data, and socioeconomic data, we adopted a method for converting different land use/cover types into construction land equivalent to calculate the HAI value and applied the Getis-Ord Gi* statistic to analyze the spatio-temporal dynamics associated with HAI since 1980 on the TP. Thereafter, we explored the forces driving the HAI changes using GeoDetector software and a correlation analysis. The main conclusions are as follows: It was observed that HAI increased slowly from 3.52% to 3.65% during the 1980–2020 period, with notable increases in the western part of the Qaidam Basin and Hehuang Valley. Spatially, HAI was associated with a significant agglomeration effect, which was mainly concentrated in the regions of the Yarlung Zangbo and Yellow-Huangshui rivers. Both natural and anthropogenic factors were identified as important driving forces behind the spatial changes in HAI, of which soil type, gross domestic product, and population density had the greatest influence. Meanwhile, the temporal changes in HAI were largely driven by economic development. This information provides crucial guidance for territory development planning and ecological-protection policy decisions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the 'third pole' of the world, the Qinghai‐Tibetan Plateau (QTP) is extremely ecologically sensitive and fragile, while also facing increasing levels of human activity, especially overgrazing. In this study, livestock data from the statistical yearbook were transformed to 1 km grazing intensity raster data by integrating the net primary productivity (NPP) data for 1990, 1995, 2000, 2005, 2010 and 2015. Furthermore, the human activity intensity (HAI) data at 1 km resolution were evaluated by the entropy weight method, which applied eight types of spatial data, including grazing intensity, Night‐Time Light, population density, Gross Domestic Product (GDP) density, the ratio of cultivated land, distance to road, distance to town and the slope of the Normalized Difference Vegetation Index (NDVI). Also, the five‐year interval human activity intensity data on the QTP from 1990 to 2015 were evaluated. By preparing the historical spatial data sets of grazing intensity and human activity intensity, our study will help to explore the influence of human disturbance on the alpine ecosystems on the QTP, as well as provide effective support for the decision‐making of the government aiming to achieve regional ecosystem management and sustainable development. [ABSTRACT FROM AUTHOR]

Bacterioplankton are essential components of riverine ecosystems. However, the mechanisms (deterministic or stochastic processes) and co-occurrence networks by which these communities respond to anthropogenic disturbances are not well understood. Here, we integrated niche-neutrality dynamic balancing and co-occurrence network analysis to investigate the dispersal dynamics of bacterioplankton communities along human activity intensity gradients. Results showed that the lower reaches (where intensity of human activity is high) had an increased composition of bacterioplankton communities which induced strong increases in bacterioplankton diversity. Human activity intensity changes influenced bacterioplankton community assembly via regulation of the deterministic-stochastic balance, with deterministic processes more important as human activity increases. Bacterioplankton molecular ecological network stability and robustness were higher on average in the upper reaches (where there is lower intensity of human activity), but a human activity intensity increase of about 10%/10% can reduce co-occurrence network stability of bacterioplankton communities by an average of 0.62%/0.42% in the dry and wet season, respectively. In addition, water chemistry (especially NO3–-N and Cl–) contributed more to explaining community assembly (especially the composition) than geographic distance and land use in the dry season, while the bacterioplankton community (especially the bacterioplankton network) was more influenced by distance (especially the length of rivers and dendritic streams) and land use (especially forest regions) in the wet season. Our research provides a new perspective of community assembly in rivers and important insights into future research on environmental monitoring and classified management of aquatic ecosystems under the influence of human activity. [ABSTRACT FROM AUTHOR]

Human activity intensity evaluation is the basis of analyzing the impact of human activity on the region. With increasing scale and quantity of human activity, the impact on the ecological environment is also more and more serious. The multifactor evaluation model of human activity intensity is established by determining the classification standard index. Firstly, population density, gross domestic product (GDP) per capita, mining points, construction land ratio and land utilization ratio are selected as quantitative indexes. Secondly, the weights of evaluation indexes are obtained by using objective valuation method. Further work, the quantitative evaluation model and systematic analysis of human a`ctivity intensity for Ji’nan city are established by multifactor method. The results show that the areas with the highest intensity of human activities are mainly distributed in Ji’nan urban area, the areas with higher intensity are mainly distributed in some towns of Licheng District and the urban areas of counties and cities, the areas with middle intensity are mainly distributed in the areas near Ji’nan urban area and counties, and the areas with lower intensity are mainly distributed in some areas far away from counties and cities. Quantitative evaluation of human activity intensity is helpful to investigate the changes of ecological environment.

Bacterioplankton are essential components of riverine ecosystems. However, the mechanisms (deterministic or stochastic processes) and co-occurrence networks by which these communities respond to anthropogenic disturbances are not well understood. Here, we integrated niche-neutrality dynamic balancing and co-occurrence network analysis to investigate the dispersal dynamics of bacterioplankton communities along human activity intensity gradients. Results showed that the lower reaches (where intensity of human activity is high) had an increased composition of bacterioplankton communities which induced strong increases in bacterioplankton diversity. Human activity intensity changes influenced bacterioplankton community assembly via regulation of the deterministic-stochastic balance, with deterministic processes more important as human activity increases. Bacterioplankton molecular ecological network stability and robustness were higher on average in the upper reaches (where there is lower intensity of human activity), but a human activity intensity increase of about 10%/10% can reduce co-occurrence network stability of bacterioplankton communities by an average of 0.62%/0.42% in the dry and wet season, respectively. In addition, water chemistry (especially NO3–-N and Cl–) contributed more to explaining community assembly (especially the composition) than geographic distance and land use in the dry season, while the bacterioplankton community (especially the bacterioplankton network) was more influenced by distance (especially the length of rivers and dendritic streams) and land use (especially forest regions) in the wet season. Our research provides a new perspective of community assembly in rivers and important insights into future research on environmental monitoring and classified management of aquatic ecosystems under the influence of human activity.

Climate warming could accelerate frozen ground degradation on the Qinghai–Tibet Plateau (QTP). Quantitative analysis of the impacts of thaw-induced hazards of the frozen ground on human activities in cold regions has become one of the most important issues in current research. To identify adverse impacts of these thawing hazards on human activities, this study explores a spatially explicit, temporally consistent and quantitative method to map human activity intensity (HAI). Four categories of variables are selected to represent some of the most important human activities on the QTP, including land use, road distribution, population density, and grazing density. By improving the human footprint index method, HAI maps of the QTP in 1995, 2005, and 2015 are created, and then quantitative analysis of the HAI under different thawing hazard levels in the frozen ground of QTP is done. The results show that, for the above three periods, the mean HAI values on the QTP are 0.10, 0.11, and 0.12, respectively. Moreover, during 1995–2015, the intensity and extent of human activities increase by 15.35% and 40.64%, respectively. The superposition results of the HAI and frozen ground thawing hazard maps show that a seasonally frozen ground region has relatively larger HAI, and its mean value is more than twice that of the permafrost region. For permafrost regions, the medium-hazard area has the highest HAI (0.09), which possibly has great impacts on the linear infrastructure. The establishment of a thawing disaster warning map can effectively shield high thaw settlement hazard areas without human activities and thus can present a more accurate early warning. These results can provide important scientific references for the disaster prevention and mitigation work in frozen ground regions, including risk assessment and infrastructure maintenance.

Eutrophication has become one of the most serious problems facing water environments. Exploring the relationship between anthropogenic activities and river trophic status provides effective information for environmental improvement and restoration, whilst the relationship remains poorly understood. Herein, 5225 water samples were collected monthly from 12 tributaries covered upstream in the Three Gorges Reservoir, China during 2014–2020. Quantitative anthropogenic activities based on land-use covers were used to establish correlations with tributary trophic status in small-scale watershed. The results showed that tributary trophic status had significant spatio-temporal variations and was the highest in summer, especially in July and August. Quantitative anthropogenic activities can explain well the eutrophic frequency and eutrophication risk in tributaries. Furthermore, it was proved that anthropogenic activities could accelerate the evolution of trophic status. Our findings increased the understanding of the interrelationship between humans and the environment and can provide practical strategies for river environmental restoration.

Human activities and landscape patterns are important representations of two major elements in the human-land system. Therefore, analyzing coupling characteristics of human activity intensity and landscape patterns is of great significance to reveal the evolution of regional human-land relationships. To fill the hole on the coupling response of human activity intensity and landscape pattern, the mountainous human activity intensity and landscape pattern coupling model was constructed, and the comprehensive dominant-recessive human activity intensity index (CHAI) was proposed. Moreover, based on the AHP and cumulative index method, we quantitatively evaluated the human activities intensity of Fengjie County in the hinterland of Three Gorges Reservoir Area (TGRA) from 2000–2020, and analyzed spatial coupling patterns between CHAI and landscape pattern through the local spatial autocorrelation model. Finally, factors influencing the type of coupled change in CHAI and landscape pattern were explored by a binary logistic regression model. The results show: (1) There are no obvious changes in CHAI of Fengjie County, but the spatial change shows the characteristics closely related to the terrain gradient and the level of economic development of Fengjie County with an overall contraction while local enhancement. (2) The spatial coupling between CHAI and landscape pattern in Fengjie County shows four types: Coordination type, antagonism type, gradually coordinated type, gradually antagonistic type. (3) The important impact factors of the coupling between CHAI and landscape pattern are distance to county administrative center, distance to the town government, and landform, etc., which reflects socio-economic factors were the main factors in the evolution of the regional social ecosystem. (4) There are diverse and special coupling responses between human activity intensity and landscape patterns in the hinterland of TGRA. In general, the hinterland of TGRA has achieved a win-win situation for the ecology and economy through partial disharmony in exchange for overall coordination. Moreover, the research results reveal the uniqueness of China's mountainous landscape pattern in response to human activities in recent years.

Human activity is becoming the key factor influencing ecological health changes and clarifying the nature of that influence could effectively promote regional ecological restoration. Commonly used human-related factors (e.g., social and economic census data) can only be constrained at the administrative division, and they lead to a loss of detailed spatial characteristics. In this study, we applied human activity intensity (HAI), mapped at the grid scale, to represent pressures of human activity on the ecosystem. Taking the Three Gorges Reservoir Region (TGRR) as the study area, the spatial heterogeneity of the influences of human pressure on ecological health was analyzed. The results showed that, in the TGRR from 2000 to 2018, there was an overall improvement of ecological health and a significant spatial reorganization of HAI. The impact of HAI on ecological health had significant spatial heterogeneity. In areas where HAI had decreased, ecological health was prone to improve significantly. In contrast, in areas where HAI had increased, ecological health tended to become worse, and only when the increase of HAI exceeded 0.08 would that tendency be significant. Compared to urban areas, HAI change had even broader impacts on ecological health in remote mountain areas, where a more remarkable restoration of the ecosystem was experienced. [ABSTRACT FROM AUTHOR]

The coupling and coordinated change characteristics of land-use production, living, and ecological functions (PLEFs) and their relationship with human activity intensity (HAI) in ecologically fragile areas are important to study, especially in promoting the sustainable development of regional land-use and revealing the evolution of the human–land relationship. In this paper, the coupling coordination degree (CCD) model was used to analyze the coordinated development level of PLEF in Wanzhou District from 2000 to 2020. The HAI was measured by the equivalent of construction land. The synchronous development model was introduced to analyze the relationship between them. The results showed that, in Wanzhou District, the PLEFs showed significant spatial distribution differences and evident spatial complementarity. The PLEFs of Wanzhou District were at a good coordination level, but exhibited a downward trend. A spatial pattern of “high in the west and low in the southeast” was presented. The CCD of the production–living function was poor, which is the critical direction of future optimization. The value of HAI in Wanzhou District showed an increasing trend and exhibited a high concentration in the central town and its surrounding regions. According to the synchronous development state of the HAI and the CCD of the PLEFs, Wanzhou District was divided into three development types. The development type of most areas of Wanzhou District was positive, but the area decreased over the past 20 years. Therefore, it is crucial to propose other regulatory strategies for regions with different development types. This research will provide a decision-making reference for promoting the coordination of the PLEFs and alleviating human–land relations in the reservoir area of central and western China, mountainous regions, and similar areas in developing countries.