Explore the vast range of titles available.

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.

Quantifying the impact of climate change and human activities on grassland dynamics is an essential step for developing sustainable grassland ecosystem management strategies. However, the direction and magnitude of climate change and human activities in driving alpine grassland dynamic over the Tibetan Plateau remain under debates. Here, we systematically reviewed the relevant studies on the methods, main conclusions, and causes for the inconsistency in distinguishing the respective contribution of climatic and anthropogenic forces to alpine grassland dynamic. Both manipulative experiments and traditional statistical analysis show that climate warming increase biomass in alpine meadows and decrease in alpine steppes, while both alpine steppes and meadows benefit from an increase in precipitation or soil moisture. Overgrazing is a major factor for the degradation of alpine grassland in local areas with high level of human activity intensity. However, across the entire Tibetan Plateau and its subregions, four views characterize the remaining controversies: alpine grassland changes are primarily due to (1) climatic force, (2) nonclimatic force, (3) combination of anthropogenic and climatic force, or (4) alternation of anthropogenic and climatic force. Furthermore, these views also show spatial inconsistencies. Differences on the source and quality of remote sensing products, the structure and parameter of models, and overlooking the spatiotemporal heterogeneity of human activity intensity contribute to current disagreements. In this review, we highlight the necessity for taking the spatiotemporal heterogeneity of human activity intensity into account in the models of attribution assessment, and the importance for accurate validation of climatic and anthropogenic contribution to alpine grassland variation at multiple scales for future studies. Attributions to alpine grassland change on Tibetan Plateau remain under debates. Future research framework on validating the reliability of attribution is constructed. More attention should be paid to quantifying human activity intensity.

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.

The acceleration of the urbanization process brings about the expansion of urban land use, while changes in land-use transformation affect the urban habitat quality, and land-use change brings a threat to regional sustainable development. Against such a backdrop, the assessment of land use on the habitat quality and the relationship between the intensity of human activities is becoming a hot spot in terms of the current land use coordinated with habitat quality. Based on the land-use data of Guiyang in 2000, 2005, 2010, 2015 and 2020, the spatial–temporal evolution characteristics of habitat quality in the study area, combined with the spatial correlation between human activity intensity and habitat quality, were hereby analyzed using the InVEST model. The impact of human activity intensity on habitat quality was correspondingly analyzed. The results show that: (1) From 2000 to 2020, the habitat quality level in Guiyang remained stable without drastic changes, but the changes showed hierarchical distribution and were scattered, mainly reflected in the urban expansion areas of the urban–rural fringe and the key areas of industrial development, and the ecological environment quality fluctuated in a small range. (2) From 2000 to 2020, the intensity of human activities in Guiyang was mainly affected by the relatively concentrated distribution, featuring obvious and significant changes. From 2010 to 2015, the high-impact area surrounded the Guanshan Lake New Area, and the regional habitat quality presented a downward trend. In 2020, the high-impact area of the main urban area and the key industrial development zone was expected to be formed, while the low-impact area was still distributed in forest areas with complex natural conditions. (3) From 2000 to 2020, there was a significant positive correlation between human activity intensity and habitat quality in Guiyang, and such a spatial correlation was weak from 2000 to 2005. The period from 2015 to 2020 witnessed the rapid development of urban construction in Guiyang, human construction activities continue to affect the urban habitat quality. The results show that the intensity of human activities on the promoting function of land use, and the dependencies between them should be considered at the same time, and that explorations on the influence of human activities on land-use intensity and habitat quality of space link are crucial to improving the efficiency of urban land use and ecological environment protection, as well as the coordination between land use and the sustainability of urban development.

Human activity intensity is a synthesis index for describing the effects and influences of human activities on land surface. This paper presents the concept of human activity intensity of land surface and construction land equivalent, builds an algorithm model for human activity intensity, and establishes a method for converting different land use/cover types into construction land equivalent as well. An application in China based on the land use data from 1984 to 2008 is also included. The results show that China＇s human activity intensity rose slowly before 2000, while rapidly after 2000. It experienced an increase from 7.63% in 1984 to 8.54% in 2008. It could be generally divided into five levels： Very High, High, Medium, Low, and Very Low, according to the human activity intensity at county level in 2008, which is rated by above 27%, 16%-27%, 10%-16%, 6%-10%, and below 6%. China＇s human activity intensity was spatially split into eastern and western parts by the line of Helan Mountains-Longmen Mountains-Jinghong. The eastern part was characterized by the levels of Very High, High, and Medium, and the levels of Low and Very Low were zonally distributed in the mountainous and hilly areas. In contrast, the western part was featured by the Low and Very Low levels, and the levels of Medium and High were scattered in Gansu Hexi Corridor, the east of Qinghai, and the northern and southern slopes of Tianshan Mountains in Xinjiang.

An in-depth exploration of the coupling and coordination relationship between urban social vulnerability and human activity intensity can provide a scientific basis for urban sustainable development, planning optimization, and policy decisions. Based on multi-source remote sensing data and social-economic statistics in 2021, Zhengzhou’s social vulnerability and human activity intensity were quantified. Subsequently, a coupling coordination degree model was applied to reveal the extent of coordination between these two factors. Finally, the geographical detector was used to analyze the impact of driving factors on the degree of coupling coordination. Results indicated that the spatial distribution pattern of social vulnerability and human activity intensity in Zhengzhou exhibits a gradual attenuation trend from the center toward the periphery, indicating a strong correlation between the two factors. The regions with high coupling coordination degrees were primarily concentrated in the central region. The total output value of the primary industry, patch aggregation index, per capita cultivated land area, and human activity intensity were identified as key drivers affecting changes in coupling coordination degree. These factors exhibit evident synergistic enhancement effects, resulting in comprehensive impacts on the spatial distribution of coupling. This study can provide a reference for urban development decision-making.

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 km 2 , primarily concentrated around urban; the critical area of natural restoration was 490 km 2 . 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.

Understanding the spatial interdependencies between cultural ecosystem services (CES) and human activity intensity (HAI) is critical for reconciling sustainable urbanization with ecological resilience. This research endeavors to conduct an exhaustive analysis of the spatial patterns between CES and HAI along Shenyang’s Hun River corridor, as well as to explore the interrelationships between these two factors. Multi-source geospatial and socioeconomic data, spatial statistical analysis software provided initial data and technical support for this research. We developed an urban riparian-specific CES evaluation framework encompassing four categories (opportunities for water/non-water activities, cultural and heritage, landscape aesthetic quality), and integrated HAI using geospatial indicators (population density, normalized difference vegetation index, traffic accessibility, nighttime light). Applying Pearson’s correlation and bivariate local Moran’s I analyses, three governance-critical patterns emerged: (1) Strong positive CES-HAI synergies ( r  = 0.52 –0.67) for most services, driven by spatial co-occurrence patterns; (2) Spatial mismatches where high HAI clusters degrade aesthetic quality ( r  = − 0.33); (3) Governance-driven heterogeneity between top-down megaprojects and bottom-up initiatives. These patterns informed an adaptive zoning framework classifying riparian areas into four management categories: synergy, conflict, potential, and latent zones. This approach provides actionable insights for optimizing cultural landscape preservation amid compact city development in China’s urbanizing river corridors.

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. The working flowchart.