{"title":"2000 - 2020年中国高质量人类活动强度图","authors":"Wenqi Xie, Yonghui Yao","doi":"10.1111/geb.70130","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aim</h3>\n \n <p>Human activity intensity (HAI) is a crucial metric for quantifying human impacts on ecosystems. It is essential for studying humans' role in macro-ecological processes such as habitat connectivity, ecosystem change and biodiversity loss. However, the lack of high-quality HAI datasets in China has hindered related research. Our goals were to develop an improved method for national HAI mapping and to present a comprehensive HAI assessment in China from 2000 to 2020.</p>\n </section>\n \n <section>\n \n <h3> Location</h3>\n \n <p>China.</p>\n </section>\n \n <section>\n \n <h3> Time Period</h3>\n \n <p>2000–2020.</p>\n </section>\n \n <section>\n \n <h3> Major Taxa Studied</h3>\n \n <p>Terrestrial ecosystem.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We developed an improved HAI mapping methodology by introducing a comprehensive eight-indicator system covering socio-economic, natural environment and resources dimensions, more rigorous scoring models (e.g., travel time calculation) and a principal component analysis-based indicator overlaying method. China's first spatiotemporally consistent HAI maps from 2000 to 2020 were produced by applying this methodology and using higher temporal resolution and more reliable data than those used in global research.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Our HAI dataset demonstrated significantly improved accuracy, with an overall root mean square error (RMSE) of 0.085; 34% lower than that of the global human footprint product (RMSE = 0.125). Additionally, it outperformed in challenging landscapes, such as rugged terrains, arid regions and natural-human transition zones. The results show between 2000 and 2015, over 620,000 km<sup>2</sup> of areas with very low human activity (HAI < 0.5) disappeared, resulting in the fragmentation of natural habitats, particularly in forest ecosystems and grassland ecosystems.</p>\n </section>\n \n <section>\n \n <h3> Main Conclusions</h3>\n \n <p>We developed a national-scale HAI mapping method framework, by which high-quality HAI datasets for China were produced. Thanks to our HAI product, we revealed details that cannot be reflected in global datasets (e.g., grazing ban) and provided critical insights into the spatiotemporal dynamics of human impacts on ecosystems in China. This methodology offers global relevance as a reference model, facilitating human-nature relationship research in other countries.</p>\n </section>\n </div>","PeriodicalId":176,"journal":{"name":"Global Ecology and Biogeography","volume":"34 10","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-Quality Human Activity Intensity Maps in China From 2000 to 2020\",\"authors\":\"Wenqi Xie, Yonghui Yao\",\"doi\":\"10.1111/geb.70130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aim</h3>\\n \\n <p>Human activity intensity (HAI) is a crucial metric for quantifying human impacts on ecosystems. It is essential for studying humans' role in macro-ecological processes such as habitat connectivity, ecosystem change and biodiversity loss. However, the lack of high-quality HAI datasets in China has hindered related research. Our goals were to develop an improved method for national HAI mapping and to present a comprehensive HAI assessment in China from 2000 to 2020.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Location</h3>\\n \\n <p>China.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Time Period</h3>\\n \\n <p>2000–2020.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Major Taxa Studied</h3>\\n \\n <p>Terrestrial ecosystem.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We developed an improved HAI mapping methodology by introducing a comprehensive eight-indicator system covering socio-economic, natural environment and resources dimensions, more rigorous scoring models (e.g., travel time calculation) and a principal component analysis-based indicator overlaying method. China's first spatiotemporally consistent HAI maps from 2000 to 2020 were produced by applying this methodology and using higher temporal resolution and more reliable data than those used in global research.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Our HAI dataset demonstrated significantly improved accuracy, with an overall root mean square error (RMSE) of 0.085; 34% lower than that of the global human footprint product (RMSE = 0.125). Additionally, it outperformed in challenging landscapes, such as rugged terrains, arid regions and natural-human transition zones. The results show between 2000 and 2015, over 620,000 km<sup>2</sup> of areas with very low human activity (HAI < 0.5) disappeared, resulting in the fragmentation of natural habitats, particularly in forest ecosystems and grassland ecosystems.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Main Conclusions</h3>\\n \\n <p>We developed a national-scale HAI mapping method framework, by which high-quality HAI datasets for China were produced. Thanks to our HAI product, we revealed details that cannot be reflected in global datasets (e.g., grazing ban) and provided critical insights into the spatiotemporal dynamics of human impacts on ecosystems in China. This methodology offers global relevance as a reference model, facilitating human-nature relationship research in other countries.</p>\\n </section>\\n </div>\",\"PeriodicalId\":176,\"journal\":{\"name\":\"Global Ecology and Biogeography\",\"volume\":\"34 10\",\"pages\":\"\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2025-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Ecology and Biogeography\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/geb.70130\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Ecology and Biogeography","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/geb.70130","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
High-Quality Human Activity Intensity Maps in China From 2000 to 2020
Aim
Human activity intensity (HAI) is a crucial metric for quantifying human impacts on ecosystems. It is essential for studying humans' role in macro-ecological processes such as habitat connectivity, ecosystem change and biodiversity loss. However, the lack of high-quality HAI datasets in China has hindered related research. Our goals were to develop an improved method for national HAI mapping and to present a comprehensive HAI assessment in China from 2000 to 2020.
Location
China.
Time Period
2000–2020.
Major Taxa Studied
Terrestrial ecosystem.
Methods
We developed an improved HAI mapping methodology by introducing a comprehensive eight-indicator system covering socio-economic, natural environment and resources dimensions, more rigorous scoring models (e.g., travel time calculation) and a principal component analysis-based indicator overlaying method. China's first spatiotemporally consistent HAI maps from 2000 to 2020 were produced by applying this methodology and using higher temporal resolution and more reliable data than those used in global research.
Results
Our HAI dataset demonstrated significantly improved accuracy, with an overall root mean square error (RMSE) of 0.085; 34% lower than that of the global human footprint product (RMSE = 0.125). Additionally, it outperformed in challenging landscapes, such as rugged terrains, arid regions and natural-human transition zones. The results show between 2000 and 2015, over 620,000 km2 of areas with very low human activity (HAI < 0.5) disappeared, resulting in the fragmentation of natural habitats, particularly in forest ecosystems and grassland ecosystems.
Main Conclusions
We developed a national-scale HAI mapping method framework, by which high-quality HAI datasets for China were produced. Thanks to our HAI product, we revealed details that cannot be reflected in global datasets (e.g., grazing ban) and provided critical insights into the spatiotemporal dynamics of human impacts on ecosystems in China. This methodology offers global relevance as a reference model, facilitating human-nature relationship research in other countries.
期刊介绍：
Global Ecology and Biogeography (GEB) welcomes papers that investigate broad-scale (in space, time and/or taxonomy), general patterns in the organization of ecological systems and assemblages, and the processes that underlie them. In particular, GEB welcomes studies that use macroecological methods, comparative analyses, meta-analyses, reviews, spatial analyses and modelling to arrive at general, conceptual conclusions. Studies in GEB need not be global in spatial extent, but the conclusions and implications of the study must be relevant to ecologists and biogeographers globally, rather than being limited to local areas, or specific taxa. Similarly, GEB is not limited to spatial studies; we are equally interested in the general patterns of nature through time, among taxa (e.g., body sizes, dispersal abilities), through the course of evolution, etc. Further, GEB welcomes papers that investigate general impacts of human activities on ecological systems in accordance with the above criteria.
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