Quantifying human activity intensity in the Qinling‐Daba Mountains

Bibliographic Data

ID19582425
AuthorsWenqi Xie (0009-0006-1280-1077, Chinese Academy of Sciences), Yonghui Yao (0000-0002-3520-1078, Chinese Academy of Sciences, corresponding author)
Year2024
Volume6
Issue4
Pages1486-1501
Publication date2024-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePeople and Nature (JOURNAL)
Journal identifiersISSN: 2575-8314 • E-ISSN: 2575-8314
PublisherWiley (PUBLISHER • GB)
DOI10.1002/pan3.10649
OpenAlexW4396972217
LanguageEN
Citations received1
References cited80

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

Bottleneck · Cartography · Environmental resource management · Geography · Geospatial analysis · Human settlement · Physical geography · Population · Principal component analysis · Statistics · Terrain · Weighting · Computer Science · Environmental Science · Land Use and Ecosystem Services · Mathematics · Urban Transport and Accessibility · Wildlife-Road Interactions and Conservation

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