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Frontiers of Earth Science

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Front. Earth Sci.    2022, Vol. 16 Issue (3) : 744-756    https://doi.org/10.1007/s11707-021-0928-3
RESEARCH ARTICLE
Identifying the spatio-temporal variability of human activity intensity and associated drivers: a case study on the Tibetan Plateau
Cai LIU1,2,3, Haiyan ZHANG4, Fuping GAN2,3(), Yunge LU3, Hao WANG3, Jiahong ZHANG3, Xing JU3
1. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
2. Key Laboratory of Airborne Geophysics and Remote Sensing Geology (Ministry of Nature Resources), Beijing 100083, China
3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
4. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Abstract

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.
Keywords Tibetan Plateau      human activity intensity      GeoDetector      spatio-temporal variability      driving factors     
Corresponding Author(s): Fuping GAN   
Online First Date: 23 December 2021    Issue Date: 29 December 2022
 Cite this article:   
Cai LIU,Haiyan ZHANG,Fuping GAN, et al. Identifying the spatio-temporal variability of human activity intensity and associated drivers: a case study on the Tibetan Plateau[J]. Front. Earth Sci., 2022, 16(3): 744-756.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0928-3
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I3/744
Fig.1  Location of study area.
Data set Data declaration Time Data sources
Land-use/cover Raster; 100 m 1980, 1990, 2000, 2010, 2015, 2018, 2020 Resource and Environment Data Cloud Platform
(available at Resource and Environment Science and Data Center website)
DEMa) Raster; 90 m 2000 Geospatial Data Cloud site, Computer Network Information Centre, Chinese Academy of Sciences (available at Geospatial Data Cloud website)
Slope Raster; 90 m 2000 National Tibetan Plateau Data Centre
(available at TPDC website)
Precipitation and Temperature Raster; 1 km 2000, 2015 National Tibetan Plateau Data Centre (Ding, 2019) (available at TPDC website)
GDPb) and Population density Raster; 1 km 2000, 2015 Resource and Environment Data Cloud Platform (available at Resource and Environment Science and Data Center website)
Soil type Vector; 1:1,000,000 1995 Institute of soil science, Chinese Academy of Sciences
Transportation Vector; 1:1,000,000 1995, 2018 Resource and Environment Data Cloud Platform (available at Resource and Environment Science and Data Center website)
Nature reserves Vector; 1:1,000,000 2018 Resource and Environment Data Cloud Platform (available at Resource and Environment Science and Data Center website)
Tab.1  Data sets used in the present study
Fig.2  Changes in the CLE area and HAI on the land surface on the TP between 1980 and 2020.
Fig.3  Spatial distribution of HAI on the land surface on the TP in (a) 1980 and (b) 2020.
Fig.4  Hotspot mapping of HAI on the land surface on the TP for (a) 1980, (b) 2020, and (c) 1980–2020.
Year Slope Altitude Precipitation Temperature Soil type GDP Population density Transportation
Tibetan Plateau 2000 0.017** 0.015** 0.035** 0.011** 0.065** 0.046** 0.054** 0.022**
2015 0.014** 0.014** 0.035** 0.007** 0.057** 0.082** 0.043** 0.023**
Qinghai 2000 0.013** 0.045** 0.060** 0.047** 0.114** 0.105** 0.108** 0.045**
2015 0.012** 0.040** 0.068** 0.031** 0.112** 0.130** 0.081** 0.036**
Tibet 2000 0.034** 0.035** 0.056** 0.018** 0.052** 0.038** 0.030** 0.010**
2015 0.029** 0.031** 0.064** 0.016** 0.047** 0.082** 0.059 0.013**
Tab.2  q value of single factors for the spatial variability in human activity intensity on the land surface
Fig.5  Relationships on the TP in 2000 and 2015 between HAI on the land surface and (a) slope, (b) altitude, (c) precipitation, (d) temperature, (e) GDP, (f) population density, (g) distance to roads, (h) nature reserves, and (i) soil type. Notes: ‘**’: p<0.01; ‘*’: p<0.05.
Year Slope Altitude Precipitation Temperature Soil type GDP Population
density		 Transportation
Slope 2000 0.017**
2015 0.014**
Altitude 2000 0.166** 0.015**
2015 0.163** 0.014**
Precipitation 2000 0.118** 0.176** 0.035**
2015 0.120** 0.147** 0.035**
Temperature 2000 0.107** 0.094** 0.125** 0.011**
2015 0.085** 0.077** 0.120** 0.007**
Soil type 2000 0.105** 0.112** 0.137** 0.105** 0.065**
2015 0.102** 0.108** 0.129** 0.102** 0.057**
GDP 2000 0.135** 0.081** 0.106** 0.085** 0.105* 0.046**
2015 0.211** 0.168** 0.217** 0.169** 0.203** 0.082**
Population
density		 2000 0.180** 0.096** 0.171** 0.112** 0.123** 0.087* 0.054**
2015 0.073** 0.061** 0.082** 0.058** 0.093* 0.094* 0.043**
Transportation 2000 0.128** 0.086** 0.124** 0.098** 0.116** 0.101** 0.128** 0.022**
2015 0.120** 0.097** 0.137** 0.078** 0.117** 0.174** 0.071** 0.023**
Tab.3  q value for the interactions between factors for the spatial variability in human activity intensity on the land surface, Tibetan Plateau
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