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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (11): 1497-1517    DOI: 10.1007/s40333-025-0032-8    
Research article     
Spatial and temporal pattern of human activity intensity and its driving mechanism in the Turpan- Hami Basin, China from 1990 to 2020
SHI Qingqing1, YIN Benfeng2, HUANG Jixia1,3, YIN Yuanyuan4,5,*(), YANG Ao2, ZHANG Yuanming2
1State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China
2State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3Academy of Plateau Science and Sustainability, People's Government of Qinghai Province & Beijing Normal University, Xining 810008, China
4Beijing Union University, Beijing 100011, China
5Institute of Science and Technology Education, Beijing Union University, Beijing 100011, China
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Abstract  

The Turpan-Hami (Tuha) Basin of China, a critical region on the Silk Road Economic Belt and a major national energy base, occupies a significant position in energy security and in the major industrial clusters in Xinjiang Uygur Autonomous Region, China. Understanding spatial and temporal evolution of human activities in this area is essential for harmonizing ecological protection with energy development, safeguarding the ecological security of the Silk Road Economic Belt, and promoting the sustainable development of the area. However, despite rapid socioeconomic advances, the trajectories of human activity intensity and the principal driving mechanisms over the past three decades remain inadequately understood. To address these gaps, this study constructed a land use dataset for the Tuha Basin from 1990 to 2020, utilizing Google Earth Engine (GEE) and random forest classification algorithm. We assessed the intensity of human activities and their spatial autocorrelation patterns and further identified key drivers influencing spatial and temporal variations using the Geodetector model. Our findings indicated that the intensity of human activities in the Tuha Basin has exhibited a "first decline and then recovery" trend over the past 30 a, accompanied by significant spatial clustering. In recent years, the aggregation of hot spots has diminished, while clustering of cold spots has intensified, suggesting a dispersion of human activity centers. Nevertheless, urban areas in the Hami and Turpan cities, along with their surrounding areas, continued to serve as core areas of human activities. Topographic features (slope gradient and aspect) and their interactions with economic variables emerged as dominant determinants shaping the spatial patterns and temporal dynamics of human activity intensity. This result provides critical insights into fostering sustainable regional development and ecological conservation in the Tuha Basin and offers valuable methodological and empirical references for studies on land use dynamics and human activity intensity in similar arid areas.

Key words： Turpan-Hami Basin      land use change      Google Earth Engine      human activity intensity      interaction     
Received: 14 May 2025      Published: 30 November 2025
Corresponding Authors: *YIN Yuanyuan (sftyuanyuan@buu.edu.cn)
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SHI Qingqing
YIN Benfeng
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ZHANG Yuanming
Cite this article:

SHI Qingqing, YIN Benfeng, HUANG Jixia, YIN Yuanyuan, YANG Ao, ZHANG Yuanming. Spatial and temporal pattern of human activity intensity and its driving mechanism in the Turpan- Hami Basin, China from 1990 to 2020. Journal of Arid Land, 2025, 17(11): 1497-1517.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0032-8     OR     http://jal.xjegi.com/Y2025/V17/I11/1497

Fig. 1 Distribution of sampling points and land use types in the Turpan-Hami (Tuha) Basin, China. DEM, digital elevation model.
Dataset Resolution Year Source
Landsat imagery 30.00 m 1990-2020 Google Earth Engine
(https://code.earthengine.google.com/)
Field collected samples - 2023 Field collection
Digital elevation model (DEM) 30.00 m 2015 European Space Agency (ESA)
(https://panda.copernicus.eu/panda0)
Climate data Temperature (TMP) 1000.00 m 1990-2020 National Earth System Science Data Center
(https://www.geodata.cn/data)
Precipitation (PRE) 1000.00 m 1990-2020 National Earth System Science Data Center
(https://www.geodata.cn/data/)
Socio-economic
data		 Gross domestic
product (GDP)		 County level 1990-2020 Xinjiang Statistical Yearbook
(https://eco.gwxll.com/datashoping)
Total agricultural
output (TAO)		 County level 1990-2020
Population (POP) 1000.00 m 1990-2020 Resource and Environmental Science Data Platform
(http://www.resdc.cn)
Table 1 Dataset used in this study
Fig. 2 Research flowchart of the study. GEE, Google Earth Engine; Cli, climate; Ter, terrain; Eco, economic data; CLCD, China Land Cover Dataset. GLC_FCS30D is the first global fine land cover dynamic product at a 30.00-m resolution. The abbreviation are the same in the following figures and tables.
Fig. S1 Land use types of CLCD (a) and GLC-FCS30D (b) in 2020. CLCD, China Land Cover Dataset. GLC_FCS30D is the first global fine land cover dynamic product at a 30.00-m resolution.
CLCD_class Reclassification CLCD_class Reclassification
Cropland Farmland Sonw/Ice Water body
Forest land Forest land Barren land Desert
Shrubland Forest land Impervious Urban land
Grassland Forest land Wetland Water body
Water body Water body
Table S1 China Land Cover Dataset (CLCD) reclassification rules
GLC-FCS30_class Reclassification GLC-FCS30_class Reclassification
Rainfed cropland Farmland Lichens and mosses Desert
Herbaceous cover cropland Forest land Sparse vegetation (fc≤15.00%) Forest land
Tree or shrub cover (Orchard) cropland Forest land Sparse shrubland (fc≤15.00%) Forest land
Irrigated cropland Farmland Sparse herbaceous (fc≤15.00%) Forest land
Open evergreen broadleaved forest Forest land Swamp Water body
Closed evergreen broadleaved forest Forest land Marsh Water body
Open deciduous broadleaved forest (15.00%<fc<40.00%) Forest land Flooded flat Water body
Closed deciduous broadleaved forest (fc≥40.00%) Forest land Saline Desert
Open evergreen needle-leaved forest (15.00%<fc<40.00%) Forest land Mangrove Forest land
Closed evergreen needle-leaved forest (fc≥40.00%) Forest land Salt marsh Desert
Open deciduous needle-leaved forest (15.00%<fc<40.00%) Forest land Tidal flat Water body
Closed deciduous needle-leaved forest (fc≥40.00%) Forest land Impervious surfaces Urban land
Open mixed leaf forest (broadleaved and needle-leaved) Forest land Bare area Desert
Closed mixed leaf forest (broadleaved and needle-leaved) Forest land Consolidated bare areas Urban land
Shrubland Forest land Unconsolidated bare areas Desert
Evergreen shrubland Forest land Water body Water body
Deciduous shrubland Forest land Permanent ice and snow Water body
Grassland Forest land
Table S2 GLC-FCS30D reclassification rules
Type of interaction Basis of judgment
Nonlinear weakening q(X1∩X2)<min(q(X1), q(X2))
Single-factor nonlinear attenuation min(q(X1), q(X1))<q(X1∩X2)<max(q(X1), q(X2))
Two-factor enhancement q(X1∩X2)<max(q(X1), q(X2))
Independent q(X1∩X2)=q(X1)+q(X2)
Nonlinear enhancement q(X1∩X2)>q(X1)+q(X2)
Table 2 Type of interaction
Year Overall accuracy K Year Overall accuracy K
1990 0.73 0.64 2010 0.86 0.81
1995 0.77 0.69 2015 0.86 0.81
2000 0.76 0.65 2020 0.92 0.89
2005 0.81 0.74
Table 3 Accuracy of land use classification
Land use type 1990 1995 2000 2005 2010 2015 2020
(%)
Forest land 1.74 1.62 0.87 1.64 0.99 0.79 1.20
Water body 0.14 0.14 0.76 0.41 0.07 0.55 0.25
Desert 87.38 89.00 90.84 89.71 92.31 90.53 91.42
Urban land 3.29 2.24 2.15 2.34 2.37 3.12 3.24
Farmland 3.33 4.58 1.89 2.01 1.73 2.04 2.34
Bare land 4.12 2.43 3.47 3.90 2.53 2.97 1.55
Table 4 Proportion of each land use type in the Tuha Basin from 1990 to 2020
Fig. 3 Trajectories of land use and cover change convention in the Tuha Basin from 1990 to 2020
Land use type Forest land Water body Desert Urban land Farmland Bare land Total Decreased area
(km2)
Forest land 2883.39 461.06 2511.42 201.26 302.38 329.61 6689.12 3805.73
Water body 11.06 190.20 186.55 74.98 6.61 62.60 531.99 341.79
Desert 1496.47 228.31 323,253.32 5391.70 2564.63 2355.99 335,290.42 12,037.10
Urban land 22.92 7.72 7277.30 3079.01 1688.84 551.99 12,627.78 9548.76
Farmland 131.23 7.99 5908.02 1357.64 4154.52 1230.30 12,789.70 8635.18
Bare land 76.14 51.17 11,650.20 2315.47 277.85 1429.70 15,800.52 14,370.83
Total 4621.20 946.46 350,786.80 12,420.06 8994.82 5960.19 383,729.53 -
Increased area 1737.81 756.26 27,533.48 9341.04 7305.99 4530.49 - -
Table 5 Transfer matrix of land use type in the Tuha Basin from 1990 to 2020
Fig. 4 Changes in urban land equivalent percentage and human activity intensity from 1990 to 2020 (a), and county-level human activity intensity in the Tuha Basin (b). TH-HAILS, Tuha Basin-Human activity intensity of land surface; GCX, SSX, TKXX, YZX, BLHSKZZX, and YWX are Gaochang District, Shanshan County, Toksun County, Yizhou District, Barkol Kazak Autonomous County, and Yiwu County, respectively. The data of China-HAILS were obtained from the study of Tan et al. (2024).
Fig. 5 Spatial distribution of HAILS at the township scale in the Tuha Basin from 1990 to 2020. (a), 1990; (b), 1995; (c), 2000; (d), 2005; (e), 2010; (f), 2015; (g), 2020.
Year Category Low Relatively low Moderate Relatively high High
1990 Numbers 31 31 23 10 8
Average intensity (%) 2.96 8.76 20.82 36.82 55.57
1995 Numbers 29 32 24 2 6
Average intensity (%) 2.36 9.48 21.37 34.39 54.99
2000 Numbers 30 23 26 9 5
Average intensity (%) 1.96 8.66 22.95 34.85 58.99
2005 Numbers 36 15 23 13 6
Average intensity (%) 2.34 10.18 22.81 35.83 58.62
2010 Numbers 36 18 20 13 6
Average intensity (%) 2.09 8.86 23.5 36.92 67.06
2015 Numbers 24 23 23 16 7
Average intensity (%) 1.89 8.46 21.99 37.67 57.36
2020 Numbers 28 22 18 13 12
Average intensity (%) 2.73 7.89 22.32 38.13 60.07
Table S3 Human activity intensity classification in the Turpan-Hami (Tuha) Basin from 1990 to 2020
Index 1990 1995 2000 2005 2010 2015 2020
Moran's I 0.52 0.49 0.46 0.68 0.56 0.56 0.91
Z score 10.72 10.14 9.43 13.90 11.50 11.40 18.48
P value 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Table S4 Spatial autocorrelation of human activity intensity in the Tuha Basin
Fig. 6 Spatial distribution of hotspot analysis on human activity intensity at the township scale in the Tuha Basin from 1990 to 2020. (a), 1990; (b), 1995; (c), 2000; (d), 2005; (e), 2010; (f), 2015; (g), 2020.
Factor POP PRE TMP DEM SLP ASP GDP TAO
q-value 0.21 0.11 0.22 0.27 0.48 0.38 0.05 0.22
Table 6 The q-value of each detected factor on the intensity of human activities in the Tuha Basin from 1990 to 2020
Fig. 7 Interaction detection results of human activity intensity influencing factors in the Tuha Basin
Dataset Land use type Forest land Water body Desert Urban land Farmland
(km2)
Tuha
Basin		 Forest land 2883.39 461.06 2841.03 201.26 302.38
Water body 11.06 190.20 249.14 74.98 6.61
Desert 1572.61 279.48 338,689.21 7707.16 2842.48
Urban land 22.92 7.72 7829.28 3079.01 1688.84
Farmland 131.23 7.99 7138.33 1357.64 4154.52
CLCD Forest land 581.45 0.00 0.49 0.00 0.00
Water body 0.56 598.90 175.97 0.27 0.50
Desert 224.76 142.88 375,936.00 424.23 1937.93
Urban land 0.00 0.28 0.02 158.26 0.01
Farmland 0.51 2.27 671.60 155.22 2638.29
GLC_FCS30D Forest land 1181.80 10.35 91.32 1.03 21.25
Water body 2.31 810.76 93.13 3.17 2.06
Desert 914.83 280.71 370,351.02 547.38 2100.14
Urban land 0.69 0.02 29.35 325.91 38.93
Farmland 71.35 41.46 2031.12 131.30 4643.51
Table 7 Transfer matrix of land use for three datasets in the Tuha Basin from 1990 to 2020
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