Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City, China

doi:10.1007/s40333-022-0097-6

Journal of Arid Land

2022, Vol. 14

Issue (6): 637-652 DOI: 10.1007/s40333-022-0097-6 CSTR: 32276.14.s40333-022-0097-6

Research article

Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City, China

DONG Jianhong¹, ZHANG Zhibin², LIU Benteng¹^,^*(

), ZHANG Xinhong¹^,^*(

), ZHANG Wenbin², CHEN Long²

¹School of Design and Art, Lanzhou University of Technology, Lanzhou 730050, China
²School of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China

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Abstract

Rapid industrialization and urbanization have led to the most serious habitat degradation in China, especially in the loess hilly area of the Yellow River Basin, where the ecological environment is relatively fragile. The contradiction between economic development and ecological environment protection has aroused widespread concern. In this study, we used the habitat quality of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST-HQ) model at different scales to evaluate the dynamic evolution characteristics of habitat quality in Lanzhou City, Gansu Province of China. The spatiotemporal variations of habitat quality were analyzed by spatial autocorrelation. A Geographical Detector (Geodetector) model was used to explore the driving factors that influencing the spatial differentiation of habitat quality, including natural factors, socio-economic factors, and ecological protection factors. The results showed that the habitat quality index of Lanzhou City decreased from 0.4638 to 0.4548 during 2000-2018. The areas with reduced the habitat quality index were mainly located in the Yellow River Basin and Qinwangchuan Basin, where are the main urban areas and the new economic development areas, respectively. The spatial distribution of habitat quality presented a trend of high in the surrounding areas and low in the middle, and showed a significant positive spatial autocorrelation. With the increase of study scale, the spatial distribution of habitat quality changed from concentrated to dispersed. The spatial differentiation of habitat quality in the study area was the result of multiple factors. Among them, topographic relief and slope were the key factors. The synergistic enhancement among these driving factors intensified the spatial differentiation of habitat quality. The findings of this study can provide a scientific basis for land resources utilization and ecosystem restoration in the arid and semi-arid land.

Key words： Habitat quality spatiotemporal variations driving factors InVEST-HQ model Geodetector model Lanzhou City Yellow River Basin

Received: 30 March 2022 Published: 30 June 2022

Corresponding Authors: * LIU Benteng (E-mail: liubt@lut.edu.cn);ZHANG Xinhong (E-mail: zhangxh1981@lut.edu.cn)

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	DONG Jianhong
	ZHANG Zhibin
	LIU Benteng
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	ZHANG Wenbin
	CHEN Long

Cite this article:

DONG Jianhong, ZHANG Zhibin, LIU Benteng, ZHANG Xinhong, ZHANG Wenbin, CHEN Long. Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City, China. Journal of Arid Land, 2022, 14(6): 637-652.

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http://jal.xjegi.com/10.1007/s40333-022-0097-6 OR http://jal.xjegi.com/Y2022/V14/I6/637

Fig. 1 Overview of Lanzhou City

Table 1 Driving factors of habitat quality used in the Geographical Detector (Geodetector) model

Fig. 2 Classification of driving factors for the spatial differentiation of habitat quality in Lanzhou City. (a), elevation; (b), slop; (c), topographic relief; (d), ≥10°C accumulated temperature; (e), precipitation; (f), soil erosion; (g), population; (h), gross domestic product (GDP); (i), road density; (j), distance from county (district); (k), nighttime light index; (l), increased area of woodland.

Table 2 Threat factors of habitat quality and calculated results from the habitat quality of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST-HQ) model

Table 3 Relative sensitivity of habitat types to threat factors

Table 4 Changes of the habitat quality index in Lanzhou City in 2000, 2010, and 2018

Fig. 3 Spatial distribution (a-c) and changes of the habitat quality index (d-f) in Lanzhou City from 2000 to 2018 at grid scale.

Fig. 4 Spatial distribution (a-c) and changes of the habitat quality index (d-f) in Lanzhou City from 2000 to 2018 at town (street) scale. The classification criteria of the habitat quality index at town (street) scale is based on the natural breakpoint method.

Fig. 5 Variations of the habitat quality index at county (district) scale in Lanzhou City from 2000 to 2018. Bars mean standard errors.

Table 5 Moran's I of habitat quality in Lanzhou City from 2000 to 2018

Table 6 Contribution rate of driving factors for the spatial differentiation of habitat quality in Lanzhou City

Table 7 Interactive detection of driving factors for the spatial differentiation of habitat quality in Lanzhou City


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