Monitoring rock desert formation caused by two different origins (ice-snow melting and drying) in the Qinghai-Tibet Plateau of China by considering topographic and meteorological elements

doi:10.1007/s40333-022-0100-2

Journal of Arid Land

2022, Vol. 14

Issue (8): 849-866 DOI: 10.1007/s40333-022-0100-2

Research article

Monitoring rock desert formation caused by two different origins (ice-snow melting and drying) in the Qinghai-Tibet Plateau of China by considering topographic and meteorological elements

JIA Wei¹^,², SHI Peijun³^,⁴^,^*(

), WANG Jing'ai¹^,⁴^,⁵, MA Weidong², XIA Xingsheng²^,³, ZHOU Yuantao²

¹Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education), Qinghai Normal University, Xining 810008, China
²School of Geographical Sciences, Qinghai Normal University, Xining 810008, China
³Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810008, China
⁴Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
⁵Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

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Abstract

Monitoring rock desert formation caused by two different origins (ice-snow melting and drying) through remote sensing is crucial to our understanding of the interaction between the underlying surface of different rock desert and land-atmosphere types, as well as the relationship between bare land and soil erosion. A number of achievements have been made in remote sensing monitoring of desert areas, but there is a lack of accurate classification and remote sensing identification of rock desert types based on formation mechanism. In this study, the north and south sides of the eastern Kunlun Mountains in the northern part of the Qinghai-Tibet Plateau of China were taken as the study areas. Landsat operational landscape imager, digital elevation model, and precipitation and temperature grid data were used as data sources. By identifying the bare areas based on the normalized difference vegetation index (NDVI), we used the multi-element fusion method of contours, isotherms, and isohyets to identify the rock desert types in the ice-snow melting and dry areas. The results showed that: (1) the rock desert areas identified by remote sensing based on topographic and meteorological elements were highly accurate, with an overall accuracy of 88.45% and kappa coefficient of 0.77. The multi-element fusion method of contours, isotherms, and isohyets could effectively identify the rock desert types in the ice-snow melting and dry areas; (2) the optimal segmentation range of the ice-snow melting and dry areas was 3600 m contour, -2°C-2°C isotherms, and 100-130 mm isohyets. The areas with elevation less than 3600 m, annual average temperature higher than 2°C, and average annual precipitation less than 100 mm were rock desert in the dry areas. The range of -2°C-2°C isotherms and 100-130 mm isohyets was the transition area between the ice-snow melting and dry areas. The areas with elevation higher than 3600 m, annual average temperature less than -2°C, and average annual precipitation higher than 130 mm were rock desert in the ice-snow melting areas; and (3) the identification accuracy of the bare areas based on the NDVI method was better, specifically, the identification accuracy of plain bare areas was generally better than that of mountain bare areas. The remote sensing identification method considers not only the topographic factors that have great influence on the spatial distribution of the two types of rock desert areas, but also the meteorological factors, which can provide a scientific reference for the effective identification of the two types of rock desert areas.

Key words： rock desert in the ice-snow melting areas rock desert in the dry areas contours isotherms isohyets Qinghai-Tibet Plateau

Received: 14 May 2022 Published: 30 August 2022

Corresponding Authors: * SHI Peijun (E-mail: spj@bnu.edu.cn)

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	JIA Wei
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	XIA Xingsheng
	ZHOU Yuantao

Cite this article:

JIA Wei, SHI Peijun, WANG Jing'ai, MA Weidong, XIA Xingsheng, ZHOU Yuantao. Monitoring rock desert formation caused by two different origins (ice-snow melting and drying) in the Qinghai-Tibet Plateau of China by considering topographic and meteorological elements. Journal of Arid Land, 2022, 14(8): 849-866.

URL:

http://jal.xjegi.com/10.1007/s40333-022-0100-2 OR http://jal.xjegi.com/Y2022/V14/I8/849

Fig. 1 Material composition and spatial distribution characteristics of the rock desert in the ice-snow melting areas (hand-painted by Prof. SHI Peijun)

Fig. 2 Material composition and spatial distribution characteristics of rock desert in the dry areas (hand-painted by Prof. SHI Peijun). The mountains are located on the southern side of the valley plain, the direction of the mountains is east-west.

Fig. 3 Overview of the study area

Fig. 4 Remote sensing identification process of the rock desert in the ice-snow melting and dry areas. OLI, operational landscape imager; DEM, digital elevation model.

Fig. 5 Landsat8 OLI images of the study area. (a and b), the images of plain areas; (c and d), the images of mountain areas. P136R034, P137R034, P136R035, and P137R035 are the path/row numbers of the remote sensing image used.

Table 1 Normalized difference vegetation index (NDVI) threshold of each land use type in the bare areas

Table 2 Accuracy verification results of the bare areas identification

Table 3 Accuracy verification results of identification of rock desert in the ice-snow melting and dry areas and transition area

Fig. 6 Calculation results of the NDVI based on OLI images. (a and b), the images in the plain areas; (c and d), the images in the mountain areas. P136R034, P137R034, P136R035, and P137R035 are the Path/Row numbers of the remote sensing image used.

Fig. 7 Identification results of the bare areas based on the NDVI method

Fig. 8 Extraction results of contours (a), isotherms (b), and isohyets (c)

Fig. 9 Identification results overlay multi-factor analysis for the two types of rock desert areas

Fig. 10 NDVI (a1-d1) and NDVI density segmentation (a2-d2) results in the plain areas

Fig. 11 NDVI (a1-d1) and NDVI density segmentation (a2-d2) results in the mountain areas

Fig. 12 Variation trend of annual average temperature (a) and average annual precipitation (b) from 1991 to 2018


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