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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 192-201     DOI: 10.6046/gtzyyg.2020187
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Remote sensing survey and driving force analysis of area change of Hongyashan Reservoir in the past twenty years
HAO Guzhuang1,2(), GAN Fuping3(), YAN Baikun3, LI Xianqing1,2, HU Huidong1,2
1. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China
2. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China; China Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing 100083, China;
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Abstract  

Hongyashan Reservoir is located in northwestern China where water resources are lacking. Reservoirs are an important support for the ecosystem in this region. Analyzing the changes in the area of the reservoir can effectively help Minqin County Government to make overall plans for water ecological protection and restoration as well as rational use of water resources and can also provide support for its decision-making. Through the extraction and analysis of the water area and vegetation coverage of the Landsat series data and GF-2 data from 2000 to 2019 and in combination with the surrounding meteorological data and the collection of local data, the authors comprehensively analyzed the influencing factors of the water area change and explored the spatial and temporal changes of the water area as well as the driving force. The results show that, on the whole, the water area of Hongyashan Reservoir has continued to increase in the past 20 years, the total area has increased by 8.98 km2, and the area change rate is as high as 42.6%, and that, in terms of monthly changes, the change in water area has an inverted “normal distribution” curve. The trend is that the wet season is mainly concentrated in March and September-October in the spring and autumn seasons, and the dry season is mainly concentrated in June in the summer. In terms of interannual variability, the water area is greatly affected by the seasons, so it is divided into spring, summer, autumn and winter. Interannual analysis shows that the water area in spring and winter continues to rise, with average annual growth rates of 5.03% and 5.22%, the lowest average annual growth rate in autumn is only 2.42%, and the average annual growth rate of summer water area is 22.19%, which is the season with the largest variation amplitude, exhibiting “V” fluctuation and rising. According to the meteorological data such as temperature, precipitation and evaporation, the correlation analysis of vegetation coverage and water area, and the analysis of related hydrological data, the following conclusions can be drawn: the direct driving forces are the change in precipitation, the increasing project expansion, and the change of runoff into the reservoir, whereas the indirect driving forces include changes in temperature, changes in vegetation coverage, the industrial, agricultural and domestic water use, and the restoration of the ecological environment.

Keywords Hongyashan Reservoir      remote sensing survey      water area      driving force     
ZTFLH:  TP79  
Corresponding Authors: GAN Fuping     E-mail: haoguzhuang@126.com;fpgan@aliyun.com
Issue Date: 21 July 2021
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Guzhuang HAO
Fuping GAN
Baikun YAN
Xianqing LI
Huidong HU
Cite this article:   
Guzhuang HAO,Fuping GAN,Baikun YAN, et al. Remote sensing survey and driving force analysis of area change of Hongyashan Reservoir in the past twenty years[J]. Remote Sensing for Land & Resources, 2021, 33(2): 192-201.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020187     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/192
Fig.1  Location of the study area
卫星/传感器 时间 分辨
率/m 		影像
数量 		重访周
期/d
Landsat5 TM 2000/03—2012/02 30 254 16
Landsat7 ETM+ 2012/03—2013/02 30 34 16
Landsat8 OLI 2013/03—2020/02 30 171 16
Tab.1  Landsat remote sensing image data
传感器 时间 分辨率/m 影像数量 重访周期/d
PAN/PMS 2015/04/10 1/4 6/6 5
2016/10/08
2016/12/21
2017/02/23
2017/11/21
2018/07/21
Tab.2  GF-2 remote sensing image data
Fig.2  Overall technical route
Fig.3  Comparison of extraction effects of four water body indexes in Landsat Images
水体提取法 水库水域
面积/km2 		绝对误
差/km2 		相对误
差/%
目视解译法 20.707
MNDWI水体指数法 20.713 0.006 0.029
NDWI水体指数法 20.583 -0.124 -0.599
NWI水体指数法 20.550 -0.157 -0.758
EWI水体指数法 20.529 -0.178 -0.860
Tab.3  Comparison of water area extraction accuracy between four water index methods and GF-2 visual interpretation method
Fig.4  GF-2 image NDWI index method to extract water
时间 GF-2数据 Landsat
数据 		绝对误
差/km2 		相对误差/%
2015/04 19.72 19.81 0.09 0.46
2016/10 21.27 20.71 -0.57 -2.66
2016/12 17.45 17.51 0.06 0.33
2017/02 20.89 20.73 -0.15 -0.72
2017/11 19.55 19.72 0.17 0.88
2018/07 13.30 12.42 -0.88 -6.63
Tab.4  Comparison of precision of water area extraction in GF-2/Landsat data
Fig.5  GF-2/Landsat water area correlation
Fig.6  Monthly variation of water area of Hongyashan Reservoir from 2000 to 2019
Fig.7  Average monthly water area of Hongyashan Reservoir from 2000 to 2019
Fig.8  Interannual change of water area of Hongyashan Reservoir from 2000 to 2019
Fig.9  Annual average water area change of Hongyashan Reservoir
Fig.10  Interannual change rate of water area of Hongyashan Reservoir
Fig.11  Influence of temperature and precipitation on the change of water area from 2000 to 2019
Fig.12  Interannual changes in vegetation coverage
Fig.13  Water area-vegetation coverage correlation
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