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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 144-154     DOI: 10.6046/zrzyyg.2021385
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Sentinel-2 MSI and Sentinel-1 SAR based information extraction of abandoned land in the western Loess Plateau:A case study of Minhe County in Qinghai
ZHANG Hao1,2,3(), GAO Xiaohong1,2,3,4(), SHI Feifei1,2,3, LI Runxiang1,2,3
1. School of Geographical Sciences, Qinghai Normal University, Xining 810008, China
2. MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological, Xining 810008, China
3. Qinghai Province Key Laboratory of Physical Geography and Environmental Process, Xining 810008, China
4. Academy of Plateau Science and Sustainability, Xining 810008, China
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Abstract  

The eastern agricultural areas of Qinghai are located in the transitional zone from the Loess Plateau to the Qinghai-Tibet Plateau. In this transitional zone, the loess hills feature various landforms, large fluctuations, and fragmentation. With the acceleration of urbanization in recent decades, the shortage of available rural labor force has aggravated land abandonment. Therefore, ascertaining the distribution of abandoned land in the eastern agricultural areas of Qinghai is very crucial to protecting cultivated and ecological land. This study investigated Minhe County in Qinghai Province based on the GEE cloud platform. According to the phenological characteristics of crops, both Sentinel-2 MSI and Sentinel-1 SAR satellite images covering the growth and planting periods of crops were selected as the main data source. With the aid of the DEM and by combining the characteristics of spectra, terrain, polarization, and tasseled cap, this study automatically classified the land cover from 2018 to 2020 in the study area using the random forest method, obtaining the three-year land cover data of the study area. Then, this study built a decision tree based on the determination rules for abandoned land and extracted and verified the abandoned land information using the decision tree. The study results are as follows. The overall classification precision of land cover in 2018, 2019, and 2020 were 86.93%, 87.36%, and 88.54%, respectively. The area of abandoned land in Minhe County in 2020 was 43.17 km2, accounting for 2.28% of the total study area. The abandoned land was mainly distributed in areas with an altitude of 2 200~2 600 m, a slope of 6°~25°, and a shady slope direction. The integration of the polarization characteristics of Sentinel-1 SAR images into Sentinel-2 MSI multi-season images can effectively improve the land cover classification precision and yield accurate information on the abandoned land. This study will provide a reference method and basis for the information extraction of abandoned land in areas with similar terrain.
Keywords abandoned land      Sentinel-1/2 satellite imagery      multi-season      random forests      western Loess Plateau      Minhe County     
ZTFLH:  TP79  
Issue Date: 27 December 2022
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Hao ZHANG
Xiaohong GAO
Feifei SHI
Runxiang LI
Cite this article:   
Hao ZHANG,Xiaohong GAO,Feifei SHI, et al. Sentinel-2 MSI and Sentinel-1 SAR based information extraction of abandoned land in the western Loess Plateau:A case study of Minhe County in Qinghai[J]. Remote Sensing for Natural Resources, 2022, 34(4): 144-154.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021385     OR     https://www.gtzyyg.com/EN/Y2022/V34/I4/144
Fig.1  Location map of the Minhe County
Fig.2  Main crop types and phenological period in Minhe County
卫星及数
据类型		 2018年 2019年 2020年
种植期 成熟期 种植期 成熟期 种植期 成熟期
Sentinel-1
SAR		 18 26 18 24 15 22
Sentinel-2
L1C		 23 91 0 0 0 0
Sentinel-2
L2A		 0 0 45 72 36 59
合计 41 117 63 96 51 81
Tab.1  
年份 耕地 林地 草地 河渠、水
库坑塘		 河滩
地		 城乡工矿
居民用地		 未利用
土地
2018年 1 807 648 1 397 164 497 1 629 999
2019年 1 805 642 1 063 164 497 1 644 983
2020年 1 810 615 1 298 164 493 1 515 961
Tab.2  
Fig.3  Technology roadmap
指数 B2 B3 B4 B8 B11 B12
亮度指数 0.082 2 0.136 0 0.261 1 0.389 5 0.388 2 0.136 6
绿度指数 -0.112 8 -0.168 0 -0.348 0 0.316 5 -0.457 8 -0.406 4
湿度指数 0.136 3 0.280 2 0.307 2 -0.080 7 -0.406 4 -0.560 2
Tab.3  Matrix coefficients of tassel cap transformation from Sentinel-2A/B
特征组合 Kappa系数 总体分类精度/%
光谱 0.723 7 77.66
光谱+地形 0.792 9 83.26
光谱+地形+纹理 0.797 1 83.59
光谱+地形+缨帽 0.803 3 84.10
光谱+地形+纹理+缨帽 0.803 7 84.14
光谱+地形+缨帽+纹理 0.803 7 84.14
光谱+地形+极化 0.829 6 86.20
光谱+地形+极化+缨帽 0.838 4 86.92
光谱+地形+极化+纹理 0.832 7 86.43
光谱+地形+纹理+缨帽+极化 0.834 7 86.62
Tab.4  Kappa coefficient and overall classification accuracies under different feature combinations
年份 重要性评估前11个特征
地形特征 极化特征 光谱特征 缨帽特征
2018年 高度 种植期-
VH		 成熟期-
EVI		 种植期-
EVI		 种植期-
wetness
坡度 成熟期-
VH		 种植期-
RRI		 种植期-
MNDWI		 成熟期-
wetness
成熟期-
B2
2019年 高度 种植期-
VH		 成熟期-
EVI		 种植期-
RRI		 种植期-
wetness
坡度 成熟期-
VH		 种植期-
EVI		 成熟期-
wetness
种植期-
VV		 成熟期-
B2
2020年 高度 成熟期-
VH		 种植期-
EVI		 成熟期-
EVI		 种植期-
wetness
坡度 种植期-
VH		 种植期-
RRI		 成熟期-
MNDWI
成熟期-
VV		 成熟期-
B2
Tab.5  Results of feature optimization selection in 2018, 2019 and 2020
年份 Kappa系数 总体分类精度/%
2018年 0.838 9 86.93
2019年 0.843 6 87.36
2020年 0.859 2 88.54
Tab.6  Kappa coefficient and overall accuracy
Fig.4  Abandoned land extraction decision tree
Fig.5  Land cover classification in 2018, 2019 and 2020
Fig.6  Spatial distribution, validation sample points and enlarged view of local areas from abandoned land
高程级数 范围/m 面积/km2 占比/%
1 [1 652,2 000) 3.26 7.56
2 [2 000,2 200) 3.81 8.81
3 [2 200,2 400) 22.77 52.74
4 [2 400,2 600) 12.74 29.50
5 [2 600,4 200] 0.59 1.39
Tab.7  Abandoned land area under different elevation series
坡度级数 范围/(°) 面积/km2 占比/%
1 [0,2) 0.35 0.81
2 [2,6) 2.09 4.85
3 [6,15) 13.78 31.90
4 [15,25) 22.48 52.08
5 [25,90] 4.47 10.36
Tab.8  Abandoned land area under different slope grades
坡向 面积/km2 占比/%
阴坡 13.77 31.89
半阴坡 10.08 23.35
半阳坡 10.35 23.99
阳坡 8.97 20.77
Tab.9  Abandoned land area in different slope directions
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