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Remote Sensing Technology and Application  2022, Vol. 37 Issue (3): 539-549    DOI: 10.11873/j.issn.1004-0323.2022.3.0539
    
Remote Sensing Monitoring of Cultivated Land Abandonment based on Multi-temporal Collaborative Change Detection
Zhonghui Wei1,2(),Hailiang Jin1,Xiaohe Gu2(),Yingru Yang3,Gengze Wang1,2,Yuchun Pan2
1.School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China
2.Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China
3.Shijiazhuang Academy of Agriculture and Forestry Sciences,Shijiazhuang 050041,China
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Abstract  

Aiming at the problem of low precision of abandoned land extraction caused by complex land cover and broken land, a method of abandoned land information extraction based on multi temporal collaborative change detection was proposed. Taking Luquan District, Shijiazhuang City, Hebei Province as the research area, the Normalized Difference Vegetation Index (NDVI) of various types of cultivated land cover was analyzed by using sentinel 2a and Landsat 7 multispectral images and supported by field samples Based on the classification system of seasonal abandonment, perennial abandonment, winter wheat and perennial garden, a multi temporal collaborative change detection model was constructed to carry out remote sensing monitoring of cultivated land abandonment in the study area. The results show that the classification accuracy of seasonal and perennial abandoned farmland based on Sentinel 2A image is 95.83% and 96.55% respectively; the classification accuracy of seasonal and perennial abandoned farmland based on Landsat 7 image is 91.67% and 93.10% respectively; the seasonal abandoned farmland accounts for 4.7% and perennial abandoned farmland accounts for 7.1% in Luquan District in 2019. This method can quickly and accurately obtain the spatial distribution and area information of cultivated land in the study area, and has good extraction accuracy for abandoned land in different resolution images.

Key words:  Cultivated land abandonment      Sentinel-2A      NDVI      Multi temporal change detection      Remote sensing monitoring     
Received:  06 January 2021      Published:  25 August 2022
ZTFLH:  TP79  
Corresponding Authors:  Xiaohe Gu     E-mail:  1024217621@qq.com;guxh@nercita.org.cn
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Articles by authors
Zhonghui Wei
Hailiang Jin
Xiaohe Gu
Yingru Yang
Gengze Wang
Yuchun Pan

Cite this article: 

Zhonghui Wei,Hailiang Jin,Xiaohe Gu,Yingru Yang,Gengze Wang,Yuchun Pan. Remote Sensing Monitoring of Cultivated Land Abandonment based on Multi-temporal Collaborative Change Detection. Remote Sensing Technology and Application, 2022, 37(3): 539-549.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.3.0539     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I3/539

Fig.1  Location of the study area
时相获取卫星传感器波段数空间分辨率/m影像质量
2018-12-24S2AMSI1310良好
2019-5-28S2AMSI1310良好
2019-7-12S2AMSI1310良好
2018-12-19Landsat7ETM830良好
2019-5-28Landsat7ETM830良好
2019-7-15Landsat7ETM830良好
Table 1  Image data list
Fig.2  Sketch map of remote sensing image
Fig.3  Cultivated land range and investigation samples in the study area
Fig.4  Technical flow chart
地物类型12月5月7月
小麦作物作物裸地
多年生园地裸地作物作物
年荒裸地裸地裸地
季荒裸地裸地作物
Table 2  Land cover types in different months
Fig.5  NDVI time series curve of typical features
地物类型时相
12月至5月5月至7月
小麦0.41 ≤ V1 ≤ 0.59-0.66 ≤ V2 ≤ -0.54
多年生园地0.24 ≤ V3 ≤ 0.490.01 ≤ V4 ≤ 0.07
季荒0.02 ≤ V5 ≤ 0.140.16 ≤ V6 ≤ 0.33
年荒-0.17 ≤ V7 ≤ 0.00-0.08 ≤ V8 ≤ 0.08
Table 3  NDVI distribution interval of typical features in different phases
Fig.6  Change detection model
Fig. 7  Classification results of change detection
Fig. 8  Remote sensing monitoring map of cultivated land abandonment in Luquan district in 2019
实测样本分类结果合计用户精度/%
季节撂荒常年撂荒小麦多年生园地

季节撂荒230012495.83
常年撂荒028002896.55
小麦308809196.70
多年生园地8349711286.61
实测样本合计34319298255
制图精度/%67.6590.3295.6598.98
总体精度/%92.19Kappa系数0.88
Table 4  Accuracy evaluation of confusion matrix of Sentinel image classification results
实测样本分类结果合计用户精度/%
季节撂荒常年撂荒小麦多年生园地

季节撂荒221012491.67
常年撂荒127002893.10
小麦638029187.91
多年生园地10559211282.14
实测样本合计39368595255
制图精度/%56.4175.0094.1296.84
总体精度/%86.33Kappa系数0.80
Table 5  Accuracy evaluation of confusion matrix of Landsat7 ETM image classification results
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