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遥感技术与应用  2022, Vol. 37 Issue (3): 539-549    DOI: 10.11873/j.issn.1004-0323.2022.3.0539
农业遥感专栏     
基于多时相协同变化检测的耕地撂荒遥感监测
韦中晖1,2(),靳海亮1,顾晓鹤2(),杨英茹3,王庚泽1,2,潘瑜春2
1.河南理工大学 测绘与国土信息工程学院,河南 焦作 454000
2.北京市农林科学院信息技术研究中心,北京 100097
3.石家庄市农林科学研究院,河北 石家庄 050041
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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摘要:

针对地表覆被复杂、地块破碎等原因导致的撂荒地提取精度较低问题,提出一种基于多时相协同变化检测的耕地撂荒信息提取方法。以河北省石家庄市鹿泉区为研究区,采用Sentinel?2A和Landsat 7多光谱影像,在野外样本的支持下,分析耕地各种覆盖类型的归一化植被指数(Normalized Difference Vegetation Index,NDVI)季相变化规律,以季节性撂荒、常年性撂荒、冬小麦、多年生园地为分类体系,构建多时相协同变化检测模型,开展研究区耕地撂荒状态遥感监测。研究结果表明:基于Sentinel?2A影像的季节性撂荒和常年撂荒耕地的分类精度分别为95.83%和96.55%;基于Landsat 7影像的季节性撂荒和常年撂荒耕地的分类精度分别为91.67%和93.10%;2019年鹿泉区季节性撂荒占耕地面积的4.7%,常年撂荒耕地占7.1%。利用该方法能够快速、准确地获取研究区耕地空间分布、面积等信息,对于不同分辨率的影像均具有较好的撂荒地提取精度。

关键词: 耕地撂荒Sentinel?2ANDVI多时相变化检测遥感监测    
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
收稿日期: 2021-01-06 出版日期: 2022-08-25
ZTFLH:  TP79  
基金资助: 陕西省重点研发计划(2022ZDLNY02-10)
通讯作者: 顾晓鹤     E-mail: 1024217621@qq.com;guxh@nercita.org.cn
作者简介: 韦中晖(1995-),男,山东德州人,硕士研究生,主要从事农业定量遥感研究。E?mail: 1024217621@qq.com
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引用本文:

韦中晖,靳海亮,顾晓鹤,杨英茹,王庚泽,潘瑜春. 基于多时相协同变化检测的耕地撂荒遥感监测[J]. 遥感技术与应用, 2022, 37(3): 539-549.

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.

链接本文:

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

图1  研究区地理位置
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
表1  影像数据列表
图2  研究区遥感影像
图3  耕地地块数据及野外调查样本
图4  技术流程图
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
表2  不同时相间典型地物的季相变化规律
图5  典型地物NDVI时间序列曲线
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
表3  不同时相间典型地物的NDVI分布区间
图6  变化检测模型
图7  变化检测分类结果
图8  鹿泉区2019年耕地撂荒遥感监测图
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
表4  Sentinel影像分类结果混淆矩阵精度评价
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
表5  Landsat-7 ETM影像分类结果混淆矩阵精度评价
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