长江流域资源与环境 >> 2021, Vol. 30 >> Issue (2): 371-381.doi: 10.11870/cjlyzyyhj202102012

• 自然资源 • 上一篇    下一篇

基于遥感影像序列的建设占用农用地时空信息提取

郝玉珠1,陈振杰1* ,侯仁福2,王贝贝1   

  1. (1.南京大学地理与海洋科学学院,江苏 南京 210046;2.安徽省第一测绘院,安徽 合肥 230031)
  • 出版日期:2021-02-20 发布日期:2021-03-18

Spatiotemporal Information Extraction of Agricultural Land Occupied by Construction Based on Time Series of Remote Sensing

HAO Yu-zhu 1, CHEN Zhen-jie 1, HOU Ren-fu 2, WANG Bei-bei 1   

  1. (1.School of Geography and Ocean Science, Nanjing University,Nanjing 210046,China;2.Anhui Provincial First Institute of Surveying and Mapping,Hefei 230031,China)
  • Online:2021-02-20 Published:2021-03-18

摘要: 随着我国城市化进程持续深入,大量农用地被建设占用,严重威胁着粮食安全和生态环境。因此,农用地保护刻不容缓,而及时获取建设占用农用地时空信息是其重要基础。利用长时间序列Landsat影像,构建了一种基于时序异常值分段检测的建设占用农用地信息提取方法。首先构造多种指数的原始时间序列和年际变化率时间序列,根据年际变化率时间序列选择能够显著表征建设占用农用地的指数,然后采用异常值检测算法对年际变化率时间序列进行异常值检测以获取发生土地利用变化的像元及其变化时点,最后根据变化时点将原始时间序列分割为三段子序列,并分别与建设用地原始时间序列、农用地原始时间序列对应的子序列进行相似性比较,识别建设占用农用地的像元。实验证明,该文提出的方法能够快速获取建设占用农用地的变化时点和空间位置信息,变化时点检测的总体精度为89.35%,Kappa系数为0.88;空间位置检测精测精度为93.49%,Kappa系数为0.91。

Abstract: With the continuous deepening of urbanization in China, a large number of agricultural land is occupied by construction, which seriously threatens the food security and ecological environment. Therefore, it is an important foundation for protecting agricultural land to obtain the time and space information of construction land in time. In this paper, based on the long time series Landsat images, a method about extracting the information of agricultural land occupied by construction is proposed. Firstly, the original time series and interannual change rate series of various indexes are constructed. According to the interannual change rate series, the indexes that can distinguish the agricultural land occupied by the construction are selected. Then, the outlier detection algorithm is used to detect the outliers of the interannual change rate series to obtain the land use changed pixels and change time. Finally, according to the change time, the original time series is segmented three sub-series, and compared with the sub-series corresponding to the original time series of construction land and agricultural land to identify the pixel of agricultural land occupied by construction. The experimental result shows that the method can quickly obtain the change time and spatial location information of the agricultural land occupied by construction land. The overall accuracy of change time is 89.35%, and the kappa is 0.88. The overall accuracy of spatial position is 93.49%, and the kappa is 0.91.

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