欢迎访问《生态学杂志》官方网站,今天是 分享到:

生态学杂志

• 方法与技术 • 上一篇    下一篇

基于稳定像元的决策树分类后比较法在林地变化检测中的应用

庞博1, 2,王浩2*,宁晓刚2   

  1. (1山东科技大学测绘科学与工程学院, 山东青岛 266590;2中国测绘科学研究院, 北京 100830)
  • 出版日期:2018-09-10 发布日期:2018-09-10

Application of decision tree post-classification comparison based on stable pixels in forestland change detection.

PANG Bo1,2, WANG Hao2*, NING Xiao-gang2   

  1. (1College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong, China; 2Chinese Academy of Surveying and Mapping, Beijing 100830, China).
  • Online:2018-09-10 Published:2018-09-10

摘要: 林地是森林资源的载体和重要组成部分。如何准确快速地获取林地变化信息,对森林资源的监测管理具有重要的意义。本研究以伊春市为试验区,采用稳定像元筛选以提高决策树分类中的训练样本的可靠性,从而提高决策树分类后比较法得到的林地分类精度和变化检测精度。结果表明:与传统的决策树分类后比较法相比,改进的决策树分类后比较法的变化检测精度提高了4.01%,且能提高阴影、薄雾、耕地等区域的检测精度,用于林地变化检测更加可靠。

关键词: 茄子, 产量, 品质, 发光二极管, 幼苗质量, 光质

Abstract: Forestland is the carrier and an important component of forest resources. How to accurately and quickly obtain information of forestland change is of important significance to monitoring and management of forest resources. In this study, we improved the training sample selection method in the decision tree classification to enhance the accuracy of forestland classification and change detection. The reliability of the improved method was tested with Yichun as a case. The results showed that the improved decision tree classification method outperformed traditional method with the accuracy of change detection being improved by 4.01%. Furthermore, the method improved the detection accuracy in regions with shadow, mist and arable land. Therefore, our method is more reliable for the detection of forestland changes.

Key words: quality., LED, seedling quality, eggplant, light quality, yield