地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (8): 1284-1294.doi: 10.12082/dqxxkx.2019.180650

• 遥感科学与应用技术 • 上一篇    下一篇

基于垂直带谱的太白山区山地植被遥感信息提取

张俊瑶1,2,姚永慧1,*(),索南东主1,2,郜丽静3,王晶1,2,张兴航1,2   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 中国科学院空天信息研究院,北京 100094
  • 收稿日期:2018-12-12 修回日期:2019-04-09 出版日期:2019-08-28 发布日期:2019-08-28
  • 通讯作者: 姚永慧 E-mail:yaoyh@lreis.ac.cn
  • 作者简介:张俊瑶(1995-),女,山东滨州人,硕士生,研究方向为植被遥感分类。E-mail: eaea330@163.com
  • 基金资助:
    国家自然科学基金项目(41871350);科技基础资源调查项目(2017FY100900)

Mapping of Mountain Vegetation in Taibai Mountain based on Mountain Altitudinal Belts with Remote Sensing

ZHANG Junyao1,2,YAO Yonghui1,*(),SUONAN Dongzhu1,2,GAO Lijing3,WANG Jing1,2,ZHANG Xinghang1,2   

  1. 1. Skate Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • Received:2018-12-12 Revised:2019-04-09 Online:2019-08-28 Published:2019-08-28
  • Contact: YAO Yonghui E-mail:yaoyh@lreis.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41871350);Scientific and Technological Basic Resources Survey Project(2017FY100900)

摘要:

遥感数据因其全覆盖的优势被广泛应用于山地植被信息的调查和研究。为了实现山区植被类型的高精度提取,本文以太白山区为实验区,结合山地植被的垂直地带性分布规律,利用太白山植被垂直带谱、高分辨率遥感影像(GF1/GF2/ZY3)和1:1万的数字表面模型(Digital Surface Model, DSM)数据,进行了多层次、多尺度的影像分割,构建了具有植被垂直带谱信息的地形约束因子,并据此进行样本选择和面向对象的分类,分类总精度达92.9%,kappa系数达到0.9160。该方法相比于未辅以垂直带谱信息的分类,总精度提高了10%。研究结果表明,分类过程中加入具有垂直带谱信息的地形约束因子,能显著地提高样本选择的效率和准确率,为后续的植被分类提供了精度的保证。通过人机交互的方式,将垂直带谱知识应用到分类中,可以有效地提高山地植被分类的精度。

关键词: 植被垂直带谱, 面向对象, 多层次分割, 植被信息提取, 地形约束因子, 太白山区, 山地植被

Abstract:

The structural function and ecological characteristics of mountain vegetation can reflect comprehe-nsively the basic characteristics and functional properties of the eco-environment. Mapping of different vegetation types is the basis for the study of vegetation cover dynamics. Therefore, studying vegetation types and their distribution patterns in montane areas is important for understanding the eco-environment and climatic spatial changes. With the development and application of satellite remote sensing technology, remote sensing data have been widely used in the investigation and research of mountain vegetation information extraction. As altitude increases, vegetation presents the characteristic of regular zonal arrangement and combination, which is called the altitudinal belts law of mountain vegetation. Through the altitudinal vegetation belts information, the altitude range of different vegetation groups and the adjacent relationship between the upper and lower layers can be determined. To achieve high-precision extraction of mountain vegetation types, in this paper, we took Taibai Mountain (the main peak of Qinling Mountains) as the experimental area, combined the obvious vertical zonal distribution law of mountain vegetation, and used the data of altitudinal belts of Taibai Mountain vegetation, high-resolution remote sensing imagery (GF1/GF2/ZY3), and 1:10 000 digital surface model (DSM). Followingly, we selected the optimal segmentation scale of different levels by calculating the mean variance, and conducted multi-level and multi-scale image segmentation. Then, we built terrain constraint factors with mountain altitudinal belts information and selected samples. After overlaying terrain constraint factors with altitudinal belts information of vegetation on the high-resolution images, the rough distribution ranges of each vegetation types were clear at a glance, which can make sample selection more efficient and accurate. Lastly, the images were used to extract vegetation information through the object-oriented classification method. The classification result had a total accuracy of 92.9% and a kappa coefficient of 0.9160. To prove the role of terrain constraint factors, some regions in the western part of the north slope were selected for comparing whether terrain constraint factors affected the classification. Compared with the classification without altitudinal vegetation belts information, this method improved the overall accuracy by 10%. The results show that adding terrain constraint factors in the classification process can significantly improve the efficiency and accuracy of sample selection, and provide a guarantee for the accuracy of subsequent vegetation classification. By man-machine interaction, this study applies the knowledge of mountain altitudinal belts to classification, and effectively improves the accuracy of mountain vegetation classification.

Key words: mountain altitudinal belts, object-oriented, multi-scale segmentation, vegetation mapping, terrain constraint factors, Taibai Mountain, mountain vegetation