生物多样性 ›› 2019, Vol. 27 ›› Issue (7): 796-812.DOI: 10.17520/biods.2019197

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基于多源遥感数据的植物物种分类与识别: 研究进展与展望

孔嘉鑫1,2,张昭臣1,2,*(),张健1,2   

  1. 1 华东师范大学生态与环境科学学院, 浙江天童森林生态系统国家野外科学观测研究站, 上海 200241
    2 上海污染控制与生态安全研究院, 上海 200092
  • 收稿日期:2019-06-17 接受日期:2019-07-11 出版日期:2019-07-20 发布日期:2019-08-21
  • 通讯作者: 张昭臣
  • 基金资助:
    国家自然科学基金(31670439);华东师范大学公共创新服务平台(008)

Classification and identification of plant species based on multi-source remote sensing data: Research progress and prospect

Kong Jiaxin1,2,Zhang Zhaochen1,2,*(),Zhang Jian1,2   

  1. 1 Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241
    2 Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092
  • Received:2019-06-17 Accepted:2019-07-11 Online:2019-07-20 Published:2019-08-21
  • Contact: Zhang Zhaochen

摘要:

物种分类与识别是生物多样性监测的基础, 明确物种的类别及其分布是解决几乎所有生态学问题的前提。为深入了解基于多源遥感数据的植物物种分类与识别相关研究的发展现状和存在的问题, 本文对2000年以来该领域的研究进行了总结分析, 发现: 当前大多数研究集中在欧洲和北美地区的温带或北方森林以及南非的热带稀树草原; 使用最多的遥感数据是机载高光谱数据, 而激光雷达作为补充数据, 通过单木分割及提供单木的三维垂直结构信息, 显著提高了分类精度; 支持向量机和随机森林作为应用最广的非参数分类算法, 平均分类精度达80%; 随着计算机技术及机器学习领域的不断成熟, 人工神经网络在物种识别领域得以迅速发展。基于此, 本文对目前基于遥感数据的植物物种分类与识别中在分类对象复杂性、多源遥感数据整合、植物物候与纹理特征整合和分类算法技术等方面面临的挑战进行了总结, 并建议通过整合多时相监测数据、高光谱和激光雷达数据、短波红外等特定波谱信息、采用深度学习等方法来提高分类精度。

关键词: 物种分类, 物种识别, 遥感监测, 生物多样性, 监督分类

Abstract

Species classification and identification is the basis of biodiversity monitoring, and is critical to deal with almost all ecological questions. In this paper, we aim to understand the current status and existing problems in plant species classification and identification using multi-source remote sensing data. We summarized the studies in this field since the year 2000, and found that most of these studies focus on temperate or boreal forests in Europe and North America, or African savanna. Airborne hyperspectral data is the most widely used remote sensing data source, and the LiDAR, as a supplementary data, significantly improves the classification accuracy through the information of single tree segmentation and three-dimensional vertical structure. Support vector machine and random forest are the most widely used non-parametric classification algorithms with an average classification accuracy of 80%. With the development of computer technology and machine learning, artificial neural network has developed rapidly in species identification. Based on the literature-based analysis, we propose that the current research in this field is still facing some challenges, including the complexity of classification objects, the effective integration of multi-source remote sensing data, the integration of plant phenology and texture characteristics, and the improvement in plant classification algorithm. The accuracy of plant classification and identification could be greatly improved by using the high-frequency data collection over time, the integration of hyperspectral and LiDAR data, the use of specific spectral information such as short-wave infrared imagery, and the development of novel deep learning techniques.

Key words: species classification, species identification, remote sensing monitoring, biodiversity, supervised classification