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

• 综述 • 上一篇    下一篇

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
  • 通讯作者: 张昭臣 E-mail:sean19880305@163.com
  • 基金项目:
    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
  • Contact: Zhang Zhaochen E-mail:sean19880305@163.com

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

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

图1

2000-2018年间遥感领域3个主流期刊(International Journal of Remote Sensing、Remote Sensing of Environment和Remote Sensing)上使用多源遥感数据进行植物物种分类与识别的论文统计"

表1

基于遥感数据进行植物物种识别的120项研究案例统计"

研究对象
Research subjects
相关案例数量
Number of case studies
案例
Selected case studies
热带/亚热带森林
Tropical/subtropical forests
10 Clark et al, 2005; Féret & Asner, 2013; 樊雪等, 2017
热带稀树草原 Savanna 8 Cho et al, 2012; Madonsela et al, 2017
温带森林 Temperate forests 56 尹凌宇等, 2016; Franklin & Ahmed, 2018
北方森林/针叶林 Boreal forest or Coniferous forest 19 Suratno et al, 2009; 于丽柯等, 2014
红树林 Mangrove 7 Yang et al, 2009; 李想等, 2018
草地 Grassland 4 钱育蓉等, 2011; Skovsen et al, 2017
城市树种 Urban tree species 11 Liu et al, 2017; 皋厦等, 2018
入侵植物 Invasive plants 5 Hung et al, 2014; Michez et al, 2015

表2

常用物种分类算法的优缺点及案例"

分类算法
Species classification algorithms
优缺点 Advantages and disadvantages 案例列举
Case studies
输入数据是否需要分布假设
Does the input data need specific distribution assumption?
是否需要训练数据
Is the training data needed?
对各种分类问题的适用性
Applicability to different kinds of species classifications
算法复杂性和计算成本
Algorithmic complexity and computational cost
可操作性
Is it easy to use for ecologists?
支持向量机
Support Vector Machine (SVM)

No
较少
Less
较高
High
较高
High
容易
Easy
Féret & Asner, 2012;
陈向宇等, 2019
随机森林
Random Forest (RF)

No
较少
Less
较高
High
较低
Low
容易
Easy
Naidoo et al, 2012;
傅锋等, 2019
最大似然分类
Maximum Likelihood Classifiers (MLC)

Yes
较多
More
较高
High
较高
High
容易
Easy
Dymond et al, 2002;
汪少华和杨婷, 2018
判别式分析
Discriminant Analysis (DA)

Yes
较多
More
较高
High
较低
Low
容易
Easy
Kim et al, 2009;
Hovi et al, 2016
k-最近邻分类
k-Nearest Neighbor (KNN)

No

No
较高
High
较高
High
容易
Easy
Jia et al, 2014;
王二丽等, 2017
人工神经网络
Artificial Neural Networks (ANN)

No
较多
More
较高
High
较高
High
较难
Difficult
Onishi & Ise, 2018;
滕文秀等, 2019
光谱角制图
Spectral Angle Mapper (SAM)

No

No
较低
Low
较高
High
容易
Easy
Clark et al, 2005;
杨可明等, 2015
广义线性模型
Generalized Linear Model (GLM)

Yes

No
较低
Low
较高
High
较难
Difficult
Waser et al, 2011;
Engler et al, 2013
分类和回归树
Classification and regression tree (CART)

No
较少
Less
较高
High
较低
Low
容易
Easy
Friedl & Brodley, 1997;
Pu & Landry, 2012
贝叶斯分类算法
Bayesian Classifiers

No

No
较高
High
较低
Low
容易
Easy
Saatchi & Rignot, 1997;
李想等, 2018

图2

常用物种分类算法的应用。(a)不同算法分类的物种数; (b)不同算法分类的总体精度。SVM: 支持向量机; RF: 随机森林; MLC: 最大似然分类; DA: 判别式分析; KNN: k-最近邻分类; ANN: 人工神经网络; GLM: 广义线性模型; SAM: 光谱角制图; CART: 分类和回归树; Bayes: 贝叶斯算法; MCS: 多分类系统。括号中N代表每种算法对应的研究案例数。"

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