Biodiversity Science ›› 2019, Vol. 27 ›› Issue (7): 796-812.doi: 10.17520/biods.2019197

• Review • Previous Article     Next Article

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:2019-08-21
  • Zhang Zhaochen

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

Fig. 1

The papers on plant species classification and identification using different types of remote sensing data published between 2000 and 2018 in three top journals in remote sensing, including International Journal of Remote Sensing, Remote Sensing of Environment, and Remote Sensing."

Table 1

Statistics of 120 selected study cases of plant species identification based on remote sensing data"

Research subjects
Number of case studies
Selected case studies
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

Table 2

Advantages and disadvantages of commonly used species classification algorithms and case studies"

Species classification algorithms
优缺点 Advantages and disadvantages 案例列举
Case studies
Does the input data need specific distribution assumption?
Is the training data needed?
Applicability to different kinds of species classifications
Algorithmic complexity and computational cost
Is it easy to use for ecologists?
Support Vector Machine (SVM)

Féret & Asner, 2012;
陈向宇等, 2019
Random Forest (RF)

Naidoo et al, 2012;
傅锋等, 2019
Maximum Likelihood Classifiers (MLC)

Dymond et al, 2002;
汪少华和杨婷, 2018
Discriminant Analysis (DA)

Kim et al, 2009;
Hovi et al, 2016
k-Nearest Neighbor (KNN)


Jia et al, 2014;
王二丽等, 2017
Artificial Neural Networks (ANN)

Onishi & Ise, 2018;
滕文秀等, 2019
Spectral Angle Mapper (SAM)


Clark et al, 2005;
杨可明等, 2015
Generalized Linear Model (GLM)


Waser et al, 2011;
Engler et al, 2013
Classification and regression tree (CART)

Friedl & Brodley, 1997;
Pu & Landry, 2012
Bayesian Classifiers


Saatchi & Rignot, 1997;
李想等, 2018

Fig. 2

Application of commonly used species classification algorithms. (a) The number of species classified by different algorithms; (b) Overall accuracy of different algorithms. SVM, Support Vector Machine; RF, Random Forest; MLC, Maximum Likelihood Classifiers; DA, Discriminant Analysis; KNN, k-Nearest Neighbor; ANN, Artificial Neural Networks; GLM, Generalized Linear Model; SAM, Spectral Angle Mapper; CART, Classification and regression tree; Bayes, Bayesian Classifiers; MCS, Multiple Classifier Systems. “N” in the brackets represents the number of study cases corresponding to each algorithm."

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