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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 10-19     DOI: 10.6046/zrzyyg.2021137
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Research progress and development trend of remote sensing information extraction methods of vegetation
HUANG Pei1(), PU Junwei1, ZHAO Qiaoqiao1, LI Zhongjie2, SONG Haokun3, ZHAO Xiaoqing3()
1. Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
2. Simao Jinlancang High-Yield Plantation Ltd., Pu’er 665699, China
3. School of Earth Science, Yunnan University, Kunming 650500, China
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Abstract  

The remote sensing information extraction of vegetation is the basis and key link for remote sensing investigation and dynamic monitoring of vegetation coverage, which is of great significance for regional ecological environment protection and sustainable development. For this purpose, the research progress on the remote sensing information extraction methods of vegetation was reviewed from prior knowledge, expert knowledge and related auxiliary information, extraction of vegetation phenological features, the fusion of multi-source remote sensing data, machine learning, and other methods. Then, the main problems and challenges existing at the present stage were pointed out, and the future development trend was put forward. The research shows that there are many methods to extract remote sensing information about vegetation, and different methods have their own advantages and disadvantages in the application. However, the research on remote sensing information extraction methods of vegetation is currently facing many challenges, such as the lack of openness of high-resolution remote sensing data, the poor stability of parameter settings in vegetation information extraction models, the prominent phenomenon of same objects with different spectra and different objects with the same spectrum, the difficulties in automatic extraction of vegetation remote sensing information based on an expert knowledge base, and the need in further research on the multiple-method fusion. Therefore, making more breakthroughs in integrating multi-source data, multiple methods and new features of multi-temporal remote sensing images will be necessary to promote the refined, automated, and intelligent development of remote sensing information extraction of vegetation.
Keywords remote sensing of vegetation      information extraction      research methods      problems and challenges      development trend     
ZTFLH:  TP79  
Corresponding Authors: ZHAO Xiaoqing     E-mail: hphyyy09@126.com;xqzhao@ynu.edu.cn
Issue Date: 20 June 2022
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Pei HUANG
Junwei PU
Qiaoqiao ZHAO
Zhongjie LI
Haokun SONG
Xiaoqing ZHAO
Cite this article:   
Pei HUANG,Junwei PU,Qiaoqiao ZHAO, et al. Research progress and development trend of remote sensing information extraction methods of vegetation[J]. Remote Sensing for Natural Resources, 2022, 34(2): 10-19.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021137     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/10
方法
类别		 方法名称 优势 不足
监督分类 最大似然法 考虑了波段间的协方差和未知像元属于不同类别的概率 对样本光谱特征要求高(正态分布);计算量大、对不同类别方差变化敏感
平行六面体法 简单,有效,考虑了不同类别间的方差 类别较多时,不同类别特征空间易重叠
最小距离法 方法简单、实用性强、计算速度快 未考虑不同类别内部方差异同,易导致不同类别边界的重叠现象出现
马氏距离法 不受量纲影响,方法简单,计算速度快 易受协方差矩阵不稳定性的影响
非监督分类 K-Means法 算法简单,对于处理大数据集具有相对优势 初始聚类中心的选择具有较大随机性,聚类结果易偏离最优值
ISODATA法 自组织能力强,可考虑类别的分裂和合并;能舍去样本数据很少的类 迭代次数难以把握;最优先验参数难以确定
Tab.1  Common supervised and unsupervised classification methods
指数名称 英文
简称		 计算公式① 参考
文献
比值植被指数 RVI RVI=ρNIR/ρRed [29]
差值植被指数 DVI DVI=ρNIR-ρRed [35]
归一化植被指数 NDVI NDVI= $\frac{\rho_{NIR}\ \ - \ \rho_{Red}}{\rho_{NIR}\ \ + \ \rho_{Red}}$ [30]
垂直植被指数 PVI PVI= $\frac{\rho_{NIR}\ \ - \ a \rho_{Red}\ \ - \ b}{\sqrt{1+a^{2}}}$
a=10.489,b=6.604		 [36]
增强植被指数 EVI EVI= $\frac{2.5(\ \ \rho_{NIR}\ \ - \ \rho_{Red}\ \ )}{\rho_{NIR}\ \ + \ 6.0\rho_{Red}\ \ - \ 7.5\rho_{Blue} \ \ + \ 1}$ [27]
土壤调整植被指数 SAVI SAVI= ( $\frac{\rho_{NIR}\ \ - \ \rho_{Red}}{\ \ + \ \rho_{Red}\ \ + \ L}\ \ $ )×(1+L) [37]
再归一化植被指数 RDVI RDVI= $\sqrt{NDVI \times DVI}$ [37]
Tab.2  Some calculation formulas of common vegetation indexes
提取方法 应用优势 限制条件
先验知识法 可避免不必要类别的出现;方法简单,实用性强 受研究者主观认知水平的影响较大;传统的监督分类方法要求大量的训练样本或样本要求必须符合正态分布
专家知识和相关辅助信息法 一定程度上可减少“同物异谱”和“同谱异物”现象,分类精度有较大提高 专家库知识规则的建立存在主观性;部分辅助数据具有时空覆盖范围小、时效性差等问题
植被物候特征提取法 能精确反映植被生长变化情况;可进行长周期、广覆盖的植被信息提取 对数据时间分辨率要求高;受数据噪声影响较大
多源遥感数据融合法 可实现不同光谱和时间分辨率的遥感影像信息重组和互补;可提高地物信息提取算法的鲁棒性和精度 像素级层次信息冗余度高;决策级层次数据预处理代价高
机器学习法 具有非线性特征;可以运行样本容量很大的数据集,泛化能力强 算法相对复杂;需要大量的训练样本;最优参数的确定较为困难
其他方法 面向对象分类法综合考虑了光谱特征、空间特征和上下文信息;较基于像元分类结果,降低了椒盐效应
混合像元分解法提升了影像信息整体精度,有效避免了土壤等地物背景的影响		 面向对象分类法算法运行速度慢;对低分辨率影像及景观破碎、地形复杂地区应用性较差
混合像元分解法算法相对复杂;单个像元的分解精度较低
Tab.3  Application advantages and limitations of different remote sensing information extraction methods of vegetation
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