Research progress on remote sensing discrimination techniques for grassland botanical species

doi:10.11686/cyxb2022278

Abstract

Abstract:

Grassland is an important resource bank for regional economic development and a crucial ecological reservoir for the security of China’s land-based ecological environment. Remote sensing technology is rapid， efficient， and low-cost， and therefore provides the mainstream technical means for large-scale grassland monitoring. The use of remote sensing technology to discriminate grassland species is an important way to monitor the population dynamics and botanical community succession in grassland. Such information is conducive to the timely and accurate detection of changes in the grassland ecological environment and provides an important reference for the scientific management of grassland ecosystems and the construction of an ecologically aware civilization. This study focuses on the problem of remote sensing discrimination of grassland species， clarifies the technical process of grassland species discrimination and introduces the latest research progress. Technical difficulties are covered from three perspectives： The characteristics of the main remote sensing data sources for grassland species discrimination and their acquisition techniques， important grassland species discrimination features and their mining techniques and the current commonly used grassland species discrimination methods and models. This study concludes that hyperspectral and LiDAR remote sensing and their fusion technologies have application prospects in the remote sensing discrimination of grassland species， and that the deep mining of multidimensional features and the effective combination of complementary features can improve the accuracy of grassland species discrimination. This study identifies the main problems of the current remote sensing discrimination technology for grassland species， and opens the prospect of the future precise discrimination of grassland species through remote sensing technology and thereby provides a theoretical reference for a thorough understanding of the field of remote sensing identification of grassland species and in-depth research on grassland species discrimination.

Key words: remote sensing, grass species discrimination, hyperspectral, LiDAR, feature mining, discriminative model

Meng-ge HUANG, Xin-hong WANG, Ling-ling MA, Xue-hua YE, Xiao-hua ZHU, Wei-ping KONG, Ning WANG, Qi WANG, Guang-zhou OUYANG, Qing-chuan ZHENG, Xiao-xin HOU, Ling-li TANG. Research progress on remote sensing discrimination techniques for grassland botanical species[J]. Acta Prataculturae Sinica, 2023, 32(6): 167-185.

Figures/Tables 9

Fig.1 Flow chart of remote sensing techniques for grass species discrimination

Fig.2 The number of annual publications on unmanned aerial vehicle hyperspectral remote sensing of vegetation on the Scopus platform

Table 1 Major remote sensing data sources and their characteristics

主要遥感数据源

Major remote sensing data sources

特点

Characteristics

高光谱遥感Hyperspectral remote sensing

获取光谱反射特性和空间分布特性，光谱分辨率高，覆盖波谱范围广，能够精细地描述草种波谱特性；但只能获取二维表面信息，且数据维度高，处理困难。Obtain spectral reflectance characteristics and spatial distribution characteristics， with high spectral resolution， covering a wide spectral range， and capable of finely describing the spectral characteristics of grass species； However， only two-dimensional surface information can be obtained， and the high dimensionality of the data makes processing difficult.

激光雷达遥感LiDAR remote sensing

获取垂直结构信息，穿透性好、抗干扰能力强；但受限于点云密度和高程精度，容易低估草层高度等参数。Obtain vertical structure information with good penetration and resistance to interference； However， limited by point cloud density and elevation accuracy， easy to underestimate parameters such as grass layer height.

Fig.3 Spectral characteristic curves of grassland grass species （leaf）

Table 2 Major spectral libraries covering vegetation spectra at home and abroad

项目 Item	波谱库名称 Spectrum library name	发布机构 Publisher	建设年份 Construction year	下载网址 Download site
国外 Foreign	USGS波谱库 USGS spectrum library	美国地质调查局 United States Geological Survey	1993	https：//speclab.cr.usgs.gov/spectral-lib.html
	ASTER波谱库 ASTER spectrum library	美国宇航局National Aeronautics and Space Administration	1998	https：//speclib. Jpl.nasa.gov
	SPECCHIO波谱库 SPECCHIO spectrum library	苏黎世大学University of Zurich	2003	www.specchio/dndex.php
	HyspIRI波谱库 HyspIRI spectrum library	美国喷气推进实验室American Jet Propulsion Laboratory	1999	https： //hyspiri. jpl. nasa.gov/documents/
	VSL植被波谱库 VSL vegetation spectrum library	得克萨斯大学University of Texas	2014	www.spectrallibrary.utep.edu
国内 Domestic	地物反射光谱特性数据库 Database of reflectance spectral properties of features	中国科学院遥感应用研究所等Institute of Remote Sensing Applications， Chinese Academy of Sciences， etc.	20世纪90年代初 In the early 1990s	_
国内 Domestic	新疆典型荒漠植物光谱数据库Spectrum database of typical desert plants of Xinjiang	新疆农业大学草业与环境科学学院College of Grass and Environmental Sciences， Xinjiang Agricultural University	2013	_

Table 3 Typical vegetation spectral features

名称 Name	指标 Index	定义 Definition	特征描述 Feature description	相关文献 Reference
光谱位置、面积特征 Spectral position，area features	红边Red edge	红边幅值：一阶导曲线680~760 nm幅值；红边位置：红边幅值位置对应的波长；红边面积：680~760 nm范围内一阶导数值总和。Red edge amplitude： The first order guide curve amplitude of 680 to 760 nm. Red edge position： The wavelength corresponding to the position of the red edge amplitude. Red edge area： The sum of the first order derivativee values in the 680-760 nm range.	对原始曲线取一阶导有助于消除土壤背景等的影响；当植被生理生化参数改变，红边位置发生“蓝移”或“红移”；红边面积可以作为红边幅值的补充。Taking a first order derivative of the original curve helps to remove the effects of soil background， etc. When the vegetation physiographic parameters change， the position of the red edge is “blue-shifted” or “red-shifted”. The area of the red edge can be used as a supplement to the red edge amplitude.	［51］，［52］，［53］
	黄边Yellow edge	黄边幅值：一阶导曲线560~640 nm幅值。Yellow edge amplitude： 560-640 nm amplitude of the first order guide curve.	同上Ditto
	蓝边Blue edge	蓝边幅值：一阶导曲线490~530 nm幅值。Blue edge amplitude： 490-530 nm amplitude of the first order guide curve.	同上Ditto
光谱吸收特征Spectral absorption features	绿峰指数Green peak index	包络线归一化曲线518~576 nm最大值。Envelope normalisation curve 518-576 nm max.	对原始光谱曲线进行包络线归一化，放大反射峰、吸收谷，可以增加不同植被光谱差异。 The envelope normalisation of the original spectral curve， amplifying the reflection peaks and absorption valleys， can increase the differences in the spectra of different vegetation.	［55］，［56］，［57］
	红谷指数Red valley index	包络线归一化曲线650~692 nm最小值。Envelope normalisation curve 650-692 nm min.
	近红外峰值Near infrared peak	原始光谱780~1000 nm最大值。 Original spectrum 780-1000 nm max.	植被在近红外波段反射率差异大于可见光，可作为植被类型补充判据。The difference in reflectance of vegetation in the near-infrared band is greater than that of visible light and can be used as a complementary criterion for vegetation type.	［46］
光谱植被指数Spectral vegetation index	归一化差分植被指数Normalized difference vegetation index	$R N I R - R r e d R N I R + R r e d$	能够较好地响应绿色生物量的变化，对中、低密度的植被更有效。Better able to respond to changes in green biomass and more effective for medium and low density vegetation.	［67］
	比值植被指数Ratio vegetation index	$R N I R R r e d$	同上Ditto	［39］
	三角植被指数Triangle vegetation index	$0.5 × [120 × (R 750 - R 550) - 200 × (R 670 - R 550)]$	植被不同生长阶段叶绿素浓度改变会导致绿波段（550 nm）反射率变化，从而影响TVI。Changes in chlorophyll concentration at different stages of vegetation growth can lead to changes in the green band （550 nm） reflectance and thus affect TVI.	［68］
	改进的土壤调整植被指数Improved soil adjusted vegetation index	$a (R 800 - a R 670 - b) [a R 800 + R 670 - a b + X (1 + a 2)] (X = 0.08, a = 1.22, b = 0.03)$	受土壤背景影响较小，在估计均匀冠层方面表现较好。Less influenced by soil context， better performance in estimating uniform canopy.	［69］

Table 3 Typical vegetation spectral features

名称 Name	指标 Index	定义 Definition	特征描述 Feature description	相关文献 Reference
光谱位置、面积特征 Spectral position，area features	红边Red edge	红边幅值：一阶导曲线680~760 nm幅值；红边位置：红边幅值位置对应的波长；红边面积：680~760 nm范围内一阶导数值总和。Red edge amplitude： The first order guide curve amplitude of 680 to 760 nm. Red edge position： The wavelength corresponding to the position of the red edge amplitude. Red edge area： The sum of the first order derivativee values in the 680-760 nm range.	对原始曲线取一阶导有助于消除土壤背景等的影响；当植被生理生化参数改变，红边位置发生“蓝移”或“红移”；红边面积可以作为红边幅值的补充。Taking a first order derivative of the original curve helps to remove the effects of soil background， etc. When the vegetation physiographic parameters change， the position of the red edge is “blue-shifted” or “red-shifted”. The area of the red edge can be used as a supplement to the red edge amplitude.	［51］，［52］，［53］
	黄边Yellow edge	黄边幅值：一阶导曲线560~640 nm幅值。Yellow edge amplitude： 560-640 nm amplitude of the first order guide curve.	同上Ditto
	蓝边Blue edge	蓝边幅值：一阶导曲线490~530 nm幅值。Blue edge amplitude： 490-530 nm amplitude of the first order guide curve.	同上Ditto
光谱吸收特征Spectral absorption features	绿峰指数Green peak index	包络线归一化曲线518~576 nm最大值。Envelope normalisation curve 518-576 nm max.	对原始光谱曲线进行包络线归一化，放大反射峰、吸收谷，可以增加不同植被光谱差异。 The envelope normalisation of the original spectral curve， amplifying the reflection peaks and absorption valleys， can increase the differences in the spectra of different vegetation.	［55］，［56］，［57］
	红谷指数Red valley index	包络线归一化曲线650~692 nm最小值。Envelope normalisation curve 650-692 nm min.
	近红外峰值Near infrared peak	原始光谱780~1000 nm最大值。 Original spectrum 780-1000 nm max.	植被在近红外波段反射率差异大于可见光，可作为植被类型补充判据。The difference in reflectance of vegetation in the near-infrared band is greater than that of visible light and can be used as a complementary criterion for vegetation type.	［46］
光谱植被指数Spectral vegetation index	归一化差分植被指数Normalized difference vegetation index	$R N I R - R r e d R N I R + R r e d$	能够较好地响应绿色生物量的变化，对中、低密度的植被更有效。Better able to respond to changes in green biomass and more effective for medium and low density vegetation.	［67］
	比值植被指数Ratio vegetation index	$R N I R R r e d$	同上Ditto	［39］
	三角植被指数Triangle vegetation index	$0.5 × [120 × (R 750 - R 550) - 200 × (R 670 - R 550)]$	植被不同生长阶段叶绿素浓度改变会导致绿波段（550 nm）反射率变化，从而影响TVI。Changes in chlorophyll concentration at different stages of vegetation growth can lead to changes in the green band （550 nm） reflectance and thus affect TVI.	［68］
	改进的土壤调整植被指数Improved soil adjusted vegetation index	$a (R 800 - a R 670 - b) [a R 800 + R 670 - a b + X (1 + a 2)] (X = 0.08, a = 1.22, b = 0.03)$	受土壤背景影响较小，在估计均匀冠层方面表现较好。Less influenced by soil context， better performance in estimating uniform canopy.	［69］

Table 4 Typical GLCM texture features

定义式Definition	特征描述Feature description
$M e a n = ∑ m = 1 i ∑ n = 1 i H (m, n) ? m$	均值Mean
$V a r i a n c e = ∑ m ∑ n (m - u) 2 H (m, n)$	方差（测量纹理异质性） Variance （measure of texture heterogeneity）
$H o m o g e n e i t y = ∑ m ∑ n 1 1 + (m - n) 2 H (m, n)$	同质性Homogeneity
$C o n t r a s t = ∑ m ∑ n (m - n) 2 H (m, n)$	对比度Contrast
$E n t r o p y = - ∑ m ∑ n H (m, n) l g [H (m, n)]$	熵（强度影像随机性的度量） Entropy （measure of intensity image randomness）
$C o r r e l a t i o n = ∑ m ∑ n (m - μ x) (n - μ y) H (m, n) / σ p σ q$	相关性（像素与其领域的相关程度） Correlation （the degree of correlation between a pixel and its neighbourhood）
$D i s s i m i l a r i t y = ∑ m ∑ n H (m, n) m - n$	不相似性Dissimilarity
$A s m = ∑ m ∑ n H (m, n) 2$	角二阶矩Asm

Table 4 Typical GLCM texture features

定义式Definition	特征描述Feature description
$M e a n = ∑ m = 1 i ∑ n = 1 i H (m, n) ? m$	均值Mean
$V a r i a n c e = ∑ m ∑ n (m - u) 2 H (m, n)$	方差（测量纹理异质性） Variance （measure of texture heterogeneity）
$H o m o g e n e i t y = ∑ m ∑ n 1 1 + (m - n) 2 H (m, n)$	同质性Homogeneity
$C o n t r a s t = ∑ m ∑ n (m - n) 2 H (m, n)$	对比度Contrast
$E n t r o p y = - ∑ m ∑ n H (m, n) l g [H (m, n)]$	熵（强度影像随机性的度量） Entropy （measure of intensity image randomness）
$C o r r e l a t i o n = ∑ m ∑ n (m - μ x) (n - μ y) H (m, n) / σ p σ q$	相关性（像素与其领域的相关程度） Correlation （the degree of correlation between a pixel and its neighbourhood）
$D i s s i m i l a r i t y = ∑ m ∑ n H (m, n) m - n$	不相似性Dissimilarity
$A s m = ∑ m ∑ n H (m, n) 2$	角二阶矩Asm

Table 5 Features and their application characteristics

特征类型 Feature type	特点 Characteristics
光谱特征Spectral features	反映草种在不同波长处的反射特性，物理意义明确，有众多可分谱段，特征提取方法多样；对草种判别具有最重要的参考意义；草种波谱库数据有待扩充。Reflects the reflectance characteristics of grass species at different wavelengths， with clear physical meaning， numerous separable spectral bands and various methods of feature extraction； The most important reference for grass species identification； Grass species spectral library data to be expanded.
纹理特征Texture features	反映草种图像的空间分布特征，是对光谱特征的有效补充。Reflects the spatial distribution of grass species images and is a useful complement to spectral features.
物候特征Phenological features	反映草种的时间变化特性，物理意义较为明确；但数据时间依赖性强，获取耗时耗力。Reflects the time-varying characteristics of grass species and has a clear physical meaning； However， the data is time-dependent and time-consuming to obtain.
基于规则的特征Rule-based features	不考虑草种自身的物理特性，自动提取遥感数据中的大量有用特征，特征提取效率高、泛化性好，在实际应用中较为可取，适用于高光谱遥感。Automatic extraction of a large number of useful features from remote sensing data without regard to the physical characteristics of the grass species itself， with high feature extraction efficiency and good generalization， which is preferable in practical applications and suitable for hyperspectral remote sensing.

Table 6 Remote sensing discrimination methods and their main characteristics

遥感判别方法

Remote sensing discrimination method

特点

Characteristics

基于纯像素假设的方法Methods based on pure pixel assumptions

简单判别方法Simple discrimination method

如目视判读，简单阈值法等。流程简单，但特征比较单一，可靠性差，不利于精细判别，不适合大面积草种监测。Such as visual interpretation， simple threshold method， etc. Simple process， but relatively single features， poor reliability， not conducive to fine discrimination， not suitable for monitoring grass species over large areas.

机器学习/神经网络分类方法Machine learning/neural network classification methods

如卷积网络，支持向量机等。特征自动提取，识别效率高，适合大范围草种监测，但对样本数量和质量要求很高，在精细判别方面有待深入研究。Such as convolutional networks， support vector machines， etc. Automatic feature extraction， high recognition efficiency， suitable for largescale grass species monitoring， but the number and quality of samples are very demanding， and in-depth research is needed in fine discrimination.

基于混合像素的方法Methods based on mixed pixel

光谱混合模型Spectral mixture model

考虑草种高度混杂情形下的判别问题，对精细判别意义重大，但草种光谱变异性问题严重，影响模型精度，相关研究最少。Consideration of discrimination in highly mixed grass species situations is of great significance for fine discrimination， but the problem of spectral variability of grass species is serious and affects the accuracy of the model， with minimal relevant studies.

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