基于高光谱卫星影像的生长期互花米草指数构建

Construction method of a Spartina alterniflora index based on hyperspectral satellite images

近年来外来入侵植物互花米草迅速扩张,对沿海区域经济社会的发展造成了一定干扰,占据生态位,危害当地生态系统稳定。精确的互花米草监测与制图对湿地生态系统的保护与恢复具有重要意义。当前研究识别互花米草以监督分类方法为主,这一类方法所需样本量大、分类器复杂多样,大区域互花米草的精细识别计算量大、效率低,而且精度对样本质量的依赖性强。为了提高大区域互花米草快速精细识别的能力,本研究提出了一种基于高光谱数据构建生长期互花米草指数的方法,利用互花米草与其他盐沼湿地植被在近红外波段和短波红外波段存在明显光谱差异的特性,选取差异敏感波段构建互花米草指数,增大复杂滨海湿地环境下互花米草与其他盐沼湿地植被的特征差异,有效减少“异物同谱”造成的滨海盐沼湿地植被难区分的现象。本研究选取黄河三角洲湿地和盐城滨海湿地区域作为研究对象,通过野外调查和Google Earth高分辨率影像生成样本数据集,基于生长期互花米草指数构建决策树对互花米草进行精细识别,与NDVI、EVI、支持向量机、随机森林、最大似然和人工神经网络方法进行互花米草提取结果对比验证。结果表明,基于互花米草指数构建决策树的方法可快速、准确地实现复杂湿地生态系统中互花米草的提取,提取精度优于其他方法,在大区域互花米草快速识别方面具有更高的潜力。

Spartina alterniflora,an introduced coastal wetland plant,has rapidly propagated and expanded in recent years due to its strong adaptability and tolerance to climate and environment.Owing to the rapid reproduction and expansion of S.alterniflora,it has invaded the ecological niche of native vegetation and caused serious damage to the local ecosystem.Remote sensing technology can realize long-term,large-scale,real-time dynamic and accurate surveys and be effectively applied to the precise monitoring of S.alterniflora.Furthermore,it can provide guidance and a basis for the management of S.alterniflora and the restoration of coastal wetland ecosystems.Wetland management provides accurate,real-time,and dynamic information and technical support.To solve the problem of rapid and precise identification of S.alterniflora,this study proposed a method to construct the growth period of a S.alterniflora index based on ZY1-02D hyperspectral data.On the basis of the characteristics of S.alterniflora and other salt marsh wetland vegetation in near-infrared and short-wavelength infrared bands,the differentially sensitive bands were selected to construct an S.alterniflora index,increasing the spectral difference between S.alterniflora and other salt marsh vegetation in the complex coastal wetland environment.This outcome effectively reduces the problem of difficult vegetation discrimination caused by the phenomenon of different spectra of the same objects and the same spectrum of different objects in surface cover.In this study,two national nature reserves,the Yellow River Delta wetland and Yancheng coastal wetland,were selected as the study area,and ZY1-02D hyperspectral images of September were selected as the data source.First,image preprocessing was performed to obtain the reflectance data of the study area.Second,the differentially sensitive bands were determined,the Growing Period Normalized Difference Spartina Alterniflora Index(GNDSAI)was constructed,and the decision tree was constructed to extract the information of S.alterniflora accurately.Finally,the qualitative and quantitative accuracy of the classification results were evaluated,with Normalized Differential Vegetation Index(NDVI),Enhanced Vegetation Index(EVI),Support Vector Machine(SVM),Random Forest(RF),Maximum Likelihood Classifier(MLC),and Artificial Neural Network(ANN)selected for comparative evaluation.Experimental results show that the proposed method has strong regional adaptability in the two study areas.S.alterniflora can be well separated from other wetland salt marsh vegetation in the box plot of the GNDSAI value.The optimal lower threshold of GNDSAI for the Yellow River Delta wetland is 0.4 and that of the Yancheng coastal wetland is 0.27.The average producer’s accuracy and user’s accuracy of S.alterniflora were 92.00%and 91.68%,respectively,which were better than those of the other classification methods.Spectral heterogeneity of S.alterniflora,selection of band combinations,variability of thresholds,and influence and uncertainty of tide levels are discussed in the text.In this study,GNDSAI was proposed using ZY1-02D hyperspectral images.Considering the phenological characteristics of S.alterniflora,GNDSAI was constructed by using the four bands of near-infrared(765 nm),near-infrared(842 nm),short-wave infrared(1644 nm),and short-wave infrared(2216 nm)to enhance the difference between S.alterniflora and other wetland salt marsh vegetation through the calculation of spectral bands.Combined with MNDWI and prior knowledge,the decision tree classification model based on GNDSAI was designed,which realized simple,rapid,and accurate extraction of S.alterniflora,providing a new idea or method for information extraction of S.alterniflora.