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