## 5 Conclusions  
In this study, based on the hyperspectral remote sensing image data of \"Zhuhai-1\", the continuum removal method was employed to process the images. It focused on five typical vegetation species in the Yellow River Delta, extracting and selecting characteristic difference bands among the vegetation. Additionally, a feature tuning scheme was designed to select appropriate classification sample categories, and the separability among the categories was verified. Finally, the classification of remote sensing images of five typical vegetation species in the Yellow River Delta was completed by the random forest classification method, and the spatial and temporal changes of vegetation in the study area were monitored and analyzed with the classification results. The following conclusions are drawn.  
(1) Combining the spectral features, red edge index, water index, and vegetation index, the classification results obtained from the random forest classification algorithm, support vector machine classification algorithm, and maximum likelihood estimation were compared for accuracy, revealing higher accuracy with the random forest classification. The  
Figure 7: Main areas of vegetation change.  
overall classification accuracy and Kappa coefficient of the five typical vegetation species in the study area in 2020 and 2022 were 85.92% and 0.84, and 86.25% and 0.84, respectively.  
(2) In both 2020 and 2022, the ranking of the area share of the five vegetation species in the Yellow River Delta remained unchanged, with _Suada glauc \(>\) Pragmites \(>\) Spartina alterniflora \(>\) Tamarisk \(>\) Typha orientalis Presl_. Except for _Spartiina alterniflora_, the distribution area of each type of vegetation increased. _Phragmites_ exhibited the most growth, with a 9.42% increase in area.  
(3) The ecological restoration and management measures implemented in the Yellow River Delta have proven effective. The proportion of _Spartiina alterniflora_ in the vegetation has decreased, the native vegetation has shown signs of recovery, and the distribution pattern of the vegetation communities is becoming more stable. Moreover, the total area covered by vegetation in the study area is exhibiting an upward trend.  
[PERSON], [PERSON] and P.F. completed the conceptualization, method design, and writing, review, and editing: [PERSON]. ([PERSON]) completed the original manuscript writing and experimental validation; [PERSON]. ([PERSON]) completed the literature review and method writing; and all authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was jointly supported by the Shandong Top Talent Special Foundation, the National Natural Science Foundation of China (Grant No. 42101388), and the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2022 MD070).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Authors [PERSON] and [PERSON] was employed by the Shandong Electric Power Engineering Consulting Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.