摘要
【目的】凤眼莲作为我国典型的外来入侵物种之一,其大规模入侵对水生生态系统破坏严重。目前在不同生境下开展的凤眼莲遥感监测方法研究精度有所不同。本研究对比了不同分类方法,拟筛选出适合我国南方地区凤眼莲的分类方法。【方法】基于Sentinel-2、Landsat8 OLI多光谱影像,选择最大似然和支持向量机监督分类、决策树分类以及植被指数阈值分类方法分别对海南省5个水库的凤眼莲遥感分类,依据无人机可见光影像目视结果对不同方法的分类精度进行评价。【结果】基于凤眼莲时相特征的决策树分类精度最高,总体精度达到90%以上;在基于光谱特征的分类方法中,最大似然监督分类的用户精度为77.88%、制图精度为72.44%,支持向量机分类的用户精度和制图精度分别达到87.00%和84.48%。【结论】基于时相特征与光谱特征的决策树分类方法精度高于仅基于光谱特征的监督分类方法,简单植被指数阈值方法难以区分不同生境内的凤眼莲,研究结果可为我国南方地区凤眼莲遥感监测与预警提供依据。
【Aim】As one of the typical alien invasive species in China,the large scale invasion of Eichhornia crassipes has caused serious damage to the aquatic ecosystem.At present,the research accuracy of remote sensing monitoring methods of E.crassipes in different habitats is different.In this study,different classification methods were compared to screen out suitable classification methods for E.crassipes in the southern region of China.【Method】Based on Sentinel-2 and Landsat8 OLI multispectral images,maximum likelihood and support vector machine supervised classification,decision tree classification and vegetation index threshold classification methods were selected to classify the E.crassipes of five reservoirs in Hainan Province.The classification accuracy of different methods was evaluated according to the visual results of UAV optical images.【Result】The results showed that the classification accuracy of the decision tree based on the time phase characteristics of E.crassipes was the highest,and the overall accuracy was more than 90%.In the classification method based on spectral features,the user accuracy of maximum likelihood supervised classification is 77.88%,the mapping accuracy is 72.44%,and the user accuracy and producer accuracy of support vector machine classification are 87.00%and 84.48%,respectively.【Conclusion】The accuracy of decision tree classification based on time phase and spectral features is higher than that of supervised classification only based on spectral features.The simple vegetation index threshold method is difficult to distinguish the different habitats of E.crassipes.The results of this study can provide scientific basis for remote sensing monitoring and early warning of E.crassipes in southern China.
作者
李淑贞
徐大伟
陈宝瑞
赵越
李静思
王旭
LI Shuzhen;XU Dawei;CHEN Baorui;ZHAO Yue;LI Jingsi;WANG Xu(Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China;College of Agronomy,Hebei Agricultural University,Baoding,Hebei 071001,China)
出处
《生物安全学报》
CSCD
北大核心
2023年第1期85-91,共7页
Journal of biosafety
基金
国家自然科学基金(32171567)。
关键词
外来入侵
凤眼莲
监督分类
决策树分类
遥感监测
alien invasion
Eichhornia
supervision classification
decision tree classification
remote sensing monitoring
