基于遥感云计算的鄱阳湖湿地植被群落分类研究  

Classification of wetland vegetation community in Poyang Lake based on remote sensing cloud computing

作　　者：韩新星 艾金泉 叶子君 牛春妹 唐鑫涛 HAN Xinxing;AI Jinquan;YE Zijun;NIU Chunmei;TANG Xintao(Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources,East China University of Technology,Nanchang 330013,China;School of Surveying and Mapping Engineering,East China University of Technology,Nanchang 330013,China)

机构地区：[1]东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室,江西南昌330013 [2]东华理工大学测绘与空间信息工程学院,江西南昌330013

出　　处：《人民长江》2023年第7期55-60,共6页Yangtze River

基　　金：自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室开放基金项目(MEMI-2021-2022-31);东华理工大学博士科研启动基金项目(DHBK2018001);东华理工大学教改课题(DHJG-21-36)。

摘　　要：针对大型通江湖泊湿地植被精细分类中精度不高、算法稳健性不强的问题,以鄱阳湖湿地植被为研究对象,基于遥感云平台GEE和Sentinel-2影像,着重研究不同训练样本数量、不同时相特征数据及不同机器学习算法对鄱阳湖湿地植被类型分类的影响。结果表明:(1)随着训练样本数量的增加,植被类型的分类精度呈现先上升后平稳的规律,当不同植被类型训练样本达到550个时,精度达到峰值平稳状态;(2)不同时相特征的数据集分类精度具有显著差异,具体为:月度时序>枯水期>四季多时相>单时相,其中,月度时序数据集的总体精度最高,总体精度及Kappa系数分别为82%和0.79;(3)不同遥感算法获得的分类结果精度不同,RF分类精度最高,SVM和CART次之;(4)当不同植被类型的训练样本达到550个时,使用Sentinel-2月时序影像和RF算法能取得最优的分类结果。研究成果可为鄱阳湖湿地精细分类提供方法借鉴,为鄱阳湖湿地保护提供技术支持。Aiming at the problems of low precision and algorithm robustness in the fine classification of wetland vegetation in large river-connected lakes,based on remote sensing cloud platform GEE and Sentinel-2 images,this paper studied the optimization scheme of vegetation classification in Poyang Lake wetland by different training sample quantity,simultaneous phase characteristics data and machine learning classification algorithms.The results showed that:①With the increase of the training samples number,the classification accuracy of vegetation types increased first and then stabilized.When the number of training samples of different vegetation types reached 550,the classification accuracy reached the peak stable state.②The classification accuracy of data sets with different phase characteristics was significantly different,specifically,monthly time series data set>dry season data set>four seasons data set>single time phase.The overall accuracy of monthly time series data set was the highest,and the overall accuracy and kappa coefficient were 82%and 0.79,respectively.③Different remote sensing classification algorithms could obtain different accuracy of classification results.RF classification accuracy was the highest,followed by SVM and CART.④When the number of training samples of different vegetation types reached 550,the Sentinel-2 time sequence image and RF algorithm could be used to obtain the best classification results.This study can be a reference for the fine classification of Poyang Lake wetland and provide technical support for its protection.

关 键 词：湿地植被 植被群落分类 机器学习 Google Earth Engine Sentinel-2 鄱阳湖 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]