基于GEE和Sentinel-2影像的杭州城市湿地精细化分类研究

Refined classification of the wetlands in Hangzhou by Google Earth Engine(GEE)and Sentinel-2 imagery

基于Google Earth Engine(GEE)云平台,使用Sentinel-2卫星影像数据,采用随机森林算法面向杭州城市湿地类型进行遥感分类研究.研究结果表明:在进行湿地类型精细化分类时,相比于使用单一特征信息分类,多特征信息的组合可以显著提高分类精度,最优特征组合包括传统光谱特征、红边光谱特征、变换特征、纹理特征和地形特征,其总体精度为81.2%,Kappa系数为0.75;不同特征信息对于不同湿地类型的提取具有重要作用,传统光谱特征对于滩地和养殖池的识别非常有利,而红边光谱特征和变换特征分别对草本沼泽和湖泊的识别更为有效.此外,红边光谱特征和纹理特征的组合有利于运河/水渠的识别.通过特征对比实验可为湿地精细化分类的案例研究提供参考,湿地分类结果可为杭州城市湿地信息的遥感识别提供数据支撑.

Hangzhou possesses diverse and abundant wetland resources.With rapid urbanization,however,the total area of wetland has been shrinking.Issues such as degradation of ecosystem quality and functional decline have become increasingly severe.Consequently,it is crucial to conduct refined land cover classification of wetland types in Hangzhou,for effective wetland protection and management.In the present work,Google Earth Engine(GEE)cloud platform and Sentinel-2 satellite imagery data were used to categorize wetlands in Hangzhou with Random Forest Algorithm.A combination of multiple feature information substantially enhanced classification accuracy,compared to single-feature classification.Feature combination encompassed traditional spectral features,red-edge spectral features,transformed features,texture features,and topographic features,yielding an overall accuracy of 81.2%and a Kappa coefficient of 0.75.Different feature information contributed significantly to the extraction of various wetland types.Traditional spectral features were particularly advantageous for identifying tidal flats and aquaculture ponds.Red-edge spectral features and transformed features were more effective for recognizing herbaceous marshes and lakes respectively.Further,combining red-edge spectral features and texture features proved beneficial for identifying canals and water channels.The feature comparison experiment in this study is useful for future studies on refined wetland classification.The wetland classification results provide data support for remote sensing identification of wetland information in Hangzhou.