摘要:
滨海湿地的遥感分类研究对于滨海湿地的保护和规划具有重要意义。为此,以黄河三角洲作为研究区,采用2019年3—10月获取的8景Landsat8 OIL作为数据源,使用GEE(Google Earth Engine)云平台,根据影像的不同特征构建了7种不同的分类方案; 然后,使用随机森林分类器对不同特征集合进行分类,并选择其中分类效果最好的用于绘制黄河三角洲地区的湿地类别图。其中8,9月份数据由于受到云的污染导致质量差,使用增强型自适应反射率时空融合模型(enhanced spatial and temporal adaptive reflectance fusion model,ESTARFM)算法对有云区域进行填补处理。结果表明: ①ESTARFM时空融合模型生成的预测影像与真实影像波段表现出较好的相关性,其 R 值均能达到 0.73 以上, 说明重构的影像可以用于本研究; ②使用随机森林算法对研究区地物类型进行分类,其中方案7通过特征优选,分类结果总体精度达92.28%,Kappa系数达0.91,分类结果与湿地实况相吻合,比常规方案分类精度更高。研究结果有助于了解和掌握该区域湿地不同类型的空间分布特征,可为区域生态环境的保护和规划提供科学依据。
Abstract:
Exploring the remote sensing-based classification of coastal wetlands is significant for their conservation and planning. Hence, this study investigated the Yellow River Delta with the 8-view Landsat8 OIL images from March to October 2019 as the data source. It constructed seven classification schemes based on different features of the images on the Google Earth Engine (GEE) cloud platform. Then, it employed the random forest classifier to classify different feature sets, with the scheme exhibiting the best classification effects selected for mapping the wetland categories of the Yellow River Delta. Considering poor data quality in August and September due to cloud contamination, this study filled in the cloudy zones using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) algorithm. The results show that: ① The predicted images generated from the ESTARFM manifested a high correlation with the real image bands, with R values above 0.73, suggesting that the reconstructed images could be used in this study; ② The random forest algorithm was used to classify the surface feature types in the study area. Through optimal feature selection, the classification results of Scheme 7 demonstrated an overall accuracy of 92.28%, higher than those of conventional schemes, with a Kappa coefficient of 0.91, aligning with the actual wetland conditions. The results of this study can assist in deeply understanding the spatial distributions of different wetlands in the area, and provide a scientific basis for the conservation and planning of the regional ecological environment.
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