1.School of Remote Sensing &2.Geomatics Engineering, Nanjing University of Information Science &3.Technology;4.Hebei Provincial Key Lab for Meteorology and Eco-environment;5.School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology
以广西北部湾为研究区,针对潮位周期性变化导致稀疏低矮红树林难以被准确提取问题,基于多潮位Landsat8 OLI图像和DEM (Digital Elevation Model)数据,通过构建红树林识别决策树模型,并以支持向量机(support vector machine,SVM)为对照,评价结合潮位和DEM信息的决策树法提取红树林信息的可行性。研究结果表明：(1)不同高度、密度红树林之间光谱以及不同潮位时红树林光谱差异均较大,稀疏低矮红树林也与阴坡林地、水体-陆生植被混合像元光谱存在严重“异物同谱”效应；(2)无论是基于低潮位、高潮位图像,还是多潮位图像,在SVM中,将红树林按高度、密度差异细分为高密红树林和稀矮红树林,其总体精度(分为红树林和非红树林两类)可分别提高4.65%、4.41%和7.22%；(3)基于多潮位图像及DEM的决策树模型识别的总体精度和Kappa系数分别为98.80%和0.973,比SVM中最佳值分别高出1.62%和0.035。因此,通过同时考虑红树林高度密度、潮位和DEM等特征,可明显提高红树林遥感识别的精度。
The sparse and low mangroves are difficult to be accurately extracted due to the periodic changes of the tide level. Taking the Beibu Gulf of Guangxi as the research area, the paper constructed a mangrove identification decision tree model using Landsat8 OLI images at low and high tidal levels and DEM (Digital Elevation Model) data, and took SVM (support vector machine) method to evaluate the feasibility of the decision tree method. The research results show that: (1) The spectra of mangroves were different due to the height and density of mangroves canopy and the tidal levels. There was a serious "same spectrum with different species" effect between sparse and low-lying mangroves and the water-terrestrial vegetation mixed pixel and shady slope forest. (2) Mangroves were classified into high-density mangroves and sparse-dwarf mangroves according to the differences in height and density of canopy. Whether using low-tide, high-tide or multi-tide images for the SVM methods, this can bring about that the overall accuracy values (Mangroves were divided into mangrove and non-mangrove categories) were improved by 4.65%, 4.41% and 7.22% respectively, (3) The overall accuracy and Kappa coefficient of decision tree model based on multi-tide image and DEM were 98.80% and 0.973 respectively, which were 1.62% and 0.035 higher than the best value in SVM method. Therefore, considering the characteristics of mangrove height, density, tide level and DEM could significantly improve the accuracy of remote sensing identification of mangroves.
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