摘要
依托中分辨率成像光谱仪完整的数据序列和丰富的光谱信息,遥感特征指数在湿地生态系统发展变化的状态、趋向和规律研究方面发挥着不可替代的优势。传统类间距离判别的遥感特征指数选取中常存在过分依赖数据统计特征、入选指数与目标地类间生态学意义不明确、分类模型普适性差等局限性。基于此,本研究以河北省白洋淀湿地自然保护区为例,提出类可分离性距离判别(Class Separation Discrimination,CSD)与类间距离判别(Class Distance Discrimination,CDD)相结合的方法构建最优遥感特征指数集,并采用QUEST算法和马氏距离判别法构建分类决策树模型用于白洋淀湿地信息的提取研究,尝试克服传统类间距离指数选取中的不足。结果表明:运用CSD和CDD相结合的方法所选取的遥感特征指数在研究区湿地信息提取过程中的总体分类精度达到了91.32%,Kappa系数0.88,较传统的分类与回归树(Classification and Regression Tree,CART)方法,分类精度提高了1.67%;其次选取的最优指数与待提取的湿地类型均具有明确的生态学意义,如挺水植物在立地干湿交替条件下的潴育化过程决定了氧化铁比率IO可成功的将混分的耕地和挺水植物进一步分离;进一步将基于研究区2017年OLI影像构建的CSD和CDD相结合方法与CART方法的模型分别应用于研究区2019年OLI影像进行分类,基于CSD和CDD相结合方法构建的模型分类总体精度和Kappa系数分别为:86.97%、0.83,基于CART方法构建的模型无法满足分类需求,研究结果较好地证明了基于CSD和CDD相结合方法构建的模型在年际之间具有良好的适用性和稳定性。总之,CSD和CDD相结合的方法在不降低湿地信息提取精度的基础上,有效避免了传统遥感特征指数选择方法的局限性,提高了分类模型的普适性,是遥感特征指数选择算法和决策树相结合在湿地信息提取方面的有益尝试。
Based on the complete data sequence and abundant spectral information of the moderate resolution imaging spectrometer, remote sensing feature indices play an irreplaceable role in studying the stage, trend, and law of wetland ecosystem development. There are some limitations in selection of remote sensing feature indices for the traditional inter-class distance discrimination, such as over-dependence on statistical characteristics of data, indefiniteness of the inclusion index and the ecological significance of the target area, and poor applicability of the classification model. Based on this, Baiyangdian Wetland Nature Reserve in Hebei Province is selected as the research area. The method of Class Separation Discrimination(CSD) and Class Distance Discrimination(CDD) is proposed to construct the optimal remote sensing feature index. In addition, QUEST algorithm and Markov distance discrimination method are used to construct the classification decision tree model for the Baiyangdian Wetland extraction, which overcomes the shortcomings in the selection of traditional inter-class distance index. The results show that, firstly, the overall classification accuracy of the remote sensing feature index selected by the method combining CSD and CDD reach 91.32% and the kappa coefficient is 0.88 for the wetland information extraction in the study area. Compared with the traditional Classification and Regression Tree(CART) method, the classification accuracy is improved by 1.67%. Secondly, the selected optimal remote sensing feature index has a clear ecological meaning for the type of wetland extracted. For example, the redoxing process of emergent water plants under alternate dry and wet conditions determines that iron oxide index(IO) can be successfully selected to further separate the mixed cultivated land and emergent water plants. Furthermore, according to the OLI image of the study area in 2017, the decision tree model based on the combination of CSD and CDD and the decision tree model based on CART algorithm are appli
作者
赵栋梁
郭超凡
吴东丽
高星琪
郭逍宇
ZHAO Dongliang;GUO Chaofan;WU Dongli;GAO Xingqi;GUO Xiaoyu(College of Resource Environment and Tourism,Capital Normal University,Beijing 100048,China;School of Resource Environment and Tourism,Anyang Normal University,Anyang 455000,China;Meteorological Observation Center of China Meteorological Administration,Beijing 100081,China)
出处
《地球信息科学学报》
CSCD
北大核心
2021年第6期1092-1105,共14页
Journal of Geo-information Science
基金
北京市自然科学基金和北京市教委联合资助重点项目(KZ20190028042)。
