CAJ | 학술논문

以崇明东滩自然保护区盐沼植被为研究对象,利用Landsat TM遥感图像,结合现场调查和前人关于东滩时空动态变化的研究结果,确定崇明岛东滩主要分布的盐沼植被类型,提出了基于知识工程师的植被分类方法.与常规非监督和监督分类相比,该方法的精度较高,总体精度为92.35%,kappa系数为0.9072,而非监督分类和监督分类(最大似然法)的总体精度分别为86.92%和89.10%.实验结果表明,该方法能够有效地对研究区植被进行分类与识别,可为实现盐沼植被的自动提取提供理论依据和有效的方法途径.
이숭명동탄자연보호구염소식피위연구대상,이용Landsat TM요감도상,결합현장조사화전인관우동탄시공동태변화적연구결과,학정숭명도동탄주요분포적염소식피류형,제출료기우지식공정사적식피분류방법.여상규비감독화감독분류상비,해방법적정도교고,총체정도위92.35%,kappa계수위0.9072,이비감독분류화감독분류(최대사연법)적총체정도분별위86.92%화89.10%.실험결과표명,해방법능구유효지대연구구식피진행분류여식별,가위실현염소식피적자동제취제공이론의거화유효적방법도경.
This paper used Chongming Dongtan Nature Reserve as the research object for salt marsh vegetation classification based on Landsat TM image. According to such image preprocessing measures as image geometric correction and subset image and on the basis of analyses of Landsat TM remotely sensed images integrated with field survey and other studies of spatio-temporal dynamics of Chongming Dongtan Nature Reserve, this paper confirmed the species of the vegetation in this area. The authors used knowledge engineer to classify the vegetation, built knowledge base on the basis of vegetation spectral information and presented a vegetation classification method based on the spectral information. The overall precision of the vegetation classification method based on knowledge engineer is 92.35%, and the kappa coefficient is 0.907 2. The precision is higher than the overall precision of the vegetation classification based on unsupervised classification and supervised classification (maximum likelihood): the overall precisions of unsupervised classification and supervised classification are respectively 86.92% and 90.10%. The result shows that the vegetation classification method can classify and discriminate vegetation effectively and the precision is higher than that of other methods. The vegetation classification method provides a theoretical foundation and effective method for automatic extraction of vegetation.