顾及植被季相节律的滨海湿地类型动态变化研究

Dynnmic Changes of Coastal Wetland Types with Taking Seasonal Rhythms into Account

基于盐城国家级珍禽自然保护区核心区2014年3个月份的Landsat 8遥感影像及其矢量数据,采用基于CART算法的决策树分类方法提取研究区芦苇、碱蓬、米草、鱼塘、浅滩、海域等湿地信息,并分析2014年植被变化情况。其中采用植被指数NDVI,RVI,DVI时间序列光谱分析曲线获得湿地植被类型窗口期,通过各植被指数、第一主成分分量、缨帽变换、原始波段(红外、近红外)、非监督分类影像等因子构建时序因子集。结果表明:1)3—12月份为植被分类窗口期,芦苇、碱蓬、米草区分度最大;2)CART算法的决策树分类方法对盐城湿地植被区分度较好,3个月份影像分类总体精度分别为99.88%,99.18%和97.61%,Kappa系数分别为0.99,0.99和0.97;3)2014年间,芦苇的面积从61.69km^2增长到63.08km2,米草从38.01km^2增加到44.78km^2,碱蓬从26.37km^2锐减到19.63km^2。

Based on three months of Landsat8 remote sensing images and vector data of the core area of Yancheng national rare birds nature reserve and vector data in 2014, using CART-based decision tree classifier to extract phragmites,suaeda,spartina,fish ponds,mud flat,waters and wetlands information and then changes of vegetation are analyzed. Using vegetation index NDVI,RVI,DVI series spectrum curves to get a wetland vegetation type in the window period, through the vegetation index, the first principal compo-nent ,tasseled Cap transformation, the original bands （ red, near-infrared ） , unsupervised classification im-age ,sequence subsets are built. Results showed that ： 1） The window period for vegetation classification is from March to December, phragmites, suaeda, Spartina discrimination of maximal order during this deci-sion tree classification of remote sensing images for the data source can improve the accuracy of classifica-t ion^） CART-based decision tree classifier to Yancheng wetland vegetation and distinguish have good o-verall classification accuracy for 99. 88% ,99. 18% and 97. 61% and good Kappa coefficient for 0. 99, 0. 99 and 0. 97 ;3） In 2014,the area of phragmites grew from 61. 69km2 to 63. 08km2and spartina from 38. 01km2to 44. 78km2 while suaeda had fallen from 26. 37km2to 19. 63km2.