摘要:
基于广西山口国家红树林生态自然保护区的Landsat 8 OLI 影像数据,选用广泛应用于植被液态水含量反演的归一化差值湿度指数( normalized difference moisture index,NDMI)和修正的归一化差值池塘指数( modified normal-ized difference pond index, MNDPI)作为分类特征,运用决策树方法进行红树林信息的自动提取。研究结果表明:红树林独特的滨海湿地生境特点,使其光谱同时包含植被和湿地信息; MNDPI和NDMI可分别反映可见光-近红外波段反射率同短波红外波段反射光谱的反差,可成功应用于湿地植被信息的提取,能有效地将红树林同其他地物相区分;采用Landsat8 OLI遥感数据,并结合NDMI和MNDPI分类特征构建的决策树模型可有效地提取红树林信息,其错分率和漏分率都较低,分别为5.34%和1.69%。
Abstract:
NDMI ( normalized difference moisture index ) is widely used to assess and retrieve vegetation liquid water content. In this study, decision tree method was employed to automatically extract mangrove forests information combining the NDMI and MNDPI ( modified normalized difference pond index) , modified according to the mangrove characteristics, with Landsat8 OLI imagery acquired at Shankou mangrove national ecosystem nature reserve in Guangxi. The research results show that mangrove forests spectra consist of vegetation and wetland characteristics due to the unique near-shore coastal habitat of mangrove forests. MNDPI and NDMI can represent the spectral contrast between shortwave infrared region and visible region, near infrared region respectively. Therefore, the two spectral indices can successfully be employed to extract wetland vegetation and effectively discriminate mangrove forests from other land cover types. The decision tree method effectively extracted mangrove forests information by combining the classification features of MNDPI and NDMI and using Landsat8 OLI remotely sensed data. The commission error and omission error of mangrove forests were 5. 34% and 1. 69% respectively.
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