地球信息科学学报 ›› 2013, Vol. 15 ›› Issue (4): 590-596.doi: 10.3724/SP.J.1047.2013.00590

• 遥感科学与应用技术 • 上一篇    下一篇

淤泥质潮滩湿地类型遥感识别分类方法与应用

王聪, 刘红玉, 候明行, 谭清梅   

  1. 南京师范大学地理科学学院, 南京 210023
  • 收稿日期:2012-11-19 修回日期:2013-01-14 出版日期:2013-08-08 发布日期:2013-08-08
  • 通讯作者: 刘红玉(1963- ),女,辽宁辽阳人,教授,博士生导师,主要从事湿地景观生态研究。E-mail:liuhongyu@njnu.edu.cn E-mail:liuhongyu@njnu.edu.cn
  • 作者简介:王聪(1974- ),女,博士,讲师,主要从事湿地景观生态和RS与GIS应用研究。E-mail:wangc74@163.com
  • 基金资助:

    国家自然科学基金项目“基于生态过程的海滨景观演变动态模拟研究”(41071119);江苏省高校自然科学研究重大项目“自然与人为影响下盐城海滨湿地景观演变模拟模型研究”(10KJA170029)。

Classification Method of Muddy Tidal Flat Wetlands Based on Remote Sensing

WANG Cong, LIU Hongyu, HOU Minghang, TAN Qingmei   

  1. Geographical Science Institute, Nanjing Normal University, Nanjing 210023, China
  • Received:2012-11-19 Revised:2013-01-14 Online:2013-08-08 Published:2013-08-08

摘要:

依据江苏盐城国家珍禽自然保护区淤泥质潮滩湿地影像特征,快速提取高精度潮滩湿地地物信息对湿地生态保护具有重要的意义。本研究以2010年TM影像为数据源,针对海滨湿地植物覆被类型复杂,以及湿地植物类型之间的生态交错带信息难以识别等问题,综合运用植被NDVI指数、波段反射率特征、环境特征和生态条件,逐级分层分类及人工选取阈值等方法,较好地解决了淤泥质潮滩湿地分类问题。结果表明,与同一时期的监督分类相比较,在识别植被交错带植被覆被类型和零星分布的植被斑块的类型方面更具优势,分类精度有明显提高。通过ROI训练区,选取了3126个包括所有类型的样本进行精度检验,分类精度达到95.87%。该方法弥补了单一分类方法的不足,对快速、高精度地提取淤泥质潮滩地物类型具有重要的参考价值和实践意义。关键词:淤泥质潮滩湿地;光谱反射率;归一化植被指数(NDVI);GIS

关键词: 光谱反射率, GIS, 淤泥质潮滩湿地, 归一化植被指数(NDVI)

Abstract:

Remote sensing technology is a kind of effective technical means to obtain information of the tidal muddy flat wetlands. It has important significance to explore the tidal flat wetland's remote sensing classification method. This study's aim is to get high-precision information of features of tidal flat wetlands according to image feature and muddy tidal flat wetlands characteristics of Yancheng National Nature Reserve in Jiangsu Province, China. The TM image of 2010 is taken as the data source. The practical problems are tidal flat wetland vegetation types' complexity and the difficulty in correctly identifying the ecotone information. Because tidal flat wetland surface features in remote sensing images have a complex spectrum, it is very difficult to effectively extract information through one single method. So, we comprehensively use methods of the NDVI, band reflectance spectral characteristics, environmental characteristics and ecological conditions, one level after another to simplify the complex tidal flat wetland classification. Firstly, we chose the best combination of the bands. Secondly, we established vegetation and non-vegetation's ROI to analyze the band reflectance spectral characteristics. The ROI include 1316 Spartina alterniflora Loisel, 633 mudflats, 1253 Suaeda salsa, 1002 Phragmites cammunis, 525 water body, and 4803 road samples. At last, we established decision tree to reality the practices. After selecting 3216 samples to accuracy inspection and compared with the same area of supervised classification, the results show that classification accuracy is up to 95.87% by ROI training area. It has advantages in identifying the ecotones' vegetation types and vegetation scattered patches. The method makes up for the lack of a single classification and has an important reference value and practical significance in effectively extracting tidal flat surface features.

Key words: GIS, spectral reflectance, muddy tidal flat wetlands, normalized difference vegetation index