地球信息科学学报

地球信息科学学报 ›› 2013, Vol. 15 ›› Issue (4): 618-624.doi: 10.3724/SP.J.1047.2013.00618

• 遥感科学与应用技术 • 上一篇    

吉林西部盐碱地数字图像植被覆盖度的自动提取

丁艳玲1,2, 赵凯1,3, 李晓峰1,3, 郑兴明1,3   

  1. 1. 中国科学院东北地理与农业生态研究所, 长春 130012;
    2. 中国科学院大学, 北京 100049;
    3. 长春净月潭遥感实验站, 长春 130012
  • 收稿日期:2012-01-04 修回日期:2013-02-21 出版日期:2013-08-08 发布日期:2013-08-08
  • 通讯作者: 赵凯(1962- ),男,研究员,主要从事微波遥感机理以及仪器研发研究。E-mail:zhaokai@neigae.ac.cn E-mail:zhaokai@neigae.ac.cn
  • 作者简介:丁艳玲(1986- ),女,博士生,主要从事遥感产品真实性检验方面的研究。E-mail:sara-0725@163.com
  • 基金资助:

    国家自然科学基金项目“东北地区季节性积雪层中粒径的谱分布特征与微波(辐射、散射)特性研究”(41001201);国家高技术研究发展计划(“863”计划)“遥感产品真实性检验关键技术及其试验验证”(2012AA12A305-5-2)。

An Automatic Extraction Approach to Fractional Vegetation Cover of Saline Land with Digital Images

DING Yanling1,2, ZHAO Kai1,3, LI Xiaofeng1,3, ZHENG Xingming1,3   

  1. 1. Northeast Institute of Geography and Agricultural Ecology, CAS, Changchun 130012, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Changchun Jingyuetan Remote Sensing Test Site of Chinese Academy of Sciences, Changchun 130012, China
  • Received:2012-01-04 Revised:2013-02-21 Online:2013-08-08 Published:2013-08-08

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4 | 1

摘要：

植被覆盖度是生态环境变化的重要指标,也是遥感反演的关键参数。盐碱地植被覆盖度的准确测量对研究地表植被蒸腾、土壤水分蒸发及土壤退化、盐碱化等具有重要意义。过绿指数(Excess Green index,ExG)对绿色植被比较敏感,能突显植被信息,去除土壤、阴影的干扰。通过对吉林西部盐碱地玉米、高粱、绿豆、杂草、土壤数字图像特征分析,利用改进过绿指数(Modified Excess Green index,MExG)算法计算植被和土壤的MExG值;并确定区分植被和土壤的MExG阈值为40,进而计算植被覆盖度。本文采用监督分类的最大似然法对比验证MExG自动提取结果,并对两种方法计算的玉米、高粱、绿豆和杂草的覆盖度,分别进行目视判读和t检验。研究表明,MExG自动提取方法具有客观性强,处理时间短,分类精度高等优点,是计算不同植被类型覆盖度的有效方法。

关键词: 植被覆盖度, 改进过绿指数, MExG自动提取, 盐碱地

Abstract:

Fractional vegetation cover is an important variable in ecological environment and a key parameter in remote sensing estimation, which is needed in the modeling of the land-atmosphere exchanges of momentum, energy, water, and trace gases. Determination of fractional vegetation cover exactly is necessary for studies on plant transpiration, ground surface evaporation, soil degradation and salinization. Excess green, highlighting vegetation and inhibiting the interference of soil and shadow, was used as a contrast enhancement for identifying plants from soil regions. This study uses modified excess green index to extract fractional vegetation cover by analyzing RGB color features of corn, sorghum, mung beans and weeds growing in saline land in western Jilin Province. The digital images are geometrically corrected in order to eliminate distortion. The automatic extraction approach using modified excess green indexes which is about 40 for vegetation growing on the saline land of western Jilin Province accurately distinguishes vegetation from soil, derives plant and soil binary images, then calculates fractional covers of the four vegetation types. This paper chooses maximum likelihood method to contrast the results of MExG automatic classification. The covers of corn, sorghum, mung beans and weeds calculated by these two methods were compared by visual interpretation and t-test. The visual interpretation shows a very high probability. The t-tests indicate that the means of the four vegetation types extracted by maximum likelihood and MExG automatic classification show a high consistency. In addition, the true values of corn, sorghum, mung bean are obtained by digitizing these images using ArcGIS software to validate MExG approach. The accuracy of MExG method can reach 99%. The results show that the MExG automatic approach which achieves good classification results and is less labor and time intensive than maximum likelihood, can be a viable ground-based method to validate fractional cover products generated by remote sensing.

Key words: MExG automatic extraction, fractional vegetation cover, saline land, modified excess green index