地理学报 ›› 2003, Vol. 58 ›› Issue (5): 695-702.doi: 10.11821/xb200305007

• 生态系统 • 上一篇    下一篇

青海湖环湖地区草地植被生物量遥感监测模型

牛志春1, 倪绍祥2   

  1. 1. 江苏省环境监测中心,南京 210036;
    2. 南京师范大学地理科学学院,南京 210097
  • 收稿日期:2002-11-18 修回日期:2003-04-28 出版日期:2003-09-25 发布日期:2003-09-25
  • 作者简介:牛志春 (1978-), 女, 山西人, 硕士, 主要从事遥感与GIS及其应用研究。
  • 基金资助:

    国家自然科学基金资助项目(49971056)

Study on Models for Monitoring of Grassland Biomass around Qinghai Lake Assisted by Remote Sensing

NIU Zhichun1, NI Shaoxiang2   

  1. ege of Geographical Sciences, Nanjing Normal University, Nanjing 210097, China
  • Received:2002-11-18 Revised:2003-04-28 Online:2003-09-25 Published:2003-09-25
  • Supported by:

    National Natural Science Foundation of China, No.49971056

摘要:

利用青海湖环湖地区2000年陆地卫星TM遥感图像数据和同期野外实测的34处样方产草量数据,分析了遥感植被指数与草地植被生物量之间的相关关系,进而分别建立了遥感植被指数与草地植被生物量的一元线性回归模型和非线性回归模型。研究表明,遥感植被指数与草地生物量之间存在较好的相关性,但不同遥感植被指数与草地植被生物量相关性程度存在一定差别。此外,所建遥感植被指数与草地植被生物量的非线性回归模型在拟合精度上优于一元线性回归模型,且由三次方程得到的非线性回归模型最适用于监测青海湖环湖地区的草地植被生物量。

关键词: 遥感, 植被指数, 草地, 生物量, 青海湖

Abstract:

Taking the region around the Qinghai Lake as the study area and using the Landsat Thematic Mapper data and the measured grass yield data, the monadic linear regression models and the non-linear regression models were established, respectively, to express the relations between grassland biomass and the vegetation indices. There are two types of sampling site, i.e., the larger one is 30 m×30 m and the smaller one is 1 m×1 m. Each larger sampling site includes one smaller one which was randomly selected. The major conclusions from this study are: 1) the fitting accuracies of the non-linear regression models are much higher than those of the non-linear regression models, namely, the results obtained from the non-linear regression models are more accordant with the measured grassland biomass data in comparison with those from the monadic linear regression models; 2) the comparison of different forms of the non-linear regression analysis on the relations between the vegetation indices and the measured grassland biomass data indicates that the cubic equation is the best one in terms of the suitability of use in the study area; 3) the results from the non-linear regression analysis show that the order is RVI, NDVI, SAVI, MSAVI and DVI in terms of the fitting accuracy between these vegetation indices and grassland biomass data; and 4) the non-linear model Y = -18.626RVI3+220.317RVI2-648.271RVI+691.093 is the best model which can be used in monitoring grassland biomass based on the vegetation indices in the region around the Qinghai Lake.

Key words: remote sensing, vegetation index, grassland, biomass, Qinghai Lake