自然资源学报 ›› 2012, Vol. 27 ›› Issue (8): 1340-1348.doi: 10.11849/zrzyxb.2012.08.008

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天山北坡典型退化草地植被覆盖度监测模型构建与评价

杨峰1,2, 李建龙2, 钱育蓉3, 杨齐4, 金国平5   

  1. 1. 四川农业大学 农学院,农业部 西南作物生理生态与耕作重点实验室,成都 611130;
    2. 南京大学 生命科学学院,南京 21009;
    3. 新疆大学 软件学院,乌鲁木齐 830046;
    4. 中国环境监测总站,北京100012;
    5. 新疆阜康市畜牧局,新疆 阜康 831500
  • 收稿日期:2011-01-18 修回日期:2012-02-09 出版日期:2012-08-20 发布日期:2012-08-20
  • 通讯作者: 李建龙(1962- ),男,教授,主要从事草地生理生态与3S应用研究。E-mail:jlli2008@nju.edu.cn E-mail:jlli2008@nju.edu.cn
  • 基金资助:
    国家重点基础研究发展计划项目(973项目)(2010CB950702);国家高技术(863计划)专题项目(2007AA10Z231)。

Estimating Vegetation Coverage of Typical Degraded Grassland in the Northern Tianshan Mountains

YANG Feng1,2, LI Jian-long2, QIAN Yu-rong3, YAN Qi4, JIN Guo-ping5   

  1. 1. College of Agronomy, Sichuan Agricultural University/Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu 611130, China;
    2. School of Life Science, Nanjing University, Nanjing 21009;
    3. College of Software, Xinjiang University, Urumqi 830046;
    4. National Environment Monitoring Center, Beijing 100012, China;
    5. Fukang Animal Husbandry Bureau, Fukang 831500, China
  • Received:2011-01-18 Revised:2012-02-09 Online:2012-08-20 Published:2012-08-20

摘要: 草地退化是当今世界面临的一个极为严峻的生态问题。植被覆盖度作为草地退化监测的重要指标之一,在草地退化、荒漠化治理方面起着重要的作用。为了构建适合于天山北坡典型草地植被覆盖度监测模型,便于对草地及时、快速的监测分析,研究利用新疆阜康市2008年9月Landsat TM遥感影像数据和相应的实测数据,分别探讨5种植被指数(NDVI、RVI、GNDVI、SAVI和MSAVI)与植被覆盖度的线性和非线性(二次多项式、指数、对数以及幂函数)关系,以便获得最佳监测草地状况的植被指数和模型。研究结果表明,MSAVI和GNDVI与植被覆盖度的相关性最好(P<0.01),而NDVI和RVI较差;通过5种植被指数和植被覆盖度进行回归分析,MSAVI和GNDVI与植被覆盖度分别建立模型最佳,即:y=138.45x-1.248 2(R2=0.502 7,P<0.01)和y=2 596.66x2-561.54x+38.488(R2=0.605 3,P<0.01),精度达到90%以上。该研究结果说明不同的植被指数适用的条件不同,为今后利用3S技术深入研究荒漠退化草地植被状况的快速监测和科学管理提供支持。

关键词: 退化草地, 监测模型, 植被覆盖度, 植被指数, 新疆草地

Abstract: Grassland is one of the most widespread vegetation types worldwide, which is also an important composition of terrestrial ecosystem. Grassland degradation is a serious ecological problem in the world. Therefore, monitoring and estimating grassland degradation correctly plays an important role for managing and controlling grassland degradation and desertification. In this study, the monitoring models of grassland degradation were built for monitoring the status of grassland using remote sensing data and vegetation coverage. In addition, the relationships between five vegetation indices (Ratio Vegetation Index, RVI; Normalized Difference Vegetation Index, NDVI; Green Normalized Difference Vegetation Index, GNDVI; Soil-Adjusted Vegetation Index, SAVI; Modified Soil-Adjusted Vegetation Index, MSAVI) were discussed. The results showed that the correlations between MSAVI, GNDVI and vegetation coverage were better than other vegetation indices. Comparing the regress models between five vegetation indices and vegetation coverage, the models built by MSAVI and GNDVI appeared better effect. The optimum models for monitoring the status of grassland degradation were y=138.45xMSAVI-1.2482 (R2=0.5027, P<0.01) and y=2596.66x2GNDVI-561.54xGNDVI+38.488(R2=0.6053, P<0.01)respectively.

Key words: degraded grassland, monitoring model, vegetation coverage, vegetation index, Xinjiang grassland

中图分类号: 

  • TP79:Q948.12