地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (5): 823-816.doi: 10.3724/SP.J.1047.2014.00815

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青藏高原典型植被生长季遥感模型提取分析

常清1,2(), 王思远1,,A;*(), 孙云晓1,2, 殷慧1,2, 尹航1,3   

  1. 1. 中国科学院遥感与数字地球研究所,北京 100094
    2. 中国科学院大学,北京 100049
    3. 中国地质大学, 北京 100083
  • 收稿日期:2013-10-11 修回日期:2013-12-04 出版日期:2014-09-10 发布日期:2014-09-04
  • 通讯作者: 王思远 E-mail:hellochangqing@126.com;sywang@ceode.ac.cn
  • 作者简介:

    作者简介:常 清(1988-),女,硕士生,研究方向为植物物候与全球变化。E-mail: hellochangqing@126.com

  • 基金资助:
    国家自然科学基金项目(41271426);国家“973”计划项目(2011CB707100)

The Remote Sensing Monitoring Model of the Typical Vegegtation Phenology in the Qinghai-Tibetan Plateau

CHANG Qing1,2(), WANG Siyuan1,*(), SUN Yunxiao1,2, YIN Hui1,2, YIN Hang1,3   

  1. 1. Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. China University of Geosciences, Beijing 100083, China
  • Received:2013-10-11 Revised:2013-12-04 Online:2014-09-10 Published:2014-09-04
  • Contact: WANG Siyuan E-mail:hellochangqing@126.com;sywang@ceode.ac.cn
  • About author:

    *The author: WANG Jiacheng, E-mail:shanqiangw@fync.edu.cn

摘要:

物候变化是衡量全球气候变化最直接、敏感的指示器,针对青藏高原这个独特地域单元上特殊的高寒植被进行关键物候期遥感提取模型及植被物候时空变化的研究具有重要的意义。本文首先以反距离加权空间插值算法与Savitzky-Golay滤波算法相结合的数据重建模型获得高质量2003-2012年青藏高原MODIS归一化植被指数(NDVI)数据。在此数据基础上,分别利用动态阈值法、最大变化斜率法、logistic曲线拟合法3种遥感植被生长季提取模型,对青藏高原地区两种典型植被的生长季(SOS生长季开始期,EOS生长季结束期,LOS生长季长度)进行提取。通过对3种模型提取结果的对比分析,并结合日均温模型对提取结果的验证发现,动态阈值法为青藏高原地区典型植被生长季的最优遥感提取模型。该模型对近10 a的高分辨率典型高寒植被物候参量的反演及时空变化特征分析表明,受青藏高原水热及海拔梯度的影响,青藏高原植被物候变化呈现出从东南向西北的空间分异规律,随春季温度的升高,近10 a来青藏高原高寒草地总体呈现生长季开始期(SOS)提前(0.248 d/a)的趋势。

关键词: 青藏高原, 物候学, 生长季, 遥感提取模型, 对比分析, 时空变化

Abstract:

Vegetation phenology changes is one of the most direct and sensitive indicators of global climate change and it has become the focus problem of the word studies. The Qinghai-Tibetan Plateau is a unique geographical unit covered by alpine vegetation types so that it is very important to study the remote sensing monitoring model of these vegetation types’ phenology. Firstly, Based on MODIS Normalized Difference Vegetation Index (NDVI) data from 2003 to 2012, we reconstructed the long-term time-series datasets through the combination of Inverse Distance Weighted Interpolation and Savitzky-Golay fitting method. After filtering, the obvious noise is removed but the detail information of vegetation growth is kept well so that the time-series curve is definitely suitable for the extraction of phenology paramethers. Then, we studied the extraction models of the typical vegetation phenology in the Qinghai-Tibetan Plteau with dynamic threshold value method, biggest change slope method and logistic curve fitting method. We compared and analyzed the monitoring results based on the nearly ten years NDVI dataset using the relationship between vegetation growing characteristics and daily mean temperature and then selected the dynamic threshold value method as the best model for typical vegetation phenology extraction in the Qinghai-Tibetan Plateau. Finally, we extracted the phenology information of grassland in the plateau with dynamic threshold value model. After the analysis of nearly ten years vegetation phenology, the results showed that the alpine grassland in the plateau experienced the trend of start of season (SOS) advancing (the ratio is 0.248d/a) as the end of season (EOS) following a more complex rule. The andvanced SOS manily caused by the rise of spring temperature and the influence of precipation is not significant. What’s more, the vegetation phenology and variation trends in the plateau showed obvious spatial distribution rule from the southeast to the northwest.

Key words: Qinghai-Tibetan Plateau, phenology, growing season, remote sensing monitoring model, comparison and analysis, temporal and spatial variation