首页 >  2008, Vol. 12, Issue (1) : 92-103

摘要

全文摘要次数: 3965 全文下载次数: 86
引用本文:

DOI:

10.11834/jrs.20080113

收稿日期:

修改日期:

2006-09-22

PDF Free   HTML   EndNote   BibTeX
中国植被绿度期遥感监测方法研究
1.中国农业科学院农业环境与可持续发展研究所 北京 100081;2.北京师范大学资源学院 北京 100875
摘要:

基于遥感技术的地表植物物候监测,有效克服了传统地面观测站点有限、资料不完整等缺陷,实现了观测方法由点向面的空间尺度转换,因此可表征植被生态系统层面的物候现象.本文选择使用"植被绿度期"来代替"植物生长期或生长季"等概念,并以中国陆地植被为研究对象,建立了中国植被绿度期遥感监测模型--基于NDVI累积频率曲线的Logistic拟合模型,来计算中国植被绿度始期和绿度末期.为验证本模型结果的可靠性和优越性,使用地面物候观测数据对其结果加以检验,并与其他常见遥感监测模型进行了比较.结果和验证表明,在地处温带地区的牡丹江、呼和浩特、北京、西安和洛阳站点,使用本模型计算的植被绿度始期与地面观测结果相差9-21d,绿度末期相差0-13d,其准确度均优于其他遥感模型,而且年际波动相对较小;在地处亚热带地区的屯溪、仁寿、贵阳和广州站点,本模型结果产生较大误差.通过误差分析可知,在南方以常绿植被为主的亚热带地区,本模型计算所得的植被绿度始期和绿度末期并不是地表植被生长季始末日期的真实反映,而往往用于指示区域气候变化的特征.此外,本研究模型比其他方法具有更好的适用性,而且适合不同空间尺度的植物物候监测.

Research on a Detection Method of Chinese Terrestrial Vegetation Greenness Periods Based on Remote Sensing
Abstract:

Vegetation phenology, the study ofrecurring vegetation cycles and theirconnection to clmi ate, is an mi portant variable in awide variety ofearth and atmospheric science applications. Vegetation phenology is also an integraph ofglobal changes and a comprehensive indicator of landscape and environment changes, and the studies on its response to global environmentchangeshave become a focusofglobalchanges field. Vegetation phenologydetectionmethodsbased on remote sensing overcome conventionalground observation’s shortcomings, such as lmi ited observation sites andmissing data, and realize the spatial scale transition of observation methods from points to coverage. Remote sensing technology greatly promotes the study on vegetation ecosystem response to clmi ate changes at regiona,l continenta,l even global scales.In order to keep consistentwith the characterof remote sensing-based vegetation phenology detection, the paperuses“vegetation greennessperiod”to replace“vegetation growing season”, and chose leafunfolding and leafcoloration of local plant communities as indicator events to show the start and end of vegetation greenness period. Then, based on NOAA/AVHRR dataset, meteorological data, ground phenology observation data, and so on, the paperbuilds a remote sensing-based vegetation greenness period detection mode,l namely, Logistic fittingmodel on cumulative frequency ofNDVI to determine the beginning date ofgreennessperiod (BGP) in spring and the end date ofgreennessperiod (EGP) in autumn of China since 1982. BGP and EGP are utilized to reflect the leaf-unfolding stage and leaf-coloring stage of the terrestrial vegetation, respectively. The computed results indicate thatBGP appeared to delay and EGP have an advance trend from south to north.Finally, through comparing the results of the modelwith the 9 ground observation sites and other remote sensing-based detectionmodels, it is found thatBGP and EGP computed by themodelhave differences of9—21 days and 0—13 days, respectively, with the ground observation inMudanjiang, Huhehot, Beijing, Luoyang andXi’an observation sites.Themodel ismore precise than the other remote sensing-based detectionmodels and the annualBGP andEGP fluctuations are comparatively smal.l It is obvious that BGP and EGP estmi ated by the model are reliable in the north temperate regions. InTunx,i Guiyang, Renshou andGuangzhou observation sites, the differences ofBGP and EGPwith the ground\nobservation aremore obvious than those inMudanjiang, Huhehot, Beijing, Luoyang and Xi’an observation sites in spite of any remote sensing-based detection mode.l Tunx,i Guiyang, Renshou and Guangzhou observation sites locate in the southern subtropical evergreen region. The vegetation has no obvious and consistent leaf-unfolding stage and leaf-coloring stage. However, obvious BGP and EGP can be computed by remote sensing-based detection models, which are mainly related to continuously overcast, rainy and foggy days during the rainy season in the sites. Therefore, BGP and EGP estmi ated by themodelare not the real startand end of the vegetation growing season, butreflectvegetation’s response to regional clmi ate changes.In a word, compared with other remote sensing-based detection mode,l the logistic fitting model on cumulative frequency ofNDVI inChina can be characterized in threeways. (1) NDVIdata needn’tbe exceedingly smoothed, which can remainmore temporal details; (2) Logistic model only includes three fitting parameter. So, computing process is relatively smi ple;(3)Multi-model ofNDVI arisen from multiple growth cycles (e. g., double or triple-crop agriculture,semiarid systemswithmultiple rainy seasons, etc.)is considered. BGP and EGP can be straightly determined by fitting the cumulative frequency ofNDVI. The logistic fittingmodel on cumulative frequency ofNDVI ismore suitable forChina than the other remote sensing-based detectionmodels and can be applied to different spatial scales.

本文暂时没有被引用!

欢迎关注学报微信

遥感学报交流群