关键词:植物物候 遥感监测 植被绿度期 NDVI 中国 地表植被 绿度 遥感模型 监测方法研究 Remote Sensing Based Periods Vegetation Terrestrial Chinese Method of Detection 空间尺度 研究模型 特征 气候变化 区域 常绿 南方
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.