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Title: | MODIS EVI與降水中氧同位素的結合 The Merging of the MODIS EVI and δ18O in Precipitation |
Authors: | Wei-Ping Chan 詹偉平 |
Advisor: | 袁孝維(Hsiao-Wei Yuan) |
Co-Advisor: | 沈聖峰(Sheng-Feng Shen) |
Keyword: | 穩定同位素, EVI, 遙感探測, Isoscape, stable isotope, EVI, remote sensing, isoscape, |
Publication Year : | 2011 |
Degree: | 碩士 |
Abstract: | 穩定同位素在水體中的自然變化可被視作時間空間上的指紋痕跡,並可用來回溯空間及時間上的源頭,目前已廣泛用於各種領域如水文學、古氣候學、生態學以及法醫學中。雖然科學家已投入許多精力於發展水中氧同位素的預測模型,但要達到高空間、高時間解析度及高準確性的氧同位素預測模型仍是一大挑戰。於本研究中,發展了一套創新的方法來預測降水中的氧同位素,不論在全球尺度或是在區域尺度下,均能使用強化型植生指數(EVI,Enhanced Vegetation Index)做出準確的氧同位素預測,本研究使用結構方程模組(SEM,Structural Equation Model)來證實了EVI和降水中氧同位素的高度相關性,並說明了EVI非常適合做為預測氧同位素的因子。再測試了EVI-δ18O模式後,可以得到高空間(250x250公尺)、高時間解析度(16天)及高準確性的資料,不論在地區性的預測力(年均δ18O以及月均δ18O的預測力分別是r=0.96和r=0.80±0.17, n=13)或是全球尺度的預測力(年均δ18O以及月均δ18O的預測力分別是r=0.96和r=0.76±0.02, n=27)皆較現存所有氧同位素預測模式為佳。作者認為EVI與氧同位素的結合將會大大拓展EVI和氧同位素在時間及空間尺度上的應用。 The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ18O in precipitation at local and global scales. Using a structural equation model, we show that the EVI and precipitated δ18O are highly correlated and thus the EVI is a good predictor of precipitated δ18O. We then test the predictability of our EVI-δ18O model and demonstrate that our approach can provide higher accuracy with finer scale spatial (250x250m) and temporal (16 days) δ18O predictions than all existing models at both regional scale (annual and monthly predictive ability are r=0.96 and r=0.80±0.17, n=13 sites, respectively) and global scales (annual and monthly predictive ability are r=0.96 and r=0.76±0.02, n=27 sites, respectively). We suggest the merging of the EVI and δ18O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47970 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 森林環境暨資源學系 |
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