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題名 應用多源遙測影像分析宜蘭地區植被物候之研究
Analyzing Vegetation Phenology in Yilan using Multisource Remote Sensing Images作者 梁鋆立
Liang, Yun-Li貢獻者 詹進發
Jan, Jihn-Fa
梁鋆立
Liang, Yun-Li關鍵詞 植被物候
MODIS
Landsat
ESTARFM
TIMESAT
Vegetation Phenology
MODIS
Landsat
ESTARFM
TIMESAT日期 2017 上傳時間 5-Sep-2019 16:56:45 (UTC+8) 摘要 地表植被物候的變化反映了氣候變遷對於生態系統所造成的影響，而透過遙測技術能夠有效率地進行大範圍且長期的植被物候監測。本研究蒐集了2001至2010年間宜蘭地區之MODIS以及Landsat TM影像，先利用MODIS的MOD13Q1植生指標產品提取出一組空間解析度為250公尺之植生指標（NDVI、EVI）時序資料，並透過影像融合模型ESTARFM（Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model）融合MODIS與Landsat TM影像來產生另一組空間解析度為30公尺之植生指標時間序列資料。接著將這兩組不同空間解析度之植生指標時間序列資料分別匯入物候分析軟體TIMESAT來計算宜蘭地區之地表植被物候參數，再配合氣象資料分析氣候因子與宜蘭地區植被及物候參數間之關係，最後比較兩種不同空間解析度之植生指標時序資料所產生的分析結果。研究結果顯示，MODIS-NDVI時序資料與氣候因子之相關性較MODIS-EVI高，並據此將該資料之物候分析成果作為基準，在2001至2010年間宜蘭地區植被生長季起點延後約6至7天；生長季終點約延後2至6天。在ESTARFM影像融合方面，以Landsat-NDVI之成果較Landsat-EVI與氣候因子有較佳的相關性，且其物候分析成果與MODIS-NDVI成果也較接近。
Land surface vegetation phenology reflects the responses of a terrestrial ecosystem to climate change. It is effective for monitoring the vegetation phenology of large-scale area using time-series remotely sensed dataset. In this study, we used MOD13Q1 and Landsat TM images for vegetation phenology analysis between 2001 and 2010 in Yilan. First, the monthly maximum value (MVC) images of MODIS were generated from 16-day VI (Vegetation Indices) time-series data. Second, the synthetic Landsat VI data were created using the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm for all dates when Landsat images were either unavailable or too cloudy. The monthly temperature and precipitation data during 2001 and 2010 were used to generate map layers using ArcGIS 10.1 for vegetation and climate analysis. Then, the MODIS and Landsat images were provided as input for the phenology analysis toolbox TIMESAT, from which phenological metrics including onset (start of growing season), offset (end of growing season) and length of growing season were derived. These phonological parameters of vegetation and climate data were used to investigate the relationship between vegetation phenology and climate change. In the end, both MODIS VI data and synthetic Landsat VI data were analyzed and compared in the study. The results show that MODIS-NDVI time-series data has higher correlation with climate data than MODIS-EVI. Moreover, the phenology analysis shows that in Yilan area the onset of vegetation was 6 days late and the offset was 2-6 days late during the 10 years period. For the ESTARFM model, Landsat-NDVI has higher coorelation with climate data than Landsat-EVI, and the phenology analysis result is more consistent to that of MODIS-NDVI.參考文獻 王宏、李曉兵、余弘婧，2006，「基於NOAA／AVHRR NDVI監測中國北方典型草原的生長季及變化」，『植物生態學報』，30（3）：365-374。行政院農業委員會，2014，『農業統計年報（102年）』，台北：行政院農業委員會。林清儒，2007，「無線感測網路於森林物候監測應用之研究」，國立臺灣大學生物資源暨農學院森林環境暨資源學系碩士論文：台北。楊麗萍、夏敦勝、陳發虎，2007，「Landsat 7 ETM+ 全色與多光譜數據融合算法的比較」，『蘭州大學學報』，4：7-17。徐雨晴、陸佩玲、于強，2004，「氣候變化對植物物候影響的研究進展」，『資源科學』，26（1）：129-136。徐森雄，2007，「從全球暖化趨勢談物候學」，『作物、環境與生物資訊』，4：304-344。侯美亭、延曉東，2012，「中國東部植被物候變化及其對氣候的響應」，『氣象科技進展』，2（4）：40。許民陽、張政亮，2002，「宜蘭平原海岸後退之研究」，『中國地理學會學刊』，30：57-76。陳超、江濤、劉祥磊，2009，「基於纓帽變化的遙感圖像融合方法研究」，『測繪科學』，34（3）：105-106。陳維勇，2000，「小波理論應用於影像套合之研究」，國立交通大學土木工程系碩士論文：新竹。張仲德，2008，「應用MODIS影像分析探討台灣地面物候變化及颱風干擾之影響」，國立彰化師範大學地理學系博士論文：彰化。張仲德、王素芬、林登秋，2011，「氣候變遷與不同尺度植被物候研究之回顧」，『地理學報』，63，4。張仲德、黃倬英，2014，「應用TIMESAT分析台灣地表植被物候空間分布」，『第三十三屆測量及空間資訊研討會論文集』，145-155。張春福，1985，『物候』，北京：氣象出版社。張春福，1995，「氣候變化對中國木本植物物候的可能影響」，『地理學報』，50（5）：403-408。董文全、蒙繼華，2018，「遙感數據時空融合研究進展及展望」，『國土資源遙感』，30（2）：1-11。楊純明，2013，「氣候智能型農業生產－從環境親和調適評估機會與挑戰」，『作物、環境與生物資訊』，10（3）：217-228。謝東佑、邱祈榮，2013，「植物物候在氣候變遷研究之回顧與展望」，『中華林學季刊』，46（3）：391-410。謝東佑，2016，「氣候變遷對物種、生態系統影響及其管理策略之探討」，『台灣林業科學』，31（3）：227-55。Acerbi-Junior, F.W., Clevers, J.G.P.W. and Schaepman, M.E., 2006, “The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian savanna”, Int. J. Appl. Earth Obs., 8, 278–288.Adole, T., Dash, J. and Atkinson, P.M., 2018, “Characterising the land surface phenology of Africa using 500 m MODIS EVI”, Applied Geography, 90, 187-199.Beck, P.S.A., Atzberger, C., Høgda, K.A., Johansen, B. and Skidmore, A.K. 2006, “Improved monitoring of vegetation dynamics at very high latitudes： A new method using MODIS NDVI”, Remote Sens. Environ, 100, 321–334.Chang, C.T. , Wang, H.C. and Huang C.Y., 2013, “Impacts of vegetation onset time on the net primary productivity in a mountainous island in Pacific Asia”, Environmental Research Letters, 8, 1-11.Chang, C.T., Wang, S.F., Vadeboncoeur M.A. and Lin, T.C., 2014, “Relating vegetation dynamics to temperature and precipitation at monthly and annual timescales in Taiwan using MODIS vegetation indices”, International journal of remote sensing, 35（2）：598-620.Chen, B., Huang, B. and Xu, B., 2015, “Comparison of Spatiotemporal Fusion Models：A Review”, International Journal of Remote Sensing, 7（2）：1798-1835.Chen, X.Q., Hu, B. and Yu, R., 2005, “Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China. Global Change Biology”, 11, 1118–1130.Chidumayo, E.N., 2001, “Climate and phenology of savanna vegetation in southern Africa”, Journal of Vegetation Sciences, 12, 347-354.Chuine, I., Cour, P. and Rousseau, D.D., 1999, “Selecting modelings to predict the timing of flowering of temperature tree：implications for tree phenology modeling”, Plant Cell Environ, 22, 1-13.Eklundh, L. and Jönsson, P., 2012, TIMESAT 3.1 Software Manual, 9-17.Gao, F., Masek, J., Schwaller, M. and Hall, F., 2006, “On the blending of the Landsat and MODIS surface reflectance：Predicting daily Landsat surface reflectance”, IEEE Trans. Geosci. Remote Sens, 44, 2207–2218.Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J.G., Gao, F. and White, J.C., 2009, “A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS.” Remote Sensing of Environment, 113, 1613–1627.Hopkins, A.D., 1918, “The bioclimatic law as applied to entomological research and farm practice”, Monthly Weather Review, 48, 335.Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, L. G. Ferreira, 2002, “Overview of the radiometric and biophysical performance of the MODIS Vegetation Indices”, Remote Sensing of Environment, 83（1-3）：195-213.Jarihani, A.A., McVicar, T.R., Niel T.G.V., Emelyanova I.V., Callow J.N. and Johansen K., 2014, “Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and“Blend-then-Index” Approaches”, IEEE Trans. Geosci. Remote Sens, 6, 9213-9238.Krawitz, L., 1974, Earth resources program scope and information needs, NASA-CR-141767, General Electric Co., Philadelphia.Lillesand, T.M., Kiefer, R.W. and Chipman, J.W., 2004, Remote Sensing and Image Interpretation 5th ed, New York：John Wiley & Sons, Inc.Moulin, S., Kergoat, L., Viovy, N. and Dedieu, G., 1997, “Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements”, Journal of climate, 10：1154-1170.Obsomer, V., Titeux N., Vancustem, C., Duveiller, G., Pekel, J.F., Connor, S., Ceccato, P. and Coosemans, M., 2013, “From Anopheles to Spatial Surveillance： A Roadmap Through a Multidisciplinary Challenge”, Anopheles mosquitoes - New insights into malaria vectors.Primack, R.B., Higuchi, H. and Miller-Rushing, A.J., 2009, “The impact of climate change on cherry trees and other species in Japan”, Biological Conservation, 142：1943-1949.Ram, B.G., F. J. Breidt, A. Dutin, and S. M. Ogle, 2009, “Predicting Enhanced Vegetation Index（EVI）curves for ecosystem modeling applications”, Remote Sensing of Environment, 113：2186-2193.Rathcke B., Lacey E.P., 1985, “Phenological patterns of terrestrial plants[J]”, Annual Review of Ecology and Systematics, 16（1）：179-214.Tian, F., Wang, Y., Fensholt, R., Wang, K., Zhang L. and Huang, Y., 2013, “Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau”, Remote Sens, 5, 4255-4279.Walter, G.R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J.C., Fromentin, J.N., Hoegh-Guldberg, O. and Bairlein, F., 2002, “Ecological responses to recent climate change”, Nature, 416：389-395.Zhang, X.Y., Hodges, J.C.F., Schaaf, C.B., Friedl, M.A., Strahler, A.H. and Gao, F., 2001, “Global Vegetation Phenology from AVHRR and MODIS Data”, Geoscience and Remote Sensing Symposium, 5：2262-2264.Zhu, X.L., Chen, J., Gao, F., Chen, X.H. and Masek J.G., 2010, “An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions”, Remote Sensing of Environment, 114, 2610–2623.陳毅青，2010，利用ArcGIS Model Builder建立差異化植生指數（NDVI）模組。http://twmtman.blogspot.tw/2010/11/model-builderndvi.html，取用日期：2014年12月13日。NASA（2015, April 4）. About MODIS. Retrieved May 5, 2015 from NASA on the World Wide Web：http://modis.gsfc.nasa.gov/about/USGS（2015, April 4）. Landsat Missions Project Descriptions. Retrieved May 5,2015 from USGS on the World Wide Web：http://landsat.usgs.gov/about_project_descriptions.php 描述 碩士
國立政治大學
地政學系
102257031資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102257031 資料類型 thesis dc.contributor.advisor 詹進發 zh_TW dc.contributor.advisor Jan, Jihn-Fa en_US dc.contributor.author (Authors) 梁鋆立 zh_TW dc.contributor.author (Authors) Liang, Yun-Li en_US dc.creator (作者) 梁鋆立 zh_TW dc.creator (作者) Liang, Yun-Li en_US dc.date (日期) 2017 en_US dc.date.accessioned 5-Sep-2019 16:56:45 (UTC+8) - dc.date.available 5-Sep-2019 16:56:45 (UTC+8) - dc.date.issued (上傳時間) 5-Sep-2019 16:56:45 (UTC+8) - dc.identifier (Other Identifiers) G0102257031 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125763 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系 zh_TW dc.description (描述) 102257031 zh_TW dc.description.abstract (摘要) 地表植被物候的變化反映了氣候變遷對於生態系統所造成的影響，而透過遙測技術能夠有效率地進行大範圍且長期的植被物候監測。本研究蒐集了2001至2010年間宜蘭地區之MODIS以及Landsat TM影像，先利用MODIS的MOD13Q1植生指標產品提取出一組空間解析度為250公尺之植生指標（NDVI、EVI）時序資料，並透過影像融合模型ESTARFM（Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model）融合MODIS與Landsat TM影像來產生另一組空間解析度為30公尺之植生指標時間序列資料。接著將這兩組不同空間解析度之植生指標時間序列資料分別匯入物候分析軟體TIMESAT來計算宜蘭地區之地表植被物候參數，再配合氣象資料分析氣候因子與宜蘭地區植被及物候參數間之關係，最後比較兩種不同空間解析度之植生指標時序資料所產生的分析結果。研究結果顯示，MODIS-NDVI時序資料與氣候因子之相關性較MODIS-EVI高，並據此將該資料之物候分析成果作為基準，在2001至2010年間宜蘭地區植被生長季起點延後約6至7天；生長季終點約延後2至6天。在ESTARFM影像融合方面，以Landsat-NDVI之成果較Landsat-EVI與氣候因子有較佳的相關性，且其物候分析成果與MODIS-NDVI成果也較接近。 zh_TW dc.description.abstract (摘要) Land surface vegetation phenology reflects the responses of a terrestrial ecosystem to climate change. It is effective for monitoring the vegetation phenology of large-scale area using time-series remotely sensed dataset. In this study, we used MOD13Q1 and Landsat TM images for vegetation phenology analysis between 2001 and 2010 in Yilan. First, the monthly maximum value (MVC) images of MODIS were generated from 16-day VI (Vegetation Indices) time-series data. Second, the synthetic Landsat VI data were created using the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm for all dates when Landsat images were either unavailable or too cloudy. The monthly temperature and precipitation data during 2001 and 2010 were used to generate map layers using ArcGIS 10.1 for vegetation and climate analysis. Then, the MODIS and Landsat images were provided as input for the phenology analysis toolbox TIMESAT, from which phenological metrics including onset (start of growing season), offset (end of growing season) and length of growing season were derived. These phonological parameters of vegetation and climate data were used to investigate the relationship between vegetation phenology and climate change. In the end, both MODIS VI data and synthetic Landsat VI data were analyzed and compared in the study. The results show that MODIS-NDVI time-series data has higher correlation with climate data than MODIS-EVI. Moreover, the phenology analysis shows that in Yilan area the onset of vegetation was 6 days late and the offset was 2-6 days late during the 10 years period. For the ESTARFM model, Landsat-NDVI has higher coorelation with climate data than Landsat-EVI, and the phenology analysis result is more consistent to that of MODIS-NDVI. en_US dc.description.tableofcontents 第一章 緒論 1第一節 前言 1第二節 研究動機 3第三節 研究目的 5第二章 文獻回顧與理論基礎 7第一節 物候學之概念 7第二節 遙測與植被物候 13第三節 衛星影像融合 18第三章 研究材料與方法 27第一節 研究區域與材料 27第二節 研究方法與流程 34第四章 研究成果與分析 43第一節 物候參數提取 43第二節 物候參數分析 69第五章 結論與建議 73第一節 結論 73第二節 建議 75參考文獻 77 zh_TW dc.format.extent 8082731 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102257031 en_US dc.subject (關鍵詞) 植被物候 zh_TW dc.subject (關鍵詞) MODIS zh_TW dc.subject (關鍵詞) Landsat zh_TW dc.subject (關鍵詞) ESTARFM zh_TW dc.subject (關鍵詞) TIMESAT zh_TW dc.subject (關鍵詞) Vegetation Phenology en_US dc.subject (關鍵詞) MODIS en_US dc.subject (關鍵詞) Landsat en_US dc.subject (關鍵詞) ESTARFM en_US dc.subject (關鍵詞) TIMESAT en_US dc.title (題名) 應用多源遙測影像分析宜蘭地區植被物候之研究 zh_TW dc.title (題名) Analyzing Vegetation Phenology in Yilan using Multisource Remote Sensing Images en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 王宏、李曉兵、余弘婧，2006，「基於NOAA／AVHRR NDVI監測中國北方典型草原的生長季及變化」，『植物生態學報』，30（3）：365-374。行政院農業委員會，2014，『農業統計年報（102年）』，台北：行政院農業委員會。林清儒，2007，「無線感測網路於森林物候監測應用之研究」，國立臺灣大學生物資源暨農學院森林環境暨資源學系碩士論文：台北。楊麗萍、夏敦勝、陳發虎，2007，「Landsat 7 ETM+ 全色與多光譜數據融合算法的比較」，『蘭州大學學報』，4：7-17。徐雨晴、陸佩玲、于強，2004，「氣候變化對植物物候影響的研究進展」，『資源科學』，26（1）：129-136。徐森雄，2007，「從全球暖化趨勢談物候學」，『作物、環境與生物資訊』，4：304-344。侯美亭、延曉東，2012，「中國東部植被物候變化及其對氣候的響應」，『氣象科技進展』，2（4）：40。許民陽、張政亮，2002，「宜蘭平原海岸後退之研究」，『中國地理學會學刊』，30：57-76。陳超、江濤、劉祥磊，2009，「基於纓帽變化的遙感圖像融合方法研究」，『測繪科學』，34（3）：105-106。陳維勇，2000，「小波理論應用於影像套合之研究」，國立交通大學土木工程系碩士論文：新竹。張仲德，2008，「應用MODIS影像分析探討台灣地面物候變化及颱風干擾之影響」，國立彰化師範大學地理學系博士論文：彰化。張仲德、王素芬、林登秋，2011，「氣候變遷與不同尺度植被物候研究之回顧」，『地理學報』，63，4。張仲德、黃倬英，2014，「應用TIMESAT分析台灣地表植被物候空間分布」，『第三十三屆測量及空間資訊研討會論文集』，145-155。張春福，1985，『物候』，北京：氣象出版社。張春福，1995，「氣候變化對中國木本植物物候的可能影響」，『地理學報』，50（5）：403-408。董文全、蒙繼華，2018，「遙感數據時空融合研究進展及展望」，『國土資源遙感』，30（2）：1-11。楊純明，2013，「氣候智能型農業生產－從環境親和調適評估機會與挑戰」，『作物、環境與生物資訊』，10（3）：217-228。謝東佑、邱祈榮，2013，「植物物候在氣候變遷研究之回顧與展望」，『中華林學季刊』，46（3）：391-410。謝東佑，2016，「氣候變遷對物種、生態系統影響及其管理策略之探討」，『台灣林業科學』，31（3）：227-55。Acerbi-Junior, F.W., Clevers, J.G.P.W. and Schaepman, M.E., 2006, “The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian savanna”, Int. J. Appl. Earth Obs., 8, 278–288.Adole, T., Dash, J. and Atkinson, P.M., 2018, “Characterising the land surface phenology of Africa using 500 m MODIS EVI”, Applied Geography, 90, 187-199.Beck, P.S.A., Atzberger, C., Høgda, K.A., Johansen, B. and Skidmore, A.K. 2006, “Improved monitoring of vegetation dynamics at very high latitudes： A new method using MODIS NDVI”, Remote Sens. Environ, 100, 321–334.Chang, C.T. , Wang, H.C. and Huang C.Y., 2013, “Impacts of vegetation onset time on the net primary productivity in a mountainous island in Pacific Asia”, Environmental Research Letters, 8, 1-11.Chang, C.T., Wang, S.F., Vadeboncoeur M.A. and Lin, T.C., 2014, “Relating vegetation dynamics to temperature and precipitation at monthly and annual timescales in Taiwan using MODIS vegetation indices”, International journal of remote sensing, 35（2）：598-620.Chen, B., Huang, B. and Xu, B., 2015, “Comparison of Spatiotemporal Fusion Models：A Review”, International Journal of Remote Sensing, 7（2）：1798-1835.Chen, X.Q., Hu, B. and Yu, R., 2005, “Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China. Global Change Biology”, 11, 1118–1130.Chidumayo, E.N., 2001, “Climate and phenology of savanna vegetation in southern Africa”, Journal of Vegetation Sciences, 12, 347-354.Chuine, I., Cour, P. and Rousseau, D.D., 1999, “Selecting modelings to predict the timing of flowering of temperature tree：implications for tree phenology modeling”, Plant Cell Environ, 22, 1-13.Eklundh, L. and Jönsson, P., 2012, TIMESAT 3.1 Software Manual, 9-17.Gao, F., Masek, J., Schwaller, M. and Hall, F., 2006, “On the blending of the Landsat and MODIS surface reflectance：Predicting daily Landsat surface reflectance”, IEEE Trans. Geosci. Remote Sens, 44, 2207–2218.Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J.G., Gao, F. and White, J.C., 2009, “A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS.” Remote Sensing of Environment, 113, 1613–1627.Hopkins, A.D., 1918, “The bioclimatic law as applied to entomological research and farm practice”, Monthly Weather Review, 48, 335.Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, L. G. Ferreira, 2002, “Overview of the radiometric and biophysical performance of the MODIS Vegetation Indices”, Remote Sensing of Environment, 83（1-3）：195-213.Jarihani, A.A., McVicar, T.R., Niel T.G.V., Emelyanova I.V., Callow J.N. and Johansen K., 2014, “Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and“Blend-then-Index” Approaches”, IEEE Trans. Geosci. Remote Sens, 6, 9213-9238.Krawitz, L., 1974, Earth resources program scope and information needs, NASA-CR-141767, General Electric Co., Philadelphia.Lillesand, T.M., Kiefer, R.W. and Chipman, J.W., 2004, Remote Sensing and Image Interpretation 5th ed, New York：John Wiley & Sons, Inc.Moulin, S., Kergoat, L., Viovy, N. and Dedieu, G., 1997, “Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements”, Journal of climate, 10：1154-1170.Obsomer, V., Titeux N., Vancustem, C., Duveiller, G., Pekel, J.F., Connor, S., Ceccato, P. and Coosemans, M., 2013, “From Anopheles to Spatial Surveillance： A Roadmap Through a Multidisciplinary Challenge”, Anopheles mosquitoes - New insights into malaria vectors.Primack, R.B., Higuchi, H. and Miller-Rushing, A.J., 2009, “The impact of climate change on cherry trees and other species in Japan”, Biological Conservation, 142：1943-1949.Ram, B.G., F. J. Breidt, A. Dutin, and S. M. Ogle, 2009, “Predicting Enhanced Vegetation Index（EVI）curves for ecosystem modeling applications”, Remote Sensing of Environment, 113：2186-2193.Rathcke B., Lacey E.P., 1985, “Phenological patterns of terrestrial plants[J]”, Annual Review of Ecology and Systematics, 16（1）：179-214.Tian, F., Wang, Y., Fensholt, R., Wang, K., Zhang L. and Huang, Y., 2013, “Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau”, Remote Sens, 5, 4255-4279.Walter, G.R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J.C., Fromentin, J.N., Hoegh-Guldberg, O. and Bairlein, F., 2002, “Ecological responses to recent climate change”, Nature, 416：389-395.Zhang, X.Y., Hodges, J.C.F., Schaaf, C.B., Friedl, M.A., Strahler, A.H. and Gao, F., 2001, “Global Vegetation Phenology from AVHRR and MODIS Data”, Geoscience and Remote Sensing Symposium, 5：2262-2264.Zhu, X.L., Chen, J., Gao, F., Chen, X.H. and Masek J.G., 2010, “An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions”, Remote Sensing of Environment, 114, 2610–2623.陳毅青，2010，利用ArcGIS Model Builder建立差異化植生指數（NDVI）模組。http://twmtman.blogspot.tw/2010/11/model-builderndvi.html，取用日期：2014年12月13日。NASA（2015, April 4）. About MODIS. Retrieved May 5, 2015 from NASA on the World Wide Web：http://modis.gsfc.nasa.gov/about/USGS（2015, April 4）. Landsat Missions Project Descriptions. Retrieved May 5,2015 from USGS on the World Wide Web：http://landsat.usgs.gov/about_project_descriptions.php zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201901024 en_US
