應用多時期MODIS衛星影像分析於蒙古地區整合型乾旱強度指標之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：27

、訪客IP：3.145.21.89

姓名

孟可竹(Munkhzul Dorjsuren) 查詢紙本館藏

畢業系所

太空科學研究所

論文名稱

應用多時期MODIS衛星影像分析於蒙古地區整合型乾旱強度指標之研究
(Multi-Temporal MODIS Data Analysis for Integrated Drought Severity Index (IDSI) over Mongolia)

相關論文

★ WVR、GPS及氣球探空觀測可降水量之比較	★ GPS斷層掃描估算大氣濕折射係數模式
★ GPS觀測大氣閃爍之研究	★ GPS 氣象中地面氣象模式之改進
★ 由GPS信號反演大氣濕折射度之數值模擬	★ 近即時GPS觀測可降水技術之研究
★ 利用水氣資訊改善降水估計之研究	★ GPS掩星觀測反演與反演誤差探討
★ 微波輻射計數位相關器之設計與實現	★ GPS與探空氣球資料觀測可降水量與降雨之關係
★ 利用GPS訊號估算對流層斜向水氣含量之研究	★ 利用遙測影像反演水稻田蒸發散量之研究
★ 利用MODIS影像反演嘉義地區水稻田蒸發散量之研究	★ 利用MODIS影像於水稻田蒸發散之研究
★ 分析以全球定位系統近即時估計可降水之可行性	★ 對流層延遲效應與全球定位系統高程定位之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

乾旱指數可用於評估使用氣象測量數據的溫度和降水的干旱檢測。此外，基於衛星的數據為區域性乾旱事件提供了空間和時間模式。本研究透過MODIS(Moderate Resolution Imaging Spectroradiometer)衛星影像，監測蒙古地區的乾旱氣候狀況。研究中所發展的乾旱指標，乃運用乾旱嚴重度指標-2(Drought Severity Index-2, DSI2)與整合型乾旱嚴重度指標(Integrated Drought Severity Index, IDSI)，並透過計算2000-2013年5月至8月的MODIS衛星資料所建立。這些指標可經由計算MODIS二波段增強型植被指數(Two-band Enhanced Vegetation Index, EVI2)的標準化特徵、地表溫度、蒸發散量與潛勢蒸發散量所求得。上述所提之乾旱嚴重度指標-2(DSI2)是以標準化的蒸發散與潛勢蒸發散比率，及二波段增強型植被指數(EVI2)為基礎所建立。並透過標準化加總的蒸發散與潛勢蒸發比率，以及二波段增強型植被指數與地表溫度比率，進行乾旱嚴重度指標之修正，即為整合型乾旱嚴重度指標(IDSI)。最後，藉由參數特徵計算二波段增強型植被指數與地表溫度的比率，並將此比率整合至乾旱嚴重度指標。除此之外，本研究蒐集並分析長達14年(2000-2013)夏季(5-8月)每月之氣溫、降雨與土壤濕度的現地觀測值(由18個氣象與農業測站取得相關資料)。氣候變數中如發現有異常的現地觀測值，將計算標準化的異常值(Standardized Anomaly)，並與乾旱嚴重度指標及整合型乾旱嚴重度指標進行比較。
接續處理多時期MODIS衛星資料的監督式分類。並利用標準化差異方法，分別計算MODIS衛星資料與現地觀測資料。因此，線性頻譜混合分析和變化矢量分析的閾值用於乾旱指數類。統計分析並計算研究期程內之乾旱嚴重度指標對氣候異常，與整合型乾旱嚴重度指標對氣候異常的相關係數。
從原位測量的標準化異常分析，降水最多的年份為2003及2011至2013年，而降水最少的年份為2001-2002、2007與2009年，其餘為降水正常的年份。一般來說，乾燥氣候常具有較低降雨量與較高溫度(如2002及2007年)；相反地，潮濕天氣常伴隨較高降水量與較低溫度(如2003、2012與2013年)，
本研究接續改善乾旱嚴重度指標之參數，結果顯示植生－溫度的特徵空間已被明確定義。而二波段增強型植被指數與地表溫度比率之驗證結果，可透過比較該比率與研究區域內的月降雨量。比較結果顯示該比率與降雨資料呈現良好一致性與敏感度。此外，ET/PET比例結果發現，ET/PET比和降水之間的關係在不同條件下具有相似的變化。表明ET/PET比率顯示出良好的濕度和乾旱條件檢測參數。
比較乾旱嚴重度指標-2(DSI2)與整合型乾旱嚴重度指標(IDSI)的結果發現，整合型乾旱嚴重度指標在分類結果的表現上，略優於乾旱嚴重度指標。於現地觀測值的時間序列分析結果可發現，整合性乾旱嚴重度指標之動力恰可體現(2001、2002、2007與2009年)與豐水時期(2003與2011-2013年)於時間與空間上的發生情況。於詳細的整合型乾旱嚴重度指標動力之空間分析亦可發現，降水最多及最少的年份(即2003與2007年)，其空間分布相較其他年份，於本研究地區佔最大影響區域約達60％和67％。
透過遙測影像與現地觀測資料間之關係，可說明整合型乾旱嚴重度指標對氣候異常值的相關性，高於乾旱嚴重度指標對氣候異常值的相關性。經由18個測站資料所得整合型乾旱嚴重度指標對氣候異常值的相關係數為0.84，並與遙測影像對觀量異常值之結果，具有良好一致性。本論文說明運用MODIS衛星資料之優點，可用來研究乾旱氣候特徵的變異性，對於農業發展與管理的乾旱監測亦十分重要和乾旱的輸入參數之一。

摘要(英)

Drought indices can be used to evaluate drought detection using meteorological measurements data of the temperature and precipitation. Moreover, the satellite-based data provides spatial and temporal patterns for the regional-scale drought occurrences. This dissertation is to investigate the drought detection in relation to climatic condition over Mongolia by using satellite remote sensing imagery, which was acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The drought index was evaluated from the MODIS data acquired during May to August from 2000 to 2013 using the Drought Severity Index-2 (DSI2) and Integrated Drought Severity Index (IDSI) methods. These indices were empirically calculated by standardized characteristics of the MODIS two-band Enhanced Vegetation Index (EVI2), Land Surface Temperature (LST), Evapotranspiration (ET), and Potential Evapotranspiration (PET) data. DSI is based on the monthly standardized ET/PET ratio and EVI2 index. The modification of DSI2, IDSI was calculated by standardization of the sum of separately monthly standardized ET/PET ratio and EVI2/LST ratio. Consequently, the ratio between EVI2 and LST was calculated by parameter features and integrated into the DSI2. In addition, fourteen-year summer monthly data for air temperature, precipitation, and soil moisture content of in-situ measurements data from the meteorological and agricultural stations were analyzed. The climatological variables anomaly of in situ measurements was also calculated by standardized anomaly to compare to the DSI2 and IDSI at the eighteen stations.
The multi-temporal of all MODIS data were processed using supervised classification. A standardized anomaly method was also calculated by both MODIS and in situ measurement data. Therefore, the linear spectral mixture analysis (LSMA) and the threshold value of change vector analysis (CVA) were used for drought-indices classes. A statistical analysis and Pearson correlation coefficients (r) for the DSI2 versus the climatological anomaly and the IDSI versus the climatological anomaly were computed for the study period.
From the standardized anomaly analysis of in situ measurements, it was shown that the wettest years were 2003 and 2011–2013, while the driest years were 2001, 2002, 2007, and 2009; the rest of the years were normal years. Generally speaking, dry weather implies lower rainfall and higher temperature, so that drought occurred in the years 2002 and 2007. By contrast, wet weather accompanies higher precipitation and lower temperature, such as the years 2003, 2012, and 2013.
For the improvement of the parameters of DSI that is the ratio between MODIS EVI2 and LST, the results showed that the vegetation-temperature feature space was well-defined. This indicated a wide range of surface wetness and drought in the study area. The validation results of EVI2/LST ratio were carried out by comparing EVI2/LST values with monthly rainfall throughout the study area. The comparison results were revealed with good agreement and sensitivity between EVI2/LST ratio and rainfall data. Moreover, ET/PET ratio results found that the relationship between the ET/PET ratio and precipitation has a similar variation in different conditions. It is indicating that the ET/PET ratio reveals a good parameter for detecting wet and drought conditions.
The comparison results between DSI2 and IDSI demonstrated that the IDSI gave slightly better classification results than the DSI2. The modification of DSI2 results was found that IDSI dynamics revealed the spatiotemporal occurrence of dry (2001, 2002, 2007 and 2009) and wet (2003 and 2011–2013) periods as shown in time series analysis of in situ measurements. From a detailed spatial analysis of IDSI dynamics, it was found that the wettest and drought occurred in 2003 and 2007 and occupied the largest region of the study area by about 60% and 67% as compared to other years.
The relationships between remotely sensed and in situ based data indicated that the correlation for IDSI versus climatological anomaly is higher than DSI2 versus climatological anomaly. Correlation coefficients obtained over the eighteen measurement stations between the IDSI and climatological anomaly (r = 0.84) show a good agreement between the satellite-derived and measured anomalies. This dissertation has demonstrated merits of using MODIS data for studying drought variability in relation to climatic characteristics, and is important for drought monitoring in agricultural management and development, and one of an input parameter for drought.

關鍵字(中)

★ 二波段增強型植被指數
★ 地表溫度
★ 蒸散
★ 整合型乾旱嚴重度指標
★ 標準化異常
★ 原位數據

關鍵字(英)

★ MODIS
★ two-band Enhanced Vegetation Index (EVI-2)
★ land surface temperature (LST)
★ evapotranspiration (ET)
★ Integrated Drought Severity Index (IDSI)
★ standardized anomaly
★ in situ data

論文目次

摘要………………………………………………………………….……………...ii
ABSTRACT……………………….………...…………………………………….v
ACKNOWLEGDMENT….…………………………………………………..vii
TABLE OF CONTENTS………………………………………………………viii
LIST OF FIGIRES.……………………………………………………..………..xii
LIST OF TABLES…………………………………………………...…………..xvi
LIST OF ABBREVIATIONS……………………………………...…………xviii
CHAPTER 1. INTRODUCTION..................................................................................1
1.1. Background...................................................................................2
1.2. Statement of the Problem.......................................................4
1.3. Research Objectives...................................................................7
1.3.1. General Objectives..................................................................7
1.3.2. Specific Objectives..................................................................7
1.4. Research Questions...................................................................8
1.5. Structure of the Dissertation..................................................9
CHAPTER 2. LITERATURE REVIEW.………………….……………….11
2.1. Overview of Meteorological Observations.....................13
2.1.1. Overview of Air Temperature............................................13
2.1.2. Overview of Precipitation...................................................15
2.1.3. Overview of Soil Moisture..................................................15
2.2. Remote Sensing Overview.....................................................16
2.3. Theoretical Description of Remotely Sensed Land Products...19
2.3.1. Overview of Vegetation Indices........................................19
2.3.1.1. Vegetation Indices Limitations......................................21
2.3.2. Overview of Land Surface Temperature........................21
2.3.2.1. Land Surface Temperature Limitations......................22
2.3.3. Overview of Evapotranspiration.......................................22
2.4. Moderate Resolution Imaging Spectroradiometer (MODIS)...25
2.4.1. MODIS Vegetation Index Product (MOD13A3)..........28
2.4.2. MODIS Land Surface Temperature Product (MOD11A1).......30
2.4.3. MODIS Evapotranspiration Product (MOD16A3).......31
2.4.4. Overview of MODIS Data Pre-processing.....................32
2.5. Drought .........................................................................................33
2.5.1. Overview....................................................................................33
2.5.2. Drought Impact.......................................................................35
2.5.3. Drought Monitoring..............................................................35
2.5.3.1. Meteorological Indices.....................................................36
2.5.3.2. Remote Sensing Indices...................................................37
2.6. Image Method Descriptions..................................................39
2.6.1. Image Pre-processing...........................................................39
2.6.2. Classification.............................................................................40
2.7. Image Analysis.............................................................................42
2.7.1. Linear Spectral Mixture Analysis (LSMA)........................42
2.7.2. Change Vector Analysis (CVA)............................................43
2.7.2.1. Change Detection of CVA.................................................45
2.8. Summary........................................................................................46
CHAPTER 3. STUDY AREA AND DATA PREPARATION……………………..........................................................48
3.1. Study Area 49
3.1.1. Topography and Land Cover..............................................49
3.1.2. Climate........................................................................................51
3.1.3. Population.................................................................................52
3.2. Data Acquisition and Pre-processing.................................52
3.2.1. MODIS Data..............................................................................53
3.2.1.1. MODIS EVI2...........................................................................54
3.2.1.2. MODIS LST.............................................................................57
3.2.1.3. MODIS ET...............................................................................57
3.2.2. In situ Measurements and Geographical Location Data.........61
CHAPTER 4. METHODOLOGY……………………………………………………....65
4.1. Conceptual Framework............................................................66
4.2. Remotely Sensed Drought Index.........................................67
4.2.1. Drought Severity Index-2 (DSI2).......................................67
4.2.2. Integrated Drought Severity Index (IDSI)......................68
4.3. Drought-Indices Using Supervised Classification and Change Vector Analysis (CVA).......................................................................70
4.4. Drought-Indices Category......................................................71
4.5. Climatological Anomaly of In situ Measurements Data.............72
4.5.1. Anomalies Analysis of Climatic Characteristic.............72
4.5.2. Anomaly Analysis among the Climatological Variables..........73
4.6. Time Series Analysis...................................................................74
4.6.1. Relationship between Drought-Indices and Climatological Anomaly..................................................................................................74
4.6.2. P-value Analysis........................................................................75
CHAPTER 5. GROUND MEASUREMENT VARIABLES………….76
5.1. Climatological Variables of In situ Measurements..........77
5.1.1. Climatological Variables among Observation Stations...........77
5.1.2. Climatic Parameter Characteristics....................................79
5.2. Standardized Anomaly of Climatological Variables........81
5.2.1. Climatic Parameter Variability..............................................81
5.2.2. Climatological Anomaly.........................................................83
CHAPTER 6. REMOTELY SENSED VARIABLES……..………….…..84
6.1. MODIS Products Characteristics.............................................85
6.1.1. MODIS EVI2 Patterns...............................................................85
6.1.2. MODIS LST Patterns.................................................................88
6.1.3. MODIS ET/PET Ratio Patterns..............................................90
6.2. EVI2 and LST Relationship.........................................................93
6.2.1. LST-EVI2 Scatter Plots.............................................................93
6.2.2. Ratio of EVI2 to LST..................................................................96
6.2.3. Comparing EVI2/LST Ratio with Precipitation Amount Data...........................................................................................................99
6.2.4. Comparing ET/PET Ratio with Precipitation Amount Data...........................................................................................................100
6.3. Standardized Anomaly of Remotely Sensed Variables.............103
6.3.1. Remotely Sensed Parameter Variability...........................103
6.3.2. Remotely Sensed Drought-Indices Variations...............104
CHAPTER 7. REMOTELY SENSED DROUGHT INDEX…………..108
7.1. Anomaly of IDSI and Climatological Variables Variations.......109
7.1.1. Spatiotemporal Variations of IDSI.......................................109
7.1.2. IDSI and Climatological Anomaly Variations among Station Basis.............................................................................................................112
7.2. IDSI and Climatological Anomaly in Wet and Dry Events........116
CHAPTER 8. DISCUSSION AND CONCLUSIONS…………...……….…...118
8.1. Discussion..........................................................................................119
8.2. Conclusions.......................................................................................121
BIBLIOGRAPHY.........................................................................................124

參考文獻

Ackerman, S.A., Strabala, K.I., Menzel, W.P., Frey, R.A., Moeller, C.C., Gumley, L.E., (1998). Discriminating clear-sky from clouds with MODIS. Journal of Geophysical Resource 103 (D24), 32 141–32 157.
Adams, J.B., Sabol, D.E., Kapos, V., Filho, R.A., Roberts, D.A., Smith, M.O., Gillespie, A.R., (1995). Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment 52, 137–154.
Allen, R.G., Walter, I.A., Elliot, R.L., Howell, T.A., (2005). ASCE Standardized Reference Evapotranspiration Equation, American Society of Civil Engineers, Reston, Virginia.
Anderson, M.C., Allen, R.G., Morse, A., Kustas, W.P., (2012). Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment 122, 50–65.
Asian Development Bank, (2005). Country environmental analysis–Mongolia 0061
Azzali, S., Menenti, M., (2000). Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data. International Journal of Remote Sensing 21 (5), 973–996.
Batima, P., Bat, B., Tserendash, S., Myagmarjab, B., (2008). Adapting to drought, zud and climate change in Mongolia’s rangelands. In Climate change and adaptation; Neil, L., Ian, B., Adejuwon, J., Barros, V., Lasco, R., Eds.; Earthscan: London, UK.
Bayarjargal, Y., Karnieli, A., Bayasgalan, M., Khudulmur, S., Gandush, C., Tucker, C.J., (2006). A comparative study of NOAA–AVHRR derived drought indices using change vector analysis, Remote Sensing of Environment 105, 9–22.
Bayarjargal, Y., Adyasuren, T., Munkhtuya, S., (2000). Drought and vegetation monitoring in the arid and semi-arid region of Mongolia using remote sensing and ground data. Available online: www.gisdevelopment.net/aars/2000/ts8/hami0004.asp
Becker, F., Li, Z.L., (1990). Toward a local split window method over land surface. International Journal of Remote Sensing 11 (3), 369–393.
Bolortsetseg, B., Bayasgalan, Sh., Dorj, B., Natsagdorj, L., Tuvaansuren, G., (2000). Impact on agriculture. In Climate Change and Its Impacts in Mongolia; Batima, P., Dagvadorj, D., Eds.; JEMR Publishing: Ulaanbaatar, Mongolia, pp. 96–198.
Cai, G., Du, M., Liu, Y., (2011). Regional Drought Monitoring and Analysing Using MODIS Data – A Case Study in Yunnan Province. In: Y. C., Li D, Liu Y, editors. CCTA 2010, part II, IFIP AICT 345, 243–251.
Campbell, J.B., (Ed), (2002). Introduction to Remote Sensing, published by The Guilford Press, A Division of Guilford Publications, Inc.
Carlson, T.N., Gillies, R.R., Perry, E.M., (1994). A method to make use thermal infrared temperature and NDVI management to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews 9, 161–173.
Chang, F.C., Wallace, J.M., (1987). Meteorological conditions during heat waves and droughts in the United States Great Plains. Monthly Weather Review 115 (7), 1253–1269.
Chang, T.Y., Liou, Y.A., Lin, C.Y., Liu, C.S., Wang, Y.C., (2010). Evaluation of surface heat ﬂuxes in Chiayi plain of Taiwan by remotely sensed data. International Journal of Remote Sensing 31, 3885–3898.
Chen, J., Gong, P., He, C., Pu, R., Shi, P., (2003). Land-Use/Land-Cover Change Detection Using Improved Change-Vector Analysis. Photometric Engineering and Remote Sensing 69 (4), 369–379.
Chen, S.T., Yu, P.S., (2007). Real-time probabilistic forecasting of flood stages. Journal of Hydrology 340, 63–77.
Chen, W., Xiao, Q., Sheng, Y., (1994). Application of the anomaly vegetation index to monitoring heavy drought in 1992 (in Chinese). Remote Sensing of Environment 9, 106–112.
Clark, P.U., Alley, R.B., Pollard, D., (1999). Northern hemisphere ice-sheet influences on global climate change. Science 286, 1104–1111.
Cohen, W., Fiorella, M., (1998). Comparison of methods for detecting conifer forest change with thematic mapper imagery. In R. S. Lunetta and C. D. Elvidge (Eds.), Remote sensing change detection: Environmental monitoring methods and applications, 89−102. Michigan, USA: Ann Arbor Press.
Crist, E.P., Cicone, R.C., (1984). A physically-based transformation of thematic mapper data – The TM tasseled cap. IEEE Transactions on Geoscience and Remote Sensing 22 (3), 256–263.
Dagvadorj, D., Natsagdorj, L., Dorjpurev, J., Namkhainyam, B., (2009). Mongolia Assessment Report on Climate Change 2009; Ministry of Environment, Nature and Tourism, Mongolia: Ulaanbaatar, Mongolia.
Dai, A., (2011). Characteristics and trends in various forms of the Palmer drought severity index during 1900–2008. Journal of Geophysical Research 116, D12115, doi:10.1029/2010JD015541.
Dash, P., Gottsche, F.M., Olesen, F.S., Fischer, H., (2002). Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends. International Journal of Remote Sensing 23 (13), 2563–2594.
de Beurs, K.M., Henebry, G.M., (2005). A statistical framework for the analysis of long image time series. International Journal of Remote Sensing 26 (8), 1551–1573.
Dingman, S.L., (2002). Chapter 6, Water in soils: infiltration and redistribution. Physical Hydrology (Second ed.). Upper Saddle River, New Jersey: Prentice-Hall, Inc. p. 646. ISBN 0-13-099695-5.
Dore, A.J., Mousavi-Baygi, M., Smith, R.I., Hall, J., Fowler, D., Choularton, T.W., (2006). A model of annual orographic precipitation and acid deposition and its application to Snowdonia. Atmospheric Environment 40 (18), 3316–3326.
Dorjsuren, M., Liou, Y.A., Cheng, C.H., (2016). Time Series MODIS and in Situ Data Analysis for Mongolia Drought, Remote Sensing 8, 509; doi:10.3390/rs8060509.
Droogers, P., Immerzeel, W., Perry, C., (2009). Application of Remote Sensing in National Water Plans: Demonstration Cases for Egypt, Saudi-Arabia and Tunisia; FutureWater Report 80; World Bank: Wageningen, The Netherlands.
Edwards, D.C., McKee, T.B., (1997). Characteristics of 20th Century Drought in the United States at Multiple Time Scales. Atmospheric Science Paper 634, 1–30.
Emmanouil, N.A., (2004). A convective/stratiform precipitation classification algorithm for volume scanning weather radar observations. Meteorological Applications. Cambridge University Press 11 (4), 291–300.
Erdenesaikhan, N., (2003). Time–series Satellite Data Analysis for Assessment of Vegetation Cover in Mongolia. Mongolian Journal of Biological Sciences 1 (2), 25–32.
Evapotranspiration from Wikipedia, Source: https://en.wikipedia.org/wiki/Evapotranspiration
Food and Agriculture Organization, (2006). Country pasture ⁄forage resource profiles – Mongolia (Faoorg ⁄ag ⁄AGP ⁄AGPC ⁄doc ⁄Counprof ⁄Mongol2htm Accessed 14 January 2008).
Frey, R.A., Ackerman, S.A., Liu, Y., Strabala, K.I., Zhang, H., Key, J.R., Wang, X., (2008). Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5. Journal of Atmospheric and Oceanic Technology 25, 1057–1072 DOI: 10.1175/2008JTECHA1052.1
Friedl, M.A., Coauthors., (2002). Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment 83, 287–302.
Fundamentals of Remote Sensing – Introduction, Source: http://www.nrcan.gc.ca/node/9363
Gao, M., Qin, Z., Zhang, H., Lu, L., Zhou, X., Yang, X., (2008). Remote sensing of agro-droughts in Guangdong province of China using MODIS Satellite Data. Sensors 8 (8), 4687–4708.
Gao, X., Huete, A.R., Ni, W., Miura, T., (2000). Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment 74, 609–620.
Ghulam, A., Qin, Q., Zhan, Z., (2007b). Designing of the perpendicular drought index. Environment of Geology 6, 1045–1052.
Gilabert, M.A., González-Piqueras, J., García-Haro, F.J., Meliá, J., (2002). A generalized soil-adjusted vegetation index. Remote Sensing of Environment 82, 303−310.
Glantz, M.H., Betsill, M., Crandall, K., (1997). Food security in Southern Africa: Assessing the use and value of ENSO information. Environmental and Societal Impacts Group, National Centre for Atmospheric Research, Boulder, CO.
Glenn, E.P., Huete, A.R., Nagler, P.L., Hirschboeck, K.K., Brown, P., (2007). Integrating remote sensing and ground methods to estimate evapotranspiration. Critical Reviews in Plant Sciences 26 (3), 139–168.
Goetz, S.J., (1997). Multi-sensor analysis of NDVI, surface temperature, and biophysical variables at a mixed grassland site. International Journal of Remote Sensing 18 (1), 71–94.
Goward, N.S., (1989). Satellite bioclimatology. Journal of Climate 2 (7), 710–720.
Guindin-Garcia, N., Gitelson, A.A., Arkebauer, T.J., Shanahan, J., Weiss, A., (2012). An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index. Agricultural and Forest Meteorology 161, 15–25.
Guttman, N.B., (1998). Comparing the Palmer drought index and the standardized precipitation index. Journal of American Water Resource Association 34, 113–121.
Guttman, N.B., Wallis, J.R., Hosking, J.R.M., (1992). Spatial comparability of the Palmer drought severity index. Water Resources Bulletin 28, 1111–1119.
Huete, A.R., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213.
Huete, A.R., Justice, C.O., Liu, H., (1994). Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment 49, 224–234.
Huete, A.R., Justice, C.O., van Leeuwen, W., (1999). MODIS Vegetation Index (MOD 13) Algorithm Theoretical Basis Document.
Huete, A.R., Liu, H.Q., Batchily, K., van Leeuwen, W.J.D., (1997). A comparison of vegetation indices over a global set of TM images for EOS–MODIS. Remote Sensing of Environment 59, 440–451.
Image Classification and Analysis, Source: http://www.nrcan.gc.ca/node/9361
Information and Research Institute of Meteorology, Hydrology and Environment (IRIMHE) of Mongolia, Source: http://www.icc.mn
International Civil Aviation Organization, (2002). World Geodetic System – 1984 (WGS-84) Manual. ICAO Doc 9674–AN/946. Second edition, Quebec.
IPCC, (2007). Climate change 2007: the physical science basis contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Solomon S et al. (eds) Cambridge Univ, Press, Cambridge.
Jiang, Z., Huete, A.R., Didan, K., Miura, T., (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112, 3833–3845.
Jin, Y., Schaaf, C.B., Woodcock, C.E., Gao, F., Li, X., Strahler, A.H., (2003). Consistency of MODIS surface BRDF/Albedo retrievals: 2. Validation, Journal of Geophysical Research 108(D5), 4159. doi:10.1029/2002JD002804.
Jones, P.D., Hulme, M., (1996). Calculating regional climatic time series for temperature and precipitation: methods and illustrations. International Journal of Climatology 16, 361–377.
Joseph, G., (2005). Fundamentals of Remote Sensing (Second edition). Universities Press (India) Private Limited 3-5-819, Hyderguda, Hyderabad 500 029.
Justice, C.O., Townshend, J.R.G., Vermote, E.F., Masuoka, E., Wolfe, R.E., Saleous, N., Roy, D.P., Morisette, J.T., (2002). An overview of MODIS Land data processing and product status. Remote Sensing of Environment 83, 3–15.
Kaufman, Y.J., Tanré, D., (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing 30, 261−270.
Keyantash, J., Dracup, J., (2002). The quantification of drought: an evaluation of drought indices Bulletin of the American Meteorological Society 83, 1167–1180.
Kogan, F.N., (1987). Vegetation index for a real analysis of crop conditions, In: Proceedings of the 18th Conference on Agricultural and Forest Meteorology, AMS, W. Lafayette, Indiana, 15–18 September 1987, Indiana, USA, 103–106.
Lambin, E.F., Ehrlich, D., (1996). The surface temperature-vegetation index space for land cover and land-cover change analysis. International Journal of Remote Sensing 17, 463–487.
Lambin, E.F., Strahler, A.H., (1994). Change-vector analysis in multitemporal space: A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment 48, 231−244.
Legates, D.R., (1991). An evaluation of procedures to estimate monthly precipitation probabilities. Journal of Hydrology 125, 129–140.
Li, Z.L., Tang, R., Wan, Z., Bi, Y., Zhou, C., Tang, B., Yan, G., Zhang, X., (2009). A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors 9, 3801–3853.
Liang, S., Wang, K., Zhang, X., Wild, M., (2010). Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3, 225–240.
Lillesand, T.M., Chipman, J.W., Kiefer, R.W., (2004). Remote Sensing and Image Interpretation, Fifth Edition, Wiley.
Loik, M.E., Breshears, D.D., Lauenroth, W.K., Belnap, J., (2004). A multi-scale perspective of water pulses in dryland ecosystems: Climatology and ecohydrology of the western USA. Oecologia 141, 269–281.
LP DAAC (Land Processes Distributed Active Archive Center), 2010a: Land Cover Type Yearly L3 Global 0.05Deg CMG, MCD12C1, retrieved Jan 20, 2012, https://lpdaac.usgs.gov/products/modis_products_table/land_cover/yearly_l3_glo bal_0_05deg_cmg/mcd12c1
LP DAAC (Land Processes Distributed Active Archive Center), (2010b). Nadir BRDF-Adjusted Reflectance 16-Day L3 0.05Deg CMG, MCD43C4, retrieved Dec. 25, 2011, https://lpdaac.usgs.gov/products/modis_products_table/nadir_brdf_adjusted_reflectance/16_day_l3_0_05deg_cmg/mcd43c4
LPDAAC. NASA Land Data Products and Services. Source: http://lpdaac.usgs.gov/
Lu, D., Mausel, P., Brondizio, E., Moran, E., (2004). Change detection techniques. International Journal of Remote Sensing 25 (12), 2365–2407.
Lucht, W., Schaaf, C.B., Strahler, A.H., (2000). An Algorithm for the retrieval of albedo from space using semiempirical BRDF models, IEEE Transactions on Geoscience and Remote Sensing 38, 977−998.
Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., Worthy, D., (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment 105, 142–154.
McKee., T.B., Doesken, N.J., Kleist, J., (1993). The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. AMS, Boston, MA, 179–184.
Middleton, W.E.K., Spilhaus, A.F., (1960). Meteorological Instruments. Third edition, University of Toronto Press.
Milly, P.C.D., Wetherald, R.T., Dunne, K.A., Delworth, T.L., (2002). Increasing risk of great floods in a changing climate. Nature 415, 514–517.
Mishra, A., Singh, V., (2010). A review of drought concepts. Journal of Hydrology 391, 202–216.
MODIS Reprojection Tool (MRT), version 4.0. (2008). Land Processes DAAC USGS Center for Earth Resources Observation and Science (EROS) in collaboration with the South Dakota School of Mines and Technology (SDSM&T) Basis Document.
Mongolia Population, (2017). Source: http://worldpopulationreview.com
Monteith, J.L., (1965). Evaporation and environment. The State and Movement of Water in Living Organisms, G. E. Fogg, Ed., Symposia of the Society for Experimental Biology, Vol. 19, Academic Press, 205–234.
Morinaga, Y., Bayarbaatar, L., Erdenetsetseg, D., Shinoda, M., (2004). Zoo-meteorological study of cow weight change in a forest steppe region of Mongolia. Proceedings of the Sixth International Workshop on Climate Change in Arid and Semi-Arid Region of Asia, Ulaanbaatar, Mongolia, pp. 100–108.
Mu, Q., Heinsch, F.A., Zhao, M., Running, S.W., (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment 111, 519–536.
Mu, Q., Jones, L.A., Kimball, J.S., McDonald, K.C., Running, S.W., (2009). Satellite assessment of land surface evapotranspiration for the pan-Arctic domain. Water Resources Research 45, W09420.
Mu, Q., Zhao, M., Kimball, J.S., McDowell, N.G., Running, S.W., (2013). A remotely sensed global terrestrial drought severity index. Bulletin of the American Meteorological Society 94, 83–98.
Mu, Q., Zhao, M., Running, S.W., (2011a). Evolution of hydrological and carbon cycles under a changing climate. Hydrological Processes 25, 4093–4102.
Mu, Q., Zhao, M., Running, S.W., (2011b). Improvements to a MODIS Global Terrestrial Evapotranspiration Algorithm. Remote Sensing of Environment 115, 1781–1800.
Mu, Q., Zhao, M., Running, S.W., (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3); Algorithm Theoretical Basis Document, Collection 5; NASA HQ, Numerical Terradynamic Simulation Group, University of Montana: Missoula, MT, USA.
Myneni, R. B., Coauthors., (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment 83, 214–231.
NASA, (2011a). Data products.
NASA, (2011b). MCST calibration Information.
National Statistical Ofﬁce of Mongolia, (2016). Mongolian statistical yearbook 2015. NSOM, Ulaanbaatar.
National Statistical Ofﬁce of Mongolia, (2017). Mongolian statistical yearbook 2016. NSOM, Ulaanbaatar.
Natsagdorj, L., (2003). Climate Change: Pasture and Animal Husbandry. In Climate Change and Environment; Batima, P., Eds.; Institute of Meteorology and Hydrology of Mongolia: Ulaanbaatar, Mongolia, pp. 13–44.
Nemani, R., Price, L.L., Running, S.W., Goward, S.N., (1993). Developing satellite derived estimates of surface moisture status. Journal of Applied Meteorology 32, 548–557.
Nemani, R.R., Keeling, C.D., Hashimoto, H., Jolly, W.M., Piper, S.C., Tucker, C.J., (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563.
Nugroho, N.P., (2006). Estimating Carbon Sequestration in Trophical Rainforest Using Integrated Remote Sensing and Ecosystem Productivity Modelling. In: Geoinformation Science and Earth Observation (2006) Enchede: International Institute for Geo Information Science and Observation Netherlands. 103.
Palmer, W.C., (1965). Meteorological drought. U.S. Weather Bureau Research Paper.
Panu, U.S., Sharma, T.C., (2002). Challenges in drought research: some perspectives and future directions. Journal of Hydrological Science 47, 19–30.
Ped, D.A., (Педь, Д.А.,) (1975). О показателе засухи и избыточного увлажнения. –Тр. ГМЦ, СССР, вып.156, с.19-38 (in Russian).
Pridhoko, L., and Goward, S. N. (1997). Estimation of Air Temperature from Remotely Sensed Observations. Remote Sensing Environment 60, 335–346.
Priestley, C.H.B., Taylor, R.J., (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100 (2), 81–92.
Purevdorj, Ts., Tateishi, R., Ishiyama, T., Honda, Y., (1998). Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing 19 (18), 3519–3535.
Qin, Q., Ghulam, A., Zhu, L., Wang, L., Li, J., Nan, P., (2008). Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China. International Journal of Remote Sensing 7, 1983–1995.
Rai, A., (2003). Remote sensing and GIS applications in agriculture. Indian Agricultural Statistics Research Institute. Library Avenue, New Delhi-110012.
Robert A. Houze, Jr., (1994). Cloud Dynamics. Academic Press. p. 348. ISBN 0080502105.
Robert, P.P., (2002). Meteorology at the Millennium. Academic Press. p. 66. ISBN 978-0-12-548035-2.
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., (1974). Monitoring vegetation systems in the Great Plains with ERTS. In; Fraden S.C., Marcanti E.P. & Becker M.A. (eds.), Third ERTS-1 Symposium, 10–14 Dec. 1973, NASA SP-351, Washington D.C. NASA, pp. 309–317.
Salomonson, V.V., Barnes, W., Masuoka, E.J., (2006). Introduction to MODIS and an Overview of Associated Activities. In: Earth Science Satellite Remote Sensing Volume 1: Science and Instruments--John J. Qu WG, Menas Kafatos, Robert E. Murphy, Vincent V. Salomonson, ed. (2006) Berlin/Heidelberg: SPRInger/Tsinghua University Press.
Sandholt, I., Rasmussen, K., Andersen, J., (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79, 213–224.
Schubert, S., Koster, R., Hoerling, M., (2007). Predicting drought on seasonal-to-decadal time scales. Bulletin of the American Meteorological Society 88, 1625–1630.
Sellers, P.J., (1985). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 6, 1335–1372.
Sivakumar, M.V.K., Motha, R.P., Wilhite, D.A., Qu, J.J., (Eds.), (2011). Towards a Compendium on National Drought Policy. Proceedings of an Expert Meeting on the Preparation of a Compendium on National Drought Policy, July 14-15, 2011, Washington DC, USA: Geneva, Switzerland: World Meteorological Organization, AGM-12; WAOB-2011, 135 pp.
Smith, M.O., Johnson, P.E., Adams, J.B., (1985). Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis. Journal of Geophysical Research 90, 797–804.
Solano, R., Didan, K., Jacobson, A., Huete, A.R., (2010). MODIS Vegetation Index User’s Guide (MOD13 Series); Collection 5; Vegetation Index and Phenology Lab, The University of Arizona: Tucson, AZ, USA.
Techniques of Remote Sensing, Source: http://www.fis.uni-bonn.de/en/recherchetools/infobox/beginners/what-remote-sensing/techniques-remote-sensing
Temperature, GPH 111 – Introduction to Physical Geography, Supplemental Lecture Materials…, by Lynn, E.N., (2007). Source: http://web.gccaz.edu/~lnewman/gph111/topic_units/temperature1/
Topography of Mongolia; Central Asian Internal Drainage Basin from Wikipedia, Source: https://en.wikipedia.org/wiki/Central_Asian_Internal_Drainage_Basin
Tserendash, S., (2000). Some policy related issues for pasture management. Newsletter 6 (38), Mongolian Academy of Sciences.
Tucker, C.J., (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 150, 127–150.
Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D., Pak, E.W., Mahoney, R., Vermote, E., Saleous, N., (2005). An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26, 4485–4498.
Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D., Pak, E.W., Mahoney, R., Vermote, E., Saleous, N., (2005). An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26, 4485–4498.
Tucker, C.J., Townsend, J.R.G., Goff, T.E., (1985). African land cover classification using satellite data. Journal of Science 227, 369–375.
Vermote, E.F., Vermeulen, A., (1999). Algorithm technical background document: Atmospheric correction algorithm: spectral correction reflectances (MOD 09) (Version 4.0).
Wan, Z., (1999). MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD) Version 3.3. Contract Number: NAS5-31370, Institute for Computational Earth System Science University of California, Santa Barbara, CA 93106-3060.
Wan, Z., (2007). Collection-5 MODIS Land Surface Temperature Products Users’ Guide; ICESS, University of California, Santa Barbara, USA.
Wan, Z., (2014). New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment 140, 36–45.
Wan, Z., Dozier, J., (1989). Land-surface temperature measurement from space: physical principles and inverse modeling. IEEE Transactions on Geoscience and Remote Sensing 27 (3), 268–278.
Wan, Z., Dozier, J., (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geosciences and Remote Sensing 34, 892–905.
Wan, Z., Wang, P., Li, X., (2004). Using MODIS land surface temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing 25, 61–72.
Wan, Z., Zhang, Y., Zhang, Q., Li, Z.L., (2002). Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment 83, 163–180.
Wan, Z., Zhang, Y., Zhang, Q., Li, Z.L., (2004). Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing 25, 261–274.
Wang, K., Dickinson, R.E., (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics 50, doi:10.1029/2011RG000373.
Wang, K., Liang, S., (2009). Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sensing of Environment 113, 1556–1565.
Wang, K., Wang, P., Li, Z., Cribb, M., Sparrow, M., (2007). A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. Journal of Geophysical Resource 112, D15107, doi:10.1029/2006JD008351.
Wang, L., Qu, J.J., Zhang, S., Hao, X., Dasgupta, S., (2007). Soil moisture estimation using MODIS and ground measurements in eastern China. International Journal of Remote Sensing 28 (6), 1413–1418.
Wang, Z., Liu, C., Huete, A.R., (2003). From AVHRR-NDVI to MODIS EVI: Advances in vegetation index research. Acta Ecologica Sinica 23, 979–987.
Wardlow, B.D., Egbert, S.L., Kastens, J.H., (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment 108, 290−310.
Wheater, C.P., Cook, P.A., (2003). Using statistics to understand the environment, Routledge, London.
Wilhite, D.A., (1992). Preparing for Drought: A Guidebook for Developing Countries, Climate Unit, United Nations Environment Program, Nairobi, Kenya.
Wilhite, D.A., (2000). Drought: A Global Assessment. London: Routledge Publishers.
Wilhite, D.A., Svoboda, M., Hayes, M., (2007). Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resources Management Journal 21, 763-774.
World Bank, (2002). Mongolia environmental monitor Ulaanbaatar.
World Meteorological Organization (WMO), (1986). Report on Drought and Countries Affected by Drought During 1974–1985, WMO, Geneva, 118 pp.
World Meteorological Organization, (1986). Report on drought and countries affected by drought during 1974–1985 World Climate Data Programme WCP-118 WMO⁄ TD 133.
World Meteorological Organization, (1992). Manual on the Global Data-Processing and Forecasting System. WMO-No. 485, Volume I (Annex IV to the WMO Technical Regulations) Global Aspects, 1992 edition, WMO, Geneva, ISBN 92-63-12485-X.
World Meteorological Organization, (2008). Guide to Meteorological Instruments and Methods of Observation. WMO-No. 8, Seventh edition, WMO, Geneva, ISBN 978-92-63-10008-5.
Wu, H., Hayes, M., Weiss, A., Hu, Q., (2001). An evaluation of the Standardized Precipitation Index: The China-Z Index and the Statistical Z-Score. International Journal of Climatology 21, 745–758.
Wu, H., Hayes, M.J., Weiss, A., Hu, Q., (2001). An evaluation the standardized precipitation index, the china-z index and the statistical z-score, International Journal of Climatology 21, 745–758.
Xiong, X., Isaacman, A., Barnes, W., (2006). MODIS Level-1B Products. In: Earth Science Satellite Remote Sensing Volume 1: Science and Instruments--John J. Qu WG, Menas Kafatos, Robert E. Murphy, Vincent V. Salomonson, ed. (2006) Berlin/Heidelberg: SPRInger/Tsinghua University Press.
Yang, X., Wu, J.J., Yan, F., Zhang, J., (2009). Assessment of regional soil moisture status based on characteristics of surface temperature/vegetation index space. Acta Ecologica Sinica. 29 (3), 1205–1216.
Yao, Y., Liang, S., Qin, Q., Wang, K., (2010). Monitoring drought over the conterminous United States using MODIS and NCEP Reanalysis-2 data. Journal of Applied Meteorology and Climatology 49, 1665–1680.
Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C., Huete, A., (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84, 471–475.

指導教授

劉說安(Yuei-An Liou)

審核日期

2017-7-19

推文