| 摘要: | 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。
;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. |
