Multi-Temporal MODIS Data Analysis for Integrated Drought Severity Index (IDSI) over Mongolia

• Main Authors: Munkhzul Dorjsuren, 孟可竹
• Other Authors: Yuei-An Liou
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/tvh336

博士 === 國立中央大學 === 太空科學研究所 === 105 === 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.