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  • 标题:Drought Monitoring using Drought Indices and GIS Techniques in Kuan Kreng Peat Swamp, Southern Thailand
  • 本地全文:下载
  • 作者:Anan KHAMPEERA ; Chao YONGCHALERMCHAI ; Kuaanan TECHATO
  • 期刊名称:Walailak Journal of Science and Technology (WJST)
  • 印刷版ISSN:2228-835X
  • 出版年度:2017
  • 卷号:15
  • 期号:5
  • 页码:357-370
  • 语种:English
  • 出版社:Institute of Research and Development, Walailak University.
  • 摘要:This research aims to study the spatial characteristics of drought throughout the year in Kuan Kreng Peat Swamp (KKPS) by using various drought indices. Meteorological drought indices were analysed by using data of precipitation during the period of the study 1984 - 2013. The standardized precipitation index (SPI) was calculated on the basis of precipitation deficit. Vegetation-based drought indices were also derived from the analysis of Landsat satellite images based on the normalized difference drought index (NDDI). In addition, hydrological drought indices were studied based on the water table level (WTL) and drought assessments were also based on the standardized water level index (SWI) calculated from data on surface water and the groundwater level in the peat swamp forest. The results are presented in the form of maps of geographic information system (GIS) based on the SPI, NDDI, WTL and SWI. The study focused on the droughts in 2 years: 2010 and 2012. The year 2010 was subject to the El Niño phenomenon while 2012 was not. However, peat fires occurred in both years. The assessment of drought using the SPI, WTL and SWI reveals that drought occurred from April to October due to there being less rainfall during that period. The NDDI reveals that vegetation was affected by the drought between February and September due to this being the summer season with high temperatures and less moisture in the air. The 3 types of drought indices used, meteorological, vegetation and hydrological for the period of April to September indicate the likelihood of peat fires in the KKPS area during that period. The results of this study contribute to the understanding of how spatial and temporal data can be used to predict and measure the severity of drought, to which the study area is vulnerable and to the concomitant risk of peat fires.
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