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´ëÇÑÁö¸®ÇÐȸÁö , v.44 n.2(2009-06) |
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ÇÏ¿ÍÀÌ ; ÁöÇ¥¿Âµµ ; °Å¸® ¿ªºñ·Ê °¡ÁßÄ¡¹ý ; °øµ¿Å©¸®±ë ; ³»»ð¹ý ; Hawaiian Islands ; MODIS LST ; IDW ; cokriging ; interpolation |
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ÁöÇü ±¼°îÀÌ ½ÉÇÑ ÇÏ¿ÍÀÌ È»ê¼¶ÀÇ °æ¿ì, ÃøÈÄ¼Ò ºÐÆ÷°¡ ¸Å¿ì Á¦ÇÑÀûÀÌ¾î¼ °ø½ÄÀûÀÎ ±â¿Â ºÐÆ÷µµ°¡ ÀÛ¼ºµÇÁö ¸øÇÏ°í ÀÖ´Â ½ÇÁ¤ÀÌ´Ù. º» ¿¬±¸¿¡¼´Â ÀÌ·¯ÇÑ ±â¿Â ÁöµµÈÀÇ ¹®Á¦Á¡À» ÇØ°áÇÏ´Â ¹æ¹ýÀ¸·Î À§¼º±â¹ÝÀÇ ÁöÇ¥¿Âµµ ÀÚ·á·ÎºÎÅÍ ±â¿ÂÃßÁ¤Ä¡¸¦ ÃßÃâÇÏ¿© ³»»ð¹ý¿¡ ÇÊ¿äÇÑ ÀÔ·ÂÀÚ·á·Î »ç¿ëÇÏ¿´´Ù. ÃßÃâµÈ ¿Âµµ°ªÀ» Ç¥º»°ªÀ¸·ÎÇÏ¿© °Å¸® ¿ªºñ·Ê °¡ÁßÄ¡¹ý (IDW)°ú °øµ¿Å©¸®±ë (cokriging)À» Àû¿ëÇÏ¿© ±â¿ÂÃßÁ¤Ä¡¸¦ ÁöµµÈÇÏ¿´´Ù. ±â¿Â°ú °íµµ°ªÀ» ÇÔ²² ÀÌ¿ëÇÑ cokrigingÀÌ IDW¿¡ ºñÇØ Å©°Ô Çâ»óµÈ ÃßÁ¤ ¿ÀÂ÷°ªÀ» ³ªÅ¸³»¾ú´Ù. CokrigingÀº ÁÖ º¯¼ö¿Í °íµµ¿Í °°Àº Ãß°¡ º¯¼ö °£ÀÇ »ó°ü°ü°è°¡ À¯ÀÇÇÏ°Ô ³ªÅ¸³¯ ¶§ È¿°úÀûÀ¸·Î »ç¿ëµÇ´Â ³»»ð¹ýÀÌÁö¸¸, ³»»ð Á¤È®µµ´Â °èÀýÀûÀÎ ±â»óÁ¶°Ç¿¡ ¹Î°¨ÇÏ°Ô ¿µÇâ¹Þ´Â °ÍÀ¸·Î Á¶»çµÇ¾ú´Ù. °¼ö·®ÀÌ Å©°Ô Áõ°¡ÇÏ´Â ¿ì±â¿¡´Â °Ç±â¿¡ ºñÇØ °ø°£ÀûÀÎ ±â¿Âº¯È°¡ Å©¸ç, ÀÌ¿¡ µû¶ó ±â¿Â ÃßÁ¤ ¿ÀÂ÷°ªµµ ¿ì±â¿¡ ³ô°Ô ³ªÅ¸³µ´Ù. |
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The accurate official map of air temperature does not exist for the Hawaiian Islands due to the limited number of weather stations on the rugged volcanic landscape. To alleviate the major problem of temperature mapping, satellite-measured land surface temperature (LST) data were used as an additional source of sample points. The Moderate Resolution Imaging Spectroradiometer (MODIS) system provides hypertemperal LST data, and LST pixel values that were frequently observed (¡Ã14 days during a 32-day composite period) had a strong, consistent correlation with air temperature. Systematic grid points with a spacing of 5km, 10km, and 20km were generated, and LST-derived air temperature estimates were extracted for each of the grid points and used as input to inverse distance weighted (IDW) and cokriging methods. Combining temperature data and digital elevation model (DEM), cokriging significantly improved interpolation accuracy compared to IDW. Although a cokriging method is useful when a primary variable is cross-correlated with elevation, interpolation accuracy was sensitively influenced by the seasonal variations of weather conditions. Since the spatial variations of local air temperature are more variable in the wet season than in the dry season, prediction errors were larger during the wet season than the dry season. |