¥ê¥â¥»¥ó¸×¤Î·ê¡ÊRemote Sensing in Tiger's Den¡Ë  Index  Search  Changes  Login

ÍúÎò_2018

ȯɽÍúÎò

1·î2Æü ǯ»ÏµÙ¤ß¡¡
1·î9Æü ǯ»ÏµÙ¤ß¡¡
1·î16Æü 17:30-19:00¡¡ºÙÀîÆࡹ»Þ¡Ê¹ñ´Ä¸¦¡Ë
1·î23Æü Ãϵå´Ñ¬¥ß¥Ã¥·¥ç¥ó¹çƱPI¥ï¡¼¥¯¥·¥ç¥Ã¥×¡Ê1/22~26¡Ë¤Ë¹çή
1·î30Æü 17:30-19:00¡¡Æິ¸¶¸²Ïº¡ÊÃÞÇÈÂç¡Ë
2·î6Æü 17:30-19:00¡¡ÀèºêÍýÇ·¡Ê¹ñ´Ä¸¦¡Ë
2·î13Æü ¤ªµÙ¤ß¡¡
2·î20Æü JapanFlux10¼þǯµ­Ç°½¸²ñ¡Ê2/20~21¡Ë¤Ë¹çή¡¡
2·î27Æü 17:30-19:00¡¡Luis Alberto Vega Isuhuaylas (¿¹ÎÓÁí¸¦)
3·î6Æü 17:30-19:00¡¡Æິ¸¶¸²Ïº¡ÊÃÞÇÈÂç¡Ë
3·î13Æü 17:30-19:00¡¡¿ÀµÜæÆ¿¿¡ÊÃÞÇÈÂç¡Ë
3·î20Æü 17:30-19:00¡¡¾®Àî·ë°á¡ÊÃÞÇÈÂç¡Ë
3·î27Æü ¿¹Îӳزñ(3/26~29)¤Ë¹çή¡¡
4·î3Æü 17:30-19:00¡¡ÌîÅĶÁ¡Ê¹ñ´Ä¸¦¡Ë
4·î10Æü EGU2018(4/8~13)¤Ë¹çή¡¡
4·î17Æü 17:30-19:00¡¡ÊÒÌڿΡÊÃÞÇÈÂç¡Ë
4·î24Æü 17:30-19:00¡¡¾®ÎӷĻҡʹñ´Ä¸¦¡Ë
5·î1Æü GWµÙ¤ß¡¡
5·î8Æü 17:30-19:00¡¡·§Ã«Ä¾´î¡Ê¹ñ´Ä¸¦¡Ë
5·î15Æü ¤ªµÙ¤ß
5·î22Æü JpGU2018(5/20~24)¤Ë¹çή¡¡
5·î29Æü 17:30-19:00¡¡¾¾°æůºÈ¡Ê¿¹ÎÓÁí¸¦¡Ë
6·î5Æü ASTER¥µ¥¤¥¨¥ó¥¹¥Á¡¼¥à¥¢¥×¥ê¥±¡¼¥·¥ç¥óWG¤Ë¹çή
6·î12Æü 17:30-19:00¡¡²¡ÈøÀ²¼ù¡Ê¹ñ´Ä¸¦¡Ë
6·î19Æü 17:30-19:00¡¡ÎÓ¿¿ÃÒ¡ÊJAXA¡Ë
6·î26Æü 17:30-19:00¡¡PHAN Cao Duong (ÃÞÇÈÂç)
7·î3Æü ¤ªµÙ¤ß
7·î10Æü ¤ªµÙ¤ß
7·î17Æü 17:30-19:00¡¡À÷ëͭ¡Ê¹ñ´Ä¸¦¡Ë¡÷2F¥»¥ß¥Ê¡¼¼¼
7·î24Æü 17:30-19:00¡¡µÜÆâãÌé¡Ê¹ñ´Ä¸¦¡Ë¡÷2F¥»¥ß¥Ê¡¼¼¼
7·î31Æü 17:30-19:00¡¡Æ£ÅÄÃι°¡Ê¹ñ´Ä¸¦¡Ë¡÷2F¥»¥ß¥Ê¡¼¼¼
8·î7Æü 17:30-19:00¡¡ÃӾ忿ÌÚɧ¡Ê¹ñ´Ä¸¦¡Ë¡÷2F¥»¥ß¥Ê¡¼¼¼
8·î14Æü ¤ªËߵ٤ߡ¡
8·î21Æü 17:30-19:00¡¡¿¼Ã«È¥°ì¡Ê¹ñ´Ä¸¦¡Ë
8·î28Æü 17:30-19:00 Hoang Trung Ta¡ÊÃÞÇÈÂç¡Ë¡÷2F¥»¥ß¥Ê¡¼¼¼
9·î4Æü ¤ªµÙ¤ß
9·î11Æü 17:30-19:00¡¡¾¾¶¶ºÌ°á»Ò(¹ñ´Ä¸¦)
9·î18Æü ¤ªµÙ¤ß
9·î25Æü 17:30-19:00¡¡µÈÀîŰϯ¡Ê¹ñ´Ä¸¦¡Ë
10·î2Æü 17:30-19:00¡¡Á긶δµ®¡ÊÃÞÇÈÂç¡Ë
10·î9Æü ¤ªµÙ¤ß
10·î16Æü 17:30-19:00¡¡¾®½ÐÂç¡Ê¹ñ´Ä¸¦¡Ë
10·î23Æü 17:30-19:00¡¡Eko Prasetyo (ÃÞÇÈÂç)
10·î30Æü 17:30-19:00¡¡º´µ×´ÖÅìÍۡʹñ´Ä¸¦¡Ë
11·î6Æü 17:30-19:00¡¡Truong Van Thinh (ÃÞÇÈÂç)
11·î13Æü 17:30-19:00¡¡YIN Shuai (¹ñ´Ä¸¦)
11·î20Æü 17:30-19:00¡¡°¤ÉôÇîºÈ¡Ê¹ñ´Ä¸¦¡Ë
11·î27Æü 17:30-19:00¡¡°æ¾å¿¸Ê¿¡Ê¹ñ´Ä¸¦¡Ë
12·î4Æü ¤ªµÙ¤ß
12·î11Æü AGU 2018 fall meeting (12/10-14)¤Ë¹çή¡¡
12·î18Æü 15:30-17:00¡¡Ê¿Åľ½»Ò¡Ê¹ñ´Ä¸¦¡Ë
12·î25Æü ¥¯¥ê¥¹¥Þ¥¹µÙ¤ß¡¡

2018ǯ12·î25Æü

  • ¥¯¥ê¥¹¥Þ¥¹¤Î¤¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ12·î18Æü

ȯɽ¼Ô¡§Ê¿Åľ½»Ò¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¡¼Ò²ñ´Ä¶­¥·¥¹¥Æ¥à¸¦µæ¥»¥ó¥¿¡¼¡¡ÃÏ°è´Ä¶­±Æ¶Áɾ²Á¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§µ¤¸õÊÑÆ°¤Ë¤È¤â¤Ê¤¦Á´µå¥¹¥±¡¼¥ë¤Ç¤Î¿¹ÎÓʬÉÛ°è¤ÎÊѲ½Í½Â¬
  • ³µÍס§µ¤¸õÊÑÆ°¤Ë¤È¤â¤Ê¤¦µ¤²¹¤Î¾å¾º¤ä¹ß¿å¥Ñ¥¿¡¼¥ó¤ÎÊѲ½¤Ï¡¢¿¹ÎÓ¤ÎʬÉۤ䵡ǽ¤ËÊѲ½¤òÍ¿¤¨¤ë¤ÈͽÁÛ¤µ¤ì¤ë¡£¤·¤«¤·¡¢¤½¤Î±Æ¶Á¤ÎÄøÅÙ¤äÊý¸þÀ­¤ÏÃÏ°è¤Ë¤è¤Ã¤ÆÍÍ¡¹¤Ç¤¢¤ë¡£´¥Áç²½¤Ë¤è¤ë¿¹ÎÓ¼ùÌڤθϻàΨ¤ÎÁý²Ã¤¬Êó¹ð¤µ¤ì¡¢¿¹ÎӤοêÂब·üÇ°¤µ¤ì¤Æ¤¤¤ëÃÏ°è¤â¤¢¤ì¤Ð¡¢µ¤²¹¤Î¾å¾º¤Ë¤È¤â¤Ê¤¦¿¹ÎÓ¤ÎʬÉÛ²Äǽ°è¤Î³ÈÂ礬ͽ¬¤µ¤ì¤Æ¤¤¤ëÃÏ°è¤â¤¢¤ë¡£¤³¤Î¤è¤¦¤Ê±Æ¶Á¤ÎÃϰ躹¤Ï¡¢¼ùÌÚ¤ÎÀ®Ä¹¤äÀ¸Â¸¤ÎÀ©¸ÂÍ×°ø¤¬ÃÏ°è¤Ë¤è¤Ã¤Æ°Û¤Ê¤ë¤³¤È¤Ëµ¯°ø¤·¤Æ¤¤¤ë¤È¹Í¤¨¤é¤ì¤ë¡£È¯É½¼Ô¤Ï¡¢µ¤¸õÊÑÆ°¤Ë¤È¤â¤Ê¤¦¿¹ÎÓ¤ÎʬÉۤ䵡ǽ¤ÎÊѲ½¤òÁ´µå¥¹¥±¡¼¥ë¤Çͽ¬¤¹¤ë¤¿¤á¤Ë¡¢¼ùÌÚ¤ÎÀ®Ä¹¤äÀ¸Â¸¤òÀ©¸Â¤¹¤ë´¥Áç¡¢Æü¼ÍÎÌ¡¢Äã²¹¤È¤¤¤Ã¤¿µ¤¸õ¾ò·ï¤òÀ¸Â֥˥åÁ¥â¥Ç¥ë¤ËÁȤ߹þ¤à¤³¤È¤Ç¡¢¸½ºß¤Î¿¹ÎÓʬÉÛ¤òµ¬Äꤷ¤Æ¤¤¤ëµ¤¸õ¾ò·ï¤ÎïçÃͤò¿äÄꤹ¤ë¤È¤È¤â¤Ë¡¢¾­Íè¤Îµ¤¸õ¥·¥Ê¥ê¥ª²¼¤Ç¤Î¿¹ÎÓʬÉÛ°è¤ÎÊѲ½¤Îͽ¬¤ò¹Ô¤Ã¤Æ¤¤¤ë¡£ËÜȯɽ¤Ç¤Ï¡¢¤³¤ì¤é¤Î²òÀÏ·ë²Ì¤ò¤´¾Ò²ð¤¹¤ë¡£

2018ǯ12·î11Æü

  • ¡ÖAGU 2018 fall meeting¡×¤Ë¹çή
  • 2018ǯ12·î10〜14Æü¡Ê²Ð¡Ë¡¢Washington, D.C.

2018ǯ12·î4Æü

  • ¥â¥Ç¥ì¡¼¥¿ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ11·î27Æü

ȯɽ¼Ô¡§°æ¾å¿¸Ê¿¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¡Ãϵå´Ä¶­¸¦µæ¥»¥ó¥¿¡¼¡¡Êª¼Á½Û´Ä¥â¥Ç¥ê¥ó¥°¡¦²òÀϸ¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§±ÒÀ±¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤òÍѤ¤¤¿ÄÅÇÈÈïºÒ¿åÅĤκîÉÕ¤±¾õ¶·ÇÄ°®¼êË¡¤Î¸¡Æ¤
  • ³µÍס§ÅìÆüËÜÂç¿ÌºÒ¤Ë¤è¤ëÄÅÇÈÈïºÒÃϤοåÅĤòÂоݤˤª¤³¤Ê¤Ã¤¿¡¢¡Ø±ÒÀ±¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤òÍѤ¤¤¿ºîÉÕ¤±¾õ¶·ÇÄ°®¼êË¡¤Î¸¡Æ¤¡Ù¤Ë¤Ä¤¤¤Æ¾Ò²ð¤¹¤ë¤È¤È¤â¤Ë¡¢¸½ºß¼è¤êÁȤó¤Ç¤¤¤ë¡¢¡ØÅ쥢¥¸¥¢¤Ë¤ª¤±¤ë¿åÅĥޥåפκîÀ®¡ÙµÚ¤Ó¡Ø¥Ç¥£¡¼¥×¥é¡¼¥Ë¥ó¥°¤äĶ¾®·¿±ÒÀ±¤òÍѤ¤¤¿±ÒÀ±¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤ÎÇÀ¶ÈʬÌî¤Ø¤Î±þÍѡ٤ˤĤ¤¤Æ¡¢¸¡Æ¤¾õ¶·¤òÊó¹ð¤¹¤ë¡£¡Ø±ÒÀ±¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤òÍѤ¤¤¿ºîÉÕ¤±¾õ¶·ÇÄ°®¼êË¡¤Î¸¡Æ¤¡Ù¤Ç¤Ï¡¢Êà¾ì¶è²è¥Ý¥ê¥´¥ó¤È±ÒÀ±²èÁü¡ÊIKONOS¤Î¥Þ¥ë¥Á¥¹¥Ú¥¯¥È¥ë²èÁü¤È¥Ñ¥ó¥¯¥í¥Þ¥Æ¥£¥Ã¥¯²èÁü¡Ë¤òÍѤ¤¤Æ¡¢ÄÅÇÈÈïºÒÃÏ°è¤Î¿åÅĤˤª¤±¤ëºîÉÕ¤±ºîʪ¡Ê¥¤¥Í¡¢¥À¥¤¥º¡¢Èó¹ÌºîÃϡˤÎʬÎà¤òÊà¾ìñ°Ì¤Ç»î¤ß¤¿¡£½¾Íè¤ÎºÇÌàË¡¤Ë¤è¤ëÅÚÃÏÈïʤʬÎà¤Î·ë²Ì¤Ë¡¢Êà¾ì¶è²è¥Ý¥ê¥´¥ó¤ò½Å¤ÍºÇÉÑÃͤò»»½Ð¤¹¤ë¼êË¡¤ËÈæ¤Ù¡¢Êà¾ì¶è²è¥Ý¥ê¥´¥óÆâ¤ÎÅý·×ÃÍ¡Ê¥Þ¥ë¥Á¥¹¥Ú¥¯¥È¥ë¥Ð¥ó¥É¤ÎDNÃͤÎÊ¿¶ÑÃÍ¡¢¥Ñ¥ó¥¯¥í¥Þ¥Æ¥£¥Ã¥¯DNÃͤÎɸ½àÊк¹¡¢NDVI¤Îɸ½àÊк¹¡Ë¤Î¥ì¥¤¥ä¡¼¥»¥Ã¥È¤òÍѤ¤¤ÆºÇÌàË¡¤Ë¤è¤ëʬÎà¤ò¤ª¤³¤Ê¤¦¤³¤È¤Ç¡¢¿åÅĤˤª¤±¤ëºÏÇݺîʪ¤ÎÃê½ÐÀºÅÙ¤¬¸þ¾å¤¹¤ë¤³¤È¤ò³Îǧ¤·¤¿¡£

2018ǯ11·î20Æü

ȯɽ¼Ô¡§°¤ÉôÇîºÈ¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¡À¸Êª¡¦À¸ÂַϴĶ­¸¦µæ¥»¥ó¥¿¡¼¡¡¥»¥ó¥¿¡¼Ä¹¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§¶Ëü¸½¾Ý¤¬±è´ß°è¤Î¼«Á³À¸ÂַϤª¤è¤Óµù¶È¡¦ÍÜ¿£¶È¤ËµÚ¤Ü¤¹±Æ¶Áɾ²Á¡ÝË̳¤Æ»¤Î²´éÚÍÜ¿£¤Ï°ÂÂÙ¤«¡©¡Ý
  • ³µÍס§°Û¾ï¹â²¹¤ä¹ë±«¤È¤¤¤Ã¤¿¶Ëü¸½¾Ý¤ÏÁ´¹ñŪ¤Ë¤ß¤ÆÁý²Ã·¹¸þ¤Ë¤¢¤ê¡¢¾­ÍèŪ¤Ë¤â¤½¤Î·¹¸þ¤Ï³¤¯¤³¤È¤¬Í½Â¬¤µ¤ì¤Æ¤¤¤ë¡£¶Ëü¸½¾Ý¤ÏľÀÜŪ¤Ë²æ¡¹¤ÎÊë¤é¤·¤Ë±Æ¶Á¤¹¤ë¤¬¡¢ÍÍ¡¹¤Ê¥×¥í¥»¥¹¤òÄ̤¸¤Æ¼«Á³À¸ÂַϤäµù¶È¡¦ÍÜ¿£¶È¤Ë¤â±Æ¶Á¤òµÚ¤Ü¤¹¡£ËÜȯɽ¤Ç¤ÏË̳¤Æ»¤Î±è´ß°è¤òÂоݤȤ·¡¢Â籫¤äµ¤²¹¤Î¾å¾º¤¬²´éÚÍÜ¿£¤ä¥¢¥Þ¥â¾ì¤ÎÀ¸»ºÀ­¤Ë¤É¤ÎÄøÅٱƶÁ¤¹¤ë¤«¤ò¿ôÃÍ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤Ã¤Æ¸¡¾Ú¤·¤¿»öÎã¤ò¾Ò²ð¤¹¤ë¡£Ä¾¶á¤Î10ǯ´Ö¤òÂоݤȤ·¤¿Î®Æ°¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Î·ë²Ì¤«¤é¤Ï¡¢²´éÚ¤ÎÚÍ»à¥ê¥¹¥¯¤Ï´ü´ÖÃæ1¡Á2²óµÞ·ã¤Ë¹â¤Þ¤ëÄøÅ٤Ǥ¢¤ë¤³¤È¤¬¼¨¤µ¤ì¤¿¡£¤·¤«¤·¤Ê¤¬¤é¡¢º£¸å¤Îµ¤¸õÊÑÆ°¤ò´Õ¤ß¤ë¤È¡¢Äã±öʬ¿å¤Ë»¯¤µ¤ì¤Ë¤¯¤¤µù¾ì¤ÎÆÃÄê¤ä¿å²¹¸ûÇÛ¤òÍøÍѤ·¤¿Å¬ÀÚ¤ÊÍÜ¿£¹©Äø¤Î¸¡Æ¤¤¬°ìÁؽÅÍפˤʤ뤳¤È¤¬¼¨º¶¤µ¤ì¤¿¡£¤Þ¤¿ºÇ¸å¤Ë¡¢¶áǯȯÀ¸¤·¤¿¶Ëü¸½¾Ý¤¬¹ñΩ¸ø±à¤Î¼«Á³À¸ÂÖ·Ï¡¦µù¶È¡¦ÍÜ¿£¶È¤ËÍ¿¤¨¤¿±Æ¶Á¤Î»öÎã¤ò´Êñ¤Ë¾Ò²ð¤·¡¢¹Ö¤¸ÆÀ¤ëŬ±þºö¤Ë¤Ä¤¤¤Æ¤â¸¡Æ¤¤¹¤ë¡£

2018ǯ11·î13Æü

ȯɽ¼Ô¡§YIN Shuai (National Institude for Environmental Studies, Center for Global Environmental Research, Biogeochemical Cycle Modeling and Analysis Section)

  • Title: Exploring the effects of crop residue burning on haze pollution in China using ground and satellite data
  • Abstract: As the largest developing country, China has experienced severe haze pollution, with fine particulate matter (PM) recently reaching unprecedentedly high levels across many cities. PM2.5, as the most important indicator of haze pollution, has been introduced into the national monitoring network in China since 2012. In this study, we used ground-measured air pollutants and various remote sensing and meteorological datasets to explore the effects of crop residue burning on haze pollution. Meanwhile, the MODIS thermal anomalies and land cover products are applied to extract the crop residue burning spots. The results indicate that the crop residue burning presents strong seasonal pattern and its spatial distribution is closely related to farming activities. In October and November 2015, three severest PM2.5 pollution episodes ever recording occurred in Northeast China, and the maximum concentration of hourly PM2.5 greatly exceeded 1,000 ¦Ìg/m3 in Shenyang on 8th November 2015. The crop residue burning was inferred to have a direct influence on the first and second pollution episodes, especially in Heilongjiang Province. Finally, the transportation of aerosols combining with certain meteorological conditions (e.g. sudden increase of relative humidity, static or no wind weather) contributed greatly to the severe PM2.5 pollution episode in Jilin and Liaoning Province. We also compared air quality indexes and pollutants from remote sensing with ground-measured datasets; the results indicated that there were certain correlations and spatial consistency between the two types of datasets, except for Ultraviolet Aerosol Index (UVAI), which is meaningful to the area without an effective ground monitoring network.

2018ǯ11·î6Æü

ȯɽ¼Ô¡§Truong Van Thinh (Univ. of Tsukuba, Faculty of Life and Environmental Science)

  • Title: Establishing Forest / Non-Forest (FNF) maps for Viet Nam using ScanSAR time series images
  • Abstract: Vietnam has wide forest area (3/4 of total land cover) so that Forest/Non-forest (FNF) map is crucial information for governments and companies. In this study, I propose to use ScanSAR time series images, optical image, SRTM ... to map FNF for Viet Nam. The software which I used to classify FNF is SACLASS. In this presentation, I will focus on my initial result for FNF classification in one test area which is located in southern part of central region of Vietnam.

2018ǯ10·î30Æü

ȯɽ¼Ô¡§º´µ×´Ö¡¡ÅìÍÛ¡ÊÃÞÇÈÂç³ØÂç³Ø±¡¡¡¥·¥¹¥Æ¥à¾ðÊ󹩳ظ¦µæ²Ê¡¡¼Ò²ñ¹©³ØÀ칶¡¤¹ñΩ´Ä¶­¸¦µæ½ê¡¡À¸Êª¡¦À¸ÂַϴĶ­¸¦µæ¥»¥ó¥¿¡¼¡¡¥»¥ó¥¿¡¼Ä¹¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§¿¹ÎӲкҥݥƥ󥷥ã¥ë¿äÄê¤Î¤¿¤á¤ÎMODIS¤Èµ¤¾Ý¥Ç¡¼¥¿¤Ë´ð¤Å¤¯»Øɸ¤ÎÍ­¸úÀ­É¾²Á
  • ³µÍס§Ëܸ¦µæ¤Ç¤Ï¡¤¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¥Ç¡¼¥¿¡ÊRS¥Ç¡¼¥¿¡Ë¤ª¤è¤Óµ¤¾Ý¥Ç¡¼¥¿¤òÁȤ߹ç¤ï¤»¤¿¹âÀ­Ç½¤Ê¿¹ÎӲкҥݥƥ󥷥ã¥ë¥â¥Ç¥ë¡ÊFFPM¡Ë¤Î¹½ÃÛ¤òÌܻؤ·¡¤¼¡¤Î¸¡Æ¤¤ò¹Ô¤Ã¤¿¡£1) RS¥Ç¡¼¥¿¤ª¤è¤Óµ¤¾Ý¥Ç¡¼¥¿¤«¤é»»½Ð¤µ¤ì¤ë´û±ý¤Î¿¹ÎӲкҥݥƥ󥷥ã¥ë»Øɸ¡ÊFFPIRS¡¤FFPIM¡Ë¤ò»»½Ð¤·¡¤2009ǯ¤«¤é2016ǯ¤Î8ǯ´Ö¤ËÆüËܤǼºݤËȯÀ¸¤·¤¿Â絬ÌÏ¿¹ÎӲкҾðÊó¥Ç¡¼¥¿¤òÍѤ¤¤Æ¡¤È¯²Ð»þ¤ÈÈóȯ²Ð»þ¤Ç³ÆFFPI¤ÎÍ­°ÕÀ­¤ò¸¡¾Ú¤·¤¿¡£2) FFPIRS¤Î¤ß¡¤FFPIM¤Î¤ß¤ª¤è¤ÓFFPIRS¤ÈFFPIM¤òÁȤ߹ç¤ï¤»¤¿FFPM¤ÎÀºÅÙ¤òÈæ³Ó¤·¤¿¡£·ë²Ì¤È¤·¤Æ¡¤´û±ý¸¦µæ¤Ë¤è¤Ã¤ÆÄó°Æ¤µ¤ì¤Æ¤¤¤ë¤Û¤È¤ó¤É¤ÎFFPI¤Çȯ²Ð¤ÈÈó²ÐºÒ»þ¤ÎÃÍ´Ö¤ÇÍ­°Õº¹¤¬³Îǧ¤µ¤ì¤¿¤â¤Î¤Î¡¤NDVIÅù¤Î¿¢À¸¤ÎÎФËÃåÌܤ·¤¿»Øɸ¤Ë¤Ä¤¤¤Æ¤ÏÍ­°Õº¹¤¬¤ß¤é¤ì¤Ê¤«¤Ã¤¿¡£FFPIM¤ÎÊý¤¬FFPIRS¤ÈÈæ³Ó¤·¤Æȯ²Ð¤ÈÈó²ÐºÒ»þ¤Îº¹°Û¤òÌÀ³Î¤Ëɽ¸½¤·¤Æ¤¤¤¿¡£·èÄêÌÚ¤òÍѤ¤¤ÆFFPIRS¤ª¤è¤ÓFFPIM¤òÁȤ߹ç¤ï¤»¤ë¤³¤È¤Ë¤è¤ê¹½ÃÛ¤·¤¿FFPM¤Ï¤É¤Á¤é¤«¤Î¥Ç¡¼¥¿¤òñÆȤǹ½ÃÛ¤·¤¿FFPM¤è¤ê¤âÀ­Ç½¤¬¸þ¾å¤¹¤ë¤³¤È¤¬¼¨¤µ¤ì¤¿¡£É¾²Á»Øɸ¤Ë¤è¤ê³ÆFFPM¤ÏÆÃÀ­¤¬°Û¤Ê¤ë¤¿¤á¡¤ÍøÍѼԤÎÌÜŪ¤ä¥Ç¡¼¥¿¼èÆÀÀ©¸Â¤Ë±þ¤¸¤ÆºÇŬ¤ÊFFPM¤òÁªÂò¤¹¤ëɬÍפ¬¤¢¤ë¡£

2018ǯ10·î23Æü

ȯɽ¼Ô¡§Eko Prasetyo (Univ. of Tsukuba, Faculty of Life and Environmental Science, Graduate School of Biosphere Resource Science and Technology)

  • Title: Predicting Teak Plantation Suitability under Climate Change in Java, Indonesia
  • Abstract: Teak (Tectona grandis) is one of the most important tree species for high-grade wood plantations in the tropics. Teak planted in 70 tropical countries. Teak plantation covers 1.2 million hectares in Java, Indonesia. Good seedlings from breeding program have begun to be used in teak plantation, and since 2002 seedlings are from clonal propagation. These clone use for next teak plantation program throughout Java. Site limitations for teak plantation are altitude more than 900 a.s.l. (ideally 0-900 m a.s.l.), annual precipitation under 750 mm and over 4.000 mm (ideally 1,200-3,800 mm) and temperature under 13oC and over 38oC (ideally 22-27oC). Teak plantation is optimum on 3-5 dry months dry season. The forests of tropical Asia are considered to be vulnerable to climate change. Impact of climate change can also occur on teak plantations. In India, 30% of teak habitats are susceptible to climate change under future climatic scenarios. Global means surface temperature has risen by 0.85o C, over 132 years (1880-2012). The increase of global mean surface temperature for 2016-2035 is projected to exceed 1.5o C. Globally, an area that encompassed by monsoon area will increase over the 21th century. The objective of this study is to know the potential impact of future climate change on teak plantation. Growth data of teak throughout Java used for making model. Site index is calculated from growth data by Mitscherlich equation and use it for response variable. Mitscherlich equation used to describe the yield response of a plant to an increase in the main factor (climatic and or edaphic) that is limiting growth. Type of soil and bioclimatic variable such as rainfall and temperature used for predictor variable. Bioclimatic variable were obtained from WorldClim v.14 and type of soil was available from Soil Research Institute, Indonesian Ministry of Agriculture. Modelling the potential suitable area for teak plantation is worth action for teak plantation management.

2018ǯ10·î16Æü

ȯɽ¼Ô¡§¾®½ÐÂç¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¢À¸Êª¡¦À¸ÂַϴĶ­¸¦µæ¥»¥ó¥¿¡¼¡¢¥»¥ó¥¿¡¼Ä¹¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§ÄêÅÀ¥«¥á¥é¤òÍѤ¤¤¿¹ÈÍդδѬ¡§Ìî³°´Ñ¬¤Ë¤ª¤±¤ë´è·ò¤Ç¹â´¶Å٤ʿ§»Øɸ¤È¥â¥Ç¥ê¥ó¥°¼êË¡
  • ³µÍס§ÄêÅÀ¥«¥á¥é¤òÍѤ¤¤¿´Ñ¬¤Ï¤³¤ì¤Þ¤Ç¿¢Êª¤Ë¤ª¤±¤ë¥Õ¥§¥Î¥í¥¸¡¼´Ñ¬¤Ç½ÅÍפÊÌò³ä¤òô¤Ã¤Æ¤­¤¿¤¬¡¢½ÅÍפÊʸ²½Åª¥µ¡¼¥Ó¥¹¤Ç¤¢¤ë¹ÈÍդ˴ؤ·¤Æ¤Ï´Ñ¬¼êË¡¤ÎÈæ³Ó¤¬¹Ô¤ï¤ì¤Æ¤³¤Ê¤«¤Ã¤¿¡£¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¢´è·ò¤Ç¹â´¶Å٤ʹÈÍÕ¤ÎÄêÅÀ¥«¥á¥é´Ñ¬¼êË¡¤ò¸«½Ð¤¹¤Ù¤¯¡¢¹ÈÍդ˴ؤ¹¤ë¿§»Øɸ¤ä¥â¥Ç¥ë¤ÎÈæ³Ó¤ò¹Ô¤Ã¤¿¡£¹ñÆ⣳²Õ½ê¤Î¹â»³ÂÓ¡ÊÂçÀ㻳°°³Ù¡¢¶Ë³ÚÊ¿¡¢Î©»³¼¼Æ²¡Ë¤Ë¤ª¤±¤ëÄêÅÀ¥«¥á¥é¥Ç¡¼¥¿¤òÍѤ¤¤Æ²òÀϤò¹Ô¤Ã¤¿¡£´Ñ¬ǯ¤Ë¤è¤Ã¤Æ¤Ï¡¢¹ÈÍÕ¤¬À¸¤¸¤Ê¤¤Ç¯¤â´Þ¤Þ¤ì¤Æ¤¤¤¿¤¿¤á¡¢¤³¤Î¸¦µæ¤Ç¤Ï¥«¥á¥é²èÁü¤Î²òÀÏÂоÝÎΰè¡ÊAOI¡Ë¤ÎRGBÃͤª¤è¤Ó¤½¤ì¤«¤é»»½Ð¤µ¤ì¤¿¿§»Øɸ¤È¡¢³ÆAOI¤¬¹ÈÍÕ¤·¤Æ¤¤¤ë¤«Èݤ«¤òÌÜ»ë¤ÇȽÃǤ·¤¿¶µ»Õ¥Ç¡¼¥¿¤òÍѤ¤¤Æ¡¢È½ÊÌʬÀϤˤè¤Ã¤Æ¹ÈÍÕ´ü´Ö¤ò²òÀϤ·¤¿¡£6¼ïÎà¤ÎȽÊÌʬÀÏ¡ÊÀþ·ÁȽÊÌ¡¢£²¼¡È½ÊÌ¡¢Â¿¹à¥í¥¸¥¹¥Æ¥£¥Ã¥¯È½ÊÌ¡¢·èÄêÌÚ¡¢¥µ¥Ý¡¼¥È¥Ù¥¯¥È¥ë¥Þ¥·¥ó¡¢¥é¥ó¥À¥à¥Õ¥©¥ì¥¹¥È¡Ë¤È¡¢7¼ïÎà¤Î¿§»Øɸ¡ÊExR, GRVI, Hue, RGR, RGB, Rcc, VARI¡Ë¤ÎÁ´¤Æ¤ÎÁȹ礻¤òcross validation¤Ë¤è¤Ã¤ÆÈæ³Ó¤·¤¿¤È¤³¤í¡¢¹ÈÍÕ»þ´ü¤ÎȽÊÌÀºÅÙ¤¬ºÇ¤â¹â¤«¤Ã¤¿¤Î¤Ï¡¢RGB¤Î£³¤Ä¤ÎÃͤò¤½¤Î¤Þ¤Þ»È¤Ã¤¿£²¼¡È½Ê̤⤷¤¯¤Ï¿¹à¥í¥¸¥¹¥Æ¥£¥Ã¥¯È½Ê̤Ǥ¢¤Ã¤¿¡£¤Þ¤¿¹ÈÍÕºÇÀ¹Æü¤È¤½¤Î¿§ÉÕ¤­¤Î¶¯¤µ¤òÌî³°¤Ç°ÂÄêŪ¤ËÃê½Ð¤Ç¤­¤ë¿§»Øɸ¡¦¥â¥Ç¥ë¤òÌÀ¤é¤«¤Ë¤¹¤ë¤¿¤á¡¢³ÆÄ´ººÃÏ¡¦Ç¯Ëè¤Ë80%¤Î¥é¥ó¥À¥à¥µ¥ó¥×¥ê¥ó¥°¤ò20²ó¹Ô¤¦¤³¤È¤Ë¤è¤Ã¤Æ¡¢°­Å·¸õ¤Ê¤É¤Ë¤è¤ë·ç¬¤ËÂФ¹¤ë´è·ò¤µ¤ò²òÀϤ·¤¿¡£¤½¤Î·ë²Ì¡¢¥¹¥×¥é¥¤¥ó¤Ç²óµ¢¤·¤¿RGR¤Þ¤¿¤ÏVARI¤Î¿§»Øɸ¤Ë¤ª¤¤¤Æ¡¢¹ÈÍÕºÇÀ¹Æü¤ò°ÂÄêŪ¤ËÃê½Ð¤Ç¤­¤ë¤³¤È¤¬ÌÀ¤é¤«¤È¤Ê¤Ã¤¿¡£¤Þ¤¿¿§¤Å¤­¤Î¶¯¤µ¤Ë´Ø¤·¤Æ¤Ï¡¢¥í¥¸¥¹¥Æ¥£¥Ã¥¯²óµ¢¤·¤¿VARI»Øɸ¤Ë¤ª¤¤¤ÆÀÖ¿§¤Î¶¯¤µ¤òºÇ¤â¹â´¶ÅÙ¤«¤Ä°ÂÄêŪ¤ËÃê½Ð¤Ç¤­¤Æ¤¤¤¿¡£¤·¤«¤·²«¿§¤Î¹ÈÍդ˴ؤ·¤Æ¤Ï¡¢¹â´¶Å٤ʻØɸ¡ÊExR¡Ë¤È°ÂÄêŪ¤Ê»Øɸ¡ÊRGR¡Ë¤¬°Û¤Ê¤Ã¤Æ¤ª¤ê¡¢ÌÜŪ¤Ë±þ¤¸¤¿»È¤¤Ê¬¤±¤¬É¬Íפȹͤ¨¤é¤ì¤¿¡£Ëܸ¦µæ¤Î¼êË¡¤Ï¥µ¥¤¥È¤ä¥«¥á¥éµ¡´ï¤â°Û¤Ê¤ë¾õ¶·²¼¤Ç¡¢¿§»Øɸ¤ä¥â¥Ç¥ë¤ÎÊ¿¶ÑŪ¤ÊǽÎϤòɾ²Á¤¹¤ë¤â¤Î¤Ç¤¢¤ê¡¢¹­°èŪ¤Ê¹ÈÍÕ´ü´Ö¡¦¿§¤Å¤­¤Î¶¯¤µ¤Î²òÀϤˤÏɬÍ×ÉԲķç¤Ê»î¤ß¤È¸À¤¨¤ë¡£

2018ǯ10·î9Æü

  • ¥â¥Ç¥ì¡¼¥¿ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ10·î2Æü

ȯɽ¼Ô¡§Á긶δµ®¡ÊÃÞÇÈÂç³Ø¡¢À¸Ì¿´Ä¶­²Ê³Ø¸¦µæ²Ê¡¢¿¹ÎÓÀ¸ÂִĶ­³Ø¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§ÃÝÎÓʬÉۤξ­Íèͽ¬-µ¤¸õÊÑÆ°¡¦¿Í¸ý¸º¾¯¿Ê¹Ô²¼¤ÎĹÌ¤Ë¤ª¤¤¤Æ-
  • ³µÍס§Ëܸ¦µæ¤Ç¤Ï¡¢Ä¹ÌÁ´°è¤ÎÃÝÎÓ(¥â¥¦¥½¥¦¥Á¥¯¡¢¥Þ¥À¥±¡¢¥Ï¥Á¥¯)¤ÎʬÉÛ¤òÃê½Ð¤·¡¢­¡¾­Íè¤Îµ¤¸õÊÑÆ°¤Ë²Ã¤¨¤Æ¿Í¸ý¸º¾¯¤òÈ¿±Ç¤µ¤»¤¿ÃÝÎÓ¤ÎÀøºßÀ¸°é°è¤òͽ¬¤¹¤ë¤È¤È¤â¤Ë¡¢­¢ÃÝÎÓÌÌÀѤȵ¤²¹¡¢¹ß¿åÎÌÅù¤Îµ¤¸õÍ×°ø¤È¤Î´Ø·¸À­¤ò²òÀϤ·¤¿¡£ÃÝÎÓ¤ÎÀøºßÀ¸°é°è¤òͽ¬¤¹¤ë¥â¥Ç¥ë¤Ë¤Ï¡¢Àè¹Ô¸¦µæ¤ÇÃÝÎÓʬÉÛ¤ò·èÄꤹ¤ëÍ×°ø¤È¤·¤Æ»ØŦ¤µ¤ì¤¿Ç¯Ê¿¶Ñµ¤²¹¡¢ÀÑÀãÎ̤˲䨡¢¿Í´Ö³èÆ°¤Î»Øɸ¤È¤·¤Æ¿Í¸ýÌ©ÅÙ¤òÁȤ߹þ¤ó¤À¡£¤½¤Î·ë²Ì¡¢­¡2050ǯ¤Ë¤ÏĹÌ¤ÎÃÝÎÓ¤ÎÀøºßÀ¸°é°è¤¬Ê¿¶Ñ¤ÇÌó30%³ÈÂ礷¡¢µ¤²¹¾å¾ºÎ¨¤È¿Í¸ý¸º¾¯Î¨¤¬¤È¤â¤Ë¹â¤¤ÅÔ»Ô°è¤ÇÃÝÎÓ¤ÎÀøºßÀ¸°é°è¤¬ºÇ¤â³ÈÂ礹¤ë¤Èͽ¬¤µ¤ì¤¿¡£¤Þ¤¿¡¢­¢º£²ó¤Î¸¦µæÂоݰè¤Ç¤Ï¡¢¹ß¿åÎ̤ÈÃÝÎÓÌÌÀѤˤϴط¸À­¤¬¸«½Ð¤»¤Ê¤«¤Ã¤¿¤¬¡¢µ¤²¹¤Î¹â¤¤ÃÏ°è¤Û¤ÉÌÌÀѤÎÂ礭¤¤ÃÝÎÓ¤¬Ê¬ÉÛ¤·¤Æ¤¤¤¿¡£µ¤²¹¤¬¹â¤¤¤Û¤ÉÃϲ¼·Ô¤Î¿­Ä¹À¸Ä¹¤¬Â¥¿Ê¤µ¤ì¡¢¤è¤ê³ÈÂ礷¤ä¤¹¤¤¤È¤¤¤Ã¤¿²ÄǽÀ­¤¬¹Í¤¨¤é¤ì¤ë¡£

2018ǯ9·î25Æü

ȯɽ¼Ô¡§µÈÀîŰϯ¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¢À¸Êª¡¦À¸ÂַϴĶ­¸¦µæ¥»¥ó¥¿¡¼¡¢À¸ÊªÂ¿ÍÍÀ­É¾²ÁŽ¥Í½Â¬¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§²Ì¼Â¿©Ä»¤Î¼ï¿ô¡¦¸ÄÂοô¤Ï¡¢¹­ÍÕ¼ùÎÓ¤ÎʬÃDz½¤Ë¤É¤Î¤è¤¦¤Ë±þÅú¤¹¤ë¤Î¤«¡©
  • ³µÍס§¸½ºß¡¢Á´À¤³¦Åª¤Ë¿¹ÎÓ¤ÎʬÃDz½¡¦ÃÇÊÒ²½¤¬¿Ê¹Ô¤·¤Æ¤¤¤ë¡£Ê¬ÃDz½¤·¤¿¿¹ÎӤǤϿ¹ÎÓÀ­¤Îưʪ¤Î¼ï¿ô¤ä¸ÄÂοô¤¬¸º¾¯¤·¡¢¤Þ¤¿¤½¤ì¤é¤Îưʪ¤¬²Ì¤¿¤·¤Æ¤¤¤ëÀ¸Âַϵ¡Ç½¤â¼Á¤¬Äã²¼¤¹¤ë¤³¤È¤¬·üÇ°¤µ¤ì¤ë¡£²Ì¼Â¿©Ä»Îà¤Ï¡¢¿¢Êª¤Î¼ï»Ò»¶ÉۤȤ¤¤¦½ÅÍפʵ¡Ç½¤òô¤Ã¤Æ¤¤¤ë¥®¥ë¥É¤Ç¤¢¤ê¡¢¤½¤Îµ¡Ç½¤ò°Ý»ý¤¹¤ë¤¿¤á¤Ë¤â¿¹ÎÓʬÃDz½¤ËÂФ¹¤ëÈà¤é¤Î±þÅú¤òÇÄ°®¤¹¤ë¤³¤È¤¬½ÅÍפǤ¢¤ë¡£ËÜ¥»¥ß¥Ê¡¼¤Ç¤Ï¡¢À¸Â©ÃϤÎʬÃDz½¤¬Æ°Êª·²½¸¤ËµÚ¤Ü¤¹±Æ¶Á¤Ë¤Ä¤¤¤Æ´Êñ¤Ë¥ì¥Ó¥å¡¼¤·¤¿¾å¤Ç¡¢Ê¬ÃDz½¤·¤¿Å·Á³ÎӤβ̼¿©Ä»·²½¸¤Ë¤Ä¤¤¤Æ¤Î¸¦µæ»öÎã¤ò¾Ò²ð¤¹¤ë¡£°ñ¾ë¸©ËÌÉô¤Î¹­ÍÕ¼ùÎӥѥåÁ¤Ë¤ª¤±¤ë²Ì¼Â¿©Ä»Îà·²½¸¤ò£³Ç¯´ÖÄ´ºº¤·¡¢¤½¤Î¼ï¿ô¤È¸ÄÂοô¤Î·èÄêÍ×°ø¤òõ¤Ã¤¿¡£¤½¤Î·ë²Ì¡¢²Ì¼Â¿©Ä»¤Ë¤È¤Ã¤Æ¤Ï·Ê´Ñ¥¹¥±¡¼¥ë¤ÎÍ×°ø¤È¶É½ê¥¹¥±¡¼¥ë¤ÎÍ×°ø¤ÎÁÐÊý¤¬½ÅÍפǤ¢¤ë¤³¤È¡¢¤½¤ì¤é¤ÎÁêÂÐŪ¤Ê½ÅÍ×ÅÙ¤¬µ¨Àá¤Ë¤è¤Ã¤ÆÊѲ½¤¹¤ë¤³¤È¡¢ÆäËÈóÈË¿£´ü¤Ï¶É½ê¥¹¥±¡¼¥ë¤Î²Ì¼Â»ñ¸»Î̤¬½ÅÍפǤ¢¤ë¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡£¤³¤ì¤é¤Î·ë²Ì¤«¤é¡¢¿¹ÎÓ´ÉÍý¤Î¤¢¤êÊý¤È¼ï»Ò»¶ÉÛµ¡Ç½¤ÎÊÝÁ´¤Ë¤Ä¤¤¤ÆµÄÏÀ¤¹¤ë¡£

2018ǯ9·î18Æü

  • ¥â¥Ç¥ì¡¼¥¿ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ9·î11Æü

ȯɽ¼Ô¡§¾¾¶¶ºÌ°á»Ò¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¢¼Ò²ñ´Ä¶­¥·¥¹¥Æ¥à¸¦µæ¥»¥ó¥¿¡¼¡¢ÃÏ°è´Ä¶­±Æ¶Áɾ²Á¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§¿å¤ò¤Ä¤«¤Ã¤ÆʬÉÛÄ´ºº¡§´Ä¶­DNAµ»½Ñ¤ò¿¢Êª¤ËŬÍѤ¹¤ë
  • ³µÍס§¿åÊմĶ­¤Ë¤ª¤±¤ëµÞ®¤ÊÀ¸ÂÖ·ÏÊѲ½¤Ï¡¢À¤³¦Åª¤Ë¿¼¹ï¤ÊÌäÂê¤È¤Ê¤Ã¤Æ¤¤¤ë¡£¤³¤¦¤·¤¿ÊѲ½¤ËÂбþ¤·¤Æ¤¤¤¯¤¿¤á¤Ë¤Ï¡¢À¸Â©À¸Êª¤ÎʬÉÛ¤ò¤¤¤«¤Ë¿×®¤ËÇÄ°®¤¹¤ë¤«¤¬½ÅÍפȤʤ롣¿åÀ¸À¸Êª¤ÎʬÉÛÄ´ºº¤òÈôÌöŪ¤Ë¸úΨ²½¤¹¤ë¼êÃʤȤ·¤Æ¡¢¶áǯÃíÌܤò½¸¤á¤Æ¤¤¤ë¤Î¤¬´Ä¶­DNAµ»½Ñ¤Ç¤¢¤ë¡£¿å¤«¤éDNA¤òÃê½Ð¤¹¤ë¤³¤È¤Ç¡¢¤½¤³¤ËÀ¸Â©¤¹¤ëÀ¸Êª¤ò¿×®³î¤Ä´ÊÊؤ˿äÄꤹ¤ë¤³¤Î¼êË¡¤Ï¡¢ÍÍ¡¹¤ÊÀ¸Êª·²¤ËŬÍѤµ¤ì¡¢µÞ®¤ÊȯŸ¤ò¿ë¤²¤Æ¤¤¤ë¡£°ìÊý¤Ç¡¢¿¢Êª¤Ë¤ª¤±¤ëÃ諤äŬÍÑÎã¤Ï̤¤À˳¤·¤¤¡£ËÜȯɽ¤Ç¤Ï¡¢´Ä¶­DNAµ»½Ñ¤òÄÀ¿å¿¢Êª¤ÎʬÉÛ¿äÄê¤ËŬÍѤ·¤¿¸¦µæ¤Ë¤Ä¤¤¤Æ¾Ò²ð¤¹¤ë¡£ÌܤǤϳÎǧ¤·¤Å¤é¤¤ÄÀ¿å¿¢Êª¤ÎÄ´ºº¤¬¡¢´Ä¶­DNAµ»½Ñ¤ÎƳÆþ¤Ë¤è¤Ã¤Æ¤É¤Î¤è¤¦¤ËÊѤï¤Ã¤Æ¤¤¤¯¤Î¤«¡£µ»½Ñ³«È¯¤ÎÆ»¤Î¤ê¤È¡¢¤½¤ì¤Ë¤è¤Ã¤ÆÆÀ¤é¤ì¤¿À®²Ì¤Ë¤Ä¤¤¤Æ¤ªÏ乤롣

2018ǯ9·î4Æü

  • ¥â¥Ç¥ì¡¼¥¿ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ8·î28Æü

ȯɽ¼Ô¡§Hoang Trung Ta¡ÊÃÞÇÈÂç³Ø¡¢Î®°è´ÉÍý¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§Land Cover Change Mapping in Southern Vietnam Using Multi-temporal High-resolution Satellite Remote Sensing Data
  • ³µÍס§Land cover maps have been increasingly used in many global or regional environmental studies as an important information source. To meet the demand, a number of land cover products have been published, but the coarse spatial resolution is considered as a challenge of the current large area land cover maps. Thus, the purpose of this study is to create 10 meter spatial resolution land cover map of Southern Vietnam in 2007 and 2017, respectively. The maps then are analyzed to find out the changes of land cover between these two years. In this study, Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Sentinel-2, AVNIR-2, ALOS PALSAR and PALSAR 2 mosaic, ALOS Global Digital Surface Model were employed to produce land cover map of the region by using Kernel Density Estimation classifier. Other ancillary data sources such as Open Street Map, Vietnam Geographical database were used for information supplement. In order to making and validating the maps, 60,000 reference data points were created based on the field GPS photos as well as visual interpretation on Google Earth images.The overall accuracy of the maps is 82% and 84% in 2007 and 2017 respectively. The maps reveal the expansion trend of orchard and urban area between the two periods whereas the decreasing in barren area is shown. The result also demonstrates the potentiality of using multi-temporal, multi-sensor satellite data in making the land cover map in the large area.

2018ǯ8·î21Æü

ȯɽ¼Ô¡§¿¼Ã«È¥°ì¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¢À¸Êª¡¦À¸ÂַϴĶ­¸¦µæ¥»¥ó¥¿¡¼¡¢À¸ÊªÂ¿ÍÍÀ­É¾²ÁŽ¥Í½Â¬¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§¹­°è¤Î¼ï¸ÄÂοôʬÉÛ¤ÈÀ¸ÊªÂ¿ÍÍÀ­¤Î¿Ê²½Åª´ðÈ×
  • ³µÍס§¼ï¸ÄÂοôʬÉÛ¡ÊSAD¡Ë¤ÏÀ¸ÊªÂ¿ÍÍÀ­¤Î´ðËÜŪ¤ÊÆÃÀ­¤Ç¤¢¤ê¡¢¤³¤ì¤òÍý²ò¤¹¤ë¤³¤È¤ÏÀ¸ÂֳؤνÅÍפÊÌäÂê¤Ç¤¢¤ë¡£¤·¤«¤·¤Ê¤¬¤é¡¢SAD¤òÄ´¤Ù¤ë¤¿¤á¤Ë¤Ï¿¤¯¤Î¼ï¤Ë¤Ä¤¤¤Æ¸ÄÂΤò¿ô¤¨¾å¤²¤ëɬÍפ¬¤¢¤ê¡¢°ìÈÌŪ¤ËÂ礭¤ÊÏ«ÎϤòÍפ¹¤ë¡£¤½¤Î¤¿¤á¡¢¸½ºßÍÍ¡¹¤ÊÀ¸ÂַϤÇÆÀ¤é¤ì¤Æ¤¤¤ëSAD¤Ï¶É½êŪ¤Ê¤â¤Î¤Ë¸Â¤é¤ì¡¢¹­°è¤ÎSAD¤Ë¤Ä¤¤¤Æ¤ÏÍý²ò¤¬¤Û¤È¤ó¤É¿Ê¤ó¤Ç¤¤¤Ê¤¤¡£Ëܸ¦µæ¤Ç¤Ï¡¢·×²èŪĴºº¤«¤éÆÀ¤é¤ì¤ë¼ï¤ÎÈ¿Éü½Ð¸½¥Ç¡¼¥¿¤È¡¢µ¡²ñŪĴºº¤äÀìÌç²È¤ÎººÄê¤Ë´ð¤Å¤¯¼ï¤ÎʬÉÛ¾ðÊó¤òÅý¹ç¤·¤Æ¡¢¹­°è¤Ç¿¿ô¼ï¤Î¸ÄÂοô¤ò¿äÄꤹ¤ë¿·¤·¤¤Åý·×¥â¥Ç¥ë¤òÄó°Æ¤¹¤ë¡£¤³¤Î¥â¥Ç¥ë¤òÆüËÜÎóÅçÁ´°è¤Ç¼ý½¸¤µ¤ì¤¿Â絬ÌϤÊÌÚËÜ·²½¸¥Ç¡¼¥¿¤ËŬÍѤ·¡¢1248¼ï¤«¤é¤Ê¤ëSAD¤ò10km»ÍÊý¤Î²òÁüÅ٤ǿäÄꤷ¤¿¡£¹­°è¤ÎSAD¤¬ÆÀ¤é¤ì¤¿¤³¤È¤Ç¡¢À¸ÂֳؤνÅÍפʳµÇ°¤Ç¤¢¤ê¤Ê¤¬¤é¤½¤Î¼ÂÂΤϪ¤¨¤É¤³¤í¤Î¤Ê¤¤¡¢¥á¥¿·²½¸¤Î¹½Â¤¤¬ÌÀ¤é¤«¤È¤Ê¤ë¡£¿äÄꤵ¤ì¤¿¥á¥¿·²½¸SAD¤Ë´ð¤Å¤¤¤Æ¡¢4¤Ä¤ÎÀ¸ÊªÃÏÍý¶è¤Ç»ÙÇÛŪ¤Ê¼ïʬ²½Íͼ°¤È¼ïʬ²½Î¨¤ÎÃÏ°è´ÖÊÑ°Û¤ò¿ä¬¤·¤¿¡£

2018ǯ8·î14Æü

  • ¤ªËßµÙ¤ß

2018ǯ8·î7Æü

ȯɽ¼Ô¡§ÃӾ忿ÌÚɧ¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¢À¸Êª¡¦À¸ÂַϴĶ­¸¦µæ¥»¥ó¥¿¡¼¡¢À¸Â֥ꥹ¥¯É¾²ÁŽ¥Âкö¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§É¸ËܾðÊ󤫤éÀ¸Êªµ¨Àá¡Ê¥Õ¥§¥Î¥í¥¸¡¼¡Ë¤ò¿äÄꤹ¤ë
  • ³µÍס§À¸Êª¤Ï¡¢µ¤²¹¤äÆü¾È¤¢¤ë¤¤¤Ï¹ß¿å¤Ê¤Éµ¨Àá¤Ë¤è¤Ã¤ÆÊѲ½¤¹¤ëÍ×°ø¤Ë±þ¤¸¤Æ¤½¤Î¹ÔÆ°¤òÊѲ½¤µ¤»¤ë¡£¤³¤Î¤è¤¦¤Êµ¨Àá¤Ë¹ç¤ï¤»¤¿À¸Êª¤Î¹ÔÆ°¤ÏÀ¸Êªµ¨Àá¡Ê¥Õ¥§¥Î¥í¥¸¡¼¡Ë¤È¸Æ¤Ð¤ì¡¢À¸Êª¤Î´Ñ»¡¤«¤éµ¨Àá¤Î°Ü¤í¤¤¤òÇÄ°®¤Ç¤­¤ë»ö¤«¤éÇÀºî¶È¤Ê¤É¤ËÌòΩ¤Ä¤¿¤áÀ¤³¦³ÆÃϤǵ­Ï¿¤¬¹Ô¤ï¤ì¤Æ¤­¤¿¡£¶áǯµ¤¸õÊÑÆ°¤Î±Æ¶Á¤È¤·¤Æ½¾Íè¤è¤êÁᤤµ¨Àá¤Î³«²Ö¤ä¹ÈÍÕ¤ÎÃٱ䡢º«Ãî¤ÎȯÀ¸¤ÎĹ´ü²½¤äÄ»¤ÎÅϤê¹ÔÆ°¤ÎÊѲ½¤Ê¤É¤¬´Ñ»¡¤µ¤ì¤Æ¤¤¤ë¤¬¡¢¤³¤ì¤Ï²áµî¤ËÃßÀѤµ¤ì¤¿¥Ç¡¼¥¿¤È¤ÎÈæ³Ó¤ÇÌÀ¤é¤«¤Ë¤Ê¤ë»ö¤Ç¤¢¤ë¡£¤·¤«¤·¡¢°ìÊý¤ÇÀ¸Êªµ¨Àá¤Î´Ñ»¡¤Ïµ¨ÀáÊѲ½¤Ø¤Î´¶ÅÙ¤¬¹â¤¤¼ï¤¬¼ç¤ÊÂоݤȤʤäƤ¤¤ë²ÄǽÀ­¤¬¹â¤¯¡¢À¸Êªµ¨Àá¤ÎÊѲ½¤¬¤É¤ÎÄøÅÙ°ìÈÌÀ­¤¬¤¢¤ë¤Î¤«¤ÏÉÔÌÀ¤Ç¤¢¤ë¡£¤·¤«¤·¡¢µ¤¾ÝÊѲ½¤ËÂбþ¤·¤Æ¤¤¤Ê¤¤¼ï¤³¤½µ¤¸õÊÑÆ°¤ËÀȼå¤Ê¼ï¤Ç¤¢¤ë²ÄǽÀ­¤¬¤¢¤ê¡¢¤½¤Î¤¿¤á½¾Íè´Ñ»¡¤µ¤ì¤Æ¤­¤¿À¸Êª¼ï¤ò´ð¤Ëµ¤¸õÊÑÆ°¤Î±Æ¶Á¤òɾ²Á¤¹¤ë»ö¤Ï²á¾®É¾²Á¤Ë·Ò¤¬¤ë²ÄǽÀ­¤¬¤¢¤ë¡£¤½¤³¤Ç¶áǯÇîʪ´Û¤Ê¤É¤Ë¼ý¢¤µ¤ì¤Æ¤¤¤ëɸËܤò¤«¤é²áµî¤ÎÀ¸Êªµ¨Àá¤òÆɤ߼è¤ë¸¦µæ¤¬¿Ê¤ß¤Ä¤Ä¤¢¤ë¡£º«Ãî¤Ç¤¢¤ì¤Ð¡¢È¯À¸¤·¤Æ¤¤¤ë»þ´ü¤ËºÎ½¸¤µ¤ì¤ë¤Ï¤º¤Ç¤¢¤ë¤·¡¢¿¢ÊªÉ¸Ëܤϲ֤¬ºé¤¤¤Æ¤¤¤ë¸ÄÂΤòºÎ½¸¤¹¤ë»ö¤¬´ðËܤǤ¢¤ë¡£°ì¤Ä¤ÎɸËܤϵ¨Àá¤Î°ìÉô¤Ç¤·¤«¤Ê¤¤¤¬¡¢¿ô¿¤¯¤ÎɸËܤκν¸Æü¤òÄ´¤Ù¤ë»ö¤Ç¡¢¿¢Êª¤Î²Ö´ü¤Î¿äÄê¤äº«Ãî¤ÎȯÀ¸»þ´üÅù¤òÇÄ°®¤¹¤ë»ö¤¬½ÐÍè¤ë¤Ï¤º¤Ç¤¢¤ë¡£ËÜȯɽ¤Ï¥¢¥á¥ê¥«¡¦¥«¥ê¥Õ¥©¥ë¥Ë¥¢½£¤Ë¤ª¤¤¤Æ¡¢É¸ËܾðÊ󤫤é¿¿ô¤Î¼ï¤Î²Ö´ü¤ò¿äÄꤷ¤¿¸¦µæ¤ò¾Ò²ð¤¹¤ë¡£Æä˿¢Êª¤ÎÀ¸³è·¿¤ËÃåÌܤ·¡¢¼ùÌÚ¡¦ÄãÌÚ¡¦ÁðËܤʤɤDzִü¤¬¤É¤Î¤è¤¦¤Ë°Û¤Ê¤ë¤«¤ò¤Þ¤º¿Þ´Õ¤Î¾ðÊó¤ò´ð¤Ë²òÀϤ·¡¢É¸Ëܤ«¤é¿äÄꤵ¤ì¤ë²Ö´ü¤ÎÂÅÅöÀ­¤ò¸¡¾Ú¤·¡¢É¸Ëܤκν¸ÃÏÅÀ¤Ë¤ª¤±¤ë³Æǯ¤Îµ¤¸õ¤«¤é¿¢Êª¤Î²Ö´ü¤Ë±Æ¶Á¤òÍ¿¤¨¤ëµ¤¾ÝÍ×°ø¤ò¿äÄꤷ¡¢À¸Êªµ¨Àá¤ËÂФ¹¤ëµ¤¸õÊÑÆ°¤Î±Æ¶Á¤òɾ²Á¤·¤¿¤¤¡£

2018ǯ7·î31Æü

ȯɽ¼Ô¡§Æ£ÅÄÃι°¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡¢À¸Êª¡¦À¸ÂַϴĶ­¸¦µæ¥»¥ó¥¿¡¼¡¢À¸ÊªÂ¿ÍÍÀ­É¾²ÁŽ¥Í½Â¬¸¦µæ¼¼¡Ë

  • ¥¿¥¤¥È¥ë¡§¿Í¸ý¤ª¤è¤Óµ¤¸õÊÑÆ°¤ò¹Íθ¤·¤¿¾­ÍèÅÚÃÏÍøÍÑ¥·¥Ê¥ê¥ª¤Î¹½ÃÛ
  • ³µÍס§ÅÚÃÏÍøÍѤÎÊѲ½¤ÏÀ¸Êª¤Î¥Ï¥Ó¥¿¥Ã¥È¤ò²þÊѤ·¡¢À¸ÊªÂ¿ÍÍÀ­¤Î¶¼°Ò¤È¤Ê¤ê¤¦¤ë¡£º£¸å¤ª¤³¤ê¤¦¤ëÅÚÃÏÍøÍѤòͽ¬¤¹¤ë¤³¤È¤ÏÀ¸ÊªÂ¿ÍÍÀ­ÊÝÁ´¤Ë¤È¤ê¡¢µ®½Å¤Ê¾ðÊó¤òÄ󶡤¹¤ë¤³¤È¤È¤Ê¤ë¡£Àè¹Ô¸¦µæ¤Ç¡¢¿Í¸ý¤äµ¤¸õÊÑÆ°¤¬ÅÚÃÏÍøÍÑÊѲ½¤ËÍ¿¤¨¤ë±Æ¶Á¤ò¸¡Æ¤¤·¤¿Îã¤Ï¾¯¤Ê¤¤¡£¤½¤³¤ÇËܸ¦µæ¤Ç¤ÏÆüËÜÁ´ÅÚ¤òÂоݤ˿͸ý¤ª¤è¤Óµ¤¸õÊÑÆ°¤ò²ÃÌ£¤·¡¢2100ǯ¤Þ¤Ç¤ÎÅÚÃÏÍøÍÑÊѲ½¥·¥Ê¥ê¥ª¤ò¹½ÃÛ¤·¤¿¡£Ëܸ¦µæ¤Ç¤Ïµ¡³£³Ø½¬¤Î°ì¼ï¤Ç¤¢¤ë¥é¥ó¥À¥à¥Õ¥©¥ì¥¹¥È¤òÍѤ¤¡¢ÅÚÃÏÍøÍÑ¥·¥Ê¥ê¥ª¤ò¹½ÃÛ¤·¤¿¡£Ê¬ÀϤǤϤޤº¡¢1985ǯ¤«¤é2005ǯ¤Î¼ÂºÝ¤ÎÅÚÃÏÍøÍÑÊѲ½¡Ê¿åÅÄ¡¦¤½¤Î¾ÇÀÃÏ¡¦¿¹ÎÓ¡¦¹ÓÃÏ¡¦·úʪÍÑÃÏ¡¦¤½¤Î¾¿Í¹©ÅªÅÚÃÏÍøÍѡˤȿ͸ý¡¦µ¤¸õÃÍ¡ÊÃȤ«¤µ¤Î»Ø¿ô¡¢ºÇ´¨·îºÇÄ㵤²¹¡¢²Æµ¨¹ß¿åÎÌ¡¢Åßµ¨¹ß¿åÎ̡˵ڤÓÃÏ·ÁÍ×°øÅù¤Î´Ø·¸¤òʬÀϤ·¡¢³Ø½¬¥â¥Ç¥ë¤ò¹½ÃÛ¤·¤¿¡£¤³¤³¤ÇÆÀ¤é¤ì¤¿¥â¥Ç¥ë¤È¿Í¸ý¤ª¤è¤Óµ¤¸õͽ¬ÃͤòÍѤ¤¡¢ÅÚÃÏÍøÍѤÎͽ¬¤ò¹Ô¤Ã¤¿¡£¿Í¸ýͽ¬ÃͤˤĤ¤¤Æ¤Ï½ÐÀ¸¿ôÈæ³Ó¡Ê½ÐÀ¸¹â°Ì¡¦Ãæ°Ì¡¦Äã°Ì¡Ë¤ÈʬÉۥѥ¿¡¼¥óÈæ³Ó¡Ê¶Ñ¼Á¡¦½¸Ãæ¡Ë¤ÎÁ´£µ¥·¥Ê¥ê¥ª¤òÍѤ¤¤¿¡£µ¤¸õͽ¬ÃͤˤĤ¤¤Æ¤ÏRCP2.6, 4.5, 8.5¤ÎÁ´£³¥·¥Ê¥ê¥ª¤òÍѤ¤¡¢Ê¬ÀϤ·¤¿¡£ÅÚÃÏÍøÍѤμ¬ÃÍ¡Ê1985ǯ¤«¤é2005ǯ¡Ë¤ÈƱ»þ´ü¤Îͽ¬ÃͤȤδ֤ˤϹ⤤Áê´Ø¤¬¤ß¤é¤ì¡¢¹½ÃÛ¤·¤¿¥â¥Ç¥ë¤Ï¹â¤¤ÀºÅÙ¤ÇÅÚÃÏÍøÍѤòͽ¬¤Ç¤­¤Æ¤¤¤ë¤³¤È¤¬ÌÀ¤é¤«¤Ë¤Ê¤Ã¤¿¡£2100ǯ¤Þ¤ÇÅÚÃÏÍøÍѤòͽ¬¤·¤¿¤È¤³¤í¡¢2005ǯ¤ËÈæ³Ó¤·¡¢¿åÅĤˤĤ¤¤Æ¤ÏÁ´¥·¥Ê¥ê¥ª¤Ç¸º¾¯¤¹¤ë¤Èͽ¬¤µ¤ì¤¿¡£¤Þ¤¿¡¢µ¤¸õ¥·¥Ê¥ê¥ª´Ö¤Ç¤Ïͽ¬ÃͤËÂ礭¤Ê¤Á¤¬¤¤¤Ï¤ß¤é¤ì¤Ê¤«¤Ã¤¿¤¬¡¢¿Í¸ý¥·¥Ê¥ê¥ª¤Ç¤Ï¤Á¤¬¤¤¤¬¤ß¤é¤ì¤¿¡£¤³¤ì¤ËÂФ·¡¢¤½¤Î¾ÇÀÃϤˤĤ¤¤Æ¤Ï¿Í¸ý¥·¥Ê¥ê¥ª¤è¤ê¡¢µ¤¸õ¥·¥Ê¥ê¥ª¤Çͽ¬ÃͤËÂ礭¤Ê°ã¤¤¤ß¤é¤ì¤¿¡£RCP2.6¤Ç¤Ï¤½¤Î¾ÇÀÃϤÏ2005ǯ¤ËÈæ³Ó¤·¡¢¸º¾¯¤¹¤ë¤Èͽ¬¤µ¤ì¤¿¤¬¡¢RCP8.5¤Ç¤ÏÌó10%¤ÎÁý²Ã¤¬Í½Â¬¤µ¤ì¤¿¡£°Ê¾å¤Î·ë²Ì¤ÏÅÚÃÏÍøÍѤˤª¤±¤ëÊѲ½¤Î¥É¥é¥¤¥Ð¡¼¤È¤·¤Æ¡¢¿Í¸ý¤ª¤è¤Óµ¤¸õÃͤνÅÍ×À­¤ò¼¨¤¹¤â¤Î¤¢¤ë¡£

2018ǯ7·î24Æü

ȯɽ¼Ô¡§µÜÆâãÌé¡Ê¹ñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§GOSAT¤Ë¤è¤Ã¤Æ´Ñ¬¤µ¤ì¤¿ÂÀÍÛ¸÷Î嵯¥¯¥í¥í¥Õ¥£¥ë·Ö¸÷¡ÊSIF¡Ë¤ÎGPP¿äÄê¤Ø¤Î´óÍ¿¤ÈΦ°èÀ¸ÂÖ·Ï¥â¥Ç¥ë¤Ø¤ÎŬÍÑ
  • ³µÍס§²¹¼¼¸ú²Ì¥¬¥¹´Ñ¬±ÒÀ±GOSAT¤Î´Ñ¬¤·¤¿¥¹¥Ú¥¯¥È¥ë¥Ç¡¼¥¿¤«¤éÂÀÍÛ¸÷Î嵯¥¯¥í¥í¥Õ¥£¥ë·Ö¸÷¡ÊSolar-induced chlorophyll fluorescence: SIF¡Ë¤¬»»½Ð²Äǽ¤Ç¤¢¤ë¤³¤È¤¬Êó¹ð¤µ¤ì¤Æ°ÊÍè¡¢SIF¤ÈÁí°ì¼¡À¸»º¡ÊGPP¡Ë¤È¤Î¶¯¤¤Áê´Ø´Ø·¸¤Ë´ð¤Å¤¤¤¿ÃºÁÇ¥Õ¥é¥Ã¥¯¥¹¿äÄê¤äΦ°èÀ¸ÂÖ·Ï¥â¥Ç¥ë¤Îͽ¬ÀºÅÙ¸þ¾å¤Ë´Ø¤¹¤ë¸¦µæ¤¬À¹¤ó¤Ë¹Ô¤ï¤ì¤Æ¤¤¤ë¡£2018ǯ¤ËÂǤÁ¾å¤²Í½Äê¤ÎGOSAT-2¤Ç¤ÏSIF¥Ç¡¼¥¿¤òÍѤ¤¤ÆΦ°èÀ¸ÂÖ·Ï¥â¥Ç¥ëVISIT¤Îͽ¬ÀºÅÙ¤ò¹â¤á¡¢L4¥×¥í¥À¥¯¥È¡ÊCO2Ç»ÅÙ¡¢¥Õ¥é¥Ã¥¯¥¹¤Ê¤É¡Ë¤Î¿®ÍêÀ­¤ò¸þ¾å¤µ¤»¤ë¤³¤Èµá¤á¤é¤ì¤Æ¤¤¤ë¡£¤½¤Î¤¿¤á¤Î½àÈ÷¤È¤·¤ÆGOSAT¤ÎSIF¤È¥Õ¥é¥Ã¥¯¥¹¥¿¥ï¡¼´Ñ¬¤Ë¤è¤ëGPP¤È¤ÎÈæ³Ó¤äÅý·×²òÀϤˤè¤Ã¤ÆSIF¤¬GPP¿äÄê¤Ë¤É¤ì¤À¤±´óÍ¿¤·¤¦¤ë¤«¤òÄ´¤Ù¤¿¡£ËÜ¥»¥ß¥Ê¡¼¤Ç¤ÏJpGU2018¤Çȯɽ¤·¤¿ÆâÍƤȸ½ºß¿Ê¤á¤Æ¤¤¤ëVISIT¤È·²ÍîÊü¼ÍÅÁã¥â¥Ç¥ë¤òÁȤ߹ç¤ï¤»¤¿SIF·×»»¤Ë¤Ä¤¤¤Æ¾Ò²ð¤¹¤ë¡£

2018ǯ7·î17Æü

ȯɽ¼Ô¡§À÷ëͭ¡Ê¹ñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§±ÒÀ±ÅëºÜÇ®ÀÖ³°¥¹¥Ú¥¯¥È¥ë¥µ¥¦¥ó¥À¡¼¤Ë¤è¤ëÂ絤Ã楢¥ó¥â¥Ë¥¢¤ÎÁ´µå´Ñ¬
  • ³µÍס§¶áǯ¤Î±ÒÀ±ÅëºÜ¥»¥ó¥µ¡¼¤Î¹âÇÈ¿ôʬ²òǽ²½¤Ë¤è¤Ã¤Æ¡¢²¹¼¼¸ú²Ì¥¬¥¹¤ò»Ï¤á¤È¤¹¤ëÂ絤ÃæÈùÎ̵¤ÂΤδѬ¤¬¿Í¹©±ÒÀ±¤òÍѤ¤¤Æ¹Ô¤¨¤ë¤è¤¦¤Ë¤Ê¤Ã¤¿¡£10¦ÌmÉÕ¶á¤Î¿å¾øµ¤¤äÆó»À²½ÃºÁǤʤɤαƶÁ¤Î¾¯¤Ê¤¤ÇÈĹÂӤˤϤ¤¤¯¤Ä¤«¤ÎÂ絤ÈùÎÌÀ®Ê¬¤ÎµÛ¼ýÂΤ¬¤¢¤ê¡¢¥¢¥ó¥â¥Ë¥¢¤Î¤â¤Î¤â¤½¤Î¤¦¤Á¤Î°ì¤Ä¤Ç¤¢¤ë¡£ËÜȯɽ¤Ç¤Ï¡¢GOSAT¤Ë¤è¤ëÌó5ǯʬ¤ÎÁ´µå´Ñ¬¥Ç¡¼¥¿¤«¤éƳ½Ð¤·¤¿Â絤Ã楢¥ó¥â¥Ë¥¢¤Îµ¤ÃìÀÑ»»Î̤λþ¶õ´ÖʬÉۤȡ¢Â¾¤Î¥µ¥¦¥ó¥À¡¼¤Ë¤è¤ëƱÍͤΥץí¥À¥¯¥È¤È¤ÎÈæ³Ó·ë²Ì¤ò¾Ò²ð¤¹¤ë¡£

2018ǯ7·î10Æü

  • ¥â¥Ç¥ì¡¼¥¿ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ7·î3Æü

  • ¥â¥Ç¥ì¡¼¥¿ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ6·î26Æü

Presenter: PHAN Cao Duong¡ÊUniv. Tsukuba¡Ë

  • Title: Analysis of land cover changes in Central Vietnam using high-resolution multi-spectral-temporal remotely sensed data
  • Abstract: Monitoring land cover changes is essential for an intensive range of studies such as climate modeling, ecosystems and environmental protection. Recently, the availability of satellite images and the advancement of computational capacity have greatly enhanced land cover mappings with high temporal dimensionality and global scale coverage. However, global land cover products have little agreement on the consistency, coarse spatial resolution (> 30 m), and low accuracy (< 80 %) due to cloud cover, suboptimal acquisition schedules, and data archive access restriction. To close such issues, combining high- resolution and multi-spectral-temporal remote sensing imagery with ancillary data is one of the best methods. In this study, a low-cost method based on kernel-based probabilistic classification is employed to analyze land cover changes over central Vietnam from 2007 to 2017, using multi-sensor satellite images. The region was classified into water, urban, rice, crops, grassland, orchard, forest, mangrove, and barren by an automatic model. The model was trained and tested by 65,000 reference data collected from field surveys and visual interpretations. Results are the 2007 and 2017 classified maps with the spatial resolutions of 10 m and the overall accuracies of 90.5 % and 90.6 %, respectively. They show that in 2007, central Vietnam covered an estimated area of 94,000 square kilometers. Approximately 90 % of them was vegetated regions (paddy, crops, grassland, orchards, forest and mangrove) and the others were water, urban & built-up and bare land. Over a decade, paddy and bare lands lost by 485 and 496 km2 respectively whereas water, grassland, mangrove and crop areas witnessed an insignificant growth about 567, 969, 932 and 1,148 km2 respectively. Surprisingly, forest dramatically increased by 2,680 km2 but a sharp reduction of 4,604 km2 was seen in orchard areas. These findings are essential for the development of resource management strategy and environmental studies.

2018ǯ6·î19Æü

ȯɽ¼Ô¡§ÎÓ¿¿ÃÒ¡ÊJAXA¡Ë

  • ¥¿¥¤¥È¥ë¡§PALSAR-2»þ·ÏÎó¥Ç¡¼¥¿¤òÍøÍѤ·¤¿¥Ü¥ë¥Í¥ªÅç¤Î¿¹ÎӥХ¤¥ª¥Þ¥¹ÃϿޤκîÀ®
  • ³µÍס§¿¹ÎӥХ¤¥ª¥Þ¥¹¤Î¶õ´ÖʬÉÛ¤ò´Ñ¬¤¹¤ëµ»½Ñ¤Ï¡¢ÃºÁǽ۴IJáÄø¤ÎÉԳμÂÀ­¤òÄ㸺¤¹¤ë¤¿¤á¤ËɬÍפǤ¢¤ë¤¿¤á¡¢¤½¤Î¼ûÍפ¬¹â¤Þ¤ê¤Ä¤Ä¤¢¤ë¡£±ÒÀ±¥»¥ó¥µ¤ÎÃæ¤Ç¤Ï¹çÀ®³«¸ý¥ì¡¼¥À¡ÊSAR¡Ë¤¬¡¢´Ñ¬¿®¹æ¤È¿¹ÎӥХ¤¥ª¥Þ¥¹¤¬Ä¾ÀÜÁê´Ø¤·¤Æ¤¤¤ëÅÀ¤ä¡¢±À¤òÆ©²á¤·¤Æ´Ñ¬¤Ç¤­¤ëÅÀ¤Ê¤É¤«¤é¡¢¤½¤Î¼ûÍפؤδüÂÔ¤¬ºÇ¤â¹â¤¤¤È¸À¤¨¤ë¡£¤·¤«¤·¡¢¹â¥Ð¥¤¥ª¥Þ¥¹ÎÓʬ¤Ç¤ÏSAR¤Î¿®¹æ¤¬Ë°Ï¤·¤Æ¤·¤Þ¤¤¡¢ÃϾåÉô¥Ð¥¤¥ª¥Þ¥¹¤¬¤ª¤è¤½100 t/ha¤òĶ¤¨¤ëÎÓʬ¤Ë¤Ï´¶ÅÙ¤¬¤Ê¤¯¤Ê¤ë¤È¤¤¤¦·çÅÀ¤¬¤¢¤ë¡£¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¢ALOS-2/PALSAR-2¤¬¹­°è´Ñ¬¥â¡¼¥É¤ÇÇ®ÂÓ°èÁ´ÂΤòÌó1.5¥ö·î¤´¤È¤Ë´Ñ¬¤·¤¿»þ·ÏÎó¥Ç¡¼¥¿¤òÍøÍѤ¹¤ë¤³¤È¤Ç¿®¹æ˰ϤÎÌäÂê¤ò²þÁ±¤Ç¤­¤Ê¤¤¤«¡¢¥Ü¥ë¥Í¥ªÅç¤òÂоݤȤ·¤Æ¸¡Æ¤¤·¤¿¡£¤½¤Î·ë²Ì¡¢Ë°ÏÂÅÀ¤ò250-300 t/ha¤Þ¤Ç°ú¤­¾å¤²¤é¤ì¡¢¥Ü¥ë¥Í¥ªÅç¤Î87%¤Î¿¹ÎÓ¤ÇÀµ³Î¤Ê¥Ð¥¤¥ª¥Þ¥¹·×¬¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤¬¼¨¤µ¤ì¤¿¡£½¾Íè¤ÎË°ÏÂÅÀ¡Ê100 t/ha¡Ë¤Ç¤Ï25%¤·¤«¥«¥Ð¡¼¤·¤Æ¤¤¤Ê¤¤¤³¤È¤«¤é¤â¡¢Ç®ÂÓÎӤδѬ¤Ë¤Ï»þ·ÏÎó¥Ç¡¼¥¿¤ÎÍøÍѤ¬Í­¸ú¤È¤¤¤¨¤ë¡£

2018ǯ6·î12Æü

ȯɽ¼Ô¡§²¡ÈøÀ²¼ù¡Ê¹ñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§¿êÂह¤ëÅÚ±º»ÔÃæ¿´»Ô³¹ÃϤÎÅßµ¨¤Î²¹Ç®´Ä¶­ ¡Ý¹Ò¶õµ¡RS¤È°ÜÆ°´Ñ¬¤Ë¤è¤ë¼ÂÂÖÇÄ°®¡Ý
  • ³µÍס§¶áǯ·úÃÛ¤¬À¹¤ó¤Ç¤¢¤ë¹âÁØ·úʪ¤¬¼þÊÕ»Ô³¹ÃϤβ¹Ç®´Ä¶­¤ØÍ¿¤¨¤ë±Æ¶Á¤òɾ²Á¤¹¤ë¤³¤È¤Ï¡¢À¸³è¤Î²÷ŬÀ­¤ä¾Ê¥¨¥Í¤Ê¤É¤Î´ÑÅÀ¤«¤é½ÅÍפǤ¢¤ë¡£Ëܸ¦µæ¤Ç¤Ï¡¢ÃÏÊýÃæ¾®ÅԻԤǤ褯¸«¤é¤ì¤ëñÅï¤Î¹âÁØ·úÃۤǤ¢¤Ã¤Æ¤â¡¢ÆäËÅßµ¨¤Ë¤Ï¼þÊդβ¹Ç®´Ä¶­¤Ø¤Î±Æ¶Á¤¬Â礭¤¤¤Î¤Ç¤Ï¤Ê¤¤¤«¤È¹Í¤¨¡¢Ã±Åï¤Î¹âÁØ·úÃÛ¼þÊÕ¤ÎɽÌ̲¹Å٤ȵ¤²¹¤ÎʬÉÛ¤òÌÀ¤é¤«¤Ë¤¹¤ë¤³¤È¤òÌÜŪ¤Ë´Ñ¬¤ò¹Ô¤Ã¤¿¡£ÂоÝÃϤȤ·¤Æ¡¢¹â¤µ100 m¤òĶ¤¨¤ë¹âÁØ·úÃÛ¤òÍ­¤¹¤ëºÆ³«È¯Ã϶è¤ËÄãÁؤλԳ¹ÃϤ¬ÎÙÀܤ¹¤ëÅÚ±º±Ø¤ÎÀ¾Â¦¥¨¥ê¥¢¤òÁª¤ó¤À¡£É½Ì̲¹Å٤˴ؤ¹¤ë¾ðÊó¤È¤·¤Æ¡¢Åßµ¨¤ÎÃë´Ö¤ÈÆüË׸å¤Ë¹Ò¶õµ¡¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤Ë¤è¤ê0.6 mʬ²òǽ¤ÎÊü¼Í²¹ÅÙʬÉÛ¤ò¼èÆÀ¤·¤¿¡£¤µ¤é¤Ëµ¤²¹¤ÎʬÉۤȡ¢É½Ì̲¹Å٤μçÍפʷÁÀ®Í×°ø¤Ç¤¢¤ëÊü¼Í¼ý»Ù¤ÎʬÉÛ¤òÇÄ°®¤¹¤ë¤¿¤á¤Ë¡¢¼«Å¾¼Ö¤òÍѤ¤¤¿°ÜÆ°´Ñ¬¤ò¹Ô¤Ã¤¿¡£¹âÁØ·úÃÛ¤ÎÆü±¢¤Ç¤ÏÃë´Ö¤Ç¤¢¤Ã¤Æ¤âµ¤²¹¤è¤ê¤â¤«¤Ê¤êÄ㤤ɽÌ̲¹Å٤Ǥ¢¤ê¶É½êŪ¤Ë¤½¤³¤Îµ¤²¹¤âÄ㤫¤Ã¤¿¤³¤È¡¢¤½¤Î¤è¤¦¤ÊÄã²¹´Ä¶­¤Î·ÁÀ®Í×°ø¤Ê¤É¤Ë¤Ä¤¤¤Æȯɽ¤¹¤ë¡£

2018ǯ6·î5Æü

  • ¡ÖASTER¥µ¥¤¥¨¥ó¥¹¥Á¡¼¥à¥¢¥×¥ê¥±¡¼¥·¥ç¥óWG¡×¤Ë¹çή
  • 2018ǯ6·î5Æü¡Ê²Ð¡Ë¡¢Åìµþ¡Êµ¡³£¿¶¶½²ñ´Û¡Ë

2018ǯ5·î29Æü

ȯɽ¼Ô¡§¾¾°æůºÈ¡Ê¿¹ÎÓÁí¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§µ¤¸õÊÑÆ°¤È¥Þ¥Ä¸Ï¤ì
  • ³µÍס§¥Þ¥Ä¤ÈÆüËܿͤȤδط¸¡¢¥Þ¥Ä¸Ï¤ì¤È¤Ï²¿¤«¡¢ÆüËܤˤª¤±¤ë¥Þ¥Ä¸Ï¤ìÈï³²¡¢³¤³°¤Ë¤ª¤±¤ë¥Þ¥Ä¸Ï¤ìÈï³²¡¢ËɽüÂкö¤Ê¤É¤Ë¤Ä¤¤¤Æ¡¢²¹ÃȲ½±Æ¶Áɾ²Á¤ÈÍí¤á¤Æ¤ªÏ䵤»¤Æ¤¤¤¿¤À¤­¤Þ¤¹¡£

2018ǯ5·î22Æü

  • ¡ÖÆüËÜÃϵåÏÇÀ±²Ê³ØÏ¢¹ç2018ǯÂç²ñ¡×¤Ë¹çή
  • 2018ǯ5·î20Æü¡ÊÆü¡Ë¡Á24Æü¡Ê¶â¡Ë¡¢ÀéÍÕ¡ÊËëÄ¥¥á¥Ã¥»¡Ë

2018ǯ5·î15Æü

  • ¥â¥Ç¥ì¡¼¥¿¡¼ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ5·î8Æü

ȯɽ¼Ô¡§·§Ã«Ä¾´î¡Ê¹ñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§´Ä¶­»Øɸ¤ÎºÇŬ²½¤Ë¤è¤ë¥µ¥ó¥´Çò²½¥ê¥¹¥¯É¾²Á¡§web»Ô̱Ĵºº¥Ç¡¼¥¿¤òÍѤ¤¤¿²òÀÏ
  • ³µÍס§¥µ¥ó¥´¾ÌÀ¸ÂַϤϹ­°è¡¦ÃÏ°èŪ¤Ê´Ä¶­ÊѲ½¤ËºÇ¤â»¯¤µ¤ì¤Æ¤¤¤ëÀ¸ÂַϤΤҤȤĤÀ¤¬¡¢¶áǯ¤Ï²á¾ê¤Ê¹â²¹¥¹¥È¥ì¥¹¤Ê¤É¤Î´Ä¶­¾ò·ï¤Ë¤è¤ë¥µ¥ó¥´¤Î¡ÈÇò²½¡É¸½¾Ý¡Ê¶¦À¸ÁôÎब¸º¾¯¤·¤¿¾õÂ֡ˤ¬À¤³¦Åª¤ÊÌäÂê¤È¤Ê¤Ã¤Æ¤¤¤ë¡£±é¼Ô¤é¤Ïweb»²²Ã·¿¤Î»Ô̱Ĵºº¡Ö¤ß¤ó¤Ê¤Ç¤Ä¤¯¤ë¥µ¥ó¥´¥Þ¥Ã¥×¡×¤Î¥µ¥ó¥´Çò²½µ­Ï¿¤òÍѤ¤¤Æ¡¢Çò²½¤¬µ¯¤³¤ë´Ä¶­¾ò·ï¡¦ïçÃͤòÅý·×³ØŪ¤ËºÇŬ²½¤·¤¿¡£¤³¤ì¤Ë¤è¤Ã¤ÆÀ¤³¦ºÇ¹âÀºÅÙ¤ÎÀµÅúΨ¤È¶õ´Ö²òÁüÅÙ(1 km) ¤Î¥µ¥ó¥´Çò²½¿äÄê¥â¥Ç¥ë¤Î¹½ÃÛ¤ËÀ®¸ù¤·¤¿¡£¤Þ¤¿¤³¤Î¥â¥Ç¥ë¤ÎŬÍÑÎã¤È¤·¤Æ¡¢²­Æ쳤°è¤ÎÇò²½¿äÄê·ë²Ì¤È¡¢Çò²½¤ò·Ú¸º¤¹¤ë¥·¥Ê¥ê¥ª¡Ê²¹ÃȲ½Å¬±þºö¡Ë¤Î¤â¤È¤Ç¤Î¥â¥Ç¥ë¿äÄê¤Î·ë²Ì¤ò¾Ò²ð¤¹¤ë¡£
  • Kumagai NH, Yamano H & Sango-Map-Project C (2018) High-resolution modeling of thermal thresholds and environmental influences on coral bleaching for local and regional reef management. PeerJ 6: e4382; DOI 10.7717/peerj.4382

2018ǯ5·î1Æü

  • ¥´¡¼¥ë¥Ç¥ó¥¦¥£¡¼¥¯´ü´ÖÃæ¤Î¤¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ4·î24Æü

ȯɽ¼Ô¡§¾®ÎӷĻҡʹñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§Ìµµï½»²½¤ÏΤ»³·Ê´Ñ¤Î¿¢Êª¼ï¤Î¿ÍÍÀ­¤ò¤É¤Î¤è¤¦¤ËÊѤ¨¤ë¤Î¤«
  • ³µÍס§Ìµµï½»²½¤Ï¡¢¤³¤ì¤Þ¤Ç¿Í´Ö³èÆ°¤Ë¤è¤Ã¤Æ°Ý»ý¤µ¤ì¤Æ¤­¤¿Æó¼¡ÎÓ¤äȾ¼«Á³ÁðÃÏ¡¢¿åÅĤʤɤòÀ¸Â©¡¦À¸°éÃϤȤ¹¤ëÀ¸Êª¤Î¾Ã¼º¡¦¸º¾¯¤µ¤»¤ë¸¶°ø¤Ë¤Ê¤ë¤È·üÇ°¤µ¤ì¤Æ¤¤¤ë¡£°ìÊý¡¢Ìµµï½»²½¤·¤¿ÃÏ°è¤ò¼«Á³¤ÎÁ«°Ü¤Ë¤æ¤À¤Í¤Æ¼«Á³¿¢À¸¤Ø°Ü¹Ô¤µ¤»¤ë¤³¤È¤¬¤Ç¤­¤ì¤Ð¡¢¿Í´Ö¤Î³«È¯°µ¤Ë¤è¤Ã¤ÆÎô²½¤·¤¿À¸ÂַϤò²óÉü¤µ¤»¤ë·Àµ¡¤È¤Ê¤ë¤«¤â¤·¤ì¤Ê¤¤¡£Ëܸ¦µæ¤Ç¤Ï¡¢Á´¹ñ³ÆÃϤÎ̵µï½»²½½¸Íî¤È¤½¤Î¶áÎ٤οͤ¬Êë¤é¤¹½¸Íî¤Î¼ïÁÈÀ®¤ò¡¢»Øɸ¿¢Êª¤òÍѤ¤¤¿Ìî³°Ä´ºº¤Ë¤è¤Ã¤ÆÈæ³Ó¤·¡¢Ìµµï½»²½¤¬¿¢Êª¼ï¤Î¿ÍÍÀ­¤ËÍ¿¤¨¤ë±Æ¶Á¤òɾ²Á¤·¤¿¡£

2018ǯ4·î17Æü

ȯɽ¼Ô¡§ÊÒÌڿΡÊÃÞÇÈÂç¡Ë

  • ¥¿¥¤¥È¥ë¡§Â¿»þ´üÅÚÃÏÈïʤ¾ðÊó¥Ç¡¼¥¿¥»¥Ã¥È¡ÈSACLAJ"¤Î³«È¯¾õ¶·
  • ³µÍס§ÅÚÃÏÍøÍÑ¡¦ÅÚÃÏÈïʤ¤Ï»þ´Ö¤È¶¦¤ËÊѲ½¤¹¤ë¤¿¤á¡¢Â¿»þ´ü¤ÎÃϾ帡¾Ú¾ðÊó¤òÀ°È÷¤¹¤ë¤³¤È¤¬ÅÚÃÏÈïʤ¸¦µæ¤Ë¤ª¤¤¤Æ½ÅÍפǤ¢¤ë¡£JAXAÀ¸ÂÖ·Ï¥°¥ë¡¼¥×¤Ï¿»þ´üÅÚÃÏÈïʤ¾ðÊó¥Ç¡¼¥¿¥»¥Ã¥È¡ÉSACLAJ"¤ò³«È¯¤·¡¢ÃϾ帡¾Ú¾ðÊó¤ò¼ý½¸¤·¤Æ¤¤¤ë¡£SACLAJ¤Ï¸½Ãϼ̿¿¤È¤½¤ÎÅÚÃÏÈïʤ¥«¥Æ¥´¥ê¤ò¤Ò¤âÉÕ¤±¤¿¥Ç¡¼¥¿¥»¥Ã¥È¤Ç¤¢¤ê¡¢JAXA¤Ï¤½¤Î¾ðÊó¤òÅÐÏ¿¤¹¤ë¥µ¥¤¥È"SACLAJ Web"¤âƱ»þ¤Ë³«È¯¡¦±¿ÍѤ·¤Æ¤¤¤ë¡£¤Þ¤¿SACLAJ Web¤Ë²Ã¤¨¡¢È¯É½¼Ô¤ÏSACLAJ¤Ø¤ÎÅÐÏ¿¤òÊä½õ¤¹¤ë¤¿¤á¤ÎAndroid¥¢¥×¥ê"SACLAJ Mobile"¤Î³«È¯¤ò¹Ô¤Ã¤Æ¤¤¤ë¡£ËÜȯɽ¤Ç¤Ï¤³¤ì¤é¤Î¥½¥Õ¥È¥¦¥§¥¢¤ò´Þ¤á¡¢¸¦µæ¡¦¥½¥Õ¥È¥¦¥§¥¢³«È¯¤ÎξÌ̤«¤éSACLAJ¥×¥í¥¸¥§¥¯¥ÈÁ´ÂΤθ½¾õ¤Èº£¸å¤ÎŸ˾¤ò½Ò¤Ù¤ë¡£

2018ǯ4·î10Æü

  • ¡ÖEuropean Geosciences Union General Assembly 2018¡×¤Ë¹çή
  • 2018ǯ4·î8Æü¡ÊÆü¡Ë¡Á4·î13Æü¡Ê¶â¡Ë¡¢¥ª¡¼¥¹¥È¥ê¥¢¡¦¥¦¥£¡¼¥ó¡Êthe Austria Center Vienna¡Ë

2018ǯ4·î3Æü 17:30-19:00

ȯɽ¼Ô¡§ÌîÅĶÁ¡Ê¹ñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§ÍîÍÕ¹­ÍÕ¼ù¤Î¸ÄÍÕ¤Îʬ¸÷ÆÃÀ­¤Îµ¨ÀáÊÑÆ°¡¡¡¼Êü¼ÍÅÁã¥â¥Ç¥ëPROSPECT¤Ë¤è¤ë²òÀÏ
  • ³µÍס§¿¢À¸¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤Ç´Ñ¬¤µ¤ì¤ë¿¢À¸¤ÎÈ¿¼ÍÆÃÀ­¤Ï¡¢¿¢À¸¤ÎÍÕ·²¹½Â¤¡ÊÍÕÌÌÀѻؿô¡¢ÍդγÑÅÙʬÉۤʤɡˤȿ¢À¸¤ò¹½À®¤¹¤ëÍÕ¤ÎÈ¿¼Í¡¦Æ©²áΨ¡Êʬ¸÷ÆÃÀ­¡Ë¤ËÂ礭¤¯±Æ¶Á¤µ¤ì¤ë¡£¤½¤·¤Æ¡¢¸ÄÍÕ¤Îʬ¸÷ÆÃÀ­¤Ï¡¢Íդο§ÁÇ´ÞÎ̤ä´Þ¿åΨ¡¢LMA¤Ê¤É¤ÎÀ¸ÍýŪ¤ÊÆÃÀ­¤Ë¤è¤ê·è¤Þ¤ë¡£Ëܸ¦µæ¤Ç¤Ï¡¢´ôÉìÂç³Ø¹â»³»î¸³ÃÏ¡Ê´ôÉ츩¹â»³»Ô¡Ë¤ÎÎä²¹ÂÓÍîÍÕ¹­ÍÕ¼ùÎӤˤª¤¤¤Æ¡¢Í¥Àê¼ù¼ï¤Ç¤¢¤ë¥À¥±¥«¥ó¥Ð¡¢¥ß¥º¥Ê¥é¤Ë¤Ä¤¤¤ÆŸÍÕ¤«¤éÍîÍդޤǤÎʬ¸÷ÆÃÀ­¤ò4ǯ´Ö¤Ë¤ï¤«¤Ã¤Æ¬Äꤷ¤¿¡£¤½¤Î·ë²Ì¡¢Å¸ÍÕ¤«¤é²Æ¤ËÍÕ¤¬À®½Ï¤¹¤ë¤Þ¤Ç¤ÎÀ®Ä¹´ü´Ö¤Ë¤Ï¡¢²Ä»ë°è¤ÎÈ¿¼ÍΨ¤Ï¤ï¤º¤«¤Ë¸º¾¯¤·¤¿¤Î¤ß¤À¤Ã¤¿¤Î¤ËÂФ·¤Æ¡¢Æ©²áΨ¤ÏÂ礭¤¯¸º¾¯¤·¤¿¡£°ìÊý¡¢À®½Ï¤·¤¿ÍÕ¤¬ÍîÍÕ¤¹¤ë¤Þ¤Ç¤ÎÏ·²½´ü´Ö¤Ë¤Ï¡¢È¿¼ÍΨ¡¦Æ©²áΨ¤¬¤È¤â¤Ë¾å¾º¤·¤¿¡£¤³¤ì¤é¤Îʬ¸÷ÆÃÀ­¤Ë¤Ä¤¤¤Æ¡¢ÍÕ¤ÎÊü¼ÍÅÁã¥â¥Ç¥ëPROSPECT-5 (Feret et al. 2006)¤Ë¤è¤ê²òÀϤ·¤¿¤È¤³¤í¡¢À¸Ä¹´ü´Ö¤Ë¤Ï¥â¥Ç¥ë¤ÇÍÕ¤ÎÆâÉô¹½Â¤¤Î»Øɸ¤È¤Ê¤ë¥Ñ¥é¥á¡¼¥¿N¤Ï¾å¾º¤·¤¿¡£¤¹¤Ê¤ï¤ÁÍÕ¤ÎÆâÉô¤ÎºÙ˦´Ö·ä¤¬Áý¤¨¤ë¤è¤¦¤Ê¹½Â¤¤ÎÊѲ½¤¬À¸¤¸¤Æ¤ª¤ê¡¢¤½¤ì¤¬Ê¬¸÷ÆÃÀ­¤Îµ¨ÀáÊѲ½¤ËÂ礭¤¯±Æ¶Á¤·¤Æ¤¤¤ë¤³¤È¤¬ÌÀ¤é¤«¤Ë¤Ê¤Ã¤¿¡£¤µ¤é¤Ë¡¢N¤ÎÃͤϲ¹Î̻ؿô¡ÊGDD)¤ËÂФ¹¤ë¥·¥°¥â¥¤¥É´Ø¿ô¤Ç¥â¥Ç¥ë²½¤¹¤ë¤³¤È¤¬¤Ç¤­¤¿¡£¤³¤ì¤é¤Î·ë²Ì¤Ï¡¢PROSPECT¥â¥Ç¥ë¤ò±þÍѤ·¤¿ÍÍ¡¹¤Ê¥â¥Ç¥ë¡ÊPROSAIL¤äSCOPE¤Ê¤É¡Ë¤òÍîÍÕÎӤˤª¤¤¤ÆÍøÍѤ¹¤ëºÝ¤ËÌòΩ¤Ä¤È´üÂÔ¤µ¤ì¤ë¡£

2018ǯ3·î27Æü

  • ¡ÖÂè129²óÆüËÜ¿¹ÎӳزñÂç²ñ¡×¤È¹çή
  • 2018ǯ3·î26Æü¡Ê·î¡Ë¡Á3·î29Æü¡ÊÌÚ¡Ë¡¢¹âÃθ©¹âÃλԡʹâÃÎÂç³Ø¡Ë

2018ǯ3·î20Æü 17:30-19:00

ȯɽ¼Ô¡§¾®Àî·ë°á¡ÊÃÞÇÈÂç¡Ë

  • ¥¿¥¤¥È¥ë¡§¹â»³ÂӤˤª¤±¤ëÀäÌÇ´í×ü¼ïÊÝÁ´¤òÌÜŪ¤È¤·¤¿»Ô̱»²²Ã·¿Ä´ºº¤ÎÀøºßŪ»²²Ã¼Ô¤ÎÆÃħ¡¼¥é¥¤¥Á¥ç¥¦¤ò»öÎã¤Ë¡¼
  • ³µÍס§»Ô̱»²²Ã·¿Ä´ºº¤ÇÀìÌç²È¤Ë¤è¤ëÄ´ºº¤òÊ䤤¡¤ÊÝÁ´¤ËɬÍפÊÀ¸Â©¾ðÊó¤ò¼èÆÀ¤Ç¤­¤ë¤è¤¦¤Ê»²²Ã¼Ô¤ò³ÎÊݤ¹¤ë¤Ë¤Ï¡¤Ä´ºº¤Ë»²²Ã°Õ»×¤Î¤¢¤ë¿Í¤«¤éÊ罸¤¹¤ëɬÍפ¬¤¢¤Ã¤¿¡£¤·¤«¤·¡¤»Ô̱»²²Ã·¿Ä´ºº¤ÏÄ´ººÃϤؤΥ¢¥¯¥»¥¹¤¬Îɤ¤¾ì½ê¤Ë¸ÂÄꤵ¤ì¤Æ¤­¤¿¤¿¤á¡¤¹â»³ÂӤˤª¤¤¤Æ¤Ï¡¤»²²Ã°Õ»×¤Î¤ß¤ÇÀìÌç²È¤òÊ䤨¤ë¤è¤¦¤Ê»²²Ã¼Ô¤òÊ罸¤Ç¤­¤ë¤«¤É¤¦¤«ÌÀ¤é¤«¤Ë¤Ê¤Ã¤Æ¤¤¤Ê¤¤¡£¤½¤³¤ÇËܸ¦µæ¤Ï¡¤¹â»³ÂӤǤλÔ̱»²²Ã·¿Ä´ºº¤Ë¤ª¤¤¤Æ¡¤ÀìÌç²È¤Ë¤è¤ëÄ´ºº¤òÊ䤦¤³¤È¤¬¤Ç¤­¤ë¤è¤¦¤ÊÀøºßŪ»²²Ã¼Ô¤ÎÆÃħ¤òÌÀ¤é¤«¤Ë¤¹¤ë¤³¤È¤òÌÜŪ¤È¤¹¤ë¡£·ëÏÀ¤È¤·¤Æ¡¤¹â»³ÂӤǤλÔ̱»²²Ã·¿Ä´ºº¤ÎÀøºßŪ»²²Ã¼Ô¤Ï¡¤´Ä¶­ÊÝÁ´³èÆ°·Ð¸³¤¬¤¢¤ëÅù¡¤Ä´ºº¤Ë»²²Ã°Õ»×¤¬¤¢¤ë¿Í¤ÎÆÃħ¤òÍ­¤·¡¤¤«¤Ä¹â»³ÂӤǤÎÅл³·Ð¸³¤òÍ­¤¹¤ëɬÍפ¬¤¢¤ë¤³¤È¤¬ÌÀ¤é¤«¤Ë¤Ê¤Ã¤¿¡£

2018ǯ3·î13Æü 17:30-19:00

ȯɽ¼Ô¡§¿ÀµÜæÆ¿¿¡ÊÃÞÇÈÂç¡Ë

  • ¥¿¥¤¥È¥ë¡§FOSS4G¤òÍѤ¤¤¿Åл³¼Ô¼«¿È¤Ë¤è¤ëÄ´ºº¼Â»Ü¤Î²ÝÂê¡¡¡ÁÉٻλ³Â¼»³¸ÅÆ»Åл³¤Ë¤ª¤±¤ë¥¹¥Þ¡¼¥È¥Õ¥©¥ó¥¢¥×¥ê¤òÍѤ¤¤¿¼Â¸³
  • ³µÍס§Åл³Æ»¤ÎŬÀڤʴÉÍý¡¤ÍøÍѼÔËþ­Å٤θþ¾å¤Î¤¿¤á¤Ë¤Ï¡¤Åл³¼Ô¤Î¹ÔÆ°¤ä´Ø¿´»ö¹à¤òÇÄ°®¤¹¤ë¤³¤È¤¬½ÅÍפǤ¢¤ë¡£¤½¤³¤Ç¡¤¶áǯ¤ÏICT¤ÎȯŸ¤â¤¢¤ê¡¤·ÈÂÓüËö¤ÎGPS¤ä¥«¥á¥éµ¡Ç½¤òÍѤ¤¤Æ¡¤Åл³¼Ô¼«¿È¤Ëµ­Ï¿¤ò¼è¤Ã¤Æ¤â¤é¤¦¤è¤¦¤ÊÄ´ºº¤¬ÌϺ÷¤µ¤ì¤Æ¤­¤¿¡£ËÜȯɽ¤Ç¤Ï¡¤¤½¤ÎÃæ¤Ç¤âÆäËFOSS4G( Free and Open Source Software for Geospatial) ¤ÎÍøÍѤËÃåÌܤ·¡¤FOSS4G¤òÍѤ¤¤¿¼Â¸³¤Î·ë²Ì¤Ë¤Ä¤¤¤Æ¾Ò²ð¤¹¤ë¡£É®¼Ô¤é¤Ï¡¤ÃÞÇÈÂç³Ø¤ÎÉٻλ³Â¼»³¸ÅÆ»Åл³¼Â½¬¤Ë¤ª¤¤¤Æ¡¤FOSS4G¤òÍѤ¤¤¿¥¹¥Þ¡¼¥È¥Õ¥©¥ó¥¢¥×¥ê¡ÖGeopaparazzi¡×¤ò»ÈÍѤ·¡¤»²²Ã¼Ô¤Î¹ÔÆ°¤È´Ø¿´¤ÎÇÄ°®¤ò»î¤ß¤¿¡£»²²Ã¼Ô¤Ï¡¤¡ÖGeopaparazzi¡×¤ò»ÈÍѤ·Åл³¤Îµ°ÀפòGPS¤Ë¤è¤êµ­Ï¿¤·¡¤¤Þ¤¿Ç¤°Õ¤ÎÃÏÅÀ¤Ë¤ª¤¤¤Æ¡¤¼Ì¿¿µ­Ï¿¡¤ÀâÌÀ¤Îʸ¾Ï¤Îµ­Ï¿¤ò¼è¤Ã¤¿¡£¤³¤Îµ­Ï¿¤òʬÀϤ¹¤ë¤³¤È¤Ç¡¤Åл³»þ¤Î¹ÔÆ°¤È´Ø¿´»ö¹à¤òÌÀ¤é¤«¤È¤·¤¿¡£¤Þ¤¿¡¤»²²Ã¼Ô¤Ø¤Î¥¢¥ó¥±¡¼¥ÈÄ´ºº¤Ë¤è¤ê¡ÖGeopaparazzi¡×¤Ø¤Îɾ²Á¤òÌÀ¤é¤«¤È¤·¡¤FOSS4G¤Ë¤è¤ëÄ´ºº¼Â»Ü¤Î²ÝÂê¤Ë¤Ä¤¤¤Æ¹Í»¡¤·¤¿¡£

2018ǯ3·î6Æü 17:30-19:00

ȯɽ¼Ô¡§Æິ¸¶¸²Ïº¡ÊÃÞÇÈÂç¡Ë

  • ¥¿¥¤¥È¥ë¡§AMSR2¤ÈPALSAR2¤ò»È¤Ã¤¿¥¤¥ó¥É¥Í¥·¥¢Sentarum¼¾ÃϤιâ»þ¶õ´Öʬ²òǽ¥â¥Ë¥¿¥ê¥ó¥°
  • ³µÍס§¥Ü¥ë¥Í¥ªÅç¤ÎSentarum¼¾ÃϤÏ, ¥¤¥ó¥É¥Í¥·¥¢ºÇĹ¤Î²ÏÀî¤Ç¤¢¤ëKapuasÀî¤Î¼«Á³Í·¿åÃϤǤ¢¤ê, Â礭¤Êµ¨ÀáÊÑÆ°¤ò¤¹¤ë¡£¿åʸ³Ø¡¦À¸ÂֳؤδÑÅÀ¤Ç, ¤³¤Î¼¾ÃϤÎù¿åÈϰϤò»þ·ÏÎó¤ÇÇÄ°®¤¹¤ë¤³¤È¤Ï½ÅÍפǤ¢¤ê, ¤½¤Î¤¿¤á¤Ë±ÒÀ±¥ê¥â¥»¥ó¤¬½ÅÍפǤ¢¤ë¡£°ìÊý¤ÇÅöÃϤÏÇ®ÂӤǤ¢¤ë¤¿¤á°ìǯÃæ, ±À¤¬Â¿¤¯, ¤½¤Î¤¿¤á±ÒÀ±ÅëºÜ¸÷³Ø¥»¥ó¥µ¡¼¤ÏÌò¤ËΩ¤¿¤Ê¤¤¡£±À¤òÆ©²á¤·¤Æ´Ñ¬¤Ç¤­¤ë¥Þ¥¤¥¯¥íÇÈ¥»¥ó¥µ¡¼¤Î±çÍѤ¬É¬ÍפÀ¤¬, ¹çÀ®³«¸ý¥ì¡¼¥À¡¼¤Ï´Ñ¬ÉÑÅÙ¤¬ÉÔ½½Ê¬, ¥Þ¥¤¥¯¥íÇÈÊü¼Í·×¤Ï¶õ´Öʬ²òǽ¤¬ÉÔ½½Ê¬¤Ç¤¢¤ë¡£¤½¤³¤Çξ¼Ô¤òÍ»¹ç¡Ê¥Õ¥å¡¼¥¸¥ç¥ó¡Ë¤¹¤ë¤³¤È¤Ç, ¹â»þ¶õ´Öʬ²òǽ¤Î¥â¥Ë¥¿¥ê¥ó¥°¤ò»î¤ß¤¿¡£¥Õ¥å¡¼¥¸¥ç¥ó¤Î¥¢¥ë¥´¥ê¥º¥à¤Ï, Mizuochi et al. (2014, 2017)¤ÎDBUX¤ò²þÎɤ·¤¿RFDBUX (¥é¥ó¥À¥à¥Õ¥©¥ì¥¹¥ÈÈÇDBUX)¤òÍѤ¤¤¿¡£Ëܸ¦µæ¤Ï½¤»Î2ǯ¤ÎÀ¾»³ÃҲ»Ҥµ¤ó¤Î½¤ÏÀ¤À¤¬, Åê¹ÆÏÀʸ²½¤Î¤¿¤á¤ËºÆ²òÀϤÈÄɲòòÀϤò¹Ô¤Ã¤Æ¤ª¤ê¡¢ÏÀʸÁð¹Æ¤È¤È¤â¤Ë³§¤µ¤ó¤Ë¤´Í÷夤¤Æ¥ì¥Ó¥å¡¼¥¢¡¼ÌÜÀþ¤Ç¤Î¤´°Õ¸«¤ò夭¤¿¤¤¡£

2018ǯ2·î27Æü 17:30-19:00

ȯɽ¼Ô¡§Luis Alberto Vega Isuhuaylas¡Ê¿¹ÎÓÁí¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§Natural forest mapping in the Andes (Peru): A comparison of the performance of machine-learning algorithms
  • ³µÍס§The Andes mountain forests are sparse relict populations of tree species that have a low percentage crown cover and grow in association with local native shrubland species. The identification of forest conditions for conservation in areas such as these is based on remote sensing techniques and classification methods. In particular, the classification of Andes mountain forests is difficult because of noise in the reflectance data within land cover classes. The noise is the result of the variations in terrain illumination resulting from complex topography and the mixture of different land cover types occurring at the sub-pixel level. Considering these issues, the selection of an optimum classification method to obtain accurate results is very important to support conservation activities. In this study, we carried out cross-validation and comparative non-parametric statistical analyses of classification performance using three supervised machine-learning techniques: Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). The SVM and RF methods were not significantly different in their ability to separate Andes mountain forest and shrubland land cover classes, whereas the kNN method had a poorer performance because it was more sensitive to noisy training data.

2017ǯ2·î20Æü

  • ¡ÖJapanFlux10¼þǯµ­Ç°½¸²ñ¡Ê·ó ¹ñΩ´Ä¶­¸¦µæ½ê Φ°èÀ¸ÂÖ·Ï¥â¥Ë¥¿¥ê¥ó¥°¸¦µæ½¸²ñ¡Ë¡×¤È¹çή
  • 2018ǯ2·î20Æü¡Ê²Ð¡Ë¡Á2·î21Æü¡Ê¿å¡Ë¡¢°ñ¾ë¸©¤Ä¤¯¤Ð»Ô¡Ê¹ñΩ´Ä¶­¸¦µæ½ê¡Ë

2018ǯ2·î13Æü

  • ¥â¥Ç¥ì¡¼¥¿ÉԺߤΤ¿¤á¡¢¤ªµÙ¤ß¤È¤·¤Þ¤¹¡£

2018ǯ2·î6Æü 17:30-19:00

ȯɽ¼Ô¡§ÀèºêÍýÇ·¡Ê¹ñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§Êƹñ¤ËÌ¢±ä¤¹¤ëÁû²»³²¤È¸÷³²¤ÏÄ»Îà¤ÎÈË¿£³èÆ°¤òÊѤ¨¤ë
  • ³µÍס§¼«Æ°¼Ö¡¢Åż֡¢Èô¹Ôµ¡¡½¿ÍÎà¤Îµ»½Ñ³×¿·¤Î»òʪ¤Ë¾ï¤ËÉÕ¤­¤Þ¤È¤¦¤â¤Î¤ËÁû²»¤ä¿Í¹©¸÷¤¬¤¢¤ë¡£¤³¤ì¤Þ¤Ç¿¤¯¤Î¸¦µæ¤¬¡¢Áû²»¤ä¿Í¹©¸÷¤¬ÌîÀ¸À¸Êª¤ÎÀ¸Íý¡¦¹ÔÆ°¡¦ÈË¿£¤ËÍ¿¤¨¤ë±Æ¶Á¤òµ­ºÜ¤·¤Æ¤­¤¿¡£¤·¤«¤·¡¢¤³¤¦¤·¤¿¸¦µæ¤Î¿¤¯¤Ï¶¹¤¤ÃÏ°è¤Ë¤ª¤±¤ëñ°ì¼ï¤òÂоݤˤ·¤Æ¤­¤¿¡£¤½¤Î¤¿¤á¡¢Áû²»¤ä¿Í¹©¸÷¤¬¹­°è¥¹¥±¡¼¥ë¤ÎÀ¸Êª·²½¸¤Ë¤É¤Î¤è¤¦¤Ê±Æ¶Á¤òÍ¿¤¨¤ë¤Î¤«¤ÏÁ´¤¯¤ï¤«¤Ã¤Æ¤¤¤Ê¤¤¡£¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¢Â絬ÌϤʻÔ̱¥Ç¡¼¥¿¤È±ÒÀ±´Ñ¬¥Ç¡¼¥¿¤âÍѤ¤¤¿¹â²òÁüÅÙ¤ÎÁû²»¤È¿Í¹©¸÷¤Îͽ¬¥Þ¥Ã¥×¤òÍѤ¤¤Æ¡¢ÊƹñÁ´°è¤Ë¤ª¤±¤ë142¼ïÎà¤ÎÄ»Îà¤ÎÈË¿£³èÆ°¡ÊÊúÍñ³«»ÏÆü¡¢°ìÊ¢Íñ¿ô¡¢ÉôʬÕÛ²½À®¸ùÅÙ¡¢ÁãΩ¤ÁÀ®¸ùÅ١ˤËÁû²»¤È¿Í¹©¸÷¤¬Í¿¤¨¤ë±Æ¶Á¤òÄ´¤Ù¤¿¡£

2018ǯ1·î30Æü 17:30-19:00

ȯɽ¼Ô¡§Æິ¸¶¸²Ïº¡ÊÃÞÇÈÂç¡Ë

  • ¥¿¥¤¥È¥ë¡§±ÒÀ±ÅëºÜ¹ß¿å¥ì¡¼¥À¡¼TRMM/PR¤ò¿¢À¸´Ñ¬¥»¥ó¥µ¡¼¤È¤·¤Æ»È¤¦
  • ³µÍס§TRMM/PR¤Ï, 18ǯ´Ö¤Ë¤ï¤¿¤Ã¤ÆÇ®ÂÓ¡¦°¡Ç®ÂӤι߿å¤ò±§Ã褫¤é´Ñ¬¤¹¤ë¤È¤¤¤¦°ÎÂç¤Ê¶ÈÀÓ¤ò»Ä¤·¤¿¤¬¡¢¼Â¤Ï¤½¤Î¥Ç¡¼¥¿¤ÎÂçÉôʬ¤òÀê¤á¤ë̵¹ß±«»þ¤Î¥Ç¡¼¥¿¤Ï³èÍѤµ¤ì¤Æ¤¤¤Ê¤¤¡£¼Â¤ÏTRMM/PR¤Î̵¹ß±«»þ¤Î¥Ç¡¼¥¿¤ÏΦÌ̾õÂ֤δѬ¤ËÍ­ÍѤ«¤â¤·¤ì¤Ê¤¤¤È¤¤¤¦Êó¹ð¤Ï´û¤Ë¤¢¤ê¡¢Î¦Ì̤ÎÆÃħ¤Ï¥ì¡¼¥À¡¼È¿¼Í¤ÎÆþ¼Í³Ñ°Í¸À­¤Ë¤¢¤é¤ï¤ì¤ë¤³¤È¤¬¤ï¤«¤Ã¤Æ¤¤¤ë¡£¤È¤³¤í¤¬¡¢¤³¤ÎÆþ¼Í³Ñ¤Ï¡¢ÃÏ·Á¤Ë¤è¤Ã¤ÆÂ礭¤¯±Æ¶Á¤ò¼õ¤±¤ë¤³¤È¤¬¤ï¤«¤Ã¤¿¡Ê¤Þ¤¢ÅöÁ³¤Ç¤¢¤ë¡Ë¡£º£²ó¡¢¤½¤Î±Æ¶Á¤ò·Ú¸º¤·¡¢ÃÏ·Á¤Î±Æ¶Á¤ò¼õ¤±¤º¤ËΦÌ̾õÂÖ¡ÊÆä˿¢À¸¡Ë¤òÃê½Ð¤¹¤ë¼êË¡¤ò³«È¯¤·¤¿¡Ê¤Æ¤¤¤¦¤«³«È¯Ãæ¡Ë¤Î¤Ç¡¢Êó¹ð¤¹¤ë¡£

2017ǯ1·î23Æü

  • ¡ÖÊ¿À®29ǯÅÙ Ãϵå´Ñ¬¥ß¥Ã¥·¥ç¥ó¹çƱPI¥ï¡¼¥¯¥·¥ç¥Ã¥×¡×¤È¹çή
  • 2018ǯ1·î22Æü¡Ê·î¡Ë¡Á1·î26Æü¡Ê¶â¡Ë¡¢ÅìµþÅÔÀéÂåÅĶè¡ÊTKP¥¬¡¼¥Ç¥ó¥·¥Æ¥£Ãݶ¶¡Ë

2018ǯ1·î16Æü 17:30-19:00

ȯɽ¼Ô¡§ºÙÀîÆࡹ»Þ¡Ê¹ñ´Ä¸¦¡Ë

  • ¥¿¥¤¥È¥ë¡§Åßµ¨µ¤¸õÊÑÆ°²¼¤Ë¤ª¤±¤ë¿¹ÎÓÀ¸ÂַϤÎÅÚ¾íÃâÁÇÊÑ´¹¤ËºÙº¬¥ê¥¿¡¼¤¬²Ì¤¿¤¹Ìò³ä
  • ³µÍס§Åßµ¨µ¤¸õÊÑÆ°¤Ë¤è¤ëÅÚ¾í¤ÎÅà·ëÍ»²ò¥Ñ¥¿¡¼¥ó¤ÎÊѲ½¤Ï¡¢¸Ï»àºÙº¬¡ÊºÙº¬¥ê¥¿¡¼¡Ë¤òÁý²Ã¤µ¤»¤ë¤³¤È¤ÇÅÚ¾íÃæ¤ÎÃâÁÇÊÑ´¹¤Ë±Æ¶Á¤ò¤ª¤è¤Ü¤¹²ÄǽÀ­¤¬¤¢¤ë¤¬¡¢ÉÔÌÀÎƤÊÅÀ¤¬Â¿¤¤¡£ËÜȯɽ¤Ç¤Ï¡¢ºÙº¬¥ê¥¿¡¼¤Î¿¢Êª¼ï¤Î±Æ¶Á¤È¡¢°Û¤Ê¤ë¿¢À¸²¼¤ÎÅÚ¾í¤Ë¤ª¤±¤ë±þÅú¤Î°ã¤¤¤Ë¤Ä¤¤¤ÆÏÀ¤º¤ë¡£Ä´ºº¤ÏË̳¤Æ»ÅìÉô¤Ë°ÌÃÖ¤¹¤ëµþÅÔÂç³ØË̳¤Æ»¸¦µæÎÓɸÃãÃ϶è¤Î¥ß¥º¥Ê¥éÆó¼¡Îӡʲ¼ÁØ¿¢À¸¡§¥ß¥ä¥³¥¶¥µ¡Ë¤Ç¹Ô¤Ã¤¿¡£¤Þ¤º¡¢ºÙº¬¥ê¥¿¡¼¤Î¿¢Êª¼ï¤Î°ã¤¤¤¬ÅÚ¾í¤ÎÃâÁÇÊÑ´¹¤ËÍ¿¤¨¤ë±Æ¶Á¤òÌÀ¤é¤«¤Ë¤¹¤ë¤¿¤á¡¢¼¼ÆâÇÝÍܼ¸³¤ò¹Ô¤Ã¤¿¡£Ä´ººÃϤμçÍ×¹½À®¼ï¤Ç¤¢¤ë¥ß¥º¥Ê¥é¤È¥ß¥ä¥³¥¶¥µ¡Ê¥µ¥µ¡Ë¤òÂоݿ¢Êª¤È¤·¤Æ¡¢¤½¤ì¤¾¤ì¤ÎºÙº¬¥ê¥¿¡¼¤òÅÚ¾í¤Ëź²Ã¤·¤¿¡£¥µ¥µºÙº¬¥ê¥¿¡¼¤ÏÅÚ¾íÅà·ë´ü´Ö¤Ë¡¢¥ß¥º¥Ê¥éºÙº¬¥ê¥¿¡¼¤ÏÍ»²ò´ü´Ö¤ËÃâÁÇ̵µ¡²½Â®ÅÙ¤òÂ¥¿Ê¤µ¤»¤¿¡£Åà·ëÍ»²ò¥¤¥Ù¥ó¥È¤Ë¤è¤Ã¤ÆºÙº¬¥ê¥¿¡¼¤«¤éÍÏ椹¤ëÍϸͭµ¡ÃâÁǤϡ¢¥µ¥µ¤ÎÊý¤¬¥ß¥º¥Ê¥é¤è¤ê¤âÍ­°Õ¤Ë¿¤«¤Ã¤¿¡£¤³¤Î¤³¤È¤«¤é¡¢¥µ¥µºÙº¬¥ê¥¿¡¼¤ÏÍϸͭµ¡Êª¤ò¶¡µë¤¹¤ë¤¿¤á¡¢¿×®¤ËÃâÁÇ̵µ¡²½Â®ÅÙ¤¬Â¥¿Ê¤µ¤ì¤¿¤â¤Î¤È¹Í¤¨¤é¤ì¤¿¡£°ìÊý¡¢¥ß¥º¥Ê¥éºÙº¬¥ê¥¿¡¼¤Îź²Ã¤Ë¤è¤ëÃ٤줿ÃâÁÇ̵µ¡²½Â®ÅÙ¤ÎÂ¥¿Ê¤Ï¡¢Åà·ëÍ»²ò¥¤¥Ù¥ó¥È¤Ë¤è¤ëʪÍýŪÇ˺ÕÅù¤ÎºîÍѤ¬´Ø·¸¤¹¤ë¤È¹Í¤¨¤é¤ì¤¿¡£¼¡¤Ë¡¢°Û¤Ê¤ë¿¢À¸²¼¤ÎÅÚ¾í¤Ë¤è¤ë°ã¤¤¤òÌÀ¤é¤«¤Ë¤¹¤ë¤¿¤á¡¢Åßµ¨µ¤¸õÊÑÆ°¤Ë¤è¤ëÀÑÀãÎ̤ÎÄã²¼¤òÌÏÊ路¤¿½üÀã¼Â¸³²¼¤ÇÅÚ¾í¤ÎÌî³°ÇÝÍܤò¹Ô¤Ã¤¿¡£Ä´ººÃϤΥߥº¥Ê¥éÎӤȡ¢¥ß¥º¥Ê¥éÎÓ¤ËÎÙÀܤ¹¤ë¥«¥é¥Þ¥ÄÎÓ¤«¤é¤½¤ì¤¾¤ìÅÚ¾í¤òºÎ¼è¤·¤¿¡£³ÆÅÚ¾í¤Ë¤Ï¡¢Î¾ÎÓʬ¤Ç¶¦Ä̤β¼ÁØ¿¢À¸¤Ç¤¢¤ë¥µ¥µºÙº¬¥ê¥¿¡¼¤òź²Ã¤·¤¿¡£ÀµÌ£ÃâÁÇ̵µ¡²½Â®Å٤ϡ¢½üÀã¶è¤ÎºÙº¬¥ê¥¿¡¼Åº²ÃÅÚ¾í¤ò½ü¤­¡¢¥ß¥º¥Ê¥éÎÓ¤ÎÊý¤¬¥«¥é¥Þ¥ÄÎÓ¤è¤êÍ­°Õ¤ËÂ礭¤«¤Ã¤¿¡£¤µ¤é¤Ë¡¢ÀµÌ£Íϸͭµ¡ÃºÁÇÀ¸À®Â®Å٤ϡ¢ºÙº¬¥ê¥¿¡¼Åº²Ã¤Ë¤è¤Ã¤Æ¡¢¥ß¥º¥Ê¥éÎӤǤÏÍ­°Õ¤Ë¸º¾¯¤·¡¢¥«¥é¥Þ¥ÄÎӤǤÏÍ­°Õ¤ËÁý²Ã¤·¤¿¡£½üÀã¶è¤Ç¤Î³Æ¿¢À¸¤Ç¤ÎÀµÌ£ÃâÁÇ̵µ¡²½Â®ÅÙ¤ÎÊѲ½¤Ï¡¢ÃâÁÇ̵µ¡²½¤Î´ð¼Á¤È¤Ê¤ëÍϸͭµ¡Êª¤Î¾ÃÈñ¡¦À¸À®¤È´Ø·¸¤·¤Æ¤¤¤¿²ÄǽÀ­¤¬¤¢¤ë¡£Ëܸ¦µæ¤«¤é¡¢Åßµ¨µ¤¸õÊÑÆ°¤Ë¤è¤ëÅà·ëÍ»²ò¥Ñ¥¿¡¼¥ó¤ÎÊѲ½¤Ï¡¢ºÙº¬¥ê¥¿¡¼¤Î¤è¤¦¤Ê¿·Á¯Í­µ¡Êª¤ÎÁý²Ã¤ò²ð¤·¤ÆÅÚ¾í¤ÎÃâÁÇÊÑ´¹¤Ë±Æ¶Á¤·¡¢¤½¤Î±þÅú¤Ï¿¢À¸¤Ë¤è¤Ã¤Æ°Û¤Ê¤ë¤³¤È¤¬ÌÀ¤é¤«¤Ë¤Ê¤Ã¤¿¡£¤Þ¤¿¡¢ºÙº¬¥ê¥¿¡¼¤Î¿¢Êª¼ï¤Î°ã¤¤¤Ï¡¢ÃâÁÇ̵µ¡²½Â®ÅÙ¤ÎÁý²Ã¥¿¥¤¥ß¥ó¥°¤Ë¥º¥ì¤ò¤â¤¿¤é¤·¤Æ¤¤¤¿¡£

2018ǯ1·î9Æü

  • ǯ»ÏµÙ¤ß

2018ǯ1·î2Æü

  • ǯ»ÏµÙ¤ß
Last modified:2019/04/27 15:46:37
Keyword(s):
References:[ͽÄê¤ÈÍúÎò]