地球観測研究センター

地球観測研究センター

EORC

宇宙航空研究開発機構特別資料

2009年度

地球観測研究センター年報

Annual Report 2009 No.13

２０１０年１２月

宇宙航空研究開発機構

2009年度 地球観測研究センター年報

Annual Report 2009 No.13 宇宙航空研究開発機構特別資料

JAXA Special Publication

December 2 0 1 0

Earth Observation Research Center (EORC)

2009ᐕᐲ ࿾⃿᷹ⷰ⎇ⓥ࠮ࡦ࠲࡯ ᐕႎ ⋡ᰴ

ߪߓ߼ߦ ૑ ᣿ᱜ ··· iii

1. ALOS೑↪⎇ⓥ 1.1 ALOS⸃ᨆࡊࡠࠫࠚࠢ࠻ߩ2009ᐕᐲߩ✚᜝ ፉ↰ ᡽ା ···2

1.2 ᫪ᨋ⋙ⷞߩὑߩ㜞ಽ⸃⢻ࠝ࡞࠰࡮൨㈩⵬ᱜᷣߺPALSARࠣࡠ࡯ࡃ࡞ࡕࠩࠗࠢ↹௝ߩ૞ᚑ ፉ↰ ᡽ା ···4

1.3 ALOS/PRISM, AVNIR-2ߩᩞᱜᬌ⸽ߦ㑐ߔࠆ⎇ⓥ ↰Ლ ᱞ㓶 ···6

1.4 ALOSߦࠃࠆ࿾Ზᄌേ෸߮࿾⴫ᄌ⁁ߩᬌ಴ߣߘߩ⸃㉼ ችၔ ᵗ੺ ···8

1.5 PALSARߦࠃࠆࠗࡦ࠼ࡀࠪࠕ࡮ࠬࡑ࠻࡜ፉߩ᫪ᨋબណᬌ಴ ⏷ญ ᴦ ··· 11

1.6 วᚑ㐿ญ࡟࡯࠳࡯ࠍ↪޿ߚἴኂ⋙ⷞ ᴡ㊁ ቱᐘ ···15

1.7 Mapping Tropical Forest Using ALOS PALSAR 50m Resolution Data with Multiscale Texture Analysis Preesan Rakwatin ···18

1.8 PALSARᄙ஍ᵄᐓᷤ⸃ᨆߦࠃࠆ࿯࿾ⵍⷒߩᛠីߦߟ޿ߡ ᄢᧁ ⌀ੱ ···20

1.9 Assessment of ALOS PALSAR 50m Orthorectified FBD Data for Regional Land Cover Classification by using Support Vector Machines Nicolas Longepe ···22

2. GOSAT೑↪⎇ⓥ 2.1 GOSAT೑↪⎇ⓥࡊࡠࠫࠚࠢ࠻ߩᚑᨐ᭎ⷐ Ꮉ਄ ୃม ···26

2.2 ቝቮ߆ࠄߩ᷷ቶലᨐ᷹ࠟࠬⷰ㧔਎⇇ߩേ߈ߣGOSATᚑᨐ㧕 ᫪ጊ 㓉 ···29

2.3 GOSAT TANSO ᩞᱜ⁁ᴫߣೋᦼ⚿ᨐ Ⴎ⷗ ᘮ ···36

2.4 GOSAT TANSO ᐞ૗ᩞᱜ⁁ᴫߦߟ޿ߡ ༑ฬ ᦮ሶ ···39

2.5 GOSATᾲ⿒ᄖࡃࡦ࠼ࠍ↪޿ߚࠝ࠱ࡦỚᐲዉ಴ߩᬌ⸛ ᄢጊ ඳผ ···41

2.6 CO2 DIALࠍ↪޿ߚ㋦⋥ࠞ࡜ࡓ᷹ⷰߩታ⸽ ႺỈ ᄢ੫ ···43

2.7 GOSAT CAI࠺࡯࠲ߣࡕ࠺࡞ࠪࡒࡘ࡟࡯࡚ࠪࡦࠍ೑↪ߒߚࠛࠕࡠ࠱࡞․ᕈዉ಴ ะ੗⌀ᧁሶ ··· 48

3. TRMM/GPM/EarthCARE೑↪⎇ⓥ 3.1 TRMM/GPM/EarthCARE೑↪⎇ⓥߩᚑᨐ᭎ⷐ ᴒ ℂሶ ···52

3.2 TRMM/PR౬㐳♽ᩞᱜߣGPM/DPR߳ߩᢎ⸠ ศ↰ ⋥ᢥ ···57

3.3 GPM/DPRᮨᡆ࠺࡯࠲૞ᚑߦะߌߚⴡᤊ៞タ㒠᳓࡟࡯࠳ࠪࡒࡘ࡟࡯࠲ߩ㐿⊒ ਭ଻↰ᜏᔒ ···60

3.4

㘵↰ ᵏਭ ···62

3.5 EarthCAREᮡḰ/⎇ⓥࠕ࡞ࠧ࡝࠭ࡓߩᬌ⸛  ᩿⑲৻㇢ ···64

3.6 GPM෸߮EarthCAREߦ߅ߌࠆ࿾਄ᬌ⸽⸘↹ߦߟ޿ߡ ᷡ᳓ ෼ม ···68

3.7 ⴡᤊߦࠃࠆో⃿㒠᳓ࡑ࠶ࡊ૞ᚑࠪࠬ࠹ࡓߩᡷ⦟ߣ࠺࡯࠲ߩᔕ↪ น⍮⟤૒ሶ ··· 70

4. GCOM೑↪⎇ⓥ

4.1 GCOM೑↪⎇ⓥߩ᭎ⷐ ੖ච፲ ଻ ···76

4.2 AMSR-Eノᐲ᷷ᐲߦߺࠄࠇࠆ㔚ᵄᐓᷤၞߩ⹏ଔ ੹ጟ ໪ᴦ ···78

4.3 AMSR-E/AMSR2䈱䈢䉄䈱ᶏ㕙᳓᷷䊶ᶏ਄㘑ㅦ▚಴䉝䊦䉯䊥䉵䊛䈱㐿⊒ ᩊ↰ ᓆ ···80

4.4 ࡒ࡝ᵄ㨪ࠨࡉࡒ࡝ᵄ᡼኿⸘ߦࠃࠆ᳓⫳᳇࡮㔕᳖᷹ⷰ ਄ᴛ ᄢ૞ ···82

4.5 AMSR-Eࠍ↪޿ߚ࿾᷷࡮࿯ფ᳓ಽផቯࠕ࡞ࠧ࡝࠭ࡓߩ㐿⊒ ⮮੗ ⑲ᐘ ···85

4.6 AMSR-E࠺࡯࠲ࠍ↪޿ߚ࿾⴫㕙᷷ᐲផቯࠕ࡞ࠧ࡝࠭ࡓߩ㐿⊒ ߅ࠃ߮AMSR-E࠺࡯࠲ߩἴኂ⋙ⷞ߳ߩᔕ↪ ೨↰ ፏ ···88

4.7 MODIS䈮䉋䉎ᣣ኿㑐ㅪ䊒䊨䉻䉪䊃䈱ផቯ䈫࿾਄᷹ⷰ䊂䊷䉺䈮䉋䉎ᬌ⸽㩷 ᧛਄ ᶈ ···90

4.8 JASMESⓍ㔐ಽᏓࡊࡠ࠳ࠢ࠻ߩᢛ஻ߣ GCOMᶏ᳖㑐ㅪࡊࡠ࠳ࠢ࠻ߩ೑↪ታ⸽ ၳ 㓷⵨ ···93

4.9 ࡑࠗࠢࡠᵄ᡼኿⸘ࠍ↪޿ߚቄ▵ᶏ᳖ၞߩᶏ᳖ෘផቯ ⋥ᧁ ๺ᒄ ···98

4.10 ᫪ᨋἫἴߩᬌ⍮ࠕ࡞ࠧ࡝࠭ࡓ㐿⊒ߣߘߩᔕ↪ ਛฝ ᶈੑ ··· 101

4.11 Development of a Long-Term Cloud Climatology for the GCOM-C/SGLI Satellite Mission: Global Cloud Types Analyses J. R. Dim ··· 104

4.12 GCOM-C/SGLI 㒽࿤ࡊࡠ࠳ࠢ࠻࡮ᬀ↢ᜰᢙߩ㐿⊒ ዊ㊁ ᦶሶ ··· 109

5. ᮮゲ⎇ⓥ 5.1 ᐔᚑ21ᐕᐲEORCࡊࡠࠫࠚࠢ࠻ᮮᢿဳ㧔ᮮゲ㧕⎇ⓥߩᚑᨐ᭎ⷐ ᪢ᴛടኼᄦ ··· 114

5.2 JAXA ో⃿㒽㕙࠺࡯࠲หൻࠪࠬ࠹ࡓ(GLDAS)ߦࠃࠆ᳓ᓴⅣࡊࡠ࠳ࠢ࠻ߩ૞ᚑ ᳓ᓴⅣ⎇ⓥࠣ࡞࡯ࡊ ᴒ ᄢᐙ ··· 117

5.3 㜞ಽ⸃⢻SARߦࠃࠆἴኂ⋙ⷞߣ੹ᓟߩ⸃ᨆ ἴኂ⎇ⓥࠣ࡞࡯ࡊ ፉ↰ ᡽ା ··· 121

5.4 ↢ᘒ♽⎇ⓥࠣ࡞࡯ࡊߩᚑᨐ᭎ⷐ ↢ᘒ♽⎇ⓥࠣ࡞࡯ࡊ ᄹ૒ේ 㗼㇢ ··· 123

6. ࠮ࡦࠨ⎇ⓥߩ᭎ⷐ ੹੗ ᱜ ··· 128

7. ࿾਄ࠪࠬ࠹ࡓ㐿⊒෸߮ㆇ↪ߩᚑᨐ᭎ⷐ ┻ፉ ᢅ᣿ ··· 144

8. EORC࿖㓙දജᵴേ㧔IARCޔSAFE㧕 ␲ῳᳯ⌀৻ ··· 154

9. ࿾⃿㔚⏛ⅣႺࡕ࠾࠲࡯ⴡᤊ⟲㧦ELMOS Constellation 㧙਎⇇ೋߩ㔚ሶ᷷ᐲ࡮㔚ሶኒᐲ࡮GPSព⭁หᤨ᷹ⷰⴡᤊ⟲ߩឭ᩺㧙 ఽ₹ ື຦ ··· 160

ઃ㍳ 2009ᐕᐲ ࿾⃿᷹ⷰⴡᤊ࠺࡯࠲ឭଏታ❣··· 166

2009ᐕEORC⎇ⓥᚑᨐ⊒⴫ ··· 167

㑐ㅪ⇛⺆㓸 ··· 174

ߪߓ߼ߦ

੹࿁ޔ EORCߩᛛⴚෳਈࠍᒁ߈ฃߌࠆߎߣߦߥߞߚޕᕁ޿㄰ߒߡߺࠆߣޔ JAXAߩ೨りߩNASDA

㧔ቝቮ㐿⊒੐ᬺ࿅㧕߆ࠄ฽߼ߡޔቝቮ߆ࠄߩ࿾⃿᷹ⷰߦߟ޿ߡߪ15ᐕએ਄ߩઃ߈ว޿ߣߥࠆޕᒰᤨ

ߩᚒޘߩ໧㗴ᗧ⼂ߪޔ ̌NASAࡑࠗ࠽ࠬ⑼ቇ߇NASDA̍ߣ޿߁⸒⪲ߦ⴫ߐࠇߡ޿ࠆࠃ߁ߦޔⴡᤊ࠺

࡯࠲ࠍ↪޿ߚࠨࠗࠛࡦࠬߩၮ⋚ߩ᜛ᄢߣ޿߁ߎߣߢ޽ߞߚޕࠕࡔ࡝ࠞߣᲧߴߡᅤ૗ߦ߽ᣣᧄߩቝ ቮ߆ࠄߩ࿾⃿᷹ⷰಽ㊁ߪᒙ૕ߢ޽ߞߚޕ߅ࠅߒ߽ޔNASDAߪޔ࿾⃿᷹ⷰⴡᤊ㧔ADEOS㧕ࠍᛂߜ

਄ߍࠃ߁ߣߒߡ߅ࠅޔߘߩ࠺࡯࠲ࠍ೑↪ߒߚࠨࠗࠛࡦࠬࠍ⏕┙ߒߡ਎⇇ߦᛂߞߡ಴ࠃ߁ߣߔࠆᗧ

᳇ㄟߺ߇޽߰ࠇߡ޿ߚޕዋߥߊߣ߽ޔᒰᤨߩਛၷߪߘߩࠃ߁ߥᗧ⼂ߢ޽ߞߚޕߘߎߢޔᦨೋߪޔ ᄢቇߥߤߩ⎇ⓥ⠪ࠍ੐ᬺ࿅ߩ᜗⡜⎇ⓥຬߣߒߡ᜗⡜ߒޔ NASDAߢߩࠨࠗࠛࡦࠬߩၮ⋚ࠍ㜞߼ࠃ߁ ߣ޿߁ߎߣ߆ࠄ‛੐߇ᆎ߹ߞߚޕ

ߒ߆ߒޔߘߩࠃ߁ߥઃߌ὾಺ߥ૕೙ߢߪࠨࠗࠛࡦࠬߪᩮઃߊ߽ߩߢߪߥ޿ޔߣ޿߁ߎߣߪᒰὼ ߢ޽ࠆޕߘߎߢޔ࿾⃿᷹ⷰߦ㑐ߔࠆࠨࠗࠛࡦࠬࠍⴕ߁⚵❱ߣߒߡEORC߇⊒⿷ߒߚߩߢ޽ࠆޕߒ߆ ߒߥ߇ࠄޔ‛੐ߪߘࠇ߶ߤ◲නߢߪߥ޿ޕ๟⍮ߩࠃ߁ߦޔߘߩᓟߩዷ㐿ߪޔጊ޽ࠅ⼱޽ࠅߢ޽ߞ ߚޕࠨࠗࠛࡦࠬࠍ⏕┙ߔࠆߣ⸒ߞߡ߽ޔታⴕߦߪ᭽ޘߥ໧㗴߇ሽ࿷ߔࠆޕߒ߆߽ޔᴺ⊛ߦߪ⎇ⓥ

⚵❱ߢߪߥ޿ߩߢޔりಽ߿੍▚ߥߤ߽ਇ቟ቯߢ޽ߞߚߣᕁ߁ޕ

ߒ߆ߒߥ߇ࠄޔߘࠎߥ໧㗴ጊⓍߣ޿߁⁁ᴫߦ߽㑐ࠊࠄߕޔ⌕ታߦᚑᨐߪߢߡ޿ࠆߣᕁ߁ޕ੹ߦ ߥߞߡ⎇ⓥᚑᨐࠍ⷗ߡߺࠆߣޔ߿ߪࠅޔ15ᐕ૛ߩᤨ㑆ߩ⚻ㆊߪή㚝ߢߪߥ߆ߞߚߣᗵߓࠄࠇࠆޕ EORC߇ࠞࡃ࡯ߒߡ޿ࠆಽ㊁߽ᐢߊޔ⌕ታߦޔEORCߣߒߡ․ᓽߠߌࠄࠇࠆᚑᨐ߽ᢙᄙߊ↢ߺ಴ߐ ࠇߡ߈ߡ޿ࠆߣᕁ߁ޕ૗ߣ⸒ߞߡ߽ޔ⎇ⓥߪޔ↢࠺࡯࠲ߦㄭ޿ߣߎࠈߦዬࠆߎߣߩఝ૏ᕈ߇ሽ࿷

ߔࠆޕ↢ߩ࠺࡯࠲ࠍ⷗ߥ߇ࠄޔߘߩᔕ↪࡮೑↪⎇ⓥߦബ߻ߎߣߦࠃߞߡޔᣂߒ޿⊒⷗ߩ㆏ࠍ⷗಴

ߔߎߣ߇ߢ߈ࠃ߁ޕ

ޟࡠ࡯ࡑߪ৻ᣣߦߒߡߥࠄߕޠߣ޿߁ߩߪߌߛߒฬ⸒ߢ޽ࠆޕߎߩ15ᐕߦ෸߱EORCߩᱧผߪޔ

߿ߪࠅޔ⾗↥ߥߩߢ޽ࠆޕㆊ෰ߩᚑഞߣᄬᢌࠍ✚᜝ߒߥ߇ࠄޔ᣿ᣣߦะ߆ߞߡ৻ᱠࠍ〯ߺ಴ߔߎ ߣ߇㊀ⷐߢ޽ࠈ߁ޕ

ߥ߅ޔEORCߦߪޔ⧯޿⎇ⓥ⠪߇ᄙߊ௛޿ߡ޿ࠆޕ੹࿁ߩᚑᨐႎ๔ᦠߦ⸥タߐࠇߡ޿ࠆౝኈࠍෳ

⠨ߦޔ ᓐࠄߩ੹ᓟߩ⎇ⓥߩㅴዷߦߟߥ߇ࠆࠕ࠼ࡃࠗࠬ߿ࠦࡔࡦ࠻ࠍ޿ߚߛߌࠇ߫ᦸᄖߩᐘ޿ߢ޽ࠆޕ

ᛛⴚෳਈ ૑ ᣿ᱜ

㩷 㩷 㩷 㩷 㩷 㩷 㩷

ᵏᵌᴾᵟᵪᵭᵱМဇᄂᆮ ^㩷

㩷

㩷

1. ALOS೑↪⎇ⓥ

1.1 ALOS⸃ᨆࡊࡠࠫࠚࠢ࠻ߩ2009ᐕᐲߩ✚᜝ ፉ↰ ᡽ା

ALOSᛂߜ਄ߍ㧠ᐕ⋡ߦߥࠅޔALOSࠍ↪޿ߚ⎇ⓥ߇ㅴዷࠍ⷗ߖߡ߈ߚޕALOS-GRߣߒߡߪޔ㧟

࠮ࡦࠨߩᩞᱜᬌ⸽ޔ㜞ᰴᚑᨐ‛ޔ⹜૞ᚑᨐຠߩ૞ᚑᬌ⸽ޔἴኂ⸃ᨆኻᔕޔALOS-2/3ࠍ࠲࡯ࠥ࠶࠻ߣ ߒߡߩࠪࠬ࠹ࡓᬌ⸛ޔฃ⸤⎇ⓥޔታ೑↪ታ⸽ࡊࡠࠫࠚࠢ࠻ߩ⸃ᨆᡰេޔ੩ㇺ࡮὇⚛᷹ⷰ⸘↹ߩታᣉޔ

⸃ᨆ⸘▚ᯏࠪࠬ࠹ࡓߩㆇ↪ޔ⛽ᜬޔGEOS/CEOSޔ᧲ධࠕࠫࠕߣߩ㑐ㅪ⎇ⓥ╬ߩ࿖㓙ᬺോ╬߇޽ࠅޔ ߎࠇࠄࠍታᣉߒߚޕᛂߜ਄ߍᒰೋߩᦨᄢߩᬺോኻ⽎ߪ㧟࠮ࡦࠨߩᩞᱜߢ޽ࠅޔಣℂᷣߺ࠺࡯࠲ߩ࡜

ࠫࠝࡔ࠻࡝࠶ࠢᕈ⢻ޔࠫࠝࡔ࠻࡝࠶ࠢᕈ⢻ࠍᦨᄢ㒢ߦ⊒ើߔࠆὑߦޔෳᾖାภḮߩ⸳⟎ޔหᦼታ㛎 ߢᓧߚⴡᤊ࠺࡯࠲ߩ⸃ᨆߣߎࠇࠄ࿾਄ၮḰὐߣߩᾖวޔᦝߦߪਔ⠪ߩ޽ࠊߖㄟߺ߇ᦨᄢߩᬺോߢ޽

ߞߚޕߒ߆ߒޔߘߩᓟޔ㧟࠮ࡦࠨߪᩞᱜߐࠇޔߘߩᓟߩਥߚࠆኻ⽎ߪޔ㜞ᰴᚑᨐຠߢ޽ࠆ㧔ࠝ࡞࠰ޔ DEM㧕ߩ♖ᐲᬌ⸽ߣ⛽ᜬ࡮㜞♖ᐲൻ߿ޔ⹜૞ᚑᨐຠߩ♖ᐲᬌ⸽ޔ㜞♖ᐲൻߢ޽ߞߚޕߘࠇએ㒠ߪޔ ࠃࠅታ೑↪ߦ㊀ᔃࠍ⟎޿ߚ⎇ⓥࠍታᣉߒߡ޿ࠆޕ ৻ߟߪޔ GEOߢઍ⴫ߐࠇࠆ᫪ᨋࡊࡠࠫࠚࠢ࠻ޔ IBAMA ߣߩ㆑ᴺબណ⋙ⷞࡊࡠࠫࠚࠢ࠻ޔ࿯࿾೑↪ಽ㘃࿑ߩ૞ᚑޔ SAPCߣㅪ៤ߒߡߩ⸃ᨆࡊࡠࠫࠚࠢ࠻ߦផ ㅴߢ޽ࠆޕALOS⸃ᨆߪᛛⴚ㓸࿅ߢ޽ࠅޔ↹௝⸃ᨆ߆ࠄᓧࠄࠇࠆࡁ࠙ࡂ࠙ߩ⫾Ⓧޔ♖ᐲะ਄ߩὑߩ ᛛⴚᖱႎߩ⫾Ⓧޔᛛⴚ㐿⊒ޔߐࠄߦߪޔࠣࡠ࡯ࡃ࡞ߥ࿾⃿ࠍ᷹ⷰኻ⽎ߣߒߡߩޔ࿾⃿‛ℂᖱႎߩ⸃

ᨆขᓧߣ㑐ଥᯏ㑐߳ߩ㈩Ꮣࠍ⋡⊛ߣߔࠆޕ⃻ᤨὐߢߪޔ᫪ᨋᖱႎ㧔ߩ߽ߣߦߥࠆ50mಽ⸃⢻SAR↹

௝ߩⶄᢙᤨᦼߢߩขᓧޔᤋ௝ൻ㧕ࠍᦨૐߢᲤᐕ㧞࿁ߩ㗫ᐲߢⴕߞߡ޿ࠆޕ એਅߦޔઍ⴫⊛ߥᚑᨐࠍᢛℂߔࠆޕ

㧝㧕ᩞᱜᬌ⸽㧦㧟࠮ࡦࠨߣ߽ߦ⛮⛯ߒߡ߅ࠅޔ㧟࠮ࡦࠨߣ߽ᚲⷐߩ♖ᐲࠍḩߚߒߡ޿ࠆޕ߹ߚޔ࿖

㓙⊛ߥᩞᱜᬌ⸽࠴࡯ࡓ㧔Calibration Validation and Science Team Meeting, CVST㧕ࠍ⸳⟎ߒߡ♖ᐲߩ ะ਄ߣ⹏ଔࠍታᣉߒߡ߈ߚޕታᣉߒߡ߈ߚ⚿ᨐࠍޔ IEEE Geoscience and remote sensing, Special Issue on ALOSߣߒߡክᩏ⺰ᢥ㓸ߣߒߡ߹ߣ߼ޔ2009ᐕ12᦬ภߣߒߡ಴ ߐࠇߚޕߎߩਛߢߪޔว⸘17

✬ߩክᩏ⺰ᢥ߇ឝタߐࠇߚޕ

㧞㧕ᄖㇱฃ⸤㧦৻ㇱߪᐔᚑ20ᐕᐲ߆ࠄ㐿ᆎߐࠇߚ߇ޔએਅߦ␜ߔᄖㇱฃ⸤ࠍㅴⴕਛߢ޽ࠆޕߎࠇࠄ ߪޔ޿ߕࠇ߽ޔ JAXA/ALOSࠍ↪޿ߚᚑᨐ߇ᄖㇱᯏ㑐ߦ⹺⼂ߐࠇߚ⚿ᨐߢ޽ࠅޔ੹ᓟߣ߽ߦࠃࠅᄙ ߊߩᒁ߈ว޿ࠍฃߌࠆߎߣߦߥࠆߣᕁࠊࠇࠆޕ

㧞í㧝㧕 JST/JICA 㧦 ޟⴡᤊ࠺࡯࠲ߦࠃࠆ࿾ᒻᖱႎࠍ↪޿ߚ᳖ᴡḓ᜛ᄢጁᱧߩ⸃ᨆ⎇ⓥޠ ࡉ࡯࠲ࡦ࡮

ࡅࡑ࡜ࡗߦ߅ߌࠆ᳖ᴡ࡮᳖ᴡḓࠗࡦࡌࡦ࠻࡝࠺࡯࠲ߩ૞ᚑਛޕ

㧞í㧞㧕᫪ᨋ✚⎇㧦 ޟPALSARߩࠗࡦ࠲࡯ࡈࠚࡠࡔ࠻࡝ᯏ⢻ࠍ೑↪ߒߚ⴫㕙ᮡ㜞ᄌൻ⸃ᨆߦࠃࠆ᫪

ᨋഠൻߩ⹏ଔᚻᴺߩ㐿⊒ޠ

㧞í㧟㧕ⅣႺ⎇㧦 ޟᤨ♽೉SAR⸃ᨆߦࠃࠆ᫪ᨋᷫዋ࡮᫪ᨋഠൻ᛽಴

㧞í㧠㧕࿖੤⋭㧦 ޟ㜞ಽ⸃⢻ࡐ࡜࡝ࡔ࠻࡝࠶ࠢSARࠍ↪޿ߚ᳓ኂ⁁ᴫᛠីᛛⴚߩ㐿⊒ޠPi-SAR⥶ⓨ

ᯏ៞タ᷹ⷰታ㛎㧦2/24ߦታᣉ੍ቯ@ጘ㒂ޔ⒳ሶፉࠍ฽߻ᣣᧄ਄ⓨޕ

1.2 ᫪ᨋ⋙ⷞߩὑߩ㜞ಽ⸃⢻ࠝ࡞࠰࡮൨㈩⵬ᱜᷣߺPALSARࠣࡠ࡯ࡃ࡞ࡕࠩࠗࠢ↹௝ߩ૞ᚑ ፉ↰ ᡽ା

㒽ၞ᷹ⷰᛛⴚⴡᤊޟAdvanced Land Observing SatelliteޔALOS㧦 ޟߛ޿ߜޠ ޠߩㆇ↪߇㐿ᆎߐࠇޔ㧠 ᐕ૛ࠅߚߟޕ᫪ᨋᄌൻ߿᫪ᨋࡃࠗࠝࡑࠬ᛽಴ߦ㜞޿ᗵᐲࠍ᦭ߔࠆL-bandวᚑ㐿ญ࡟࡯࠳࡯㧔Synthetic Aperture Radar (SAR)㧕ߪޔᛂߜ਄ߍᒰೋࠃࠅޔቝቮ⥶ⓨ⎇ⓥ㐿⊒ᯏ᭴㧔JAXA㧕ߩ⎇ⓥࡊࡠࠫࠚࠢ

࠻ߢ޽ࠆALOS੩ㇺ὇⚛⸘↹ߩ߽ߣߦޔࠪࠬ࠹ࡑ࠹࠶ࠢߦో⃿᫪ᨋၞࠍ฽߻ࠣࡠ࡯ࡃ࡞᷹ⷰޔᓧࠄ ࠇߚ࠺࡯࠲ߩහᤨಣℂ㧔ࡉ࡜࠙࠭ಣℂ㧕 ޔ⸘↹ߦၮߠߊ㜞ಽ⸃⢻ಣℂߣߣ߽ߦޔ 50ࡔ࡯࠻࡞ࡕࠩࠗࠢ

↹௝ߩ૞ᚑࠍⴕߞߡ߈ߚޕ࠺࡯࠲ߪޔ࠙ࠚࡉࠍㅢߒߡ৻⥸߳౏㐿ޔදቯᯏ㑐ߦߪࠗࡦ࠲࡯ࡀ࠶࠻ࠍ ㅢߒߡޔහᤨ࠺࡯࠲ឭଏߒߡ߅ࠅޔ᫪ᨋᷫዋ⋙ⷞ╬߳ߩᵴ↪߇࿑ࠄࠇߡ᧪ߚޕ

SARߪ᷹ⷰᣇะ߇ᢳ߼ߦ޽ࠆߎߣޔSAR↹௝ࠍ࿾ᒻ࿑ߦ޽ࠊߖㄟ߻ߩߪᢙ୯࿾ᒻ࿑ࠍ↪޿ߚⶄ㔀 ߥ⸘▚߇ᔅⷐߢ޽ࠅޔߎࠇࠄߩᄌ឵ߪJAXA/EORCߢⴕߞߡ޿ࠆޕᓟᣇᢔੂᢿ㕙Ⓧߪ࿾ᒻ൨㈩ߦࠃߞ ߡᄌൻߔࠆߎߣ߆ࠄޔShuttle Radar Topography Mission (SRTM)ߩ෼㓸ߒߚ࿾ᒻᖱႎࠍⓍᭂ⊛ߦ૶↪

ߔࠆ⵬ᱜᣇᴺ߇ᄙ↪ߐࠇߡ޿ࠆޕߎࠇߪ൨㈩⵬ᱜߣ⸒ࠊࠇࠆᚻᴺߢ޽ࠅޔߘߩ⚿ᨐޔ↹௝ߪ࿾ᒻߦ

޽߹ࠅଐሽߖߕޔ᷹ⷰኻ⽎‛ߦଐሽߔࠆޕߎߩᣇ߇ᬀ↢ಽ㘃╬ߦലᨐ⊛ߢ޽ࠅޔㄭᐕᄙߊ↪޿ࠄࠇ ࠆޕ

᫪ᨋ᷹ⷰߦኻߔࠆ࿖㓙⊛ߥ࠾࡯࠭ߪ㜞߹ࠅࠍ⷗ߖߡ߅ࠅޔ Global Earth Observation System of systems (GEOSS)ߢߪޔ᫪ᨋᷫዋࠍⴡᤊ࠺࡯࠲ߢ㧔శቇߣSAR㧕ታᣉߔࠆ࿖㓙⊛ࡔࠞ࠾࠭ࡓࠍ⸳┙ߐࠇߡ߈ ߚޕ

ᱧผ⊛ߦߪޔߎࠇ߹ߢޔJERS-1 SARࠍ↪޿ߚᾲᏪ㔎ᨋ⋙ⷞࡊࡠࠫࠚࠢ࠻ޔർᣇᨋ⋙ⷞࡊࡠࠫࠚࠢ

࠻ࠍታᣉߒߡ߈ߡ߅ࠅޔ L-band SARߦࠃࠅ᫪ᨋ⋙ⷞ߇น⢻ߢ޽ࠆߎߣߪᛠីߐࠇߡ޿ࠆޕ ߹ߚޔ PALSAR ߦߟ޿ߡߪޔߎࠇ߹ߢߦᩞᱜᬌ⸽߇ߥߐࠇߡ߅ࠅޔᐞ૗ቇ♖ᐲޔ࡜ࠫࠝࡔ࠻࡝࠶ࠢ♖ᐲߪ਎⇇⊛࡟

ࡌ࡞એ਄ࠍ⛽ᜬߒߚ߽ߩߢ޽ࠆޕ

࿾⃿᷷ᥦൻߩ৻ߟߩⷐ࿃ߣߒߡޔ㐿⊒ㅜ਄࿖ߩ᫪ᨋ㕙Ⓧߩഠൻ߇ᄢ߈ߊነਈߔࠆߎߣ߇ㄭᐕႎ๔ ߐࠇޔౝኈߩ㊀ᄢߐ߆ࠄ໧㗴ⷞߐࠇߡ޿ࠆޕ଀߃߫ޔ2007ᐕߩ࿖㓙ㅪว㘩ᢱㄘᬺᯏ㑐㧔FAO㧕ႎ๔ ߦࠃࠆߣޔ㒽ၞ߆ࠄᄢ᳇ਛߦឃ಴ߐࠇߚ὇⚛㊂ߩౝޔ᫪ᨋᷫዋ߿ഠൻߦ઻߁ߩߪޔ㒽ၞ⿠Ḯ㧔଀߃

߫⍹ᴤޔ⍹὇ޔᶧൻᄤὼࠟࠬ╬㧕ో૕ߩ߁ߜ25㧑ߢ޽ࠆߎߣޔ᫪ᨋᷫዋߪᐕ㑆ߦ10ਁᐔᣇࠠࡠࡔ࡯

࠻࡞ߢᣣᧄߩ㕙Ⓧߩ㧟ഀߦኻᔕߔࠆߎߣޔᷫዋ㊂ߪޔධ☨ޔਛᄩࠕࡈ࡝ࠞޔ᧲ධࠕࠫࠕߩ㗅ߦᄢ߈

޿ߎߣ߇ႎߓࠄࠇߡ޿ࠆޕ JAXA/EORCߪPALSAR࠺࡯࠲ߩ1992ᐕ߆ࠄ2009ᐕ߹ߢߩᤨ㑆⊛ᄌൻ߆ࠄ

᫪ᨋᄌൻ㊂ࠍ᛽಴ߔࠆߎߣࠍ⋡⊛ߣߒߡޔߘߩరߣߥࠆ㜞ಽ⸃⢻↹௝㧔10m㧕ࡕࠩࠗࠢߩ૞ᚑޔ᫪

ᨋಽ㘃࿑ߩ૞ᚑޔ᫪ᨋಽᏓߩᤨ㑆ᄌൻߩ᛽಴╬ࠍ⋡⊛ߣߔࠆ߽ߩߢ޽ࠆޕࠨࡦࡊ࡞↹௝ߣߒߡޔࠝ

࡞࠰ಣℂޔ൨㈩⵬ᱜߩᲧセޔࠣࡠ࡯ࡃ࡞ࡕࠩࠗࠢߣߒߡࠕࡈ࡝ࠞߩ଀ࠍߒ߼ߔޕ

1.3 ALOS/PRISM, AVNIR-2ߩᩞᱜᬌ⸽ߦ㑐ߔࠆ⎇ⓥ ↰Ლ ᱞ㓶

1. ߪߓ߼ߦ

㒽ၞ᷹ⷰᛛⴚⴡᤊޟߛ޿ߜޠ (Advanced Land Observing Satellite, ALOS)ߪ2006ᐕ1᦬24ᣣߦ⒳ሶፉቝ ቮ࠮ࡦ࠲࡯߆ࠄᛂߜ਄ߍࠄࠇޔ4ᐕࠍ⚻ㆊߒߚ⃻࿷߽㗅⺞ߦㆇ↪ࠍ⛯ߌߡ޿ࠆޕᧄ⎇ⓥߪޔALOS៞

タߩశቇ࠮ࡦࠨPRISMޔ AVNIR-2ߩᩞᱜᬌ⸽ߦ㑐ߔࠆ߽ߩߢ޽ࠆޕ PRISMޔ AVNIR-2ߩᩞᱜߪ2006 ᐕ10᦬24ᣣߩ࠺࡯࠲৻⥸㈩Ꮣ㐿ᆎએ㒠ޔቯᏱᩞᱜߣߒߡ⛮⛯⊛ߦ♖ᐲ⹏ଔ߅ࠃ߮⹏ଔ⚿ᨐߦ߽ߣߠ ߊಣℂࡄ࡜ࡔ࡯࠲ߩᦝᣂࠍታᣉߒ♖ᐲߩ⛽ᜬ▤ℂࠍⴕߞߡ޿ࠆޕᧄ⎇ⓥߢߪቯᏱᩞᱜߣߒߡታᣉߒ ߡ޿ࠆᮡḰᚑᨐ‛ߩ♖ᐲ⹏ଔ⚿ᨐޔ߹ߚᩞᱜ⚿ᨐߦ߽ߣߠߊ⎇ⓥᚑᨐ‛ߩᬌ⸽ߦߟ޿ߡㅀߴࠆޕ

2. ᩞᱜ

PRISMߩᐞ૗ᩞᱜߪᛂ਄ߍ೨߆ࠄ࠮ࡦࠨࠕ࡜ࠗࡔࡦ࠻ߩ๟࿁ᄌേޔቄ▵ᄌേ߇ෂᗋߐࠇߡ޿ߚ ߎߣ߆ࠄޔ⛮⛯⊛ߦขᓧߒߡ޿ࠆᤤ㑆࡮ᄛ㑆ߩ࿾਄ၮḰὐ(GCP)᷹ⷰ࠺࡯࠲ࠍ↪޿ߡ⹏ଔࠍ⛯ߌߡ

޿ࠆޕࠕ࡜ࠗࡔࡦ࠻ᄌേࡕ࠺࡞ࠍ๟࿁ᄌേߣ⚻ᤨᄌേᚑಽߦಽߌߡࡕ࠺࡞ൻߔࠆߎߣߢޔGCPߥ ߒߩࠪ࡯ࡦߦߟ޿ߡ߽ᐞ૗⛘ኻ♖ᐲߩ㜞♖ᐲൻࠍታ⃻ߒߡ߅ࠅ2009ᐕ7᦬1ᣣᤨὐߢ⋥ਅ࡮೨ᣇⷞ

7.8mޔᓟᣇⷞ8.7mࠍ㆐ᚑߒߚ ¹⁾ (⃻࿷ߩ౏⴫୯)ޕߐࠄߦ⚂3ᐕ㑆(ቯᏱㆇ↪⒖ⴕᓟ)ߩ⹏ଔ⚿ᨐࠍ↪޿

ߡޔࠕ࡜ࠗࡔࡦ࠻ផቯߦᐕ๟ᦼᄌേࠍ⠨ᘦߒߚࡕ࠺࡞ࠍዉ౉ߒޔ 2007ᐕ4᦬22ᣣએ㒠ߩ᷹ⷰ࠺࡯࠲

ߦㆡ↪ߔࠆࡄ࡜ࡔ࡯࠲ߩᦝᣂ ࠍ2010ᐕ2᦬4ᣣߦ࿾⃿᷹ⷰ࠮ࡦ࠲࡯(EOC)ߩᮡḰᚑᨐ‛ಣℂ࠰ࡈ࠻

࠙ࠚࠕ߳࡝࡝࡯ࠬߒߚޕၮᧄ⊛ߦޔㆊ෰᷹ⷰ࠺࡯࠲ߦኻߔࠆࡄ࡜ࡔ࡯࠲ߩ⷗⋥ߒߪ੹࿁߇ೋ߼ߡ

ߢ޽ࠆޕ 2007ᐕ4᦬એ㒠᷹ⷰ࠺࡯࠲ߩᣂߒ޿ࡄ࡜ࡔ࡯࠲ߦࠃࠆౣಣℂࡊࡠ࠳ࠢ࠻ߪ⃻࿷♖ᐲ⹏ଔਛ

ߢ޽ࠆ߇⋥ਅⷞ6.7mޔ೨ᣇⷞ6.2mޔᓟᣇⷞ7.5m⒟ᐲߦߥࠆ⷗ㄟߺߢ޽ࠆޕ

AVNIR-2ߩᐞ૗ᩞᱜߪޔᛂ਄ߍ߆ࠄߩ⚻ᐕᄌൻ߇᣿ࠄ߆ߦߥߞߚߎߣ߆ࠄ2008ᐕ10᦬22ᣣ࠮ࡦ ࠨࠕ࡜ࠗࡔࡦ࠻ࡄ࡜ࡔ࡯࠲ߩᦝᣂࠍⴕߞߚޕߘߩᓟ߽⛮⛯⊛ߦࡕ࠾࠲࡯ߒߡ޿ࠆޕ࿑1ߪ2008ᐕ10

᦬એ㒠ߩAVNIR-2ᐞ૗♖ᐲᄌേߩ࠻࡟ࡦ࠼ࠍ⴫ߒߚ߽ߩߢ޽ࠆ߇ޔ Lineᣇะ⺋Ꮕߪ2009ᐕ4᦬એ㒠ޔ ᓢޘߦᖡߊߥࠆ௑ะ߇⏕⹺ߢ߈ࠆߎߣ߆ࠄࡄ࡜ࡔ࡯࠲ߩౣᦝᣂࠍ੍ቯߒߡ޿ࠆޕPixelᣇะ⺋Ꮕߪ ߎࠇ߹ߢߣห⒟ᐲߩ⺋Ꮕߣߥߞߡ޿ࠆޕ

ノᐲᩞᱜߦߟ޿ߡߪޔ AVNIR-2ߪMODISߣߩ⋧੕ᩞᱜ ²⁾ ޔ PRISMߪAVNIR-2ߣߩ⋧੕ᩞᱜߣߒߡ ታᣉߒߡ޿ࠆ ¹⁾ ޕ⃻⁁ޔᄢ߈ߥ♖ᐲߩഠൻߪ⏕⹺ߐࠇߡ޿ߥ޿ޕ

࿑1㧦AVNIR-2ᐞ૗⛘ኻ♖ᐲ⹏ଔ⚿ᨐ(ࡐࠗࡦ࠹ࠖࡦࠣⷺ0ᐲޔ2008ᐕ10᦬એ㒠)

3. PRISMߦࠃࠆᢙ୯࿾⴫ࡕ࠺࡞(DSM)ߩᬌ⸽

⎇ⓥᚑᨐ‛ߪ․ߦAVNIR-2ࠝ࡞࠰⵬ᱜ(ORI)࡮ᄢ᳇⵬ᱜ↹௝ࠍ౉ജߣߒߚ㜞♖ᐲ࿯࿾ⵍⷒಽ㘃࿑

ߩ૞ᚑޔ PRISMߦࠃࠆᢙ୯࿾⴫ࡕ࠺࡞(DSM)/ORIߩ૞ᚑߣPRISM/AVNIR-2 ORIߣPRISM/DSMߦࠃ ࠆ᳖ᴡ࡮᳖ᴡḓࡕ࠾࠲࡝ࡦࠣࠍㅴ߼ߡ޿ࠆޕ࿯࿾ⵍⷒಽ㘃࿑૞ᚑߪᮮゲ⎇ⓥޟ↢ᘒ♽RGޠߣ౒ห ߢታᣉߒߡ߅ࠅޔߎߎߢߪ․ߦPRISM/DSMߩᬌ⸽ߦߟ޿ߡㅀߴࠆޕ

EORCߢߪᩞᱜᬌ⸽ߩ৻ⅣߣߒߡPRISM DSM/ORI૞ᚑ࠰ࡈ࠻࠙ࠚࠕࠍ㐿⊒ߒ ³⁾ ޔ⎇ⓥᚑᨐ‛ߣߒ

ߡቯᏱ⊛ߦ↢↥ߒߡ޿ࠆޕ╳ᵄጊࠍਛᔃߣߔࠆ⚂8km྾ᣇࠍ࠹ࠬ࠻ࠨࠗ࠻ߣߒߡPRISM/DSMߩ♖

ᐲᬌ⸽ࠍⴕߞߚ ⁴⁾ ޕ࿑2ᏀߪPRISM/DSMޔਛᄩߪෳᾖ↪࠺࡯࠲ߣߒߚ⥶ⓨᯏ៞タLidarߦࠃࠆDSMޔ ฝߪੑߟߩDSMߩᏅ↹௝ࠍ⴫ߔޕ⴫2ߪᏅ↹௝ߩ⛔⸘୯ࠍ߹ߣ߼ߚ߽ߩߢ޽ࠆ߇ޔ࠹ࠬ࠻ࠨࠗ࠻ో

ၞߦ߅޿ߡᮡḰ஍Ꮕ4.91mޔ RMSE5.13mߣ޿߁⚿ᨐࠍᓧߚޕ࿯࿾ⵍⷒ೎ߦ⷗ࠆߣ᮸ᧁࠍ฽߹ߥ޿

႐ᚲߢߪRMSE3mએਅߩ㜞ߐ♖ᐲ߇޽ࠆߎߣ߇ಽ߆ߞߚޕ੹ᓟޔ⛮⛯⊛ߦ⎇ⓥᚑᨐ‛ߩ♖ᐲᬌ⸽

ࠍታᣉߔࠆߣߣ߽ߦޔߐࠄߥࠆ㜞♖ᐲൻߦദ߼ߚ޿ߣ⠨߃ࠆޕ

࿑2㧦╳ᵄጊᬌ⸽ࠨࠗ࠻ߦ߅ߌࠆPRISM/DSM(Ꮐ)ޔෳᾖ↪DSM(ਛ)ޔ㜞ߐߩᏅ↹௝(ฝ)

⴫1㧦╳ᵄጊᬌ⸽ࠨࠗ࠻ߦ߅ߌࠆPRISM/DSMᬌ⸽⚿ᨐ(ోၞޔዊ㗔ၞ) Terrain type Points Bias (m) Standard

deviation (m) RMSE (m) Max (m) Min (m)

Whole 1,287,801 -1.50 4.91 5.13 33 -71

Mountain top 10,000 -1.57 5.40 5.62 30 -37 Mountain side 10,000 -3.10 6.44 7.15 24 -33 Mountain valley 10,000 -3.44 6.12 7.01 20 -32 Mountain ridge 10,000 -1.96 6.09 6.40 22 -53

Paddy 10,000 -0.04 2.53 2.53 20 -15

Paddy and trees 10,000 -1.09 4.16 4.31 18 -31

Village 10,000 -0.20 2.94 2.94 9 -20

ෳ⠨ᢥ₂

1) T. Tadono, M. Shimada, H. Murakami, and J. Takaku, "Calibration of PRISM and AVNIR-2 Onboard ALOS

"Daichi"," IEEE Trans. Geoscience and Remote Sensing, Vol. 47, No. 12, pp.4042-4050, Dec. 2009.

2) H. Murakami, T. Tadono, H. Imai, J. Nieke, and M. Shimada, "Improvement of AVNIR-2 Radiometric Calibration by Comparison of Cross-Calibration and Onboard Lamp Calibration," IEEE Trans. Geoscience and Remote Sensing, Vol. 47, No. 12, pp.4051-4059, Dec. 2009.

3) J. Takaku and T. Tadono, “PRISM On-Orbit Geometric Calibration and DSM Performance,” IEEE Trans.

Geoscience and Remote Sensing, Vol. 47, No. 12, pp.4060-4073, 2009.

4) T. Tadono, M. Shimada, and J. Takaku, “Validation of Precise Digital Surface Model Generated by PRISM Onboard ALOS,” Proc. SPIE, International Society for Optics and Photonics, Vol. 7474, pp.74740H-1 - 74740H-12, 2009.

0m Height Scale

900m +30m

0 m

-30m

Height

Scale

1.4 ALOSߦࠃࠆ࿾Ზᄌേ෸߮࿾⴫ᄌ⁁ߩᬌ಴ߣߘߩ⸃㉼ ችၔ ᵗ੺

1. ߪߓ߼ߦ

ᧄ⊒⴫ߢߪޔ 2009ᐕᐲߦⴕࠊࠇߚ࿾㔡࡮ἫጊྃἫߦኻߔࠆALOS᷹ⷰ࠺࡯࠲ࠍ↪޿ߚ⎇ⓥߦߟ޿

ߡ⚫੺ߔࠆޕ ALOSߩ࠺࡯࠲ࠍ࿾㔡࡮ἫጊྃἫߩ⍴ᦼ੍᷹ߦ೑↪ߔࠆ੐ߪ⃻⁁ߢߪᭂ߼ߡ࿎㔍ߢ޽

ࠆޕߘࠇߪALOSߩᜬߟᤨ㑆ಽ⸃⢻߇ᄢ߈ߥ೙⚂ߦߥߞߡ޿ࠆߎߣߪ߽ߜࠈࠎޔ࿾㔡ߦ㑐ߒߡߪ⍴

ᦼ੍᷹⥄૕߇㔍ߒ޿ߣ⸒߃ࠆ߆ࠄߢ޽ࠆޕߒ߆ߒޔ࿾㔡߿ྃἫߩ࠲ࠗࡊߦࠃߞߡߪޔਛ㐳ᦼ੍᷹

ߦALOS࠺࡯࠲߇ᓎߦ┙ߟࠤ࡯ࠬ߇޽ࠆޕᧄ⊒⴫ߢߪ2007ޔ 2010ᐕߩੑߟߩ࠰ࡠࡕࡦ⻉ፉ࿾㔡ߦߟ

޿ߡޔ2008ᐕߦྃἫߒߚࠝࠢࡕࠢἫጊߦߟ޿ߡޔߘߒߡ2009ᐕߦྃἫߒߚࠨ࡝࠴ࠚࡈ࡮ࡇ࡯ࠢߦ ߟ޿ߡALOSߦࠃࠆ᷹ⷰߩ⚿ᨐߣߘߩ⸃㉼ޔਛ㐳ᦼ੍᷹߳ߩ೑↪ߦߟ޿ߡ߅⹤ߔࠆޕ߹ߚᦨᓟߦޔ 2009ᐕᐲߦⴕߞߚ࿾㔡ߦኻߔࠆ✕ᕆ᷹ⷰ߳ߩኻᔕߦߟ޿ߡ߽⚫੺ߔࠆޕ

2. ࠰ࡠࡕࡦ⻉ፉ๟ㄝߢ⊒↢ߒߚ࿾㔡ߩਛ㐳ᦼ੍᷹

৻⸒ߦޟ࿾㔡੍⍮࡮࿾㔡੍᷹ޠߣ޿ߞߡ߽ޔ࠲ࠗࡊߦࠃߞߡߪ⍴ᦼޔਛ㐳ᦼ㑐ࠊࠄߕ⃻⁁ߢߪ 㔍ߒ޿ߣ⸒ࠊߑࠆࠍᓧߥ޿ޕߚߛޔࡊ࡟࡯࠻Ⴚ⇇ဳ࿾㔡ߩౝޔᶏḴဳ࿾㔡ߣ๭߫ࠇࠆ࠲ࠗࡊߩ࿾

㔡ߦߟ޿ߡߪޔࡔࠞ࠾࠭ࡓ⊛ߦ޽ࠆ⒟ᐲߩℂ⸃߇ㅴߺޔਛ㐳ᦼ੍᷹ߢߪ޽ࠆ߇੍᷹߇น⢻ߥ႐ว ߇޽ࠆޕ2007ᐕ4᦬1ᣣߦ࠰ࡠࡕࡦ⻉ፉߢ⊒↢ߒߚM8.1ߩ࿾㔡ߪߘߩࠃ߁ߥᶏḴဳ࿾㔡ߢ޽ߞߚޕ

⃻࿾᷹ⷰ࠺࡯࠲ߩਲߒ޿ᧄ࿾ၞߢޔALOS/PALSAR࠺࡯࠲ߩᏅಽᐓᷤ⸃ᨆ㧔InSAR⸃ᨆ㧕ᚻᴺࠍ↪

޿ߚ࿾Ზᄌേߩᬌ಴㧔࿑1Ꮐ㧕߇ⴕࠊࠇޔߎߩ࿾Ზᄌേᖱႎࠍ૶޿㔡Ḯᢿጀ㕙㧔ࡊ࡟࡯࠻Ⴚ⇇㕙㧕

਄ߦ߅ߌࠆṖࠅಽᏓ㧔࿑1ฝ㧕߇ផቯߐࠇߚ[Miyagi et al., 2009]ޕMiyagi et al., [2009]ߢߪޔ2007ᐕ ߩ࿾㔡ߪㆊ෰ߩ࿾㔡⊒↢ᱧ߆ࠄផቯߐࠇࠆ࿾㔡ⓨ⊕ၞߩᄢㇱಽࠍၒ߼ߚ߽ߩߢ޽ࠆ߇ޔ2007ᐕߩ 㔡Ḯၞධ᧲ߦᧂ⎕უ㗔ၞ߇ᱷߞߡ߅ࠅޔߎߎߢ੹ᓟM7⒟ᐲ࿾㔡߇⊒↢ߔࠆน⢻ᕈ߇޽ࠆߣ⸒෸ߐ ࠇߚ㧔࿑1ߪߤߜࠄ߽Miyagi et al., [2009]߆ࠄᒁ↪ߒട╩㧕 ޕߘߒߡ2010ᐕ1᦬ޔߘߩਛ㐳ᦼ੍᷹ߦ ᴪߞߚᒻߩ࿾㔡߇⊒↢ߒߚޕ

㩷 㩷 㩷 㩷 㩷 㩷 㩷 㩷 㩷

࿑1Ꮐ㧦2007ᐕ࠰ࡠࡕࡦ⻉ፉ࿾㔡೨ᓟߩPALSARᐓᷤ⸃ᨆߦࠃࠅᓧࠄࠇߚᏅಽᐓᷤ↹௝ޕ࿾㔡ߦ઻߁

࿾Ზᄌേࠍ⴫ߔޕ

࿑1ฝ㧦࿑1Ꮐߩ࿾Ზᄌേ࠺࡯࠲߆ࠄផቯߐࠇߚᢿጀ㕙਄ߩṖࠅಽᏓߣޔᧂ⎕უ㗔ၞ㧔㕍ᨒ㧕 ޔߘࠇ߆

ࠄ2010ᐕ1᦬ߦ⿠ߎߞߚ࿾㔡ߩ㔡ᄩ㧔⿒ᤊ㧕 ޕ

3. ࠝࠢࡕࠢἫጊߦ߅ߌࠆࡑࠣࡑḳࠅߩផቯ

ࠕ࡜ࠬࠞ࡮ࠕ࡝ࡘ࡯ࠪࡖࡦ೉ፉߦ޽ࠆࠝࠢࡕࠢἫጊߪ20਎♿ਛߦ10࿁એ਄ߩྃἫࠍ⸥㍳ߒߚᵴ⊒

ߥἫጊߢޔ 2008ᐕ7᦬ߦ1997ᐕߩᄢྃἫએ᧪11ᐕ߱ࠅߩྃἫࠍ⿠ߎߒߚޕࠝࠢࡕࠢἫጊߪߎࠇ߹ߢห ߓྃἫญ߆ࠄ߶߷หߓ࠲ࠗࡊߩྃἫࠍ➅ࠅ㄰ߒߡ߈ߚ߇ޔ2008ᐕߩྃἫߪᣂߒߊߢ߈ߚྃἫญ߆ࠄ

⿠ߎߞߚ߽ߩߛߞߚޕ߹ߚ1997ᐕྃἫߦ㑐ଥߒߚߣᕁࠊࠇࠆࡑࠣࡑḳࠅ߇ޔߘࠇએ㒠߽⫾Ⓧࠍ⛯ߌ ߡ޿ߚߎߣߪߎࠇ߹ߢߩ⎇ⓥ߆ࠄಽ߆ߞߡ޿ࠆ[e.g. Lu et al., 2005; Miyagi et al., 2004]ޕ੹ᓟߩྃἫᵴ

േࠍ੍᷹ߔࠆ਄ߢޔࡑࠣࡑଏ⛎♽ߘߩ߽ߩ߇ᄌൻߒߚߩ߆ޔߘࠇߣ߽ߎࠇ߹ߢㅢࠅߥߩ߆ࠍᛠីߔ ࠆߎߣߪᭂ߼ߡ㊀ⷐߢ޽ࠆޕߘߎߢޔ2008ᐕྃἫ೨ᓟߩPALSAR࠺࡯࠲߆ࠄྃἫߦ઻ߞߚ࿾Ზᄌേ

ࠍᬌ಴ߒޔᄌേḮߩផቯࠍⴕߞߚ㧔࿑2㧕 ޕߎࠇߦࠃࠆߣޔㆊ෰ߩ⎇ⓥߢផቯߐࠇߚࠝࠢࡕࠢἫጊߩ ᄌേḮߣ㕖Ᏹߦࠃߊ৻⥌ߒߡ޿ࠆߎߣ߇ಽ߆ߞߚޕᣂߒ޿ྃἫญ߇ߢ߈ߚߎߣߢޔ੹ᓟߩྃἫᵴേ

㧔ྃἫᒻᘒޔ㗫ᐲ╬㧕ߦᄌൻ߇⃻ࠇࠆน⢻ᕈߪุ߼ߥ޿߇ޔዋߥߊߣ߽ߎࠇ߹ߢߣห᭽ߩࡑࠣࡑḳ߹

ࠅ߳ߩࡑࠣࡑߩ⫾ⓍߣᰴߩྃἫᵴേ߇㑐ଥߔࠆߣᕁࠊࠇޔ੹ᓟ߽࠺࡯࠲ߩ⫾Ⓧ߇ᦸ߹ࠇࠆޕ

㩷 㩷

࿑2Ꮐ㧦2007ᐕ8᦬ߣ2009ᐕ8᦬ߦ᷹ⷰߐࠇߚPALSAR࠺࡯࠲ࠍᐓᷤಣℂߒߡᓧࠄࠇߚᏅಽᐓᷤ↹௝ޕ

ྃἫߦ઻ߞߚ෼❗ߩ࿾Ზᄌേࠍ␜ߔޕ

࿑2ฝ㧦࿑2Ꮐߢᬌ಴ߐࠇߚ࿾Ზᄌേ࠺࡯࠲ࠍ↪޿ߡផቯߐࠇߚᄌേḮ㧔⿒ਣޔᷓߐ㨪3.2km㧕ߣޔߘ ߩᄌേḮ߆ࠄᓧࠄࠇࠆࠪࡒࡘ࡟࡯࡚ࠪࡦ↹௝ޕ

4. ජፉ೉ፉࠨ࡝࠴ࠚࡈ࡮ࡇ࡯ࠢߦ߅ߌࠆྃἫ೨ᓟߩDEMᲧセ

2009ᐕ6᦬11ᣣޔජፉ೉ፉMatuaፉߦ޽ࠆࠨ࡝࠴ࠚࡈ࡮ࡇ࡯ࠢ㧔⦹⬂ጊ㧘೎ฬߪ᧻ベን჻㧕ߢྃἫ ߇⊒↢ߒߚޕ⃻࿷ߪήੱፉߢྃἫߦࠃࠆἴኂߪ߶ߣࠎߤߥ޿߇ޔྃᾍߩ㜞ߐ߇10kmࠍ⿥߃ޔ⥶ⓨળ

␠ߪ࿖㓙✢ߩ⚻〝ࠍᄌᦝߔࠆᔅⷐߦㄼࠄࠇߚޕߎߩྃἫ೨ᓟߦ᷹ⷰߐࠇߚALOS/PRISMߩ࠺࡯࠲߆ ࠄ૞ᚑߐࠇߚDEM㧔Digital Elevation Model: ᢙ୯ᮡ㜞࠺࡯࠲㧕ࠍ૶޿ޔྃἫ೨ᓟߩ࿾⴫㕙ߩᲧセࠍ ⴕߞߚޕߎࠇߦࠃࠆߣྃἫ೨ᓟߩᮡ㜞ߩᏅ߇㗼⪺ߥㇱಽߣޔྃἫᓟߩAVNIR-2↹௝ߢ⏕⹺ߢ߈ࠆṁ ጤᵹߩᵹ〔߇৻⥌ߔࠆ㧔࿑3Ꮐ㧕 ޕߘߎߢߎߩ㗔ၞߩ㕙Ⓧߣ㜞ߐߩᏅࠍដߌߡ૕Ⓧࠍ⸘▚ߔࠆߣޔ1.4

˜10 ⁷ m ³ ߣផቯߐࠇߚޕ߹ߚޔἫญᐩߩᮡ㜞࠺࡯࠲ߩౝޔྃᾍ߇Ყセ⊛ዋߥ޿ㇱಽࠍᲧセߔࠆߣޔྃ

Ἣߦ઻ߞߡἫญᐩ߇ᦨᄢߢ50m㒱ᴚߒߡ޿ࠆߎߣ߇ಽ߆ߞߚ㧔࿑3ฝ㧕 ޕἫญᐩߩ㒱ᴚߪྃἫ೨ᓟߩ

ྃἫญߩ↹௝Ყセ߆ࠄ߽᣿ࠄ߆ߢ޽ࠆޕ

㩷 㩷

࿑3Ꮐ㧦ྃἫ೨ᓟߩDEMߩᏅಽߦࠃࠅ૕Ⓧ߇ផቯߐࠇߚྃ಴‛㧔ṁጤᵹ㧕 ޕ

࿑3ฝ㧦ྃἫ೨ᓟߩἫญᐩߩ㜞ߐᄌൻޕྃἫߦࠃࠅ߅ࠃߘ50mἫญᐩ߇㒱ᴚޕ

5. 2009ᐕᐲߦታᣉߐࠇߚ࿾㔡ߦኻߔࠆALOS✕ᕆ᷹ⷰ߳ߩኻᔕ

2009ᐕᐲ߽਎⇇ਛߢᄙߊߩⵍኂ࿾㔡߇⊒↢ߒޔALOSߦࠃࠆ✕ᕆ᷹ⷰ߇ⴕࠊࠇߚޕታ㓙ߦ࿾㔡߇

⊒↢ߒߚ㓙ߦⴕ߁ߎߣߪޔ࿾㔡ᵄ⸃ᨆߦࠃࠆᢿጀࡕ࠺࡞ࠍෳ⠨ߦߒߚޔInSARࠪࡒࡘ࡟࡯࡚ࠪࡦ↹

௝૞ᚑψ✕ᕆ᷹ⷰψ 㧔ᕆ޿ߢ޿ࠆ႐วߪ੍᷹୯ޔ ߘ߁ߢߥ޿႐วߪ㜞♖ᐲ゠㆏ᖱႎࠍᓙߞߡ㧕 PALSAR Ꮕಽᐓᷤ⸃ᨆψࠕࡦ࡜࠶ࡇࡦࠣߢ࿾Ზᄌേᖱႎߩ᛽಴ψ㧔߆ߥࠅࠪࡦࡊ࡞ߥ㧕ᢿጀࡕ࠺࡞ߩផቯޔ ߢ޽ࠆޕߎߎߢߪޔߘߩ✕ᕆ᷹ⷰ⚿ᨐߩ৻ㇱࠍ⚫੺ߔࠆޕ

㩷

1.5 PALSARߦࠃࠆࠗࡦ࠼ࡀࠪࠕ࡮ࠬࡑ࠻࡜ፉߩ᫪ᨋબណᬌ಴ ⏷ญ ᴦ

㩷

1. ߪߓ߼ߦ

JAXA߇ਥዉߔࠆ੩ㇺࠞ࡯ࡏࡦ(K&C)ࡊࡠࠫࠚࠢ࠻╬ߩᵴേߦࠃߞߡޔALOSߦ៞タߐࠇߡ޿ࠆL ࡃࡦ࠼วᚑ㐿ญ࡟࡯࠳(PALSAR)ߩ᫪ᨋ᷹ⷰߦኻߔࠆ᦭ലᕈ߇ታ⸽ߐࠇࠆࠃ߁ߦߥߞߚޕ․ߦޔᄙߊ ߩ⥄ὼᨋ߇ᄬࠊࠇߡ޿ࠆࠕࡑ࠱ࡦᾲᏪᨋߢߪޔࡉ࡜ࠫ࡞ⅣႺ⋭(IBAMA)߿ࡉ࡜ࠫ࡞ㅪ㇌⼊ኤߣߩ౒

ห⎇ⓥߣߒߡޔPALSARߩḰ࡝ࠕ࡞࠲ࠗࡓ↹௝߆ࠄᣂߚߥબណၞࠍᬌ಴ߒޔߘߩ⚿ᨐࠍၮߦ⃻࿾߳

⿞߈બណ⠪ࠍขࠅ✦߹ࠆߣ޿߁ታ೑↪ߢߩ೑↪߇ⴕࠊࠇᚑᨐࠍ޽ߍߟߟ޽ࠆޕ৻ᣇߢޔ⃻⁁ߢߪ↹

௝್⺒⠪߇⋡ⷞߢબណၞࠍ․ቯߔࠆߣ޿߁૞ᬺࠍⴕߞߡ߅ࠅޔ 㧔ඨ㧕⥄േߢߩબណၞᬌ಴ߩᔅⷐᕈ߇

᳞߼ࠄࠇߡ޿ࠆޕ

ᧄ⎇ⓥߩኻ⽎࿾ၞߢ޽ࠆࠗࡦ࠼ࡀࠪࠕ࡮ࠬࡑ࠻࡜ፉߪޔࠕࡑ࠱ࡦߣਗࠎߢᦨ߽᫪ᨋબណ߇ⴕࠊࠇ ߡ޿ࠆ࿾ၞߩ߭ߣߟߢ޽ࠅޔ᫪ᨋબណ߇ᄢ߈ߥ὇⚛᡼಴Ḯߣߥߞߡ޿ࠆޕ౒ห⎇ⓥ⋧ᚻߢ޽ࠆWWW

ࠗࡦ࠼ࡀࠪࠕߢߪ⃻࿾᷹ⷰ߿శቇ࠺࡯࠲ࠍၮߦޔ⥄ὼᨋߩᄌൻᬌ಴ࠍⴕߞߡ޿ࠆޕᧄ⎇ⓥߢߪޔߘ ࠇࠄࠍ࠻࠘࡞࡯ࠬ࠺࡯࠲ߣߒߡPALSARߦࠃࠆ᫪ᨋબណၞᬌ಴ߩቯ㊂⊛⹏ଔࠍⴕ޿ޔ᫪ᨋબណ⋙ⷞ

ࠪࠬ࠹ࡓߩ㐿⊒ᬌ⸛ࠍⴕߞߚޕ

2. ࠺࡯࠲

PALSARߩ㧞஍ᵄ㜞ಽ⸃⢻(FBD)࠺࡯࠲ࠍ૶↪ߒߚޕSIGMA-SARࡊࡠ࠮࠶ࠨࠍ↪޿ߡޔࠝ࡞࠰࡮

ᢳ㕙൨㈩⵬ᱜࠍᣉߒߚኻ⽎㗔ၞߩᤨ♽೉SAR↹௝ࠍ૞ᚑߒߚޕ࠻࠘࡞࡯ࠬ࠺࡯࠲ߣߒߡޔWWWࠗ

ࡦ࠼ࡀࠪࠕߦࠃࠅ૞ᚑߐࠇߚ2007ᐕߣ2009ᐕߩࠬࡑ࠻࡜ፉߦ߅ߌࠆ⥄ὼᨋࡌࠢ࠲࡯࠺࡯࠲ࠍ૶↪ߒ ߚޕߎࠇߪޔਥߦޔㆊ෰㧞ᐕ㑆ߦ᷹ⷰߐࠇߚ㔕ߩᓇ㗀ߩዋߥ޿LANDSAT࠺࡯࠲ߩ↹௝್⺒ߦࠃࠅ

૞ᚑߐࠇߚ߽ߩߢ޽ࠆޕ․ቯߩ㗔ၞߢߪ᧦ઙߩ⦟޿LANDSAT࠺࡯࠲ߦࠃࠅⶄᢙ࿁ߩᄌൻ᛽಴߇ⴕ ࠊࠇߡ߅ࠅޔબណၞߩᤨ㑆ᄌൻ᛽಴ߩ⹏ଔߦ૶↪ߒߚޕ

3. ⚿ᨐ

a. ᫪ᨋબណߦኻߔࠆᓟᣇᢔੂᄌൻ․ᕈ

ኻ⽎㗔ၞߩ 2007ᐕ߆ࠄ2009ᐕߦ߆ߌߡߩબណ㗔ၞࠍ᳞߼ޔ ᫪ᨋၞޔ ᄌൻၞߘࠇߙࠇߦ߅ߌࠆ PALSAR ߩᓟᣇᢔੂᢿ㕙Ⓧ(NRCS)ߩᄌൻࠍ⺞ߴߚޕ᫪ᨋၞߦኻߒߡߪޔ HHޔ HVߦኻߒߡߘࠇߙࠇޔ 1.0dBޔ 0.7dB⒟ᐲߩᄌേ߇⷗ࠄࠇߚޕߎߩᄌേߪ࿯ფ᳓ಽ㧔߹ߚߪޔ᮸౰ߩ᳓ಽ㊂㧕ߩᄌൻߦࠃࠆ߽ߩߣ⠨

߃ࠄࠇޔ࿾⴫ߩᄌൻࠍᬌ಴ߔࠆ਄ߢߪޔㆡಾߦ⷗Ⓧ߽ࠄߥߌࠇ߫ߥࠄߥ޿㊂ߣߥࠆޕ৻ᣇޔᮡḰ஍

Ꮕ/㗔ၞᐔဋߢቯ⟵ߐࠇࠆ߫ࠄߟ߈ᐲ㧔࠹ࠢࠬ࠴ࡖ㧕ߦ㑐ߒߡߪޔᤨ㑆ᣇะߦ߶߷৻ቯߢᄢ߈ߥᄌൻ ߪ⷗ࠄࠇߥ߆ߞߚޕ

બណߦኻߒߡߪHHߩNRCS߇બណᓟߦ਄᣹ߔࠆࠤ࡯ࠬ߇⷗ࠄࠇߚ߇ޔో૕ߣߒߡ᣿⍎ߥ୯ߩᄌൻ ߪ⏕⹺ߢ߈ߥ߆ߞߚޕ৻ᣇޔ HVߩାภߪ޿ߕࠇߩબណၞߦኻߒߡ߽2-4dBߩૐਅ߇⏕⹺ߐࠇߚޕ HV ߩᷫዋߪ૕Ⓧᢔੂߩᷫዋߣߒߡ⺑᣿ߐࠇࠆޕ৻ᣇޔ HHߦ㑐ߒߡߪޔࠕࡑ࠱ࡦၞߦ߅޿ߡߪାภߩૐ ਅ߇બណၞߦኻᔕߔࠆߣ⹺⼂ߐࠇߡ߈ߚޕࠕࡑ࠱ࡦߩબណၞߩਛߦߪᓟߩࡊ࡜ࡦ࠹࡯࡚ࠪࡦߩߚ߼

ߦᢛ࿾ߔࠆ႐ว߇ᄙ޿߇ޔߘߩࠃ߁ߥબណߦኻߒߡߪHHߩૐਅߣߒߡ⹺⼂߇น⢻ߢ޽ࠆޕ৻ᣇޔࠬ

ࡑ࠻࡜ߩࠃ߁ߦޔબណᓟߦ․೎ߥᢛ࿾ࠍⴕࠊߕޔࡊ࡜ࡦ࠹࡯࡚ࠪࡦߦ⒖ⴕߔࠆ႐วߦߪHHߪᬌ಴ߦ ㆡߒߡ޿ߥ޿ߚ߼ߣ੍ᗐߐࠇࠆޕ

બណၞߢߩ߫ࠄߟ߈ᐲ㧔࠹ࠢࠬ࠴ࡖ㧕ߦ㑐ߒߡߪޔHHޔHVߣ߽ߦબណਛ߆ࠄޔબណᓟߦ߆ߌߡ ୯ߩ਄᣹߇⏕⹺ߐࠇޔબណၞᬌ಴ߦ᦭ലߥࡄ࡜ࡔ࡯࠲ߣߥࠆน⢻ᕈ߇␜ߐࠇߚޕ

b. ೨ಣℂ

೨▵ߢ⺞ߴߚାภ․ᕈࠍၮߦબណၞߩ⥄േᬌ಴ᚻᴺߩᬌ⸛ࠍⴕ߁ޕSAR↹௝ߢߪ㧔㧕ࠬࡍ࠶ࠢ࡞

߇ሽ࿷ߔࠆߚ߼ޔ৻⥸⊛ߦࡇࠢ࠮࡞ࡌ࡯ࠬߩᄌൻ᛽಴ߢߪࡁࠗ࠭⁁ߩ᭴ㅧ߇ᱷࠆߎߣ߇ᄙ޿ޕᧄ⎇

ⓥߢߪޔ↹௝ࠍห᭽ߩ․ᓽࠍᜬߟ㗔ၞߢ඙ಽߔࠆ㧔࠮ࠣࡔࡦ࠹࡯࡚ࠪࡦ㧕↹௝ࠝࡉࠫࠚࠢ࠻ࠍၮߦ ߒߚᚻᴺࠍណ↪ߔࠆޕ࠮ࠣࡔࡦ࠹࡯࡚ࠪࡦࠍല₸⊛ߦⴕ߁ߚ߼ߦޔSARߩᝄ᏷↹௝ߦኻߒߡࡈࠖ࡞

࠲࡝ࡦࠣಣℂࠍⴕ߁ޕߎߎߢߪޔ(1)ᑼߦ␜ߔࠛ࠶ࠫᒝ⺞ߦ᦭ലߥޔ㕖╬ᣇᄙᤨᦼ㕖✢ᒻ᜛ᢔㆊ⒟

(Anisotropic multi-temporal nonlinear diffusion process㧦ෳ⠨ᢥ₂)ߦࠃࠆࡈࠖ࡞࠲࡝ࡦࠣࠍⴕߞߚޕ

䋨䋱䋩

⛯޿ߡޔᐔṖൻߐࠇߚ࠺࡯࠲ߦኻߒߡ㗔ၞ᜛ᒛಣℂߦࠃࠆ࠮ࠣࡔࡦ࠹࡯࡚ࠪࡦࠍⴕߞߚޕ೨ಣℂ ߩ଀ࠍ࿑1ߦ␜ߔޕ

࿑1㧚೨ಣℂߩ੐଀ޕ(a)ࠝ࡝ࠫ࠽࡞ᝄ᏷↹௝ޔ(b)ࡈࠖ࡞࠲࡝ࡦࠣᓟޔ(c)ޔ(d)࠮ࠣࡔࡦ࠹࡯࡚ࠪࡦ⚿ᨐޕ

c. બណၞᬌ಴

ฦ࠮ࠣࡔࡦ࠻ߦኻߒߡHHޔ HVߩᐔဋ୯ޔ࠹ࠢࠬ࠴ࡖࠍ⸘▚ߔࠆޕ WWWߩ࠺࡯࠲ࡌ࡯ࠬࠍၮߦޔ 2007ᐕߦ⥄ὼᨋߢ਌ߟޔ࠹ࠢࠬ࠴ࡖߩᤨ㑆ᄌൻ߇ዊߐ޿㗔ၞࠍᄌൻ߇ߥ޿⥄ὼᨋߣ઒ቯߒߡߘߩ㗔

ၞߩᄌൻߩᐔဋ୯ࠍ᳞߼ޔ࿯ფ᳓ಽ╬ߩᄌൻߦࠃࠆࡃࠗࠕࠬᚑಽߣߒߡ⷗Ⓧ߽ࠆޕࡃࠗࠕࠬࠍ⠨ᘦ ߒߚNRCSߩᄌൻ㧔ǍHHޔǍHV㧕 ޔ࠹ࠢࠬ࠴ࡖߩᄌൻ(ǍTX _HH ޔǍTX _HV )ࠍࡄ࡜ࡔ࡯࠲ߣߒߡޔ WWW ߩᬌ಴ၞߦኻߒߡᱜ⸃₸߇ᦨᄢߣߥࠆࠃ߁ߦ࠴ࡘ࡯࠾ࡦࠣࠍⴕߞߚޕ

3.aߢⴕߞߚNRCSߩᄌൻ․ᕈߩ⚿ᨐ߆ࠄ੍߽ᗐ಴᧪ࠆࠃ߁ߦǍHHߪᬌ಴ߦ߅޿ߡ᦭ലߥࡄ࡜ࡔ࡯

࠲ߣߪߥࠄߥ߆ߞߚޕ․ߦޔHHߪ㔎ቄߩᵩ᳓ᨋߦኻߒߡ߽୯߇Ⴧടߔࠆߚ߼ޔǍHHߩߺߢߪ⺋Ꮕ

¸¸ ¹

·

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’ §

’

’

˜ w ’ w

c b

b I a

D I

I t D

I

i

i ( ( & ) ), ( & )

ⷐ࿃ߣߥࠆޕ߹ߚޔ࠹ࠢࠬ࠴ࡖߩᄌൻࠍട߃ࠆߎߣߦࠃߞߡ߽ᬌ಴ߩ♖ᐲ߇ᄢ߈ߊะ਄ߔࠆߎߣߪ ߥ߆ߞߚߚ߼ޔᧄᬌ⸛ߢߪǍHVߩߺࠍᬌ಴ࡄ࡜ࡔ࡯࠲ߣߒߡㆡ↪ߔࠆޕ࠴ࡘ࡯࠾ࡦࠣߩ⚿ᨐޔǍ HV <-0.6dBߩ㗔ၞࠍᣂߚߥબណၞߣߒߡ․ቯߔࠆߎߣߣߒߚޕ2008ᐕߣ2009ᐕߦ߅ߌࠆᬌ಴⚿ᨐߩ Ყセࠍ⴫1ޔ࿑2ߦ␜ߔޕ WWF(LANDSAT)ߣPALSARߢߪᬌ಴ᣣ߇㆑߁ߚ߼ޔᱜ⏕ߥ࠻࠘࡞࡯ࠬߣߪ ߥࠄߥ޿߇ޔWWFߩ⚿ᨐߦኻߒߡPALSARߪ2008ᐕޔ2009ᐕߢߘࠇߙࠇ߅߅ࠃߘ70%ޔ85%ߩ♖ᐲ ߢબណၞࠍᬌ಴ߔࠆߎߣ߇ߢ߈ߚޕ․ߦޔ2009ᐕߩᲧセ⚿ᨐߦ⷗ࠄࠇࠆࠃ߁ߦޔ↹௝ࠝࡉࠫࠚࠢ࠻

ࠍၮᧄߣߒߚᚻᴺࠍㆡ↪ߒߚߎߣߦࠃࠅޔᲧセ⊛ⶄ㔀ߥⓨ㑆᭴ㅧࠍᜬߟબណၞߩᬌ಴ߦ߽ᚑഞߒߡ

޿ࠆޕ৻ᣇߢޔ PALSARߩᬌ಴ߪWWFߩ(LANDSATߦࠃࠆ)⚿ᨐߣᲧߴߡᬌ಴ߩᤨ㑆࡟ࠬࡐࡦࠬ߇ㆃ

޿௑ะ߇⷗ࠄࠇࠆޕ2008ᐕ6᦬22ᣣ᷹ⷰߩLANDSATߦࠃࠆᬌ಴ߣ2008ᐕ6᦬6ᣣ᷹ⷰߩPALSARߦࠃ ࠆ⚿ᨐߢߪޔPALSARߪLANDSATߩ߅߅ࠃߘ50%ߩ㗔ၞߩᬌ಴ߦ⇐߹ߞߡ޿ࠆޕબណߩೋᦼᲑ㓏ߦ ߅޿ߡSARߢߪାภߩᄌൻ߇᣿⍎ߢߥ޿ߎߣࠍ␜ໂߒߡ޿ࠆ߇ޔ ੹ᓟ⹦⚦ߥᬌ⸛ࠍⴕ߁ᔅⷐ߇޽ࠆޕ

࿑2㧚 (a)WWFߦࠃࠆLANDSATࠍ↪޿ߚޔ (b)PALSARߦࠃࠆ2007ᐕ߆ࠄ2009ᐕߦ߆ߌߡߩ᫪ᨋᄌൻࡑ࠶ࡊޕ

⴫1. ᬌ಴⚿ᨐ

ᬌ಴ᐕ WWF PALSAR accuracy

2007-2008ᐕ 2008/6/22 2008/10/22 70.5%

2007-2009ᐕ 2009/7/31 2009/9/9 85.8%

4. ߹ߣ߼

ࠗࡦ࠼ࡀࠪࠕ࡮ࠬࡑ࠻࡜ፉࠍ࠹ࠬ࠻ࠨࠗ࠻ߣߒߡPALSARߦࠃࠆ᫪ᨋબណ⥄േᬌ಴ߩᬌ⸛ࠍⴕߞ ߚޕߪߓ߼ߦޔ WWFߩ⥄ὼᨋ࠺࡯࠲ࡌ࡯ࠬࠍၮߦબណၞߢߩPALSARߩାภᄌൻ․ᕈࠍ⺞ߴߚޕ HH ߩNRCSߪબណ೨ᓟߢ᣿⍎ߥᄌൻߪ⷗ࠄࠇߥ߆ߞߚ߇ޔHVߪ♽⛔⊛ߦ2-4dBߩᷫዋ߇⏕⹺ߐࠇߚޕ

߹ߚޔାภߩ߫ࠄߟ߈ᐲ(࠹ࠢࠬ࠴ࡖ)ߦ㑐ߒߡߪޔHHޔHVߣ߽ߦબណߦࠃࠆჇട߇⏕⹺ߐࠇߚޕ

WWFߩ᫪ᨋࡑ࠶ࡊࠍࡌ࡯ࠬߦޔ਄⸥ߩNRCSߣ࠹ࠢࠬ࠴ࡖߩᤨ㑆ᄌൻࠍࡄ࡜ࡔ࡯࠲ߣߒߡޔ PALSAR

ߦࠃࠆબណၞᬌࠍⴕߞߚ⚿ᨐޔHVߩାภᄌൻ(ǍHV)߇ᣂߚߥબណၞߩᬌ಴ߦ᦭ലߢ޽ࠆߎߣ߇␜

ߐࠇߚޕ࠮ࠣࡔࡦ࠹࡯࡚ࠪࡦࠍࡌ࡯ࠬߣߒߡǍHVߩᦨㆡࡄ࡜ࡔ࡯࠲ߦࠃࠅPALSARߦࠃࠆᬌ಴ࠍⴕ ߞߚ⚿ᨐޔLANDSATࠍ↪޿ߚWWFߩᬌ಴⚿ᨐߦኻߒߡޔ70%-85%ߩ♖ᐲߢᬌ಴߇น⢻ߢ޽ࠆߎߣ ߇⏕⹺ߐࠇߚޕ

ᧄᐕᐲߪޔㆇ↪ࡈࠚ࡯࠭߳ߩ⒖ⴕḰ஻ߣߒߡޔࠬࡑ࠻࡜ፉߩFBD࠺࡯࠲ߦኻߒߡᬌ⸛ߒߚᚻᴺࠍ

ㆡ↪ߒޔᬌ಴㗔ၞࠍબណน⢻ᕈ㗔ၞߣߒߡWWFࠗࡦ࠼ࡀࠪࠕߦឭଏߔࠆߎߣࠍ੍ቯߒߡ޿ࠆޕ WWF

ߢߪᬌ಴⚿ᨐࠍၮߦ⃻⺞ᩏࠍⴕ߁੍ቯߢ޽ࠆߚ߼ޔߘࠇࠄߩ⚿ᨐࠍࡈࠖ࡯࠼ࡃ࠶ࠢߣߒߡᬌ಴♖ᐲ

ࠍ㜞߼ߡⴕߊޕ

1.6 วᚑ㐿ญ࡟࡯࠳ࠍ↪޿ߚἴኂ⋙ⷞ ᴡ㊁ ቱᐘ

1. ߪߓ߼ߦ

2009ᐕ4᦬6ᣣ㧔⃻࿾ᤨ㑆4᦬6ᣣඦ೨3ᤨ32ಽ㧕ࠗ࠲࡝ࠕਛㇱࠕࡉ࡞࠶࠷ࠝᎺ࡜ࠢࠗ࡜Ꮢㄭ㇠ࠍ㔡Ḯ ߣߔࠆࡑࠣ࠾࠴ࡘ࡯࠼6.3ߩ࿾㔡߇⊒↢ߒޔਥߦᑪ‛ୟუߥߤߦࠃߞߡ300ฬߦㄼࠆᄙᢙߩ‶†⠪߇

಴ߚޕALOS/PALSAR✕ᕆ᷹ⷰߪ2009ᐕ4᦬10ᣣߦታᣉߒޔ 2008ᐕ4᦬2ᣣߩࠕ࡯ࠞࠗࡧߣᲧセߒߚ⚿

ᨐޔ࡜ࠢࠗ࡜(L`Aquila)ᏒᣥᏒⴝߣࠝ࠽(Onna)᧛ߦ߅޿ߡ࿾㔡೨ᓟߩᒝᐲ↹௝ߦ㗼⪺ߥᄌൻ߇⏕⹺ߐ ࠇߚޕᧄ⺞ᩏߪߎߩᒝᐲ↹௝ߩᄌൻၞߦ߅ߌࠆታ㓙ߩᑪ‛៊უߩ⁁ᴫࠍᲧセߔࠆߎߣߢޔ ALOS/PALSARߦࠃࠆᄢⷙᮨἴኂ⊒↢ᤨߩⵍኂ⁁ᴫᛠីߩᬌ⸽ߣน⢻ᕈࠍតࠆߎߣࠍ⋡⊛ߣߔࠆޕ

⃻࿾⺞ᩏߪ2009ᐕ4᦬26ᣣ㧔ᣣ㧕㨪5᦬1ᣣ㧔㊄㧕ߦ߆ߌߡⵍἴ࿾ࠍ⸰໧ߒޔ⃻࿾ߩ㑐ㅪᯏ㑐ߦ⋥ធ੤

ᷤߔࠆߎߣߢ⸵นࠍᓧߡታᣉߒߚޕߎߎߢߪߘߩ⚿ᨐࠍ◲නߦ␜ߔޕ

2. ࿾㔡ߩ᭎ⷐߣ⺞ᩏ࿾ߩ૏⟎㑐ଥ

ᧄ㔡ߪ2009ᐕ4᦬6ᣣ㧔⃻࿾ᤨ㑆ඦ೨3ᤨ32ಽ㧕⊒↢ޔ USGS(US Geological Survey) National Earthquake Information Centerߦࠃࠆߣޔ㔡Ḯߪർ✲ 42.33ᐲޔ᧲⚻ 13.33ᐲޔᷓߐ 8.8kmޔࡑࠣ࠾࠴ࡘ࡯࠼6.3ޕ

⺞ᩏ࿾ߪ㔡Ḯ߆ࠄ⚂6km᧲ർ᧲L`AquilaᏒᣥᏒⴝޔห⚂12km᧲Onna᧛ޔOnna᧛߆ࠄ⚂2kmධ⷏

Monticchio᧛ޔ㔡Ḯ߆ࠄ⚂22km᧲ධ᧲Sinizzoḓߢ޽ࠆޕߘߩ૏⟎㑐ଥࠍ࿑1ߦ␜ߔޕ

࿑1. 㔡Ḯ㧔⿒ਣ㧕ߣ㧠⺞ᩏ࿾㧔㕍ਣ㧕ߩ૏⟎㑐ଥ(Google mapࠃࠅ)ޕ⿒ᨒߪ࿑2ߣ࿑3ߦ᜛ᄢߒߡ␜ߔޕ

3. ALOS/PALSAR✕ᕆ᷹ⷰߣᄌൻ᛽಴ߩ᭎ⷐ

ALOS/PALSAR✕ᕆ᷹ⷰߪ2009ᐕ4᦬10ᣣ㧔㊄㧕21:35:33 - 21:37:25 (UT)FBS38.8ᐲޔ᷹ⷰࡄࠬ641 ࠕ࠮ࡦ࠺ࠖࡦࠣߦߡታᣉޕ ࠕ࡯ࠞࠗࡧߪ2008ᐕ4᦬2ᣣFBS34.3ᐲ ᷹ⷰࡄࠬ638ࠕ࠮ࡦ࠺ࠖࡦࠣࠍ੐೨

౉ᚻޕ⥄േಣℂߦࠃࠆᄌൻ᛽಴ࠍታᣉߒޔἴኂᓟߩᒝᐲ↹௝߇ᒙߊߥߞߚ▎ᚲ߇⿒⦡ߢ⴫␜ߐߖࠆ ࠃ߁ߦ㈩⦡ߒߚޕߎࠇߦࠃࠅޔ⿒⦡߇㗼⪺ߦ⴫ߐࠇࠆㇱಽߪޔἴኂᓟߦᢔੂᒝᐲ߇ᒙߊߥߞߚߎߣ

࿑㧞㧔᜛ᄢ࿑㧕

࿑㧟㧔᜛ᄢ࿑㧕

㔡Ḯ

L`AquilaᣥᏒⴝ

Onna᧛

Monticchio

Sinizzoḓ

ࠍᗧ๧ߒޔᏒⴝ࿾ߢߪᑪ‛ߥߤߩ᭴ㅧ‛ߩୟუ╬߇⠨߃ࠄࠇࠆޕ

࿑2. L`AquilaᏒၞߦ߅ߌࠆPALSARἴኂ೨ᓟߩᒝᐲ↹௝Ყセ㧔Ꮐ㧕 ޕἴኂ೨㧔⿒㧕ἴኂᓟ㧔✛㕍㧕ߦ

㈩⦡ޕ෸߮ห࿾ၞߩ⃻࿾⺞ᩏߦࠃࠆ៊უ₸ߩಽᏓ㧔ฝ㧦Google map㧕 ޕ⿒ᨒߪ៊უ₸㧣ഀએ਄ޔ

ࠝ࡟ࡦࠫᨒߪ៊უ₸4ഀ㨪6ഀޔ㕍ᨒߪ៊უ₸3ഀᧂḩޕ⌕⦡ߥߒߪᧂ⺞ᩏၞޕ✛ߪ✛࿾Ꮺޕ

࿑3. Onna᧛ߦ߅ߌࠆPALSARἴኂ೨ᓟߩᒝᐲ↹௝Ყセ㧔Ꮐ㧕 ޕἴኂ೨㧔⿒㧕ἴኂᓟ㧔✛㕍㧕ߦ㈩⦡ޕ

෸߮ห࿾ၞߩ⃻࿾⺞ᩏߦࠃࠆ៊უ₸ߩಽᏓ㧔ฝ㧦Google map㧕 ޕ⿒ᨒߪ៊უ₸㧣ഀએ਄ޔࠝ࡟ࡦ

ࠫᨒߪ៊უ₸4ഀ㨪6ഀޔ㕍ᨒߪ៊უ₸3ഀᧂḩޕ⌕⦡ߥߒߪਥߦ᭴ㅧ‛ήߒޕ

4. ߹ߣ߼

วᚑ㐿ญ࡟࡯࠳ࠍ↪޿ߚἴኂ⋙ⷞߩ৻଀ߣߒߡޔ 2009ᐕ4᦬ߩࠗ࠲࡝ࠕਛㇱ࿾㔡ᤨߩALOS/PALSAR ߩ᷹ⷰ⚿ᨐࠍ␜ߒߚޕᧄᚑᨐႎ๔ળߢߪઁߦޔ⥶ⓨᯏ៞タวᚑ㐿ญ࡟࡯࠳ࠍ↪޿ߚ᳓ኂࠍᗐቯߒߚ

᷹ⷰታ㛎ߩ᭎ⷐߥߤ߽ႎ๔੍ቯߢ޽ࠆޕ

5. ⻢ㄉ

ᧄ⃻࿾⺞ᩏߦදജߒߡ㗂޿ߚએਅߩᣇޘߦᷓߊ⻢ᗧࠍ⴫ߔࠆޕ VVF Terni ̄ Vigili Del Fuoco㧔࠹࡞࠾▤඙ᶖ㒐ዪ㧕

VVF Roma ̄ Vigili Del Fuoco㧔ࡠ࡯ࡑ▤඙ᶖ㒐ዪ㧕

Mr. Paolo Busilacchi & Ms. Machiko Nagasawa

Mr. Cappelli Liovanni㧔Sinizzoḓ ⥄ὼ౏࿦▤ℂੱ㧕

1.7 Mapping Tropical Forest Using ALOS PALSAR 50m Resolution Data with Multiscale Texture Analysis.

Preesan Rakwatin

1. Introduction

We investigate the abilities and the limitations of ALOS PALSAR 50m resolution data for land cover classification in Tropical rainforest as part of the ALOS Kyoto and Carbon (K&C) Initiative Project. The K&C Initiative forms the continuation of the Global Rain Forest and Boreal Forest Mapping (GRFM/GBFM) project, in which 100m spatial resolution mosaics of the entire tropical and boreal zones using data acquired by the Japanese Earth Resources Satellite (JERS-1) L band HH SAR were generated. Since only two bands (HH and HV) have been proven to be limitation in land cover differentiation in SAR data, textures have been used as feature dimensions for classification 㧔Nyoungui2002,Podest2002㧕 . A large number of techniques for texture analysis have been investigated for SAR image classification. Among texture analysis methods, the most prevalent technique used for deriving texture is the use of the grey-level co-occurrence matrix (GLCM) (Haralick1979). This technique uses a spatial co-occurrence matrix that computes the relationships of pixel values and uses these values to compute the second-order statistical properties (Coburn2004, Haralick1979).

This study focus on comparing various second-order texture parameters and features at multiple scales to demonstrate their contributions in land cover classification which are importance for ALOS K&C Initiative projects.

Incorporated with World Wildlife Fund (WWF), a part of Riau province, in central Sumatra, was selected as a test site. Riau hosts some of the most biodiversity ecosystems and unique species. It is covered by vast peat lands estimated to hold Indonesia's largest store of carbon. However, Riau have been under serious threat because of rapid large-scale deforestation. (Uryu2008)

2. Methodology 2.1 Image texture

Second-order texture measurements based on Haralick's grey-level co-occurrence matrices (GLCM)

(Haralick1973) outline the distance and angular spatial relationships between pixels within the moving

window. The GLCM compute the joint probability of occurrence of the pairs of grey levels separated by a

given distance and direction (Nyoungui2002, Kuplich2005). GLCM textures were calculated for all directions

for more closely replicate variance measured within a window Coburn2004}. Several statistical measurements

can be extracted form the GLCM which have been used effectively in many SAR applications

(Kuplich2005,Vandersanden1999). In this research, six second-order texture measurements were calculated

which are the angular second moment, contrast, correlation, entropy, inverse difference moment, and

maximum probability.

2.2 Classification

A maximum likelihood (ML) classification was used to classify all of the images produced in this study.

However, we used a priori information about the expected distribution of classes to improve the classification accuracy. A priori information is incorporated though the use of a priori probability, i.e., probabilities of occurrence of classes that are based on separated, independent knowledge concerning the area to be classified (Alesheikh2007). Used in their simplest form, the probabilities weigh the classes according to their expected distribution in the output dataset by shifting decision space boundaries to produce larger volumes in measurement space for classes that are expected to be large and smaller volumes for classes that are expected to be small.

The classification starts at the low resolution scale (400 m). At this scale, the a priori probability is assumed equal for all classes. The results of the classifier at this scale are used to develop a priori probabilities for the next scale (200 m). These are probabilities with which the class membership of a pixel could be estimated before classification.

2.3 Feature Selection

For each resolution scale, the texture features used in the classification are selected by using Transformed divergence (TD) (VanDerSanden1997). It evaluates the performance in land cover discrimination by calculating the statistic distance between land cover classes included in the image. This is an indirect and a priori estimate of the probability of correct classification.

3. Conclusions

From the feature selection result, we found that angular second moment with distance length equal to one at

eight quantization bit scales was the best parameter in discriminate the class types. However, the ability in

discriminate the class types is reduce when the resolution scale is increase. This is because of the higher

resolution scale images were influenced by noise. This research also show that a multiscale texture-based

classifier can provide accurate thematic information from ALOS PALSAR at 50 m. resolution by compared

with the 2007 WWF map for Riau province.

1.8 PALSARᄙ஍ᵄᐓᷤ⸃ᨆߦࠃࠆ࿯࿾ⵍⷒߩᛠីߦߟ޿ߡ ᄢᧁ ⌀ੱ

1. ߪߓ߼ߦ

ᧄ⊒⴫ߢߪޔ ALOS/PAL1SARߩࡐ࡜࡝ࡔ࠻࡝࠶ࠢ࡮ࠗࡦ࠲࡯ࡈࠚࡠࡔ࠻࡝㧔ᄙ஍ᵄᐓᷤޔ PolInSAR㧕

⸃ᨆߦࠃࠆ࿯࿾ⵍⷒಽ㘃╬ߩ⎇ⓥ⁁ᴫࠍႎ๔ߔࠆޕ

PALSARߪ਎⇇ೋߩࡈ࡞࡮ࡐ࡜࡝ࡔ࠻࡝㧔4஍ᵄ㧕ߩ᷹ⷰ߇น⢻ߥⴡᤊ៞タSARߢ޽ࠅޔLࡃࡦ࠼

ⴡᤊSARߣߒߡߪ⃻࿷߽਎⇇໑৻ߩ߽ߩߢ޽ࠆޕPALSARߦࠃࠅᓥ᧪ߩ⥶ⓨᯏSARߢߪਇน⢻ߥᐢ

▸࿐࡮㜞㗫ᐲߢߩ㒽ၞࡐ࡜࡝ࡔ࠻࡝᷹ⷰ߇ೋ߼ߡታ⃻ߒޔᓟ⛮ᯏALOS-2/SARߢߪߐࠄߦಽ⸃⢻ޔ

౉኿ⷺߥߤߩὐߢࠃࠅᨵエߥࡐ࡜࡝ࡔ࠻࡝᷹ⷰ߇ⴕ߃ࠆࠃ߁ߦߥࠆޕ

╩⠪ߪߎࠇ߹ߢPALSARࡐ࡜࡝ࡔ࠻࡝࠺࡯࠲߆ࠄߩᖱႎ᛽಴ߩน⢻ᕈࠍᬌ⸽ߔࠆߚ߼ޔ࿯࿾ⵍⷒ ಽ㘃ߩ⎇ⓥࠍⴕߞߡ߈ߚޕSARߪ㔕ߦᓇ㗀ߐࠇߕߦ࿾⴫ߩᖱႎ෼㓸߇น⢻ߥߎߣ߆ࠄޔߎࠇ߇ታ↪

ൻߔࠇ߫ޔᐢ▸࿐࡮㜞㗫ᐲߩ࿯࿾ⵍⷒಽ㘃ޔ࿯࿾ⵍⷒᄌൻ᛽಴ࠍታ⃻ߔࠆߎߣ߇ߢ߈ࠆޕ

2. ᄙ஍ᵄᐓᷤ⸃ᨆߦࠃࠆ࿯࿾ⵍⷒಽ㘃

╩⠪ߩߎࠇ߹ߢߩ⸃ᨆ߆ࠄޔⶄᢙᤨᦼߩ࠺࡯࠲ࠍ↪޿ߕන৻ᤨᦼߩࡐ࡜࡝ࡔ࠻࡝㧔PolSAR㧕࠺࡯

࠲ߢᓥ᧪߆ࠄࠃߊ↪޿ࠄࠇࠆⶄ⚛࠙ࠖࠪࡖ࡯࠻ಽ㘃ᴺߦࠃࠅᢎᏧߥߒ෸߮ᢎᏧઃ߈ಽ㘃ࠍⴕߞߚ႐ วޔ(1)᳓ၞޔ(2)᳓↰ޔ(3)᳓↰એᄖߩㄘ૞࿾ޔ(4)ⓨ߈࿾(ࠣ࡜࠙ࡦ࠼߿ࠦࡦࠢ࡝࡯࠻㕙╬ߩᐔမߥ࿯

࿾)ޔ (5)᫪ᨋޔ(6)Ꮢⴝ࿾ߣ޿߁6ࠞ࠹ࠧ࡝࡯ߩන⚐ߥಽ㘃ߢ߽♖ᐲߪ60㧑บߦߣߤ߹ࠆߣ޿߁ೋᦼ⚿

ᨐ߇ᓧࠄࠇߡ޿ࠆޕ߹ߕߎߩ⺋ಽ㘃ߩේ࿃ߣኻಣᴺߦߟ޿ߡᬌ⸛ߒߚޕ

ේ࿃ߦߟ޿ߡߪޔ᳓ၞߣⓨ߈࿾ߪᭂ߼ߡᓟᣇᢔੂ߇ዊߐߊޔ᳓↰߽౰᳓ਛߩ႐วߪ᳓ၞߣหߓ⁁

ᘒߢޔߎࠇࠄߪේℂ⊛ߦ඙೎߇࿎㔍ߢ޽ߞߚޕ߹ߚޔ᫪ᨋߣᏒⴝ࿾ߪޔHV஍ᵄᚑಽ߇Ყセ⊛ᒝ޿ߣ

޿߁㘃ૃߒߚ஍ᵄ․ᕈࠍ߽ߟߚ߼ᷙห߇⿠߈ߡ޿ߚޕ

ߎߩߎߣ߆ࠄޔන৻ᤨᦼߩPolSAR࠺࡯࠲ߢߪᖱႎ㊂߇ਇචಽߣ⠨߃ޔੑᤨᦼߩࡐ࡜࡝ࡔ࠻࡝࠺࡯

࠲ߢPolInSAR⸃ᨆࠍⴕ޿ޔᖱႎ㊂ࠍჇടߐߖߡಽ㘃ࠍⴕߞߚޕPolInSAR⸃ᨆߪޔㄭᐕ᫪ᨋࡃࠗࠝࡑ

ࠬ╬ߩᖱႎ᛽಴ߢᵈ⋡ߐࠇߡ޿ࠆ߇ޔPALSARߩ࿁Ꮻᣣᢙ㧔46ᣣ㧕ߢߪᦼ㑆߇㐿߈ߔ߉᫪ᨋߩࠦࡅ

࡯࡟ࡦࠬ߇ૐਅߔࠆߚ߼ޔ᫪ᨋߩ⹦⚦ߥࡄ࡜ࡔ࡯࠲ࠍផቯߔࠆߩߪ⃻ታ⊛ߢߥ޿ޕ৻ᣇߢޔ࿯࿾ⵍ ⷒಽ㘃ߣ޿߁ⷰὐߢߪޔ᫪ᨋߢࠦࡅ࡯࡟ࡦࠬ߇ૐ޿ߎߣࠍㅒߦ᫪ᨋߩ᛽಴ߦ೑↪ߢ߈ࠆߣ⠨߃ࠄࠇ ࠆޕห᭽ߦ᳓ၞ߽ࠦࡅ࡯࡟ࡦࠬ߇ૐ޿ߎߣ߆ࠄޔ᳓ၞߩ᛽಴ߦ߽᦭ᦸߢ޽ࠆޕ

ಽ㘃ߩᚻᴺߪޔᲧセߩߚ߼ᓥ᧪ߣหߓᢎᏧઃ߈ⶄ⚛࠙ࠖࠪࡖ࡯࠻ಽ㘃ᴺࠍ૶↪ߒޔฦ↹⚛ߦߟ޿

ߡන৻ᤨᦼߩࡐ࡜࡝ࡔ࠻࡝࠺࡯࠲ߩ3˜3ࠦࡅ࡯࡟ࡦࠬⴕ೉ࠍਈ߃ࠆߎߣߢPolSARಽ㘃ࠍⴕ޿ޔ߹ߚ

ੑᤨᦼߩࡐ࡜࡝ࡔ࠻࡝࠺࡯࠲ߩᖱႎࠍోߡ฽߻6˜6PolInSARࠦࡅ࡯࡟ࡦࠬⴕ೉ࠍਈ߃ࠆߎߣߢ PolInSARಽ㘃ࠍⴕ޿ޔ⚿ᨐࠍᲧセߒߚޕߎߩࠃ߁ߦⶄ⚛࠙ࠖࠪࡖ࡯࠻ಽ㘃ᴺߪหߓಽ㘃ᴺߢ౉ജࠍ ᄌ߃ࠆߛߌߢPolSARಽ㘃߽PolInSARಽ㘃߽ⴕ߃ࠆޕ࠹ࠬ࠻࠺࡯࠲ߣߒߡߪޔߟߊ߫ࠍ฽߻⨙ၔ⋵⷏

ㇱ࡮ජ⪲⋵⷏ㇱࠍࠞࡃ࡯ߔࠆ㧔ࡄࠬ400ࡈ࡟࡯ࡓ710㧕 1࿁Ꮻ㧔46ᣣ㧕㔌ࠇߚ2ᤨᦼߩ࠺࡯࠲㧔2007ᐕ4

᦬2ᣣޔ2007ᐕ5᦬18ᣣ㧕ࠍ૶↪ߒޔᢎᏧ࠺࡯࠲߅ࠃ߮ᬌ⸽࠺࡯࠲ߪ࿖࿯࿾ℂ㒮ߩ࿖࿯ᢙ୯ᖱႎ࡮࿯

࿾೑↪⚦ಽࡔ࠶ࠪࡘ(2006ᐕᐲ)߅ࠃ߮ALOSశቇ↹௝㧔AVNIR-2ޔ2007ᐕ5᦬15ᣣ㧕ࠍ್⺒ߔࠆߎߣ

ߦࠃࠅᚻ૞ᬺߢ૞ᚑߒߚޕ

3. ᐓᷤಣℂ߅ࠃ߮ಽ㘃ߩ⚿ᨐ

PolInSARಣℂࠍⴕߞߡฦ⒳ಣℂ↹௝ࠍ಴ജߒߚ⚿ᨐޔ໧㗴ߣߥߞߡ޿ߚ᳓ၞߣⓨ߈࿾ߩ್೎ߦߟ

޿ߡߪHH+VV஍ᵄߢޔ ᫪ᨋߣᏒⴝ࿾ߩ್೎ߪHV஍ᵄߢࠦࡅ࡯࡟ࡦࠬߩ୯ߦᏅ߇⷗ࠄࠇޔ න৻ߩPolSAR

࠺࡯࠲ߢߪᓧࠄࠇߥ޿㊀ⷐߥᖱႎ߇ᐓᷤಣℂߢᓧࠄࠇࠆߎߣ߇␜ߐࠇߚޕ

ᰴߦߎࠇࠄߩ࠺࡯࠲ࠍ↪޿ߚಽ㘃ࠍⴕ޿ޔ න৻ᤨᦼߩࡐ࡜࡝ࡔ࠻࡝࠺࡯࠲ߦࠃࠆPolSARಽ㘃ߢ 68.5㧑 ߢ޽ߞߚ♖ᐲ߇ޔੑᤨᦼߩࡐ࡜࡝ࡔ࠻࡝࠺࡯࠲ߦࠃࠆPolInSARಽ㘃ߢ80.1㧑ߦะ਄ߒߚޕ࿯࿾ⵍⷒ

୘೎ߦᬌ⸽ߔࠆߣޔ᳓↰ߩಽ㘃♖ᐲ߇ᄢ߈ߊะ਄ߒޔߘߩઁߩ࿯࿾ⵍⷒ߽߿߿ᡷༀߒߚޕ᫪ᨋߣᏒ ⴝ࿾ߩ⺋ಽ㘃ߪචಽߦߪ⸃ᶖߖߕޔ᫪ᨋߩ㕙Ⓧࠍㆊᄢ⹏ଔߔࠆ௑ะߢ޽ࠆޕ

PolInSARߩ6˜6ࠦࡅ࡯࡟ࡦࠬⴕ೉߆ࠄ࠺࡯࠲ࠍ೥ࠅޔ5˜5ⴕ೉޽ࠆ޿ߪ4˜4ⴕ೉ߦߒߡ⸘▚㊂ࠍ

ᷫࠄߒߡಽ㘃ࠍⴕߞߚ⚿ᨐޔ ♖ᐲߪᄢᏅߥ޿৻ᣇޔ ⸘▚ᤨ㑆ߪ4˜4ⴕ೉ߩ႐วߢ⚂1/3ߦ⍴❗ߢ߈ߚޕ ߎࠇߪޔ዁᧪ో࿖ⷙᮨߥߤᄢ㊂ߩ࠺࡯࠲ߢಽ㘃ࠍⴕ߁႐วߥߤߦޔ⸘▚ᯏߩ⢻ജߦᔕߓߡ⸘▚㊂ࠍ

೥ᷫߔࠆᣇᴺߣߒߡ᦭ᦸߢ޽ࠆޕ

4. ੹ᓟߩ⺖㗴

੹࿁ߩಽ㘃ߪޔ1࿁Ꮻ㔌ࠇߚ2ᤨᦼߩ࠺࡯࠲ࠍ↪޿ߚ߇ޔ᫪ᨋߣᏒⴝ࿾ߩࠦࡅ࡯࡟ࡦࠬߩᏅ߇ߐ߶

ߤᄢ߈ߊߥ޿ߥߤߩ໧㗴߇޽ࠅޔ੹ᓟߪࠃࠅ᫪ᨋߩࠦࡅ࡯࡟ࡦࠬ߇ਅ߇ࠆߣᕁࠊࠇࠆ2࿁Ꮻએ਄㔌ࠇ ߚᐓᷤࡍࠕࠍ↪޿ࠆߥߤߒߡ♖ᐲߩะ਄ࠍ⋡ᜰߔޕ੹࿁↪޿ߚ࠙ࠖࠪࡖ࡯࠻ಽ㘃ᴺߪޔಣℂ߇㜞ㅦ ߛ߇♖ᐲߢߪഠࠆߣᜰ៰ߔࠆ⎇ⓥ߇ㄭᐕᄙߊޔ੹ᓟߪઁߩಽ㘃ᴺߢߩಽ㘃♖ᐲ߽⹏ଔߔࠆޕ዁᧪⊛

ߦߪἴኂ⁁ᴫᛠីޔㄘᬺޔ㒽ၞ↢ᘒ♽ࡕ࠾࠲࡝ࡦࠣ߳ߩᔕ↪ࠍⷞ㊁ߦ౉ࠇߡ޿ࠆޕ

(a) (b) (c)

࿑1 ಽ㘃⚿ᨐߥߤޕ(a)SARర↹௝㧔ࡄ࠙࡝↹௝㧕 ޔ(b)ಽ㘃⚿ᨐޔ(c)శቇ↹௝(AVNIR-2)

1.9 Assessment of ALOS PALSAR 50m Orthorectified FBD Data

for Regional Land Cover Classification by using Support Vector Machines

Nicolas Longepe

This study introduces a methodology for land cover classification in tropical rainforest as part of the ALOS Kyoto & Carbon initiative project. The Riau province in central Sumatra (Indonesia) was selected as test site.

Riau hosts some of the most biodiversity ecosystems and unique species in the world. It is covered by vast peat lands estimated to hold Indonesian largest stock of carbon. However, Riau has been under serious threat because of rapid large-scale deforestation. In this study, 50m ortho-rectified dual-polarized PALSAR mosaic products are solely used to make an attempt to monitor these changes. The following points detailed in [1] are presented in the following.

1. QUALITATIVE ANALYSIS OF SCATTER PLOT

WWF listed 27 different land covers within this area. In the framework of this study, a set of 14 classes is analyzed for the sake of clarity and of negligible land cover types. Using dual-polarized SAR data only (HH and HV channels), the capability of PALSAR data for discriminating these different classes is first analyzed.

Some preliminary scatter plots (HH vs. HV) are studied showing some limited possibilities with radiometric information only. Oil palm and acacia plantations widely spread over this area. Their dual-polarized signatures are slightly different from the natural forest ones providing some information in a classification context. Clear cut areas clearly induce a strong modification of the dual-polarized signature. However, in a general manner, the variance of these histograms is very large and it is obvious that a simply threshold-based method can not work properly over this dataset.

2. QUALITATIVE ANALYSIS OF TEXTURAL INFORMATION

By using PALSAR mosaic products, no time-dependent information is available. In that framework, spatial

statistics may be useful in classifying these natural media. Many methodologies exist in computer vision in

order to analyze textural statistics. Variogram, gray-level co-occurrence matrix (GLCM), Markov-Random

fields (MRF) and wavelet transforms are some of the most common methods. An original methodology is

introduced in this study in order to analyze textural information. In a similar vein as in [2, 3] which is based

on wavelet frames, a new tool for analyzing multi-scale information from SAR data is built. Based on the

Support Vector Machine (SVM) technique, the Recursive Feature Elimination algorithm, namely the

SVM-RFE [4], is a simple but efficient algorithm which was first implemented in the context of cancer gene

selection. As inputs of this algorithm, the Haralick’s parameters [5] based on the GLCM are computed at

specific locations from the 50m dual-polarized PALSAR data for a large range of lag distances, windows sizes

and quantization levels. All of these parameters (about 900) are then ranked by the SVM-RFE algorithm

based on their respective weight in the land cover classification. By analyzing the ranked GLCM parameters,

the textural information can be assessed for each land cover type. Since the most relevant textural parameters

are known for a given land cover once and for all, a limited number of parameters has to be computed at global scale while including an optimized range of spatial information.

3. SUPPORT VECTOR MACHINES FOR LAND COVER ANALYSIS

In this study, the area of interest is very wide (about 14 000 by 24 000 pixels at 50m resolution) and the use of a limited number of textural parameters is crucial for any future operational applicability. The radiometric information (HH and HV channels) and the best 10 textural parameters (see Section 2) are used as input of the Support Vector Machine classifier so as to discriminate the different land covers. Accuracy assessment is carried out at regional scale over the whole Riau province, showing the very good capabilities of 50m dual-polarized PALSAR data for land cover classification. The results over the entire Riau province are shown in Fig. 1. Some outlooks such as the implementation of pre/post processing methods (clustering, pattern recognition) are discussed. In the coming months, the forest/non forest map over the whole Sumatra Island will be also processed using the same technique.

[1] N. Longépé, P. Rakwatin, O. Isoguchi, M. Shimada and Y. Uryu, “Assessment of ALOS PALSAR 50m Orthorectified FBD Data for Regional Land Cover Classification by using Support Vector Machines,”

IEEE Trans. Geosci. Remote Sens., under review, 2010

[2] M. Simard, S. Saatchi, and G.F. De Grandi, “The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest,” IEEE Trans. Geosci. Remote Sens., vol. 38, pp.

2310–2321, 2000.

[3] G.D. De Grandi, R.M. Lucas, and J. Kropacek, “Analysis by wavelet frames of spatial statistics in SAR data for characterizing structural properties of forests,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 2, pp. 494–507, 2009.

[4] I. Guyon, J. Weston, S. Barnhill, and V.N. Vapnik, “Gene selection for cancer classification using Support Vector Machines,” Machine Learning, vol. 46, pp. 389–422, 2002.

[5] R.M. Haralick and K. Shaunmugam, “Textural features for image classification,” IEEE Trans. Syst. Man

Cybernetics, vol. 3, pp. 610–621, 1973.

Figure 1: Land cover estimated manually by WWF using Landsat images (top) and by our methodology using

PALSAR FBD 50m orthorectified data (bottom).

㩷 㩷 㩷 㩷 㩷 㩷 㩷

ᵐᵌᴾᵥᵭᵱᵟᵲМဇᄂᆮ ^㩷

2. GOSAT೑↪⎇ⓥ

2.1 GOSAT೑↪⎇ⓥࡊࡠࠫࠚࠢ࠻ߩᚑᨐ᭎ⷐ Ꮉ਄ ୃม

1. ߪߓ߼ߦ

᷷ቶലᨐ᷹ࠟࠬⷰᛛⴚⴡᤊGOSAT㧔Greenhouse gases Observing SATellite㧕ߪޔᄢ᳇ਛߩੑ㉄ൻ὇

⚛߿ࡔ࠲ࡦߥߤߩ᷷ቶലᨐࠟࠬߩో⃿ಽᏓࠍቝቮ߆ࠄ㜞♖ᐲߦ᷹ⷰߔࠆߚ߼ߦޔ 2009ᐕ1᦬23ᣣߦ⒳

ሶፉቝቮ࠮ࡦ࠲࡯߆ࠄᛂߜ਄ߍࠄࠇߚޕGOSATߩㆇ↪ᦼ㑆ߪޔ੩ㇺ⼏ቯᦠߩ╙㧝⚂᧤ᦼ㑆㧔2008㨪 2012ᐕ㧕ࠍ฽ߺޔో⃿ߢੑ㉄ൻ὇⚛ߩ᳇ᩇ㊂ࠍ᷹ቯߔࠆߎߣߦࠃࠅޔࠗࡦࡃ࡯ࠬࡕ࠺࡞߆ࠄዉ಴ߐ ࠇࠆ࿾⴫㕙ߢߩੑ㉄ൻ὇⚛ߩ෼ᡰߩផቯ⺋Ꮕࠍඨᷫߔࠆߎߣ߇⋡⊛ߢ޽ࠆޕ ៞タߐࠇߚ࠮ࡦࠨߪޔ Thermal And Near infrared Sensor for carbon Observation - TANSOߣ✚⒓ߐࠇޔ᷷ቶലᨐ᷹ࠟࠬⷰ࠮ࡦࠨ TANSO-FTS 㧔Fourier Transfer Spectrometer㧕 ޔ߅ࠃ߮ޔ㔕࡮ࠛࠕࡠ࠰࡞ࠗࡔࠫࡖTANSO-CAI 㧔Cloud and Aerosol Imager㧕ߢ᭴ᚑߐࠇߡ޿ࠆޕ

JAXAߪޔⴡᤊ෸߮៞タ࠮ࡦࠨߩ㐿⊒ޔᛂ਄ߍޔL0/L1ࡊࡠ࠳ࠢ࠻ಣℂޔᩞᱜࠍᜂᒰߒޔ࿖┙ⅣႺ

⎇ⓥᚲ(NIES)ߪޔCO2ߩๆឃ಴㊂ߩផቯࠍ฽߼ߚL2એ㒠ߩࡊࡠ࠳ࠢ࠻ಣℂࠍᜂᒰߒߡ޿ࠆޕEORC GOSAT೑↪⎇ⓥߪޔᛂ਄ߍᓟߩઍᦧᩞᱜ߿TIRࡃࡦ࠼೑↪⎇ⓥࠍᜂᒰߒߡ޿ࠆޕ

1᦬ߩᛂ਄ߍᓟ3߆᦬߹ߢޔೋᦼᯏ⢻⏕⹺ㆇ↪ᦼ㑆ߢ޽ࠅޔⴡᤊ࡮࠮ࡦࠨߩᯏ⢻⏕⹺ࠍታᣉߒߚޕ

ᰴߦ⛯ߊ3߆᦬㑆ߩೋᦼᩞᱜᬌ⸽ㆇ↪ᦼ㑆ߦ߅޿ߡޔೋᦼᩞᱜᬌ⸽ߦᔅⷐߣߥࠆ࠺࡯࠲ࠍ෼㓸ߒޔข ᓧߐࠇߚ࠺࡯࠲ߩᩞᱜ♖ᐲࠍ⹏ଔߒߚޕߘߩᓟޔTANSO࡟ࡌ࡞1ࡊࡠ࠳ࠢ࠻ߪޔGOSAT RA PIߦឭ ଏߐࠇޔ9߆᦬ᓟߢ޽ࠆ2009ᐕ10᦬ߦL1ࡊࡠ࠳ࠢ࠻ߩ৻⥸ឭଏ߇㐿ᆎߐࠇߚޕL2ࡊࡠ࠳ࠢ࠻ߦߟ޿

ߡߪޔNIESߦߡೋᦼᬌ⸽ᓟޔL2ࡊࡠ࠳ࠢ࠻ߣߒߡߩੑ㉄ൻ὇⚛࡮ࡔ࠲ࡦߩ᳇ᩇ㊂߇2010ᐕ2᦬ߦ৻

⥸ߦ౏㐿ߐࠇߚޕ

2. GOSAT೑↪⎇ⓥ

GOSAT೑↪⎇ⓥࡊࡠࠫࠚࠢ࠻ߦ߅޿ߡߪޔ GOSAT࠺࡯࠲߇᷷ᥦൻ⎇ⓥߥߤߩ⑼ቇ߿਎⇇ߩ᷷ᥦൻ

ⴕ᡽ߦ⽸₂ߢ߈ࠆࠃ߁GOSATߩᩞᱜᬌ⸽ޔ೑↪⎇ⓥޔ೑↪ଦㅴࠍ⌕ታߦㅴ߼ޔ GOSAT࠺࡯࠲ߩຠ⾰

ߩ⛽ᜬ࡮ะ਄ࠍ⋡ᜰߒߡ޿ࠆޕߎߩߚ߼ޔ21ᐕᐲߩ⸘↹ߣߒߡߪޔೋᦼᩞᱜࠍቢੌߒޔቯᏱ᷹ⷰㆇ

↪ᦼ㑆ߩᩞᱜࠍታᣉߒޔ߹ߚޔᾲ⿒ᄖ࠺࡯࠲╬ߩ㜞ᰴಣℂࠍⴕ߁ߎߣߣߒߚޕ2009ᐕᐲߩᚑᨐߪޔ

ࠬࠤࠫࡘ࡯࡞ㅢࠅߦޔೋᦼᩞᱜࠍቢੌߒޔGOSATᛂ਄ߍᓟ9߆᦬ᓟߩL1ࡊࡠ࠳ࠢ࠻ߩ৻⥸౏㐿ޔ෸

߮1ᐕᓟߩL2ࡊࡠ࠳ࠢ࠻ߩ৻⥸౏㐿ߦ⽸₂ߢ߈ߚߎߣߢ޽ࠆޕ

ೋᦼᩞᱜߪޔ゠㆏਄ᩞᱜߣߒߡߩ᜛ᢔ᧼ࠍ↪޿ߚᄥ㓁ᾖᐲᩞ෸߮ᷓቝቮᩞᱜޔ෸߮ઍᦧᩞᱜߣߒ

ߡޔ TANSO-FTSߢ᷹ⷰߐࠇߚノᐲࠍޔ㜞ノᐲ෸߮ૐノᐲߩ࿾⴫ߩ቟ቯߒߚ႐ᚲࠍㆬ߮Ყセࠍߔࠆߎ

ߣߢታᣉߔࠆޕ․ߦઍᦧᩞᱜߪޔ゠㆏਄ᩞᱜߣߪ⁛┙ߦ࠮ࡦࠨߩ․ᕈࠍᛠីߔࠆᚻᴺߣߒߡ㊀ⷐߢ

޽ࠆޕ߹ߚޔ᦬ᩞᱜߪޔේೣߣߒߡ1ᐕߦ৻ᐲޔⴡᤊࠍ᦬ߦะߌߡ᦬ߩノᐲࠍ᷹ቯߒޔ࠮ࡦࠨᗵᐲߩ

቟ቯᕈࠍࡕ࠾࠲ߔࠆޕਥⷐߥ⚿ᨐߪޔ TANSO-FTS SWIRࡃࡦ࠼ߩノᐲ♖ᐲߪ10%ࠍ㆐ᚑߒߚ߇ޔ TIR

ߩᩞᱜߦߟ޿ߡߪ⵬ᱜߦࠃࠅᡷༀߐࠇߚ߽ߩߩޔ⛘ኻ♖ᐲߣߒߡߪ߹ߛ⋡ᮡߦ㆐ߒߡ޿ߥ޿ߣ⠨߃

ࠄࠇޔᦝߥࠆ♖ᐲะ਄ࠍ⋡ᜰߔޕ TANSO-FTSߩᐞ૗♖ᐲߦߟ޿ߡߪޔࡐࠗࡦ࠹ࠖࡦࠣࡒ࡜࡯ߩ㕒ቯ

⇣Ᏹߦ઻޿ޔᐞ૗⹏ଔߪ⛮⛯⊛ߦⷞ㊁ࡕ࠾࠲ࠞࡔ࡜(CAM-H8)ࠍ↪޿ߡ⛮⛯⊛ߦ⹏ଔߒߡ޿ࠆ߇ޔ⺋

Ꮕ߇ᤨ♽೉⊛ߦᄌേߒߡ޿ࠆߚ߼ޔ࡟ࡌ࡞1ಣℂ߳ߩ෻ᤋࠍታᣉߔࠆߦߪ⥋ߞߡ߅ࠄߕޔᐞ૗♖ᐲߩ ᖱႎߣߒߡ⹏ଔ⚿ᨐࠍ࡙࡯ࠩߦឭଏߒߡ޿ࠆޕTANSO-CAIߩᐞ૗⹏ଔߦߟ޿ߡޔᛂ਄ߍᓟ6߆᦬ߦ ታᣉߒߚೋᦼᩞᱜߢࡄ࡜ࡔ࡯࠲ߩᦝᣂࠍታᣉߒޔᚲቯߩᐞ૗♖ᐲߢ޽ࠆߎߣ߇⏕߆߼ࠄࠇߚޕ᦬ᩞ

ᱜ࠺࡯࠲ขᓧࠍታᣉߒߚ߇ޔ᦬ߩၮḰߣߥࠆノᐲߩ౉ᚻߩߚ߼ߩᚻ⛯߈߇ㆃࠇߡ⹏ଔࠍታᣉߢ߈ߡ

޿ߥ޿ޕ 㧔Ⴎ⷗ޔ༑ฬ㧕 ޕ

TANSO-FTSߩ⛘ኻᗵᐲᩞᱜߦⷐ᳞ߐࠇࠆ♖ᐲߪr10㧑⒟ᐲߢ޽ࠆ߇ޔᧄ♖ᐲߢ᷹ⷰ߇ⴕࠊࠇߡ޿

ࠆߎߣࠍઍᦧᩞᱜߦࠃࠅ⏕⹺ߔࠆߎߣࠍ⋡⊛ߣߒޔ GOSAT࠴࡯ࡓߪޔ NASA ACOS 㧔Atmospheric Carbon Observation from Space㧕 ࠴࡯ࡓߣ౒หߢޔ 2009ᐕ6᦬23ᣣ㨪7᦬5ᣣߦޔ ࿾਄หᦼ᷹ⷰታ㛎ࠍ☨࿖Railroad Valleyߦ߅޿ߡታᣉߒߚޕRailroad Valleyߦߡขᓧߒߚ࿾਄᷹ⷰ࠺࡯࠲ࠍ↪޿ߡTANSO-FTSޔCAIߩ ਔ࠮ࡦࠨߦ㑐ߔࠆઍᦧᩞᱜ⹏ଔࠍታᣉߒޔฦ࠮ࡦࠨߩ᷹ⷰノᐲߣ࿾਄᷹ቯ࠺࡯࠲߆ࠄߩࠪࡒࡘ࡟࡯

࡚ࠪࡦノᐲߣߩᲧセࠍⴕ޿ޔ᭎ߨ10%ߩ♖ᐲ߇ᓧࠄࠇߚޕ⹏ଔ⚿ᨐߩ߫ࠄߟ߈ߦߪޔ࿾਄᷹ቯ୯ߩ

♖ᐲ߿ⷞ㊁⋧ᒰߩノᐲߩ⷗Ⓧ߽ࠅߩઁޔᄥ㓁ᾖᐲ࠺࡯࠲ߩ♖ᐲޔᛂߜ਄ߍ೨ᗵᐲᩞᱜ♖ᐲ߅ࠃ߮ᛂ

਄ߍᓟߩᄌേޔ࿾⴫㕙BRDFߩᓇ㗀╬߽⠨߃ࠄࠇࠆ੐߆ࠄޔ੹ᓟޔߎࠇࠄߩⷐ࿃ࠍ♖ᩏߒߡ⹏ଔࠍ ⴕ߁੍ቯߢ޽ࠆ㧔Ⴎ⷗ޔᎹ਄ޔᄢጊ㧕 ޕ

߹ߚޔ GOSATߩᩞᱜᬌ⸽ߩߚ߼㐳ᦼ㑆ߦࠊߚࠅ࿾਄߆ࠄᄢ᳇ਛߩੑ㉄ൻ὇⚛ߩࠞ࡜ࡓ㊂ࠍขᓧߔ

ࠆߚ߼ߦޔᄢ᳇᷹ⷰ↪⿥㜞ಽ⸃⿒ᄖࡈ࡯࡝ࠛᐓᷤಽశ⸘(ㅢ⒓㧦࿾਄FTS)ߩ᷹ⷰḰ஻ࠍታᣉߒߚޕ࿾

਄FTSࠍޔ12ࡈࠖ࡯࠻ߩਛฎᶏ਄ࠦࡦ࠹࠽ౝߦ෼⚊ߒߚᓟޔ╳ᵄቝቮ࠮ࡦ࠲࡯ౝߦ⸳⟎ߒޔ㐳ᦼⷰ

᷹ߦะߌߚḰ஻᷹ⷰࠍ㐿ᆎߒߚޕḰ஻᷹ⷰߢታᣉߔࠆߎߣߪޔ᷹ⷰߩ⥄േൻޔ࿖┙ⅣႺ⎇ߩ࿾਄FTS ߣ᷹ቯ⚿ᨐߩᲧセޔ㘧ⴕᯏ᷹ⷰߩ⃻႐᷹ቯ࠺࡯࠲߆ࠄᓧࠄࠇࠆCO2ߩ㋦⋥ಽᏓߦࠃࠆ࿾਄FTSߩࠦ

࡜ࡓ㊂ߩᬌቯߢ޽ࠆ㧔Ꮉ਄ޔᄢጊ㧕 ޕ

ᾲ⿒ᄖ࠺࡯࠲╬ߩ㜞ᰴಣℂߦߟ޿ߡߪޔ TANSO-FTSߩᾲ⿒ᄖࡃࡦ࠼࠺࡯࠲ࠍ೑↪ߔࠆ㜞ᰴࡊࡠ࠳

ࠢ࠷ࠍ૞ᚑߔࠆߚ߼ߩࠕ࡞ࠧ࡝࠭ࡓࠍ㐿⊒ߒޔ♖ᐲᬌ⸽ࠍⴕ޿ޔࡊࡠ࠳ࠢ࠻ߩ૞ᚑߣ౏㐿ࠍⴕ߁ߎ ߣࠍ੍ቯߒߡ޿ߚޕߒ߆ߒޔᾲ⿒ᄖࡃࡦ࠼ߩೋᦼᩞᱜᓟߩ⵬ᱜߩታᣉ߇ᔅⷐߛߞߚߚ߼ޔࡊࡠ࠳ࠢ

࠻ߩ૞ᚑߣ౏㐿ߦߪ⥋ߞߡ޿ߥ޿߇ޔGOSATߩᾲ⿒ᄖ㗔ၞߩࠬࡍࠢ࠻࡞࠺࡯࠲ߩ9.6umᏪઃㄭߩࠝ

࠱ࡦߩๆ෼✢߆ࠄዉ಴ߐࠇࠆࠝ࠱ࡦߩోࠞ࡜ࡓ㊂߅ࠃ߮ኻᵹ࿤ࠞ࡜ࡓ㊂ߩ♖ᐲࠍ⹏ଔ෸߮Ḱ஻⊛ߥ

⸃ᨆ߹ߢⴕߞߚ㧔ᄢጊ㧕 ޕ

߹ߚޔᩞᱜᬌ⸽෸߮ᰴ਎ઍߩੑ㉄ൻ὇⚛᳇ਛ㊂ߩ᷹ቯᣇᴺߩ㐿⊒ߣߒߡޔᐔᚑ18ᐕᐲࠃࠅᩞᱜᬌ

⸽↪ੑ㉄ൻ὇⚛Ꮕಽๆ෼࡜ࠗ࠳࡯ߩ⹜૞ࠍታᣉߒߡ޿ࠆޕ࿾਄᷹ቯߣߒߡߪޔ⋡ᮡ♖ᐲ⚂㧝㧑(4ppm) ࠍ⋡ᜰߒ㐿⊒ࠍㅴ߼ޔ ࿾਄⹜㛎ߢߪin-situ⸘᷹ߣߩᏅಽ߇ 2 ppm⒟ᐲޔ ᗵᐲ⸃ᨆ߆ࠄ⋧ኻ♖ᐲ 2.8 ppm

(0.5%) ߢ৻⥌ߒ⸳⸘ㅢࠅߩᯏ⢻߇ᓧࠄࠇߚ㧔ႺỈ㧕 ޕ

TANSO-CAIߩࠛࠕࡠ࠱࡞ዉ಴♖ᐲࠍะ਄ߐߖࠆߎߣߢޔTANSO-FTS࠺࡯࠲ߦ฽߹ࠇࠆࠛࠕࡠ࠱

࡞ലᨐߩ⵬ᱜ♖ᐲะ਄ߦ⽸₂ߢ߈ࠆߚ߼ޔⴡᤊ࠺࡯࠲߆ࠄዉ಴ߐࠇߚࠛࠕࡠ࠱࡞ࡊࡠ࠳ࠢ࠻ߩ♖ᐲ

⹏ଔᚻᴺߩᬌ⸛ޔ෸߮ޔⴡᤊ࠺࡯࠲ߣࡕ࠺࡞ࠪࡒࡘ࡟࡯࡚ࠪࡦ߆ࠄᓧࠄࠇࠆࠛࠕࡠ࠱࡞․ᕈࠍ೑↪

ߒߡޔࠛࠕࡠ࠱࡞ߩ᳇୥ᓇ㗀ߦ㑐ߔࠆ⎇ⓥࠍⴕߞߚ㧔ะ੗㧕 ޕ

ቝቮ߆ࠄߩ᷷ቶലᨐࠟࠬ࿖㓙ᆔຬળߩᵴേߣߒߡޔ CEOS὇⚛࠲ࠬࠢࡈࠜ࡯ࠬࠍ⚵❱ߒߡޔ GOSAT

࠺࡯࠲೑↪ߩଦㅴޔᚑᨐߩ౏㐿ࠍታᣉߔࠆߣߣ߽ߦޔGOSATߦ⛯ߊᰴᦼࡒ࠶࡚ࠪࡦߩഃ಴߳ߩ⽸₂

ࠍⴕߞߚޕ߹ߚޔߎࠇࠄߩᵴേᚑᨐࠍUNFCC/COP15ߥߤߢ⚫੺ߔࠆߎߣߢGEO/GEOSSߢ㊀ὐൻߐ ࠇߚ᷷ᥦൻ࠲ࠬࠢߦ⽸₂ߒߚޕ ߎࠇࠄߩᵴേࠍㅢߓߡޔ ਎⇇ߩ᷷ᥦൻ⎇ⓥᯏ㑐ߣදജ㑐ଥࠍ᭴▽ߒޔ

GOSAT࠺࡯࠲ߦࠃࠆᚑᨐߩࠕࡇ࡯࡞ᯏળࠍ᜛ᄢߒߚ㧔᫪ጊ㧕 ޕ

2.2 ቝቮ߆ࠄߩ᷷ቶലᨐ᷹ࠟࠬⷰ ̆਎⇇ߩേ߈ߣGOSATᚑᨐ̆ ᫪ጊ 㓉

1. ߪߓ߼ߦ

࿾⃿᷷ᥦൻ߇ਥߚࠆⷐ࿃ߣ⇼ࠊࠇࠆⅣႺߩᄌൻ߿᳇⽎࡮᳇୥ߩᄌേ߇᭽ޘߥᒻߢ㗼࿷ൻߒߡ߈ߡ

޿ࠆޕ․ߦ࿾⃿ⷙᮨߢ⿠ߎࠆᄢⷙᮨߥᄌൻߩ޽ࠆㇱಽߪޔⴡᤊ᷹ⷰߥߤߦࠃߞߡߘߩᄌൻߩㆊ⒟ࠍ นⷞൻߔࠆߎߣ߇ߢ߈ޔ਎⇇߇ද⺞ߒߡኻಣߔࠆߎߣߩᔅⷐᕈࠍ౒᦭ߔࠆߎߣ߇ߢ߈ࠆޕߘߩ࿾⃿

᷷ᥦൻߩේ࿃ߣߒߡ⇼ࠊࠇࠆߩ߇ޔ࿾⃿ᄢ᳇ߩCO2߿ࡔ࠲ࡦߥߤߩ᷷ቶലᨐࠟࠬߩੱ㑆ߩ↢↥ᵴേ

ߦ⿠࿃ߔࠆჇടߢ޽ࠆޕ᭽ޘߥ᷷ᥦൻᙬ⇼⺰ߣߘࠇߦኻߔࠆᛕ್߇޽ࠆࠃ߁ߦޔ᳿ቯ⊛ߥ⑼ቇ⊛ᩮ

᜚ߪᓙߚߨ߫ߥࠄߥ޿߇ޔߎࠇࠄߦነਈߔࠆᱜߒ޿⸘᷹ࠍⴕ߁ߎߣߪ༛✕ߩ⺖㗴ߢ޽ࠆߎߣߪ㑆㆑

޿ߥ޿ޕ․ߦߘࠇ߇ੱὑ⿠Ḯߥߩ߆⥄ὼ⿠Ḯߥߩ߆ߩ⼂೎㧔࿾⃿ⷙᮨߢߩ㒽㕙࡮ᶏ㕙ߩࡈ࡜࠶ࠢࠬ

ߩ᳿ቯ㧕߿ޔ࿾ၞ࡮࿖೎ߩๆ෼ߣឃ಴㊂߇᳞߼ࠄࠇࠆࠃ߁ߦޔ․ߦኻᵹ࿤ਅጀߩ᷷ቶലᨐࠟࠬߩ㜞

♖ᐲ࡮㜞㗫ᐲߢߩ⛮⛯⊛ߥ᷹ⷰߣޔࡕ࠺࡞ߣߩ⚿ว߇ਇนᰳߢ޽ࠆޕ

࿾⃿᷹ⷰߩ਎⇇ⷙᮨߩ⚵❱૕ߢ޽ࠆGEO߿CEOSߪޔ᳇୥ᄌേߩⷐ࿃ߣߐࠇࠆ࿾⃿᷷ᥦൻࠍᦨఝ వ⺖㗴ߣߒߡ᭽ޘߥ࠲ࠬࠢࠍ⸳ቯߒߡቝቮ᷹ⷰߩఝ૏ᕈޔ㊀ⷐᕈߩࠕࡇ࡯࡞ߣߘߩᚑᨐߩ␠ળ߳ߩ

⽸₂ߦ㊀ὐࠍ⟎޿ߚᵴേࠍⴕߞߡ޿ࠆޕ JAXAߪߎߩਛᩭߣߥࠆࠞ࡯ࡏࡦ࠲ࠬࠢߩ࡝࡯࠳࡯ࠍോ߼ޔ

࿖ౝᄖߩ⑼ቇ⠪߿਎⇇ߩቝቮᯏ㑐ߣදജߒߡޔᣂߚߥ᷷ቶലᨐࠟࠬ࡮ࡊࡠ࠳ࠢ࠻ߩ૞ᚑ߿੹ᓟߩቝ ቮ߆ࠄߩ᷷ቶലᨐ᷹ࠟࠬⷰߩ⑼ቇⷐ᳞ߩขࠅ߹ߣ߼ޔ᷹ⷰ⸘↹ߩ⺞ᢛࠍታᣉߒߡ޿ࠆޕ

2. ᵴേ⚻✲

࿾⃿᷷ᥦൻߥߤ࿾⃿ⷙᮨߢߩ⺖㗴ߦขࠅ⚵߻ߦߪޔ࿖㓙ᵴേ߇ᰳ߆ߖߥ޿ޕⴡᤊߥߤߦࠃࠆ᷹ⷰ

ᚑᨐࠍ᷷ᥦൻߦኻᔕߔࠆ᡽╷ߥߤߦ෻ᤋߐࠇࠆ㓙ߦߪޔ ࿖㓙⊛ߥࠦࡦ࠮ࡦࠨࠬ߇ਇนᰳߢ޽ࠆޕ GOSAT ߪߎࠇ߹ߢ㒢ቯ⊛ߛߞߚ࿾਄ߩ᷹ⷰࡐࠗࡦ࠻ࠍᄢ᏷ߦᡷༀߒޔ ో⃿ߢߩ᷹ⷰࠍน⢻ߣߔࠆߎߣ߆ࠄޔ

਎⇇߇ᵈ⋡ߒߡ޿ࠆޕⴡᤊ࠺࡯࠲ߩᩞᱜᬌ⸽߿CO2, CH4ߥߤࠍᱜߒ޿‛ℂ㊂㧔ᤨⓨ㑆ᐔဋࠞ࡜ࡓ㊂

߿෼ᡰߥߤ㧕ߢ᛽಴ߔࠆߚ߼ߩࠕ࡞ࠧ࡝࠭ࡓߩ㐿⊒࡮ᡷ⦟ޔ࠺࡯࠲หൻࠍㅢߒߚฦ⒳ࡕ࠺࡞߳ߩೋ

ᦼ୯࠺࡯࠲ߣߒߡߩዉ౉ߥߤᄙߊߩදജ⺖㗴߇޽ࠆޕߎࠇࠄࠍታᣉߔࠆߚ߼ߦߪޔᓥ᧪ဳߩ౏൐⎇

ⓥ߿౒ห⎇ⓥߛߌߢߪචಽߢߥߊޔ߽ߞߣᄢ߈ߥ਎⇇⊛ߥᨒ⚵ߺࠍേ߆ߒߡޔ⸒⪲ࠍ឵߃ࠇ߫ޔ਎

⇇ߩᄙߊߩࠦࡦ࠮ࡦࠨࠬߣᡰេࠍᓧߡᵴേߔࠆߎߣߦࠃࠅޔࠃࠅ㜞޿࡟ࡌ࡞ߩᚑᨐߩㆶర߇น⢻ߣ ߥࠆޕ

GEO߿CEOSߪ࿾⃿᷹ⷰߩߚ߼ߩ࿖㓙ᵴേ⚵❱ߢ޽ࠅޔGOSATࠍᆎ߼ߣߔࠆቝቮ߆ࠄߩ᷷ቶലᨐ

᷹ࠟࠬⷰߩ࠺࡯࠲ߩ೑↪ࠍଦㅴߒޔ⛮⛯⊛ߥ࿖㓙දജߦࠃࠆ᷹ⷰࠍታ⃻ߔࠆߚ߼ߦߪᦨㆡߥࡊ࡜࠶

࠻ࡈࠜ࡯ࡓߢ޽ࠆޕGEOߪ਎⇇㧤㧝ࠞ࿖ߩ㑑௥⚖࡟ࡌ࡞ߢߩ࿾⃿᷹ⷰᵴേߩᨒ⚵ߺߢ޽ࠅޔ৻ᣇޔ CEOSߪߎߩቝቮㇱಽࠍᜂ߁ᓎഀߣߥߞߡ޿ࠆޕCEOSߪᤓᐕߩ㧝㧞᦬ߦࠦࡍࡦࡂ࡯ࠥࡦߢ㐿௅ߐࠇ ߚCOP-15ߦ߅޿ߡࠨࠗ࠼ࠗࡌࡦ࠻ࠍડ↹ߒޔቝቮߩ࿾⃿᷷ᥦൻ᡽╷߳ߩ⽸₂ࠍ⸷߃ߚޕࡄࡀ࡝ࠬ࠻

ߦߪCEOS⼏㐳ޔEUMETSAT✚ⵙޔࡁ࡯ࡌ࡞ൻቇ⾨ࠍฃ⾨ߒߚࡄ࠙࡞࡮ࠢ࡟࠷ࠛࡦ᳁ޔ᫪ጊ߇ෳട ߒߡࡊ࡟࠯ࡦ࠹࡯࡚ࠪࡦࠍⴕ޿ޔળ႐ߩෳട⠪ߣߣ߽ߦᵴ⊒ߥ⾰⇼ᔕ╵ࠍⴕ߁ߥߤචಽߥࠕ࠙࠻࡝

࡯࠴߇ߢ߈ߚޕ

3. GEOߩ᳇୥࠲ࠬࠢߣCEOSߩᵴേ

GEOߩ᳇୥࠲ࠬࠢ(CL-09-03a,b,c)ߣCEOS߇ߤߩࠃ߁ߥଥࠊࠅߢදജߒߡᵴേߒߡ޿ࠆ߆ࠍ࿑㧝ߦ

␜ߔޕCL-09-03ߣߒߡ⸳ቯߐࠇߡ޿ࠆ࠲ࠬࠢߦߪޔ(a) IGCO, (b) Forest Carbon Tracking, (c) GHG monitoring from Spaceߩ㧟ߟ߇޽ࠆޕߎࠇࠄߪ⋧੕ߦ㑐ㅪߔࠆ߽ߩߢ޽ࠆߩߢޔ⋧ਸ਼ലᨐࠍ਄ߍࠆߚ

߼ߦ߽ޔද௛ߔࠆࡊ࡜࠶࠻ࡎ࡯ࡓ߇ᔅⷐߦߥࠅޔߘߩᯏ⢻ߣߒߡCEOSౝㇱߦCarbon Task Forceࠍ⸳

⟎ߒߡ޿ࠆޕ IGCOߩᵴേߪޔ᷷ቶലᨐࠟࠬ߇Ⴧടߒߡ޿ࠆߎߣߦࠃࠆ੐⽎ߩ⑼ቇ⊛⸃᣿ࠍㅴ߼ޔ߹

ߚ዁᧪ࠍ੍᷹ߒޔ IPCCߥߤ᡽╷ߦ⋥⚿ߔࠆ⑼ቇࠕ࠮ࠬࡔࡦ࠻࡟ࡐ࡯࠻ߦᚑᨐࠍㆶరߔࠆߎߣߦ޽ࠆޕ

߹ߚޔ࿾⃿ⷙᮨߢߩ᷷ቶലᨐࠟࠬߩ᷹ⷰࠍ⛮⛯ߔࠆߚ߼ߩ⑼ቇⷐ᳞㧔᷹ⷰ‛ℂ㊂ޔ♖ᐲޔ㗫ᐲޔ⛮

⛯ޔ࠺࡯࠲หൻߥߤ㧕ࠍᦨᣂߩ⑼ቇᚑᨐࠍ෻ᤋߒߡขࠅ߹ߣ߼ޔⴡᤊ᷹ⷰࠍታᣉߔࠆCEOS߿ቝቮ ᯏ㑐ߦߘߩᩮ᜚ࠍឭ␜ߔࠆߎߣߢ޽ࠆޕ⑼ቇⷐ᳞߇ⴡᤊ᷹ⷰߩᛛⴚ⊛㒢⇇ߦኻߒߡㆡಾ߆ߤ߁߆ࠍ

ࠗࡦ࠲࡯࡜ࠢ࠹ࠖࡉߦ⹏ଔߔࠆࡔࠞ࠾࠭ࡓߣߒߡޔCEOSߢߪኾ㐷ኅߦࠃࠆࠡࡖ࠶ࡊಽᨆࠍⴕ߁࠴

࡯ࡓ߇ෳടߒޔᡰេࠍⴕ߁ޕ߹ߚޔⴡᤊ᷹ⷰߛߌߢ᷷ቶലᨐࠟࠬߩࡈ࡜࠶ࠢࠬߥߤࠍᱜߒߊ᳞߼ࠄ ࠇࠆ߽ߩߢߪߥߊޔ࿾਄᷹ⷰ߿ࡕ࠺࡞⎇ⓥᯏ㑐ߣߩදജ߇ਇนᰳߢ޽ࠅޔ IGCOߩ࠴࡯ࡓߪߎߩࠃ߁ ߥᵴേࠍࠨࡐ࡯࠻ߔࠆޕᄙ᭽ߥⴡᤊ᷹ⷰߩ࠺࡯࠲ࠍ⎇ⓥ⠪߇૶޿߿ߔߊߔࠆߚ߼ߩࡐ࡯࠲࡞ߩ⸳⟎

ߥߤ߽ㅴ߼ߡ޿ࠆޕ

࿑1 GEOߩ᳇୥࠲ࠬࠢߣCEOSߩᵴേ