システム農学 (J. JASS), 30(1) ： 9 ～ 18, 2014 䝅䝇䝔䝮㎰Ꮫ(J. JASS), ᢞ✏ㄽᩥ, 20XX 1 ◊✲ㄽᩥ
◁ୣⲡཎ䛻䛚䛡䜛䝕䝆䝍䝹䜹䝯䝷⏬ീゎᯒ䛻䜘䜛᳜⏕ㄪᰝᡭἲ㛤Ⓨ䛾ヨ䜏
ி㒔ᏛᏛ㝔ሗᏛ◊✲⛉*ୖ ెᏕ
ி㒔ᏛᏛ㝔ሗᏛ◊✲⛉*ᑠᒣ㔛ዉ
せ᪨ ⌧ᅾከ䛟⏝䛔䜙䜜䛶䛔䜛┠ど䛻䜘䛳䛶⿕ᗘ䜢グ㘓䛩䜛᳜⏕ㄪᰝ䛻䛿䚸ㄪᰝ⤖ᯝ䛜⌧⬟䛷䛒䜛䛣䛸䜔ᐈほⓗ䛷 䛺䛔䛣䛸䛺䛹䛾ḞⅬ䛜䛒䜛䚹ᮏ◊✲䛷䛿䚸䝣䜱䞊䝹䝗䛷ᙳ䛥䜜䛯ᆅୖ┿䜢⏝䛔䛶⌧ྍ⬟䚸ᐃ㔞ⓗ䛻ㄪᰝ䛷䛝䜛 ᳜⿕⋡䜔⿕ᗘ䛾ㄪᰝᡭἲ䛾㛤Ⓨ䜢ヨ䜏䛯䚹2012 ᖺ 8 ᭶䛻㫽ྲྀ◁ୣ䛻䛚䛔䛶䚸ᑐ㇟᳜≀✀䛾ⴥ⩌ཬ䜃〄ᆅ䛾 RGB 䝞䞁䝗⏬ീཬ䜃㏆㉥እ⥺䠄NIR䠅䝞䞁䝗⏬ീ䜢ྲྀᚓ䛧䛶䝃䞁䝥䝹䝕䞊䝍䛸䛧䛯䚹ྲྀᚓ䛧䛯䝃䞁䝥䝹䝕䞊䝍䜢ᩍᖌ䝕䞊䝍䛸 䝔䝇䝖䝕䞊䝍䛻ᆒ➼䛻ศ䛧䛯䚹ᩍᖌ䝕䞊䝍䛻ᑐ䛧䛶䚸䝔䜽䝇䝏䝱ゎᯒ䜢㐺⏝䛧䚸䝔䜽䝇䝏䝱≉ᚩ㔞䜢ኚᩘ䛸䛧䛶᳜⿕䜔 ✀䛾ุู䜢⾜䛖䝰䝕䝹䜢సᡂ䛧䛯䚹䝰䝕䝹䛻⏝䛩䜛ኚᩘ䛾㑅ᢥ䛻㝿䛧䛶䛿䚸┦㛵䛾㧗䛔ኚᩘ⩌䜢ྲྀ䜚㝖䛔䛯ᚋ䚸 ከ㡯䝻䝆䝇䝔䜱䝑䜽ᅇᖐ䜢⏝䛔䛶᳜⏕䛾ศ㢮䛻᭷ຠ䛺ኚᩘ⩌䜢᫂䜙䛛䛻䛧䛯䚹㑅ᢥ䛧䛯ኚᩘ⩌䜢⏝䛔䛶⥺ᙧุูศ ᯒ䛻䜘䜚䝰䝕䝹䜢సᡂ䛧䛯䚹䝔䝇䝖䝕䞊䝍䛻ᑐ䛧䛶䝰䝕䝹䜢㐺⏝䛧䚸䝰䝕䝹䛾⢭ᗘ䜢᳜⿕䛸〄ᆅ䛾ุู䚸ศ㢮⩌䠄⥘䠅 䛾ุู䚸✀䛾ุู䛻䛴䛔䛶᳨ウ䛧䛯䚹䜎䛯䚸RGB 䝞䞁䝗䜢⤌䜏ྜ䜟䛫䛯䝰䝕䝹䛸 NIR 䝞䞁䝗༢⊂䛾䝰䝕䝹䛾⢭ᗘ䛾 ẚ㍑䜢⾜䛳䛯䚹᳜⿕䛸〄ᆅ䛾ุู䛻䛚䛔䛶䛿䚸RGB 䝰䝕䝹䛸 NIR 䝰䝕䝹䛜䛭䜜䛮䜜 96%䚸87%䛸㧗䛔ṇゎ⋡䜢♧䛧 䛯䚹NIR 䝰䝕䝹䛻䛚䛔䛶䛿䚸〄ᆅ䛾⌧⋡䛜 50%௨ୗ䛸ప䛛䛳䛯䛜 RGB 䝰䝕䝹䛷䛿䛶䛾㡯┠䛾⌧⋡䛜 80%௨ ୖ䛸㧗䛛䛳䛯䚹ศ㢮⩌䠄⥘䠅䛾ุู䛻䛚䛔䛶䛿䚸RGB 䝰䝕䝹䛸 NIR 䝰䝕䝹䛾ṇゎ⋡䛿䛭䜜䛮䜜 86%䚸74%䛸㧗䛛䛳䛯䚹 NIR 䝰䝕䝹䛷䛿䚸༢Ꮚⴥ㢮䛾⌧⋡䛜 30%௨ୗ䛸ప䛛䛳䛯䛾䛻ᑐ䛧䛶䚸RGB 䝰䝕䝹䛷䛿䛶䛾㡯┠䛜 70%௨ୖ䛸 㧗䛔್䛷䛒䛳䛯䚹✀䛾ุู䛻䛚䛔䛶䛿䚸RGB 䝰䝕䝹䛾ṇゎ⋡䛜 55%䚸NIR 䝰䝕䝹䛜 39%䛸୧䝰䝕䝹䛸䜒ప䛔್䜢♧ 䛧䛯䚹✀䛻䜘䛳䛶⌧⋡䛻 0%䡚95%䛸䛝䛺ᕪ䛜䛒䜚䚸ㄗุู䛥䜜䛯✀䛾ከ䛟䛿ྠ䛨ศ㢮⩌䠄⥘䠅䛻ุู䛥䜜䛯䚹䛣䜜 䜙䛾⤖ᯝ䛛䜙䚸ᮏ◊✲䛷㛤Ⓨ䛥䜜䛯ᡭἲ䜢⏝䛔䛯ᆅୖ┿䜢ᑐ㇟䛸䛧䛯ศ㢮⩌䠄⥘䠅䛾ุู䛸᳜⿕⋡᥎ᐃ䛾ྍ⬟ᛶ 䛜♧䛥䜜䛯䛜䚸✀䛾ุู䜢⾜䛖ᚲせ䛜䛒䜛⿕ᗘ᥎ᐃ䛾ᐇ⏝䛻䛿䜎䛰ᨵⰋ䛜ᚲせ䛷䛒䜛䛣䛸䛜♧၀䛥䜜䛯䚹 䜻䞊䝽䞊䝗䠖᳜⏕ㄪᰝ䚸ᆅୖ┿䚸䝔䜽䝇䝏䝱ゎᯒ1. 䛿䛨䜑䛻
㝣ୖ⏕ែ⣔䛾◊✲䛻䛚䛔䛶᳜⏕䛾䝕䞊䝍䛿᭱䜒ᇶ ᮏⓗ䛺ሗ䛾୍䛴䛷䚸⿕ᗘ䜔✀ᵓᡂ䚸ಶయᩘ䜔ಶయ 䛾䝃䜲䝈䛺䛹䛻ᇶ䛵䛔䛶ᑐ㇟ᆅ䛾᳜⏕䛜グ㍕䛥䜜䜛䚹 ⲡཎ䜔పᮌ䛜䛺ᵓᡂ✀䛸䛺䛳䛶䛔䜛⣔䛻䛚䛔䛶䛿䚸 ಶయᩘ䠄ᐦᗘ䠅䜔ⴥ⩌ᵓ㐀䚸᳜⿕⋡䚸䜎䛯䛿⣔䜢ᵓᡂ 䛩䜛✀䛻䜘䜛ᆅ⾲䛾༨᭷ྜ䛷䛒䜛⿕ᗘ䛜ᣦᶆ䛸䛥䜜 䜛䛣䛸䛜ከ䛔䚹 ㄪᰝᑐ㇟䛾䝇䜿䞊䝹䛻䜘䛳䛶⿕ᗘ䛾ㄪᰝ᪉ἲ䛿␗ 䛺䜛䚹䝬䜽䝻䝇䜿䞊䝹䜢ㄪᰝ䛩䜛ሙྜ䛻䛿䚸ㄪᰝᆅ䛾 ୍㒊䜢ᢳฟ䛧䚸⌧ᆅ䛻䛚䛔䛶᳜≀䛾✀ᵓᡂ䜔䝃䜲䝈䜢 ㄪᰝ䛧䛶ㄪᰝᆅయ䛻እᤄ䛩䜛᪉ἲ䛜ᚑ᮶ከ䛟⏝䛔 䜙䜜䛶䛝䛯䛜䚸䝸䝰䞊䝖䝉䞁䝅䞁䜾ᢏ⾡䛾Ⓨᒎ䛻క䛔䚸 ⾨ᫍ⏬ീ䜔⯟✵┿䛺䛹䛾⏬ീ䜢ゎᯒ䛧䛯᳜⏕ㄪᰝ 䜒ከ䛟⾜䜟䜜䜛䜘䛖䛻䛺䛳䛶䛝䛯䚹䝸䝰䞊䝖䝉䞁䝅䞁䜾䛻 䛚䛔䛶䛿䚸ᗈ⠊ᅖ䛾⏬ീ䜢ᑐ㇟䛸䛧䛶䚸〄ᆅ䛸ྛ᳜≀ ✀䛾ྛ䝞䞁䝗䛻䛚䛡䜛ᑕ⋡䛾ᕪ䜢⏝䛩䜛᪉ἲ䜔 䠄Katoh 2004䠅䚸⏬ീ䛻䛚䛔䛶ྛ᳜⏕䛾㍤ᗘ䛾㐪䛔䜔 つ๎ⓗ䛺ᶍᵝ䛾ኚ䛷䛒䜛䝔䜽䝇䝏䝱䜢⤫ィᏛⓗ䛻ゎ ᯒ䚸ศ㢮䛩䜛䝔䜽䝇䝏䝱ゎᯒ䛸䛔䛖᪉ἲ䛜ᗈ䛟⏝䛔䜙䜜 䛶䛔䜛䠄ຍ⸨ 2004䠅䚹 䝔䜽䝇䝏䝱ゎᯒ䛸䛿䚸⏬ീ䛻䛚䛔䛶䚸⃰ῐ䛾⧞䜚㏉䛧 䛺䛹䛾䝟䝍䞊䞁䠄䝔䜽䝇䝏䝱䠅䜢䚸Ỵ䜑䜙䜜䛯䜴䜱䞁䝗䜴䝃 䜲䝈ෆ䛻䛚䛡䜛ྛ䝢䜽䝉䝹䛻䛚䛡䜛㍤ᗘ್䛾ᖹᆒ䚸ศ ᩓ䚸䜶䞁䝖䝻䝢䞊䛺䛹ᵝ䚻䛺⤫ィⓗ≉ᚩ䛛䜙ィ 䛧䚸䝔 䜽䝇䝏䝱≉ᚩ㔞䛸䛧䛶ゎᯒ䛧䛶䛔䛟ᡭἲ䛷䛒䜛䚹䝸䝰䞊䝖 䝉䞁䝅䞁䜾ศ㔝䛻䛚䛔䛶䛿䚸ྎ‴༡㒊䛾ᅜ❧බᅬ䜢 ᑐ㇟䛻⾨ᫍ⏬ീ䛛䜙ྲྀᚓ䛧䛯䝔䜽䝇䝏䝱≉ᚩ㔞䜢⏝䛔 䛶᳜⏕䜢ᅾ᮶✀䚸እ᮶✀䚸⏿ᆅ䛻ศ㢮䛧 䛯◊✲䜔 䠄Tsai et al. 2006䠅䚸䜰䝯䝸䜹ྜ⾗ᅜ༡㒊䛾䝏䝽䝽ᅜ❧බ ᅬ䛻䛚䛔䛶⾨ᫍ⏬ീ䛛䜙ྲྀᚓ䛧䛯ከ✀䛾䝞䞁䝗䛾䝔䜽 * 䛈606-8501 ி㒔ᗓி㒔ᕷᕥி༊ྜྷ⏣ᮏ⏫ (Correspondence: [email protected])䝇䝏䝱≉ᚩ㔞䛸㫽㢮䛾ከᵝᛶ䛾┦㛵䜢♧䛧䛯◊✲䛺䛹 䠄St-Louis et al. 2006; 2009䠅䚸ᵝ䚻䛺ඛ⾜◊✲䛜Ꮡᅾ 䛩䜛䚹䜎䛯䚸䛣䛾ᡭἲ䛿䝸䝰䞊䝖䝉䞁䝅䞁䜾䛾䚸་⒪ ⏬ീฎ⌮䛺䛹ᵝ䚻䛺ศ㔝䛻㐺⏝䛥䜜䛶䛔䜛䚹 ୍᪉䚸䝣䜱䞊䝹䝗䛷⾜䜟䜜䜛䝭䜽䝻䝇䜿䞊䝹䛾᳜⏕ㄪ ᰝ䛷䛿䚸᳜⿕⋡䜔⿕ᗘ䛾 ᐃ䛿䛸䛧䛶ㄪᰝ⪅䛾┠ ど䛻䜘䜚⾜䜟䜜䛶䛚䜚䚸ㄪᰝᡭἲ䛾⮬ື䚸≉䛻⏬ീ ゎᯒᢏ⾡䛾ᑟධ䛿䛒䜎䜚㐍䜣䛷䛔䛺䛔䚹ⲡཎ䜔పᮌ䛜 䛺ᵓᡂ✀䛷䛒䜛⣔䛷䛿䚸㠃✚䜔㛗䛥䛾ᇶ‽䛸䛺䜛ᯟ 䜔Წ䛸䛾ẚ㍑䛛䜙୍ᐃ㠃✚ෆ䛾᳜≀䛾⿕ᗘ䜔✀㢮䜢 ┠ど䛷ㄞ䜏ྲྀ䜛➼䛾᪉ἲ䛜᥇䜙䜜䜛䚹䛣䛾䜘䛖䛺᪉ἲ 䛻䛿ㄪᰝ⤖ᯝ䛜⌧ྍ⬟䛺䛣䛸䚸ᗈ⠊ᅖ䜢ㄪᰝ䛩䜛 㝿䛻ㄪᰝ㛫䛜㛗䛟䛺䛳䛶䛧䜎䛖䛣䛸䛺䛹䛾ḞⅬ䛜䛒䜛䚹 䛭䜜௨እ䛻䜒┠ど䛻䜘䜛ィ 䛷䛿䚸ㄪᰝ⪅䛾⇍⦎ᗘ䛻 䜘䛳䛶⤖ᯝ䛻ᕪ䛜ฟ䛶䛧䜎䛖䛣䛸䜔䚸⿕ᗘ䜢ṇ☜䛻 ᐃ 䛩䜛䛣䛸䛿㞴䛧䛟ᐃ㔞ᛶ䛻䛚䛸䜛䛸䛔䛖ḞⅬ䛜䛒䜛䚹 ᳜≀䜢ᑐ㇟䛸䛧䛯䝭䜽䝻䝇䜿䞊䝹䛾⏬ീゎᯒ䛻䛿䚸⏬ ീ䛛䜙䛾✀䛾ศ㢮䜢┠ⓗ䛸䛧䛯ከ䛟䛾ඛ⾜◊✲䛜Ꮡᅾ 䛩䜛䛜䚸䛣䜜䜙䛾◊✲䛷䛿䚸ಶⴥ䜔ⰼ䛺䛹᳜≀䛾ჾᐁ 䛾ᙧ≧䛻╔┠䛧䛶ศ㢮䜢⾜䛳䛶䛔䜛䠄Wang et al. 2003, Hsu et al. 2011䠅䚹䜎䛯䚸iOS⏝䜰䝥䝸䜿䞊䝅䝵䞁䝋䝣䝖䜴 䜵䜰䛻䚸ⴥ䛾⏬ീ䛛䜙᳜≀䛾✀ྡ䜢ㄪ䜉䜛“Leaf Snap” 䜔䠄Columbia University et al. 2011䠅䚸ⰼ䛾⏬ീ䛛䜙✀ ྡ䜢ㄪ䜉䜛䛂ⰼ䛧䜙䜉䛃䛺䛹䛜䛒䜛䜘䛖䛻䠄Knowledge System Inc. 2012䠅䚸⏬ീゎᯒ䛾ᢏ⾡䜢୍⯡䛾ே䚻䛜 ⏝䛷䛝䜛䜒䛾䜒䛒䜛䚹䛧䛛䛧䚸䛣䜜䜙䛿ಶⴥ䜔ⰼ䜢ṇ 㠃䛛䜙ᙳ䛧䚸䛭䛾⏬ീ䜢ゎᯒ䛧䛶䛔䜛䛯䜑䚸᳜⏕ㄪ ᰝ䛻⏝䛔䜙䜜䜛⏬ീ䛾䜘䛖䛺ಶⴥ䜔ⰼ䛜ᵝ䚻䛺ゅᗘ䛷 ⌧䜜䜛⏬ീ䛻ᑐ䛩䜛㐺⏝ᛶ䛿ప䛔䚹 ᮏ◊✲䛷䛿䚸ᆅୖ┿䛻ᑐ䛧䛶䝔䜽䝇䝏䝱ゎᯒ䜢㐺 ⏝䛧䛶䚸⌧ྍ⬟䛛䛴ᐃ㔞ⓗ䛺⿕ᗘ䜔᳜⿕⋡䛾ィ 䜢⾜䛖᪂䛯䛺ㄪᰝᡭἲ䜢㛤Ⓨ䛩䜛䛣䛸䜢ヨ䜏䛯䚹㛤Ⓨ䛧 䛯ᡭἲ䛻䛴䛔䛶䚸〄ᆅ䛸᳜⿕䛾ุู䚸ศ㢮⩌䠄⥘䠖༢ Ꮚⴥ㢮䛸Ꮚⴥ㢮䠅䛾ุู䚸✀䛾ุู䛸䛔䛖䠏䛴䛾ẁ㝵 䛻ศ䛡䚸䛭䜜䛮䜜䛾ẁ㝵䛻䛚䛡䜛⢭ᗘ䜢ㄪ䜉䛯䚹
2. ㄪᰝᆅ
ㄪᰝᆅ䛸䛧䛶䚸㫽ྲྀ┴㫽ྲྀᕷ㫽ྲྀ◁ୣ䛾す㒊䠄⦋ 35䁸32ƍ䚸ᮾ⤒134䁸11-12ƍ䠅䜢㑅ᐃ䛧䛯䠄ᅗ1䠅䚹㫽ྲྀ◁ୣ 䛿㫽ྲྀᕷ㒊䛻ᗈ䛜䜛◁ୣ䛷䛒䜚䚸1955ᖺ䛻ᅜ䛾ኳ ↛グᛕ≀䛻䜒ᣦᐃ䛥䜜䛶䛔䜛䚹䛧䛛䛧䚸➨ḟୡ⏺ ᡓᚋ䛻㎰ᆅ➼䛾ಖㆤ䛾䛯䜑䛻◁ୣ࿘㎶䛻᳜᱂䛥䜜䛯 㜵㢼䞉㜵◁ᯘ䛾ᙳ㡪䜒䛒䜚䚸㏆ᖺⲡཎ䛜㐍⾜䛧䛶䛔 䜛䠄Ọᯇ䞉ᐩỌ 2007䠅䚹䛭䛾䛯䜑䚸⌧ᅾ䛿㫽ྲྀ┴䛸㫽ྲྀ ᕷ䛜య䛸䛺䛳䛶㝖ⲡ䜢⾜䛔䚸㫽ྲྀ◁ୣ䛾ᬒほಖ䜢 ⾜䛳䛶䛔䜛䠄㫽ྲྀ◁ୣᬒほಖㄪᰝ◊✲ົᒁ 2007䠅䚹 ◁ୣ䛾ᮾ㒊䛿ほග◁ୣ䛸䛧䛶⏝䛥䜜䛶䛚䜚䚸୍⯡ ⓗ䛻㫽ྲྀ◁ୣ䛸䜀䜜䜛䛾䛿䛣䛾㒊ศ䛷䛒䜛䚹䛣䛾㒊 ศ䛷䛿䚸䛛䜙ኟ䛻䛛䛡䛶ⲡᮏ䛾ධ䛜ぢ䜙䜜䜛䛜䚸 ẖᖺᐃᮇⓗ䛻㝖ⲡ䛜⾜䜟䜜䜛䛣䛸䛷䚸◁ୣ䛾ᬒほ䛜Ᏺ 䜙䜜䛶䛔䜛䚹 䛧䛛䛧䚸ほග◁ୣ䛸䛺䛳䛶䛔䛺䛔◁ୣ䛾す㒊䛷䛿㝖 ⲡ䛜⾜䜟䜜䛶䛔䛺䛔䛯䜑䚸᳜≀䛾ධ䛸ᐃ╔䛜㉳䛣䜚䚸 ⲡཎ䞉᳃ᯘ䛜㐍⾜䛧䛶䛔䜛䚹◁ୣす㒊䛿㫽ྲྀᏛ ⇱ᆅ◊✲䝉䞁䝍䞊䛻ᡤᒓ䛧䚸◁ᆅ䜢⏝䛧䛯ᐇ㦂䛺 䛹䛻䜒⏝䛔䜙䜜䛶䛔䜛䚹ᮏ◊✲䛷䛿䚸䛣䛾◁ୣす㒊䜢 ㄪᰝᑐ㇟ᆅ䛸䛩䜛䚹 㫽ྲྀ◁ୣ䛻⏕⫱䛩䜛᳜≀䛿⣙20 ✀㢮䛒䜚䚸ඃ༨䛩䜛 ✀䛸䛧䛶䛿䚸䜹䝽䝷䝶䝰䜼䠄Artemisia capillaris䠅䜔䜿䜹䝰䝜 䝝䝅䠄Ischaemum anthephoroides䠅䛺䛹䛜䛒䜛䠄ᒣ୰䜙 2000䠅䚹䜎䛯䚸ศᕸ䛩䜛᳜≀䛾✀䛿ᾏᓊ䛛䜙䛾㊥㞳䛻䜘䜚 䛝 䛺 㐪 䛔 䛜 䛒 䜚 䚸 ᾏ ᓊ 䛛 䜙 㞳 䜜 䜛 䜋 䛹 䜰 䜻 䜾 䝭 䠄Elaeagnus umbellate䠅䛺䛹䛾పᮌ䛜ቑ䛘䜛ഴྥ䛜䛒䜛䚹 ᮏ◊✲䛻䛚䛔䛶ᑐ㇟䛸䛧䛯᳜≀䛿ඛ⾜◊✲䛻䛚䛔䛶㫽 ྲྀ◁ୣ䛻ศᕸ䛩䜛䛣䛸䛜☜ㄆ䛥䜜䛶䛔䜛᳜≀䜢ᇶᮏ䛸䛧 䛯䠄ᒣ୰䜙 2000䠅䚹䛭䛾୰䛛䜙䚸ྲྀᚓ䛧䛯ᆅୖ┿䝕䞊 䝍ෆ䛻Ꮡᅾ䛧䛺䛔᳜≀䜢㝖䛝䚸䛛䛴ඛ⾜◊✲䛷䛿グ㍕䛥 䜜䛶䛔䛺䛔䛜ྲྀᚓ䝕䞊䝍ෆ䛻Ꮡᅾ䛧䛯᳜≀䜢ຍ䛘䚸⾲1 䛻♧䛩ィ13 ✀䛾᳜≀䜢ᑐ㇟䛸䛩䜛䛣䛸䛸䛧䛯䚹3. ᪉ἲ
ᅗ2 䛻⏬ീ䝕䞊䝍ྲྀᚓ䛛䜙ᡭἲ䛾ホ౯䜎䛷䛾ὶ䜜䜢 ♧䛩䚹௨ୗ䛻䛭䜜䛮䜜䛾ẁ㝵䛻䛴䛔䛶ヲ㏙䛩䜛䚹 ᅗ 1 㫽ྲྀ◁ୣ䛾ᴫほ䠄ᯟෆ䛜ㄪᰝᆅ䠅上 佳孝ほか ： 画像解析による草原植生調査手法 䝅䝇䝔䝮㎰Ꮫ(J. JASS), ᢞ✏ㄽᩥ, 20XX 3 3.1 ⏬ീ䝕䞊䝍䛾ྲྀᚓ ⏬ീ䝕䞊䝍䛸䛧䛶䚸ᑐ㇟䛸䛧䛯13✀䛾᳜≀䛾䛔䛪䜜䛛 䛜ྵ䜎䜜䜛⏬ീ䜢ྲྀᚓ䛧䛯䚹ᙳ䛿 1m×1m 䛾ṇ᪉ᙧ 䛾ᯟෆ䛜䛶ྵ䜎䜜䜛䜘䛖䛻㧗ᗘ⣙1.5m䛛䜙⾜䛔䚸ᗄఱ ⿵ṇ䜢䛺䜛䜉䛟ᑠ䛥䛟䛩䜛䜘䛖䛻┤ୖ䛛䜙ᙳ䛧䛯䚹 ᙳ䛿䚸ᕷ㈍䛾䝕䝆䝍䝹䜹䝯䝷䠄Canon IXY 200F䠅䜢⏝ 䛔䚸RGB 䝞䞁䝗䛾⏬ീ䚸䛚䜘䜃㏆㉥እ⥺䠄௨ᚋ NIR 䛸䛩 䜛䠅䝞䞁䝗⏬ീ䜢ྲྀᚓ䛧䛯䚹RGB 䝞䞁䝗䛾⏬ീ䛿㏻ᖖ ᙳ䛾䜹䝷䞊⏬ീ䜢䚸Matlab䠄ver. 7.6, Mathworks ♫〇䠅䜢 ⏝䛔䛶䚸3 䝞䞁䝗䛻ศ䛡䛯䚹୍⯡ⓗ䛺䝕䝆䝍䝹䜹䝯䝷䛿䚸 ㉥እ⥺㐽᩿䝣䜱䝹䝍䛻䜘䜚㉥እ㡿ᇦ䜢㐽᩿䛧ཷගྍ⬟⠊ ᅖ䜢ྍどග㡿ᇦ䛾400-800nm 䛻タᐃ䛥䜜䛶䛔䜛䜒䛾䛜 ከ䛔䛜䚸タᐃ䛥䜜䛶䛔䜛ཷගឤᗘ⠊ᅖ䛻䜘䛳䛶䛿䚸ྍど ග㝖ཤ䝣䜱䝹䝍䜢ྲྀ䜚䛴䛡䛶䚸㟢ග㛫䜢㛗䛟䛩䜛䛣䛸䛻 䜘䛳䛶㏆㉥እ⥺ᙳ䛜ྍ⬟䛷䛒䜛䠄㮛㔝㇂2008; 2012䚸 ⏣ 2009䠅䚹ᮏ◊✲䛷䛿䚸NIR 䝞䞁䝗⏬ീ䛸䛧䛶 760nm ௨ୗ䛾Ἴ㛗䜢㐽᩿䛩䜛䝣䜱䝹䝍䠄IR-76,ᐩኈ䝣䜱䝹䝮♫〇䠅 䜢䜹䝯䝷䛾䝺䞁䝈๓䛻╔䛧䛶ᙳ䛧䚸3 䝞䞁䝗ศ䛾㍤ᗘ ್䛾ᖹᆒ䜢⏝䛧䛯䚹 3.2 ⏬ീ䛾ᗄఱ⿵ṇ䞉ゎീᗘㄪᩚ 䝣䜱䞊䝹䝗䛷ྲྀᚓ䛧䛯┿䛿ᡭ䛷䜹䝯䝷䜢ಖᣢ䛧䛶 ᙳ䛧䛶䛔䜛䛯䜑䚸䛻┤ୖ䛛䜙䛾⏬ീ䜢ྲྀᚓ䛷䛝䛶 䛔䛺䛔ሙྜ䛜ከ䛛䛳䛯䚹䛭䛾䛯䜑䚸ᐇ㝿䛻䛿ṇ᪉ᙧ䛾 ᯟ䛜䝕䞊䝍ෆ䛷䛿ྎᙧ䛒䜛䛔䛿➼㎶ᅄゅᙧ䛸䛺䛳䛶 䛔䜛䚹䛭䛣䛷ゎᯒ䜢⾜䛖๓䛻⏬ീෆ䛾ᯟ䜢ṇ᪉ᙧ䛻⿵ ṇ䛧䛯䚹⿵ṇసᴗ䛻䛿䚸Matlab䠄ver. 7.6, Mathworks♫ 〇 䠅 䛾 Image Processing Toolbox 䠄 ver. 6.1, Mathworks♫〇䠅䜢⏝䛧䛯䚹ᯟ䛾ᗄఱ⿵ṇ䜢⾜䛳䛯 ᚋ䛻䝕䞊䝍䝃䜲䝈䛾ㄪᩚ䜢⾜䛳䛯䚹䛶䛾䝕䞊䝍䛾ゎ ീᗘ䜢2mm䛸䛧䚸1㎶1m䛾ᯟ䛜䝢䜽䝉䝹ᩘ䛷䛿1㎶500 䝢䜽䝉䝹䛸䛺䜛䜘䛖䛻䛧䛯䠄ᅗ3ཧ↷䠅䚹 3.3 ✀ẖ䛾䝃䞁䝥䝹䝕䞊䝍䛾ྲྀᚓ ⏬ീ䛛䜙ᑐ㇟᳜≀✀䛾ⴥ⩌䜢ᢳฟ䛧䛶䝃䞁䝥䝹䛸䛧 䛯䚹⿕ᗘ䜔᳜⿕⋡䛸䛧䛶⟬ฟ䛥䜜䜛䛾䛿䛻ⴥ䛻䜘䜛 ᆅ⾲䛾⿕そ䛷䛒䜛䛯䜑䛷䛒䜛䚹ྛ✀䛾ⴥ⩌䛻ຍ䛘䚸〄 ᆅ䛾䝃䞁䝥䝹䛸䛧䛶䚸◁ᆅ䛚䜘䜃䝸䝍䞊䜢ྵ䜑䛯䛯䜑䚸 䝃䞁䝥䝹䛿ィ15✀䛸䛺䛳䛯䚹䝃䞁䝥䝹⮬య䛾ศᩓ䜒ㄪ 䜉䜛䛯䜑䚸䝃䞁䝥䝹䛿ྛ✀䛻䛴䛔䛶10×10䝢䜽䝉䝹䠄2 ×2cm䠅䛾䜾䝸䝑䝗䛸䛧䚸ྛ✀䛻䛴䛔䛶3-5ಶయ䛛䜙ィ40 䝃䞁䝥䝹䜢ྲྀᚓ䛧䛯䚹䛯䛰䛧䚸䝡䝻䞊䝗䝔䞁䝒䜻䚸䝝䝬䝙 䜺䝘䚸䝁䝬䝒䝶䜲䜾䝃䛾3✀䛿ྲྀᚓ⏬ീ䝕䞊䝍ᩘ䛜༑ศ ⾲ 1 ㄪᰝᑐ㇟䛸䛧䛯᳜≀ Ꮫྡ ྡ ศ㢮⩌ ⴥࡢࡁࡉ Artemisia capillaris ࢝࣡ࣛࣚࣔࢠ Ꮚⴥ㢮 ⴥࡣ⣒≧ Carex kobomugi ࢥ࣒࢘࣎࢘ࢠ ༢Ꮚⴥ㢮 㛗ࡉ⣙10-20cmࠊ ᖜ1cm ⛬ᗘ Calystegia soldanella ࣁ࣐ࣄࣝ࢞࢜ Ꮚⴥ㢮 㛗ࡉ⣙2-4cm Elaeagnus umbellate 䜰䜻䜾䝭 Ꮚⴥ㢮 㸦పᮌ㸧 㛗ࡉ⣙4-8cm Fimbristylis sericea ࣅ࣮ࣟࢻࢸࣥࢶ࢟ ༢Ꮚⴥ㢮 㛗ࡉ⣙5cmࠊ ᖜ0.5cm ⛬ᗘ Ischaemum anthephoroides ࢣ࢝ࣔࣀࣁࢩ ༢Ꮚⴥ㢮 㛗ࡉ⣙3-4cmࠊ ᖜ1cm ⛬ᗘ Ixeris repens ࣁ࣐ࢽ࢞ࢼ Ꮚⴥ㢮 㛗ࡉ⣙3-5cm Linaria japonica ࢘ࣥࣛࣥ Ꮚⴥ㢮 㛗ࡉ⣙2cm Oenothera erythrosepala ࣐࢜࢜ࢶࣚࢢࢧ Ꮚⴥ㢮 㛗ࡉ⣙5-6cm Oenothera laciniata ࢥ࣐ࢶࣚࢢࢧ Ꮚⴥ㢮 㛗ࡉ⣙3-6cm Vitex rotundifolia ࣁ࣐ࢦ࢘ Ꮚⴥ㢮 㸦పᮌ㸧 㛗ࡉ⣙3-5cm Wedelia prostrate ࢿࢥࣀࢩࢱ Ꮚⴥ㢮 㛗ࡉ⣙1-3cm Zoysia macrostachya ࢜ࢽࢩࣂ ༢Ꮚⴥ㢮 㛗ࡉ⣙2-3cmࠊ ᖜ0.5cm ௨ୗ ᅗ 3 ᗄఱ⿵ṇ䛾䠄ᕥ䛜⿵ṇ๓䚸ྑ䛜⿵ṇᚋ䠅 ᅗ 2 ᡭἲ☜❧䜎䛷䛾ὶ䜜䛾ᴫせ
䛷䛺䛛䛳䛯䛯䜑䚸ྛ20䝃䞁䝥䝹䛸䛧䚸15✀䛛䜙ィ540䛾 䝃䞁䝥䝹䝕䞊䝍䜢ྲྀᚓ䛧䛯䚹䛣䛾䝃䞁䝥䝹䜢ྛ✀䛾䝃
䞁䝥䝹ᩘ䛜ᆒ➼䛻䛺䜛䜘䛖䛻ᩍᖌ䝕䞊䝍䠄ィ270䝃䞁䝥
䝹䠅䛸䝔䝇䝖䝕䞊䝍䠄ィ270䝃䞁䝥䝹䠅䛻ศ䛧䛯䚹䝃䞁䝥 䝹 ྲྀ ᚓ 䛻 䛿Matlab 䠄 ver. 7.6, Mathworks ♫ 〇 䠅 䛾 Image Processing Toolbox 䠄ver. 6.1, Mathworks♫〇䠅 䜢⏝䛧䛯䚹 3.4 䝔䜽䝇䝏䝱ゎᯒ ྛ᳜≀✀䛾ᩍᖌ䝕䞊䝍䛻䛴䛔䛶䝔䜽䝇䝏䝱≉ᚩ㔞䜢 ⟬ฟ䛧䛯䚹୍ḟ≉ᚩ㔞䛸䛧䛶䝺䞁䝆䚸ᶆ‽೫ᕪ䚸䜶䞁䝖䝻 䝢䞊 䚸 ḟ≉ᚩ㔞䛸䛧 䛶 ྠ⏕㉳⾜ิ䠄Gray Level Co-occurrence Matrix䚸௨ୗ GLCM 䛸䛩䜛䠅䛾䝁䞁䝖䝷䝇䝖䚸 䜶䝛䝹䜼䞊䚸ᆒ୍ᛶ䠄Homogeneity䠅䛾ィ 6 ✀㢮䜢ྲྀᚓ䛧 䛯䚹୍ḟ≉ᚩ㔞䛸䛿ᣦᐃ䜴䜱䞁䝗䜴䝃䜲䝈ෆ䛾䝢䜽䝉䝹㍤ ᗘ್䛛䜙ᚓ䜙䜜䜛ᑬᗘ䚸ṍᗘ䚸ᶆ‽೫ᕪ䚸䝺䞁䝆䛺䛹䛾 ⤫ィ㔞䛷䛒䜚䚸ィ⟬㔞䛜ᑡ䛺䛔䛸䛔䛖Ⅼ䛜䛒䜛䛜䚸✵ 㛫ⓗ䝟䝍䞊䞁䜢↓ど䛧䛶䛔䜛䚹୍᪉䚸ḟ≉ᚩ㔞䛸䛿 2 䛴䛾䝢䜽䝉䝹㛫䛾㍤ᗘ್䛾㛵ಀ䜢⪃៖䛧䛯≉ᚩ㔞䛷䛒䜚䚸 ᮏ◊✲䛷⏝䛔䜛 GLCM 䛿䛒䜛୍ᐃ㊥㞳䛻䛒䜛䝢䜽䝉䝹 㛫䛷䚸⤌䜏ྜ䜟䛫䛾䝢䜽䝉䝹್䛾㢖ᗘ䜢⾲䛩ṇ᪉⾜ิ 䛷䛒䜛䚹GLCM 䛷䛿䚸䛒䜛⛬ᗘ䛾✵㛫ⓗ䝟䝍䞊䞁䛿⪃៖ 䛥䜜䛶䛔䜛䚹䛧䛛䛧䚸ḞⅬ䛸䛧䛶䛿䚸ィ⟬㔞䛜䛝䛟䚸⟬ฟ 䛻㛫䛜䛛䛛䜛䛣䛸䛜ᣲ䛢䜙䜜䜛䚹GLCM 䛻䜘䛳䛶ồ䜑 䜙䜜䜛≉ᚩ㔞䛿ඛ⾜◊✲䛷䛿 14 ✀ᣲ䛢䜙䜜䛶䛔䜛䛜 䠄Haralick et al. 1973䠅䚸ᮏ◊✲䛷䛿䝁䞁䝖䝷䝇䝖䚸䜶䝛䝹䜼 䞊䚸ᆒ୍ᛶ䛾 3 ✀䜢⏝䛩䜛䛣䛸䛸䛧䚸0䁸䚸45䁸䚸90䁸䚸135䁸 䛾4 䛴䛾᪉ྥ䛾 GLCM 䜢ィ⟬䛧䛯䚹 ྛ᳜≀䛾ᩍᖌ䝕䞊䝍䛻ᑐ䛧䚸䜶䞁䝖䝻䝢䞊䜢㝖䛟5䛴 䛾≉ᚩ㔞䛿䚸3×3䝢䜽䝉䝹䠄0.6×0.6cm䠅䛾䜴䜱䞁䝗䜴䝃 䜲䝈䛷ィ⟬䛧䛯䚹䜶䞁䝖䝻䝢䞊䛿9×9䝢䜽䝉䝹䠄1.8× 1.8cm䠅䛷ィ⟬䜢⾜䛳䛯䚹ィ⟬䛻䛿Matlab䠄ver. 7.6䠅䛾 Image Processing Toolbox䠄ver. 6.1䠅䜢⏝䛧䛯䚹
䛭䛾ᚋ䚸ᩍᖌ䝕䞊䝍䛾✀㛫ẚ㍑䜢⾜䛖䛯䜑䚸䝔䜽䝇 䝏䝱ゎᯒᚋ䛾ᩍᖌ䝕䞊䝍⏬ീ䛾ྛ䜾䝸䝑䝗䛾ᖹᆒ䛸ᶆ ‽೫ᕪ䜢ồ䜑䛯䚹䜎䛯䚸䝔䜽䝇䝏䝱ゎᯒ䜢⾜䛖๓䛾ཎ⏬ ീ䛾䜾䝸䝑䝗䛾ᖹᆒ䛸s.d.䠄ᶆ‽೫ᕪ䠗௨㝆䚸ྛ≉ᚩ㔞䛾 䜾䝸䝑䝗䛾ᶆ‽೫ᕪ䜢s.d.䚸䝔䜽䝇䝏䝱ゎᯒ䛾୍ḟ≉ᚩ 㔞䛷䛒䜛ᶆ‽೫ᕪ䜢ᶆ‽೫ᕪ䠄୍ḟ䠅䛸䛩䜛䠅䜒✀ุู 䛻᭷ຠ䛺ᣦᶆ䛸䛺䜛ྍ⬟ᛶ䛜䛒䜛䚹䛭䛾䛯䜑䚸䝔䜽䝇䝏 䝱ゎᯒ䜢⾜䛖䛸ྠ䛻䚸ཎ⏬ീ䛾ᖹᆒ䛸s.d.䜢ồ䜑䛯䚹 䛭䛾⤖ᯝ䚸ᩍᖌ䝕䞊䝍䛾ศ㢮䜢⾜䛖㝿䛻⏝䛔䜛ኚᩘ 䛿䚸NIR ⏬ീ䛷䛿䝔䜽䝇䝏䝱≉ᚩ㔞 6 ✀ཬ䜃ཎ⏬ീ䛾䛭 䜜䛮䜜䛻䛴䛔䛶䛾ᖹᆒ䛸ᶆ‽೫ᕪ䛸䛺䜚䚸ィ14 ኚᩘ䛸䛺 䛳䛯䚹RGB ⏬ീ䛷䛿 14 ኚᩘ䛜 RGB 䛾 3 䝞䞁䝗䛻䛒䜛䛯 䜑䚸ィ42 ኚᩘ䛸䛺䛳䛯䠄⾲ 2 ཧ↷䠅䚹 3.5 ┦㛵䛜㧗䛔ኚᩘ䛾㝖ཤ ᮏ◊✲䛷䛿䚸䝰䝕䝹సᡂ䛻ᙜ䛯䛳䛶䚸䛶䛾ኚᩘ䜢 ⏝䛫䛪䚸䝰䝕䝹ෆ䛷ྠᵝ䛾ാ䛝䜢䛩䜛䛸⪃䛘䜙䜜䜛 」ᩘ䛾ኚᩘ䛻㛵䛧䛶䛿୍䛴䛾ኚᩘ䛾䜏䜢⏝䛩䜛䛣䛸 䛸䛧䛯䚹䛭䛾䛯䜑䚸ண䜑ྛኚᩘ㛫䛾┦㛵䜢ㄪ䜉䚸┦㛵 䛜㧗䛔⤌䜏ྜ䜟䛫䛾䛖䛱୍᪉䜢㝖䛔䛯ᚋ䚸⥺ᙧุู ศᯒ䛻䜘䜛✀ุู䛻䛚䛔䛶᭱㐺䛺ኚᩘ⩌䛾㑅ᢥ䜢⾜ 䛳䛯䚹䝰䝕䝹䛻⏝䛔䜛ኚᩘ⩌䜢㑅䜆㝿䛻䛿䛺䜛䜉䛟ィ ⟬㔞䛜ᑡ䛺䛔ኚᩘ䜢㑅䜆䛣䛸䛸䛧䛯䚹ᮏ◊✲䛷⏝䛔䛯 ኚᩘ䛷䛿䚸ཎ⏬ീ䚸୍ḟ≉ᚩ㔞䚸ḟ≉ᚩ㔞䛾㡰䛻 ィ⟬㔞䛜ᑡ䛺䛔䚹䜎䛯䚸ḟ≉ᚩ㔞䛸୍ḟ≉ᚩ㔞䛾ィ ⟬㔞䛾ᕪ䛻ẚ䜉䚸ḟ≉ᚩ㔞ෆ䜔୍ḟ≉ᚩ㔞ෆ䛷䛾 ኚᩘ㔞䛾ᕪ䛿ᑠ䛥䛔䛯䜑䚸ྠḟඖ䛷䛾ィ⟬㔞䛾ᕪ䛿 ⪃៖䛧䛺䛛䛳䛯䚹RGB䝞䞁䝗䛾⏬ീ䛾ሙྜ䛻䛿ኚᩘ䜢 ๐㝖䛩䜛㝿䛻䛷䛝䜛䛰䛡R䛸G䛸B䛾䛭䜜䛮䜜䛾䝞䞁䝗 䛜ᣢ䛴ኚᩘ䛾ᩘ䛜ᆒ➼䛻䛺䜛䜘䛖䛻䛧䛯䚹௨ୖ䛾ᇶ‽ 䛻ᇶ䛵䛝䚸Pearson┦㛵ಀᩘ䛜0.9௨ୖ䜒䛧䛟䛿-0.9௨ୗ 䛷䛒䜛ኚᩘ䛜↓䛟䛺䜛䜎䛷ኚᩘ䛾㝖ཤ䜢⾜䛳䛯䚹ゎᯒ 䛻䛿䚸⤫ィゎᯒ䝋䝣䝖R䠄ver. 2.15.1䠅䜢⏝䛧䛯䚹 3.6 ከ㡯䝻䝆䝇䝔䜱䝑䜽ᅇᖐ ┦㛵䛾䛒䜛ኚᩘ䜢㝖䛔䛯ᚋ䛻ከ㡯䝻䝆䝇䝔䜱䝑䜽ᅇ ᖐ䛻䜘䜚䚸ุู䛻᭱㐺䛺ኚᩘ⩌䜢ồ䜑䛯䚹ከ㡯䝻䝆䝇 䝔䜱䝑䜽ᅇᖐ䛸䛿┠ⓗኚᩘ䛜3䛴௨ୖ䛾ྡ⩏ኚᩘ䛷䛒 䜛ሙྜ䛻⏝䛔䜙䜜䜛୍⯡⥺ᙧ䝰䝕䝹䛾䠍䛴䛷䛒䜛䚹 ᮏ◊✲䛷䛿䚸᭱㐺䛺䝰䝕䝹䜢స䜛䛯䜑䛻⏝䛔䜙䜜䜛ㄝ ᫂ኚᩘ䜢㑅ᢥ䛩䜛㝿䛻䚸䜎䛪ᐃᩘ㡯䛾䜏䛻䜘䜛ከ㡯䝻 䝆䝇䝔䜱䝑䜽ᅇᖐ䜢⾜䛔䚸ḟ䛻ኚᩘቑῶἲ䜢⏝䛔䛶᭱ 㐺䛺䝰䝕䝹䛸䛺䜛ኚᩘ䛾⤌䜏ྜ䜟䛫䜢ㄪ䜉䛯䚹䛣䛾䚸 䝰䝕䝹㑅ᢥ䛾ᇶ‽䛸䛧䛶䛿AIC䠄Akaike Information Criterion䠅䜢⏝䛔䛯䚹ゎᯒ䛻䛿⤫ィゎᯒ䝋䝣䝖R䠄ver. 2.15.1䠅䜢⏝䛔䛯䚹᳜⿕䛸〄ᆅ䛾ุู䚸ศ㢮⩌䠄⥘䠅䛾ุ ู䚸✀䛾ุู䛸䛔䛖ẁ㝵䛾䛭䜜䛮䜜䛻䛴䛔䛶䚸᭱㐺䛺 ኚᩘ䛾⤌䜏ྜ䜟䛫䜢ồ䜑䛯䚹 3.7 ⥺ᙧุูศᯒ䛾㐺⏝䛸䝰䝕䝹䛾ホ౯ ⥺ᙧุูศᯒ䛸䛿┠ⓗኚᩘ䛸䛧䛶」ᩘ䛾ྡ⩏ኚᩘ 䛜䛒䜛䛻䚸┠ⓗኚᩘ䛾⩌㛫䛾ศᩓ䜢᭱䛻䛧䚸⩌ෆ 䛾ศᩓ䜢᭱ᑡ䛻䛩䜛⥺ᙧุู㛵ᩘ䜢ồ䜑䜛䛣䛸䛷ุู 䜢⾜䛖᪉ἲ䛷䛒䜛䚹 ホ౯䛩䜛㝿䛻䛿䜎䛪๓㡯䜎䛷䛷ồ䜑䛯᭱㐺䛺ኚᩘ⩌ 䜢⏝䛔䛶ุู㛵ᩘ䜢సᡂ䛧䛯䚹䛭䛾ᚋ䚸䝔䝇䝖䝕䞊䝍䛻 ᑐ䛧䚸ồ䜑䛯ุู㛵ᩘ䛻䜘䜛⥺ᙧุูศᯒ䛻䜘䜛ุู䜢
上 佳孝ほか ： 画像解析による草原植生調査手法 䝅䝇䝔䝮㎰Ꮫ(J. JASS), ᢞ✏ㄽᩥ, 20XX 5 ⾜䛳䛯䚹ุู䛻㝿䛧䛶䛿䚸〄ᆅ䛸᳜⿕䛾ุู䚸ศ㢮⩌ 䠄⥘䠅䛾ุู䚸✀䛾ุู䛸䛔䛖3 ẁ㝵䜢タᐃ䛧䚸䛭䜜䛮䜜 䛾ẁ㝵䛻䛚䛡䜛䝰䝕䝹䛾⢭ᗘ䜢⟬ฟ䛧䛯䚹
4. ⤖ᯝ䛸⪃ᐹ
4.1 ┦㛵ಀᩘ䛾⤖ᯝ䜢ᇶ‽䛸䛧䛯ኚᩘ䛾㑅ᢥ ┦㛵ࡢ㧗࠸ኚᩘࡢ㛵ಀࢆ㝖࠸ࡓ⤖ᯝࠊRGBࣂࣥࢻ ࡢ⏬ീ࡛ࡣኚᩘࡀ42ಶࡽ19ಶῶᑡࡋࠊNIRࣂࣥ ࢻࡢ⏬ീ࡛ࡣ14ಶࡽ7ಶῶᑡࡋࡓ㸦⾲2㸧ࠋ RGB䝞䞁䝗䛾⏬ീ䛻䛚䛔䛶䚸┦㛵䛾㧗䛔㛵ಀ䛻╔ ┠䛩䜛䛸䚸␗䛺䜛䝞䞁䝗䛾ྠ䛨ኚᩘ䛜┦㛵䛾㧗䛔㛵ಀ 䛻䛒䜛䛣䛸䛜ከ䛔䛣䛸䛜䜟䛛䛳䛯䚹ྠ䛨ኚᩘ䛷R䛸G䛸B 䛾3䝞䞁䝗䛜䛶⊂❧䛰䛳䛯䛾䛿䚸䜶䞁䝖䝻䝢䞊䛾s.d.䚸 䜶䝛䝹䜼䞊䛾ᖹᆒ䛸ᆒ୍ᛶ䛾s.d.䛸䛔䛖3ኚᩘ䛾䜏䛷䛒 䜚䚸䛾ኚᩘ䛿ᚲ䛪1䛴䛛2䛴䛾␗䛺䜛䝞䞁䝗䛾ྠ䛨ኚ ᩘ䛸㧗䛔┦㛵䜢♧䛧䛯䚹ᩍᖌ䝕䞊䝍⏬ീ䛾㍤ᗘ䛭䛾䜒 䛾䜢♧䛩ཎ⏬ീ䛾ᖹᆒ䛷䛿䚸R䛸G䛾┦㛵䛜0.9௨ୖ䛷 䛒䛳䛯䛜䚸B䛿2䝞䞁䝗䛸᭷ព䛺┦㛵䛜ぢ䜙䜜䛺䛛䛳 䛯䚹䜎䛯䚸ཎ⏬ീ䛾s.d.䚸䝺䞁䝆䛾ᖹᆒ䛚䜘䜃s.d.䚸ᶆ‽ ೫ᕪ䠄୍ḟ䠅䛾ᖹᆒ䛚䜘䜃s.d.䛾5䛴䛾ኚᩘ䛷䛿䚸␗䛺 䜛䝞䞁䝗䛾ྠ䛨ኚᩘ㛫䛷㧗䛔┦㛵䛜䛒䛳䛯䚹௨ୖ䜘䜚 RGB䝞䞁䝗䛾⏬ീ䛾ኚᩘ䛻㛵䛧䛶䚸䝞䞁䝗㛫䛾ᕪ䛜ᑠ 䛥䛔䛣䛸䛜䜟䛛䛳䛯䚹RGB䝞䞁䝗䛷ṧ䛳䛯ኚᩘ䛾ಶᩘ 䛿R䛜6ಶ䚸G䛜6ಶ䚸B䛜7ಶ䛷䛒䛳䛯䚹 NIRࣂࣥࢻࡢ⏬ീ࠾࠸࡚ࡣࠊࣞࣥࢪᶆ‽೫ᕪ 㸦୍ḟ㸧ࡢ┦㛵ࡀࢹ࣮ࢱࡢᖹᆒ࠾ࡼࡧs.d.ࡢ࠸ࡎࢀ ࡘ࠸࡚ࡶ㠀ᖖ㧗ࡗࡓࠋࢸࢡࢫࢳࣕ≉ᚩ㔞࠾ ࠸࡚ࠊᶆ‽೫ᕪ㸦୍ḟ㸧ࡣ࢘ࣥࢻ࢘ෆ࡛㍤ᗘࡀᖹ ᆒ್ࡽ㞳ࢀࡓࣆࢡࢭࣝࡀከ࠸ࡁ್ࡀ㧗ࡃ࡞ ࡾࠊࣞࣥࢪࡣ≀యࡢ㍯㒌ࢆྲྀࡾฟࡍᛶ㉁ࡀ࠶ࡿࠋࡑ ࡢࡓࡵࠊ᳜≀࡛ࡣࠊⴥࡀ⣽ࡃศࢀ࡚࠸ࡿሙྜࡸࠊ ⴥ㗬ṑࡀ࠶ࡿሙྜ࡞ࡣࠊ㍯㒌ࡀ⣽ࡃ࡞ࡿࡓ ࡵࠊᶆ‽೫ᕪ㸦୍ḟ㸧ࣞࣥࢪࡀఝ㏻ࡗࡓ್ࢆᣢࡘ ⪃࠼ࡽࢀࡿࠋࡲࡓࠊ࢚ࣥࢺࣟࣆ࣮ࡢs.d.ࡣࡢኚ ᩘࡢ┦㛵ࡢ್ࡀ㠀ᖖపࡗࡓࠋRGB䝞䞁䝗䛻䛚 䛔䛶Ꮡᅾ䛧䛯䜶䞁䝖䝻䝢䞊䛜䛾ኚᩘ䛸⊂❧䛷䛒䜛ഴ ྥ䛿䚸NIR䝞䞁䝗䛻䜒ྠᵝ䛻ぢ䜙䜜䛯䚹 4.2 ከ㡯䝻䝆䝇䝔䜱䝑䜽ᅇᖐ䛻䜘䜛᭱㐺䝰䝕䝹 ᳜⿕䛸〄ᆅ䛾ุู䛻䛚䛔䛶䚸RGB䝞䞁䝗䛷䛿5ಶ䚸 NIR䝞䞁䝗䛷䛿4ಶ䛾ኚᩘ䛜⏝䛔䜙䜜䛯䠄⾲3䠅䚹ศ㢮⩌ 䠄⥘䠅䛾ุู䛷䛿RGB䝞䞁䝗䛷䛿7ಶ䚸NIR䝞䞁䝗䛷䛿2 ಶ䛾ኚᩘ䛜⏝䛔䜙䜜䛯䚹✀䛾ุู䛷䛿RGB䝞䞁䝗䛷7 ಶ䚸NIR䝞䞁䝗䛷䛿3ಶ䛾ኚᩘ䛜⏝䛔䜙䜜䛯䚹 䛶䛾䝰䝕䝹䛻䛚䛔䛶䚸ཎ⏬ീ䜢⏝䛔䛯ኚᩘ䛜ከ䛟 ⏝䛔䜙䜜䚸ḟ≉ᚩ㔞䛿䛒䜎䜚⏝䛔䜙䜜䛺䛛䛳䛯䚹䛣䜜 䛿䚸┦㛵䛜㧗䛔ኚᩘ䜢㝖䛟㝿䛻䚸ィ⟬㔞䛜ᑡ䛺䛔ཎ⏬ ീ䛾ኚᩘ䛜ඃඛⓗ䛻ṧ䛥䜜䛯䛛䜙䛷䛒䜛䛸⪃䛘䜙䜜䜛䚹 4.3 ⥺ᙧุูศᯒ䛻䜘䜛ྛẁ㝵䛾䝰䝕䝹䛾⢭ᗘ ๓㡯䛷ồ䜑䛯ྛẁ㝵䛻䛚䛡䜛᳜≀䛾ุู䛻᭱㐺䛺 ኚᩘ⩌䜢⏝䛔䛶䚸ྛẁ㝵䛾䝰䝕䝹䛾⢭ᗘ䜢ồ䜑䛯䚹䜎 䛪䚸᳜⿕䛸〄ᆅ䛾ุู䛾ẁ㝵䛷䛿䚸ṇゎ⋡䠄ṇ䛧䛟ุ ⾲ 2 ከ㡯䝻䝆䝇䝔䜱䝑䜽ᅇᖐ䛻⏝䛔䜙䜜䛯ኚᩘ⩌ 䝔䜽䝇䝏䝱≉ᚩ㔞 RGB NIR R G B ཎ⏬ീ ᖹᆒ ż ż ż s.d. ż ż ୍ḟ 䝺䞁䝆 ᖹᆒ ż s.d. ż ᶆ‽೫ᕪ ᖹᆒ s.d. 䜶䞁䝖䝻䝢䞊 ᖹᆒ ż ż s.d. ż ż ż ż ḟ 䝁䞁䝖䝷䝇䝖 ᖹᆒ ż ż s.d. 䜶䝛䝹䜼䞊 ᖹᆒ ż ż ż ż s.d. ż ż ᆒ୍ᛶ ᖹᆒ ż s.d. ż ż ż ż ż䛿㑅ᢥ䛥䜜䛯ኚᩘ䜢♧䛩䚹┦䛻┦㛵䛾㧗䛔ኚᩘ䛛 䜙1ኚᩘ䜢㑅ᢥ䛧䛶⏝䛧䛯䚹 ⾲ 3 ྛẁ㝵䛷ồ䜑䜙䜜䛯᭱㐺ኚᩘ⩌ 䝔䜽䝇䝏䝱≉ᚩ㔞 RGB NIR R G B ཎ⏬ീ ᖹᆒ 1,2,3 1,2,3 1,2,3 s.d. 1,2,3 3 ୍ḟ 䝺䞁䝆 ᖹᆒ 1,2,3 s.d. 3 ᶆ‽೫ᕪ ᖹᆒ s.d. 䜶䞁䝖䝻䝢䞊 ᖹᆒ 2 1,3 s.d. * * * 2 ḟ 䝁䞁䝖䝷䝇䝖 ᖹᆒ 2,3 1,3 s.d. 䜶䝛䝹䜼䞊 ᖹᆒ * * 2 1 s.d. * 1 ᆒ୍ᛶ ᖹᆒ * s.d. * * * * 1: 〄ᆅ䛸᳜⿕䛾ุู䛻⏝䛔䜙䜜䛯ኚᩘ 2: ศ㢮⩌䠄⥘䠅䛾ุู䛻⏝䛔䜙䜜䛯ኚᩘ 3: ✀䛾ุู䛻⏝䛔䜙䜜䛯ኚᩘ *: 䛹䛾ุู䛻䜒⏝䛔䜙䜜䛺䛛䛳䛯ኚᩘู䛥䜜䛯䝃䞁䝥䝹ᩘ/䝃䞁䝥䝹ᩘ䠅䛿 RGB 䝰䝕䝹䛜 96%䠄259/270䠗⾲ 4䠅䚸NIR 䝰䝕䝹䛜 87%䠄235/270䠗⾲ 5䠅 䛸䚸䛹䛱䜙䜒㧗䛛䛳䛯䚹䛧䛛䛧䚸RGB 䝰䝕䝹䛷䛿䛶䛾 㡯┠䛜80%௨ୖ䛷䛒䛳䛯䛾䛻ᑐ䛧䚸NIR 䝰䝕䝹䛷䛿〄 ᆅ䛾⌧⋡䠄䛒䜛 1 䜽䝷䝇䛻䛚䛔䛶ṇ䛧䛟ุู䛥䜜䛯䝃 䞁䝥䝹ᩘ/䛭䛾䜽䝷䝇䛾䝃䞁䝥䝹ᩘ䠅䛜 45%䠄18/40䠅䚸 ⢭ᗘ䠄䛒䜛 1 䜽䝷䝇䛻䛚䛔䛶ṇ䛧䛟ุู䛥䜜䛯䝃䞁䝥䝹 ᩘ/䛭䛾䜽䝷䝇䛻ุู䛥䜜䛯䝃䞁䝥䝹ᩘ䠅䛜 58%䠄18/31䠅 䛸ప䛔್䛷䛒䜚䚸〄ᆅ䛾ุู䛜䛷䛝䛺䛔䛣䛸䛜䜟䛛䛳䛯䚹 NIR 䝰䝕䝹䛷〄ᆅ䛾ุู䛜䛷䛝䛺䛔⌮⏤䛸䛧䛶䚸〄ᆅ 䛿◁ᆅ䛸䝸䝍䞊䛻䜘䛳䛶ᵓᡂ䛥䜜䛶䛚䜚䚸䝸䝍䞊䛜᳜⿕ 䛻ㄗุู䛥䜜䛶䛔䜛䛾䛷䛿䛺䛔䛛䛸⪃䛘䜙䜜䜛䚹䝸䝍䞊 䛿ᙧ≧䛸䛧䛶䛿༢Ꮚⴥ㢮䛸ఝ㏻䛳䛯ᙧ≧䜢䛧䛶䛔䜛䛣䛸 䛜ከ䛟䚸ᙧ≧䛾ᕪ䛜ᑠ䛥䛔䛯䜑䚸ㄗุู䛜㉳䛝䜛䛸⪃䛘 䜙䜜䜛䚹 䛭䛣䛷䚸NIR 䝰䝕䝹䛻䛚䛔䛶䚸◁ᆅ䚸䝸䝍䞊䛚䜘䜃᳜ ⿕䛾3 ✀䛷ุู䜢⾜䛳䛯䛸䛣䜝䚸75%䠄15/20䠅䛾䝸䝍䞊䛜 ᳜⿕䛻ㄗุู䛥䜜䛶䛔䜛䛣䛸䛜☜ㄆ䛥䜜䛯䚹௨ୖ䛾⤖ ᯝ䛛䜙䚸RGB 䝰䝕䝹䜢⏝䛔䜛䛣䛸䛷䚸〄ᆅ䛸᳜⿕䛾ุ ู䛿ྍ⬟䛷䛒䜚䚸᳜⿕⋡䛾᥎ᐃ䛜ྍ⬟䛷䛒䜛䛣䛸䛜䜟 䛛䛳䛯䚹 ศ㢮⩌䠄⥘䠅䛾ุู䜢⾜䛳䛯ሙྜ䛾ṇゎ⋡䛿 RGB 䝰䝕䝹䛷 86%䠄231/270䠗⾲ 6䠅䚸NIR 䝰䝕䝹䛷 74% 䠄199/270䠗⾲ 7䠅䛷䛒䛳䛯䚹RGB 䝰䝕䝹䛷䛿䛶䛾㡯┠ 䛜70%௨ୖ䛷䛒䛳䛯䛾䛻ᑐ䛧䚸NIR 䝰䝕䝹䛷䛿༢Ꮚⴥ 㢮 䛾 ⌧ ⋡ 䛜 29%䠄15/70䠅䜔〄ᆅ䛾⢭ᗘ䛜 60% 䠄33/55䠅䛸䛺䜛䛺䛹䚸Ꮚⴥ㢮௨እ䛾㡯┠䛷ప䛔್䛸䛺 䛳䛯䚹RGB 䝰䝕䝹䛻䛚䛔䛶䚸༢Ꮚⴥ㢮䛸Ꮚⴥ㢮䛾ุ ู䛜ྍ⬟䛷䛒䛳䛯䛾䛿䚸ⴥ䛾ᙧ≧ᕪ䛜䛝䛔䛯䜑䚸2 ×2cm 䛾䜾䝸䝑䝗䜒༑ศ䛻ุูྍ⬟䛷䛒䛳䛯䛛䜙䛷䛒䜛 䛸⪃䛘䜙䜜䜛䚹 ✀䛾ุูẁ㝵䛾ṇゎ⋡䛿 RGB 䝰䝕䝹䛷䛿 55% 䠄148/270䠗⾲ 8䠅䛷䛒䜚䚸NIR 䝰䝕䝹䛷䛿 39%䠄106/270䠗 ⾲ 9䠅䛷䛒䛳䛯䚹᳜⿕⋡䛾ุู䛻ẚ㍑䛧䛶䛹䛱䜙䜒ప䛔 ⢭ᗘ䛸䛺䛳䛯䛜䚸䛣䛾ẁ㝵䛻䛚䛔䛶䜒RGB 䝰䝕䝹䛾᪉ 䛜㧗䛔⢭ᗘ䜢♧䛧䛯䚹䛭䛣䛷䚸RGB 䝰䝕䝹䛻䛚䛔䛶䚸 ྛ✀䛾⌧⋡䜢ㄪ䜉䛯䛸䛣䜝䚸᭱䜒㧗䛔✀䛷䛒䜛䜿䜹 䝰䝜䝝䝅䛿95%䠄19/20䠅䛷䛒䛳䛯䛾䛻ᑐ䛧䚸᭱䜒ప䛔✀ 䛷䛒䜛䝡䝻䞊䝗䝔䞁䝒䜻䛿 0%䠄0/10䠅䛷䛒䛳䛯䚹䜎䛯䚸 ⌧⋡䛜ప䛔✀䛻䛴䛔䛶䚸䛹䛾✀䛻ㄗุู䛥䜜䛯ሙྜ 䛜ከ䛔䛛ㄪ䜉䛯䛸䛣䜝䚸ྠ䛨ศ㢮⩌䠄⥘䠅䛻ุู䛥䜜䜛 ሙྜ䛜ከ䛔䛣䛸䛜䜟䛛䛳䛯䚹䛣䛾⤖ᯝ䛿䚸ศ㢮⩌䛾ุ ู䛾⢭ᗘ䛜㧗䛛䛳䛯䛣䛸䛸୍⮴䛩䜛䚹✀䛾ุูẁ㝵䛻 䛚䛡䜛ṇゎ⋡䛜ప䛔⌮⏤䛸䛧䛶䚸ᮏ◊✲䛻䛚䛔䛶ᑐ㇟ 䛸䛧䛯✀䛿ⴥᖜ䛜⣙0.5cm䚸ⴥ㛗䛜⣙ 5cm ⛬ᗘ䛾䝡䝻 䞊䝗䝔䞁䝒䜻䛾䜘䛖䛺ⴥ䝃䜲䝈䛾ᑠ䛥䛔✀䛛䜙䚸ⴥᖜ䛜 ⣙2-3cm䚸ⴥ㛗䛜⣙ 4-8cm ⛬ᗘ䛾䜰䜻䜾䝭䛾䜘䛖䛺ⴥ䝃 䜲䝈䛾䛝䛔✀䜎䛷䛥䜎䛦䜎䛷䛒䛳䛯䛣䛸䛜⪃䛘䜙䜜䜛䚹 䛭䛾䛯䜑䚸2×2cm 䛾䜾䝸䝑䝗䝃䜲䝈䛷䛿✀䛾ᵝ䚻䛺ᙧ ≧䜢୍䛴䛾䜾䝸䝑䝗ෆ䛻䜑䛝䜜䛪䚸ㄗุู䛜䛚䛝䛯 ྍ⬟ᛶ䛜䛒䜛䚹䝔䜽䝇䝏䝱≉ᚩ㔞⟬ฟ䛻⏝䛔䛶䛔䜛䜴䜱 ⾲ 4 RGB 䝞䞁䝗⏬ീ䜢⏝䛔䛯ሙྜ䛾᳜⿕䛸〄ᆅ䛾ุ ู⢭ᗘ ุู⤖ᯝ ⌧⋡ 䠄%䠅 〄ᆅ ᳜⿕ ྜィ 〄ᆅ 37 3 40 93 ᳜⿕ 8 222 230 96 ྜィ 45 225 270 ⢭ᗘ䠄%䠅 82 99 ṇゎ⋡䠖95.9%䚸䜹䝑䝟⤫ィ㔞䠖0.85 ⾲ 5 NIR 䝞䞁䝗⏬ീ䜢⏝䛔䛯ሙྜ䛾᳜⿕䛸〄ᆅ䛾ุู ⢭ᗘ ุู⤖ᯝ ⌧⋡ 䠄%䠅 〄ᆅ ᳜⿕ ྜィ 〄ᆅ 18 22 40 45 ᳜⿕ 13 217 230 94 ྜィ 31 239 270 ⢭ᗘ䠄%䠅 58 91 ṇゎ⋡䠖87.0%䚸䜹䝑䝟⤫ィ㔞䠖0.43 ⾲ 6 RGB 䝞䞁䝗⏬ീ䜢⏝䛔䛯ሙྜ䛾ศ㢮⩌䠄⥘䠅䛾ุ ู⢭ᗘ ุู⤖ᯝ ྜィ ⌧⋡ 䠄%䠅 〄ᆅ Ꮚⴥ㢮༢Ꮚⴥ㢮 〄ᆅ 38 0 2 40 95 Ꮚⴥ㢮 4 139 17 160 87 ༢Ꮚⴥ㢮 4 12 54 70 77 ྜィ 46 151 73 270 ⢭ᗘ䠄%䠅 83 92 74 ⾲ 7 NIR 䝞䞁䝗⏬ീ䜢⏝䛔䛯ሙྜ䛾ศ㢮⩌䠄⥘䠅䛾ุ ู⢭ᗘ ุู⤖ᯝ ྜィ ⌧⋡ 䠄%䠅 〄ᆅ Ꮚⴥ㢮 ༢Ꮚⴥ㢮 〄ᆅ 33 4 3 40 83 Ꮚⴥ㢮 7 146 7 160 91 ༢Ꮚⴥ㢮 15 35 20 70 29 ྜィ 55 185 30 270 ⢭ᗘ䠄%䠅 60 79 67 ṇゎ⋡䠖73.7%䚸䜹䝑䝟⤫ィ㔞䠖0.51 ṇゎ⋡䠖85.6%䚸䜹䝑䝟⤫ィ㔞䠖0.75
上 佳孝ほか ： 画像解析による草原植生調査手法 䝅䝇䝔䝮㎰Ꮫ(J. JASS), ᢞ✏ㄽᩥ, 20XX 7
⾲ 8 RGB 䝞䞁䝗⏬ീ䜢⏝䛔䛯ሙྜ䛾✀䛾ุู⢭ᗘ ุู⤖ᯝ
ྜィ ⌧⋡䠄%䠅 A.c C.k C.s E.u F.s I.a I.r L.j O.e O.l V.r W.p Z.m Li Sa
A.c 16 0 0 0 0 3 0 0 0 0 1 0 0 0 0 20 80 C.k 0 7 1 0 0 6 2 0 3 0 0 1 0 0 0 20 35 C.s 0 3 9 0 0 2 0 1 0 1 4 0 0 0 0 20 45 E.u 0 0 4 11 0 2 1 0 0 0 0 0 1 1 0 20 55 F.s 0 0 0 0 0 2 0 0 0 0 0 0 8 0 0 10 0 I.a 0 0 0 0 0 19 0 0 1 0 0 0 0 0 0 20 95 I.r 0 1 2 1 0 0 4 0 0 1 0 0 0 1 0 10 40 L.j 0 0 0 6 0 0 0 10 0 2 1 0 0 1 0 20 50 O.e 0 0 3 0 0 4 1 0 7 1 1 2 1 0 0 20 35 O.l 0 0 2 0 0 0 0 0 0 2 3 3 0 0 0 10 20 V.r 0 1 4 4 0 0 0 0 0 3 7 1 0 0 0 20 35 W.p 3 1 0 0 0 2 0 0 2 0 4 8 0 0 0 20 40 Z.m 0 2 0 1 0 1 0 0 0 1 0 0 11 4 0 20 55 Li 0 0 0 0 0 0 0 0 0 0 0 0 3 17 0 20 85 Sa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 20 100 ྜィ 19 15 25 23 0 41 8 11 13 11 21 15 24 24 20 270 ⢭ᗘ䠄%䠅 84 47 36 48 NA 46 50 91 54 18 33 53 45 70 100 ṇゎ⋡䠖55.2%䚸䜹䝑䝟⤫ィ㔞䠖0.51 ᳜≀䛾␎⛠䛿䛭䜜䛮䜜A.c䠖䜹䝽䝷䝶䝰䜼䚸C.k䠖䝁䜴䝪䜴䝮䜼䚸C.s䠖䝝䝬䝠䝹䜺䜸䚸E.u䠖䜰䜻䜾䝭䚸F.s䠖䝡䝻䞊䝗䝔䞁䝒䜻䚸I.a䠖 䜿䜹䝰䝜䝝䝅䚸I.r䠖䝝䝬䝙䜺䝘䚸L.j䠖䜴䞁䝷䞁䚸O.e䠖䜸䜸䝬䝒䝶䜲䜾䝃䚸O.l:䝁䝬䝒䝶䜲䜾䝃䚸V.r䠖䝝䝬䝂䜴䚸W.p䠖䝛䝁䝜䝅䝍䚸 Z.m䠖䜸䝙䝅䝞䚸Li:䝸䝍䞊䚸Sa:◁ ⾲ 9 NIR 䝞䞁䝗⏬ീ䜢⏝䛔䛯ሙྜ䛾✀䛾ุู⢭ᗘ ุู⤖ᯝ ྜィ ⌧⋡䠄%䠅 A.c C.k C.s E.u F.s I.a I.r L.j O.e O.l V.r W.p Z.m Li Sa
A.c 8 1 2 1 0 3 0 0 2 0 1 1 1 0 0 20 40 C.k 1 3 1 0 0 3 0 0 1 1 2 5 0 2 1 20 15 C.s 1 2 3 3 0 3 0 0 0 2 0 3 3 0 0 20 15 E.u 2 1 0 10 0 2 0 4 0 0 0 0 1 0 0 20 50 F.s 0 0 1 0 0 3 0 0 0 0 0 0 3 3 0 10 0 I.a 4 0 3 2 0 3 0 0 0 0 1 1 4 1 1 20 15 I.r 0 1 1 0 0 1 0 0 0 0 1 2 4 0 0 10 0 L.j 0 0 0 2 0 0 0 17 0 1 0 0 0 0 0 20 85 O.e 4 0 1 1 0 1 0 0 5 0 1 2 1 0 4 20 25 O.l 0 0 0 3 0 0 0 0 0 4 0 3 0 0 0 10 40 V.r 0 1 0 1 0 1 0 0 0 1 7 0 1 5 3 20 35 W.p 2 5 2 3 0 1 0 1 0 1 1 3 0 1 0 20 15 Z.m 1 2 0 0 0 3 0 0 1 0 2 0 8 2 1 20 40 Li 0 2 2 0 0 1 0 0 0 0 0 0 0 15 0 20 75 Sa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 20 100 ྜィ 23 18 16 26 0 25 0 22 9 10 16 20 26 29 30 270 ⢭ᗘ䠄%䠅 35 17 19 38 NA 12 NA 77 56 40 44 15 31 52 67 ṇゎ⋡䠖39.3%䚸䜹䝑䝟⤫ィ㔞 0.35 ᳜≀䛾␎⛠䛿⾲8 䛻ྠ䛨䚹
䞁䝗䜴䝃䜲䝈䜒0.6×0.6cm 䛒䜛䛔䛿 1.8×1.8cm 䛷䛒䜛 䛯䜑䚸✀䛾ุู䜢⾜䛖䛻䛿ᑠ䛥䛩䛞䜛ྍ⬟ᛶ䛜䛒䜛䚹䛭 䛾䛯䜑䚸䜾䝸䝑䝗䝃䜲䝈䜔䝔䜽䝇䝏䝱ゎᯒ䛾䜴䜱䞁䝗䜴䝃 䜲䝈䜢ኚ᭦䛩䜛䛣䛸䛷䜘䜚㧗䛔⢭ᗘ䛾✀䛾ุู䛜ᮇᚅ 䛷䛝䜛䚹 4.4 ᡭἲ䛾ỗ⏝ᛶ䛻㛵䛩䜛᳨ウ ᮏ◊✲䛾ㄪᰝᆅ䛷䛿䚸పᮌ䚸ⲡᮏ䛜ඃ༨䛧䛶䛚䜚䚸 ᳜≀㛫䛻䛚䛡䜛㧗䛥䛾ᕪ䛜ᑠ䛥䛔䛯䜑䚸3 ḟඖ䝕䞊䝍 䜢ྲྀ䜚ᢅ䜟䛺䛛䛳䛯䚹䛧䛛䛧䚸✀ุู䜢⾜䛳䛯㝿䛻ⲡ 䛾ప䛔᳜≀䛜㧗䛔᳜≀䛻ㄗุู䛥䜜䜛䛣䛸䜔䛭䛾㏫䛾 ሙྜ䜒Ꮡᅾ䛧䛯䚹䛭䛾䛯䜑䚸⏬ീ䜢ྲྀᚓ䛩䜛㝿䛻ⲡ 䛾䝕䞊䝍䜒ྠ䛻ྲྀᚓ䛧䚸✀ุู䛻䛚䛡䜛ㄝ᫂ኚᩘ䛻 ⏝䛔䜛䛣䛸䛷✀ุู䛾⢭ᗘ䛜ྥୖ䛩䜛ྍ⬟ᛶ䛜䛒䜛䛸 ⪃䛘䜙䜜䜛䚹䜎䛯䚸ᮏ◊✲䛷䛿⏬ീ䜢㧗ᗘ⣙1.5m 䛛䜙 ྲྀᚓ䛧䛶䛔䜛䛯䜑䚸ⲡ䛾㧗䛔᳜≀䜢ṇ☜䛻ᙳ䛩䜛 䛣䛸䛜䛷䛝䛪䚸㛗ⲡᆺ䛾᳜⏕䛻䛿㐺⏝䛷䛝䛺䛔䛸⪃䛘䜙 䜜䜛䚹䛧䛛䛧䚸5m 䜔 10m 䛺䛹䜘䜚㧗䛔⨨䛛䜙ᙳ䛩 䜛䛣䛸䛷䚸ⲡ䛾㧗䛔᳜≀䜢ṇ☜䛻ᙳ䛩䜛䛣䛸䛜ྍ ⬟䛸䛺䜚䚸ᮏ◊✲䛻䛚䛡䜛ᡭἲ䜢㛗ⲡᆺ䛾᳜⏕䛻䜒㐺 ⏝䛷䛝䜛ྍ⬟ᛶ䛜䛒䜛䚹 䜎䛯䚸ᮏ◊✲䛷㛤Ⓨ䛧䛯ᡭἲ䜢ᐇ㝿䛾᳜⏕ㄪᰝ⏬ ീ䛻㐺⏝䛩䜛㝿䛻ண䛥䜜䜛ၥ㢟Ⅼ䛸䛧䛶䛿䚸ᆅ⾲䛻 䛷䛝䛯᳜≀䛾ᙳ䛾ᙳ㡪䛜⪃䛘䜙䜜䜛䚹ᮏ◊✲䛷䝰䝕䝹 䛾సᡂ䛸ホ౯䛻⏝䛔䜙䜜䛯⏬ീ䝕䞊䝍䛻䛿ᙳ䛜ྵ䜎 䜜䛶䛔䛺䛛䛳䛯䛯䜑䚸ᙳ䛜ྵ䜎䜜䛶䛔䛯ሙྜ䛾ุู⢭ ᗘ䛿᫂䛷䛒䜛䚹䝰䝕䝹సᡂ䛻ᙳ䜢㏣ຍ䛩䜛䛣䛸䜔 ᳜⏕ᣦᶆ䜢⏝䛔䜛䛺䛹䛧䚸䝰䝕䝹䛾⢭ᗘ䜢᳨ド䛩䜛ᚲ せ䛜䛒䜛䚹
5. 䛚䜟䜚䛻
ᮏ◊✲䛷䛿䚸䝣䜱䞊䝹䝗䛷ᙳ䛥䜜䛯⏬ീ䜢⏝䛔䛶䚸 ┠ど䛻䜘䜙䛪᳜⏕䜢ㄪᰝ䛩䜛ᡭἲ䛾㛤Ⓨ䜢䝔䜽䝇䝏䝱 ゎᯒ䛾㐺⏝䛻䜘䛳䛶ヨ䜏䛯䚹RGB 䛾 3 䝞䞁䝗䜢⤌䜏ྜ 䜟䛫䛶ゎᯒ䛩䜛᪉ἲ䛸NIR 䝞䞁䝗䜢⏝䛔䛶ゎᯒ䛩䜛᪉ ἲ䜢䚸᳜⿕䛸〄ᆅ䛾ุู䚸ศ㢮⩌䠄⥘䠅䛾ุู䚸✀䛾ุ ู䛸䛔䛖 3 ẁ㝵䛷ẚ㍑䜢⾜䛳䛯䚹⤖ᯝ䚸䛶䛾ẁ㝵䛻 䛚䛔䛶 RGB 䝰䝕䝹䛾䜋䛖䛜ඃ䜜䛯⢭ᗘ䜢♧䛧䛯䚹 RGB 䝰䝕䝹䜢⏝䛔䛯ሙྜ䚸᳜⿕䛸〄ᆅ䛾ุูཬ䜃ศ 㢮⩌䠄⥘䠅䛾ุู䛿ṇゎ⋡䛜85%௨ୖ䛸㠀ᖖ䛻㧗䛔⢭ ᗘ䛷ศ㢮䛜ྍ⬟䛷䛒䜚䚸᳜⿕⋡䛾᥎ᐃ䛜ྍ⬟䛷䛒䜛䛣 䛸䛜ศ䛛䛳䛯䚹䛧䛛䛧䚸✀䛾ุู䛻䛚䛔䛶䛿䚸ṇゎ⋡䛜 55%䛸ప䛟䚸⿕ᗘ䛾᥎ᐃ䛿ᐇ⏝䝺䝧䝹䛻㐩䛧䛶䛔䛺䛔 䛸ゝ䛘䜛䚹䛣䜜䛿䜾䝸䝑䝗䝃䜲䝈䜔䝔䜽䝇䝏䝱䜴䜱䞁䝗䜴䝃 䜲䝈䜢ㄪᩚ䛩䜛䛣䛸䛷ᨵၿ䛩䜛ྍ⬟ᛶ䛜䛒䜛䚹 ㅰ㎡ 㫽ྲྀᏛ䛾ᒣ୰ᩍᤵ䛻䛿㫽ྲྀ◁ୣ䛻䛚䛡䜛ㄪ ᰝ䛻䛚䛔䛶ᵝ䚻䛺䛤ᑾຊ䜢䛔䛯䛰䛔䛯䚹䜎䛯䚸ி㒔 Ꮫ䛾Ᏺᒇᖾᩍᤵ䚸㓇ᚭᮁᩍᤵ䛻䛿䚸ศᯒᡭἲ䛻 䛴䛔䛶᭷┈䛺ຓゝ䜢ᩘከ䛟䛔䛯䛰䛔䛯䚹䛣䛣䛻グ䛧䛶 ㅰព䜢⾲䛩䜛䚹 ᘬ⏝ᩥ⊩Columbia University, University of Maryland, and Smithonian Institution, 2011, LeafSnap ver. 1.05, In-http://itunes.apple.com/jp/app/leafsnap/id4306498
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䝁䜴䝪䜴䝮䜼䠄Carex kobomugi Ohwi䠅䛾ᇙᅵ✀Ꮚ㞟
ᅋ䛾ᙧᡂ䛸ᐇ⏕䛾ືែ䛻䛘䜛ᙳ㡪, ᪥ᮏ◁ୣᏛ
Contributed paper
Vegetation Survey using Images obtained during Field Sampling with Digital Cameras
Yoshitaka KAMI* and Lina KOYAMA*
Graduate School of Informatics, Kyoto University*
(Received 16 November 2012; in final form 30 November 2013)
Summary
Current vegetation surveys are mainly conducted through the visual observation of coverage and species. This method has the disadvantage that it is not quantitative or reproducible. The aim of this study was to develop an objective method to survey vegetation using ground images obtained in the field. The data used in this paper were obtained in August 2012 at the Tottori Sand Dunes. The data were divided into training data and test data. First, we selected training data from our dataset and applied texture analysis. Then, classification models were obtained by using the texture characters as variables. Variables used in the models were selected as follows: 1) variables highly correlated with other variables were excluded based on the Pearson product-moment correlation coefficient, 2) multinomial logistic regression analysis was conducted to obtain the best combination of variables for classification, and 3) the accuracy of the models was estimated by linear discriminant analysis using the best combination of variables. Estimation was performed based on three categories: plant and soil classification, class classification (i.e., distinction between monocotyledon and dicotyledon) and species classification. A comparison of the RGB combined model and the NIR single model was also conducted. In the plant and soil classification, the accuracy of the RGB model and the NIR model were 96% and 87%, respectively. While the soil recall rate of the NIR model was low (lower than 50%), the RGB model showed high rates in all categories. In the class classification, the accuracy of the RGB model and the NIR model were 86% and 74%, respectively. Although the NIR model showed low rates in some categories (i.e., the recall rate of monocotyledons was 28%), the RGB model showed a high rate in all categories (higher than 70%), which was similar to the plant and soil classification. In the species classification, both the RGB model and the NIR model showed low accuracy (RGB: 55%, NIR: 39%). The accuracy differed largely among species (0 - 95%) and many of the misclassifications occurred within the same class. These results demonstrate the potential of coverage estimation and plant classification using this method.
Key Words: Ground Photo, Texture Analysis, Vegetation Survey
* Yoshida Honmachi, Sakyo-ku, Kyoto 606-8501, Japan (Correspondence: [email protected])
