JayaI Nengah Surati. “Landslide Detection Technique Using Multidate SPOT Imageries: A Case Study in Teradomari, Tochio and Shitada Mura, Niigata, Japan”. Jurnal Manajemen Hutan Tropika 11, no. 1 (December 20, 2012): 31. Accessed November 20, 2024. https://journal.ipb.ac.id/index.php/jmht/article/view/%20%3Cp%20class%3D%22MsoBodyText%22%20style%3D%22text-align%3Ajustify%3Btext-indent%3A1cm%3B%22%3E%3Cem%3E%3Cspan%20style%3D%22font-size%3A9pt%3B%22%20lang%3D%22en-us%22%20xml%3Alang%3D%22en-us%22%3EThis%20study%20describes%20the%20use%20of%20multitemporal%3Cspan%3E%C2%A0%20%3C%2Fspan%3Eprincipal%20component%20analisys%20%28MPCA%29%2C%20vegetation%20index%20differencing%20%28VIDN%29%20and%3Cspan%3E%C2%A0%20%3C%2Fspan%3Econventional%20maximum%20likelihood%20classifier%20%28MLC%29%20for%3Cspan%3E%C2%A0%20%3C%2Fspan%3Edetecting%20landslides.%3Cspan%3E%C2%A0%20%3C%2Fspan%3EThe%20study%20found%20that%20the%20synthetic%20images%20derived%20from%20stable%20greenness%2C%20delta%20greenness%20and%20delta%20brightness%20of%20MPCA%20summarized%20the%20information%20of%20landslides%20effectively%20producing%20accuracy%20of%2088%25%20for%20Teradomari%20and%2091%25%20for%20Tochio%20and%20Shitada%20Mura.%3Cspan%3E%C2%A0%20%3C%2Fspan%3EThe%20VIDN%20provides%20relatively%20lower%20accuracies%20than%20those%20from%20MPCA%2C%20i.e.%2C%20only%2062.5%25%20for%20Teradomari%20and%2064%25%20for%20Tochio%20and%20Shitada%20Mura.%20The%20MLC%20method%20also%20provided%20very%20low%20user%E2%80%99s%20accuracy%2C%20i.e.%2056.9%25%20for%20Teradomari%20and%2063.7%25%20for%20Tochio%20and%20Shitada%20Mura%20but%20high%20producer%E2%80%99s%20accuracies%2C%20i.e.%20100%25%20for%20Teradomari%20and%2098.3%25%20for%20Tochio%20and%20Shitada%20Mura.%20The%20study%20also%20found%20that%20the%20landsides%20that%20could%20be%20detected%20should%20be%20more%20than%20the%20size%20of%20spatial%20resolution%20of%20the%20SPOT%20imagery%2C%20i.e.%2010%20m%20x%2010%20m.%20Detecting%20landslides%20using%20SPOT%20imagery%20is%20more%20efficient%20than%20using%20only%20ground%20survey%2C%20providing%20an%20%3Cspan%20style%3D%22color%3A%23000000%3B%22%3Eefficiency%20of%202.7.%3C%2Fspan%3E%20%3Cspan%3E%C2%A0%C2%A0%3C%2Fspan%3E%3C%2Fspan%3E%3C%2Fem%3E%3C%2Fp%3E%20%3Cstrong%3E%3Cspan%20style%3D%22font-size%3A10pt%3B%22%20lang%3D%22en-us%22%20xml%3Alang%3D%22en-us%22%3EKeywords%3A%3C%2Fspan%3E%3C%2Fstrong%3E%3Cem%3E%3Cspan%20style%3D%22font-size%3A10pt%3B%22%20lang%3D%22en-us%22%20xml%3Alang%3D%22en-us%22%3E%20%3C%2Fspan%3E%3C%2Fem%3E%3Cspan%20style%3D%22font-size%3A10pt%3B%22%20lang%3D%22en-us%22%20xml%3Alang%3D%22en-us%22%3EAnalisis%20komponen%20utama%20multiwaktu%20%28%3Cem%3EMultitemporal%3Cspan%3E%C2%A0%20%3C%2Fspan%3Eprincipal%20component%20analisys%29%2C%20disparitas%20indeks%20vegetasi%20%28vegetation%20index%20differencing%29%2C%20%3C%2Fem%3Emetode%20peluang%20maksimum%3Cem%3E%20%28maximum%20likelihood%20classifier%29%2C%20%3C%2Fem%3Ekestabilan%20kehijauan%3Cem%3E%20%28stable%20greenness%29%2C%20%3C%2Fem%3Eperubahan%20kehijauan%3Cem%3E%20%28delta%20greenness%29%2C%20%3C%2Fem%3Eperubahan%20kecerahan%3Cem%3E%20%28delta%20brighntess%29%20%3C%2Fem%3Edan%20efisiensi%20relatif%20%28%3Cem%3Eefficiency%20relative%3C%2Fem%3E%29%3C%2Fspan%3E.