The synergy of the 0.05° (~5 km) AVHRR long term data record (LTDR) and landsat TM archive to map large fires in the North American boreal region from 1984 to 1998
Por: Moreno Ruiz, José A. ... [et al.]
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Colaborador(es): García Lázaro, José R | Riaño, David | Kefauver, Shawn C.
Tipo de material: 
ArtículoTema(s): Satélites artificiales en detección a distancia | Satélites artificiales en meteorología | Procesamiento óptico de datos | Incendios-America del Norte | Sistemas de imágenes | Procesamiento de imágenes
En: Vol. 7, núm. 4 April, 2014, pp. 1157-1166 Journal of selected topics in applied earth observations and remote sensing R0141542Resumen: A bayesian network classifier based algorithm was applied to map the burned area (BA) in the North American boreal region using the 0.05o (5 km) advanced very high resolution radiometer (AVHRR) long term data record (LTDR) data version 3 time series. The results showed an overall good agreement compared to reference maps (slope = 0.62 R2 = 0.75). The study site was divided into six sub-regions, where south western Canada performed the worst (slope - 0.25; R2 - 0.47). The algorithm achieved good results as long as a year with high fire incidence was employed to train the bayasian network, and the vegetation response to fire remained consistent across the region
| Tipo de ítem | Biblioteca de origen | Colección | Signatura | Vol info | Copia número | Estado | Fecha de vencimiento | Código de barras | Reserva de ejemplares |
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Revista
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Campus II | Hemeroteca | PP-2 550 I592j (Navegar estantería) | Vol. 7, núm. 4 (April, 2014) | Ej. 1 | Disponible | R0141542 |
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CAMPUS II
A bayesian network classifier based algorithm was applied to map the burned area (BA) in the North American boreal region using the 0.05o (5 km) advanced very high resolution radiometer (AVHRR) long term data record (LTDR) data version 3 time series. The results showed an overall good agreement compared to reference maps (slope = 0.62 R2 = 0.75). The study site was divided into six sub-regions, where south western Canada performed the worst (slope - 0.25; R2 - 0.47). The algorithm achieved good results as long as a year with high fire incidence was employed to train the bayasian network, and the vegetation response to fire remained consistent across the region