Preparation of a 1:25000 Landsat map for assessment of burnt area on Etajima Island

Abstract

The Etajima fire of 1978 in Japan was used to assess the accuracy of a method of classification, mapping and area estimation of the devastation caused by forest fire using Landsat MSS data. The classification and mapping method developed was limited to field sizes of about 10km x 10km, and the registration of Landsat data for mapping was performed by affine' transformation using corresponding ground control points both on the Landsat imagery and the' topographical map.'

Monitoring forest regrowth following large scale fire using satellite data-A case study of Yellowstone National Park, USA-

“…Fire severity is defined as the degree of environmental or ecological change caused by a wildfire event [Tanaka et al, 1993;Key and Benson, 2005]. Various methods can be used to estimate fire severity from remote sensing, but a common approach is the Differenced Normalized Burn Ratio (dNBR) .…”

Utilisation de l'imagerie numérique aéroportée (capteurs Daedalus et Meis) pour identifier différents niveaux de perturbations d'un feu de forêt /

“…Depuis plusieurs années, un autre outil a fait son apparition soit l'imagerie numérique (aéroportée ou satellitaire); l'imagerie satellitaire est la plus utilisée. Elle a déjà été employée pour cartographier ou évaluer les superficies affectées par les feux de forêt (Husson 1982, Tanaka et al 1983, Milne 1986, Jakubauskas et_al. 1990, Lopez Garcia et Casselles 1991.…”

“…La matrice de Nonobstant ces résultats, l'imagerie numérique est sûrement un outil non négli-geable pour caractériser les territoires incendiés. Plusieurs études ont démontré la possibilité d'utiliser l'imagerie satellitaire pour discriminer les brûlis et les brûlis partiels (Husson 1982;Tanaka et_al. 1983, Milne 1986, Chuvieco et Congalton 1988, Jakubauskas_et al 1990, Dagorne et al 1990, Simpson 1990).…”

Remote sensing of burned areas via PCA, Part 1; centering, scaling and EVD vs SVD

“…The first newly derived components hold the variance related to unchanged landscape features, while succeeding components may feature an enhanced variance of the changed features. Early works demonstrate the usability of PCA in remote sensing ( [13][14][15][16][17][18][19][20]). Multi-spectral bands treated as columns build up a data matrix, which is then centered and whose variance-covariance (or correlation) matrix is the basis for the components extraction algorithm.…”