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TitleLarge area Forest Classification and Biophysical Parameter Estimation using the 5-Scale Reflectance Model in Multiple-Forward-Mode
AuthorPeddle, D; Johnson, R L; Cihlar, J; Guindon, B; Latifovic, R
SourceIGARSS 2002, IEEE International Geoscience and Remote Sensing Symposium and the 24th Canadian Symposium on Remote Sensing, Toronto, Canada, June 24-28; 2002 p. 896-898
Alt SeriesEarth Sciences Sector, Contribution Series 20043127
Mediapaper; CD-ROM
AbstractThe Multiple-Forward-Mode approach to running the 5-Scale geometric-optical reflectance model (MFM-S-Scale) provides an inversion modeling capability for powerful but non-invertible models, and yields both landcover classification and forest biophysical-structural information. Unlike regular forward mode, MFMd Scale does not require exact physical stand descriptors, but instead requires only input ranges and model increments which are more easily obtained, with results determined by matching satellite image and modeled reflectance values. In this work, MFM-5-Scale was applied to a mosaic of 7 multiyear Landsat TM scenes covering the BOREAS region in western Canada, with results compared with the Enhancement Classification Method (.ECM), a highly accurate yet subjective and labour intensive approach which involves considerable user judgement and expertise. The goal was to approach ECM accuracy using MFMd-Scale, but without the subjectivity of ECM. MFM-5-Scale classification of a full set of 28 forest and non-forest classes adhering to Global Observation of Forest Cover (GOFC) specifications was in 77% agreement with the ECM product (n=13,046). MFM-5-Scale biophysical analysis of 63 BOBEAS plots showed LA1 was estimated within f 0.5 LA1 compared with ground-based LA1 validation data (biophysical information is not provided by ECM). These results represent significant progress towards defining an operational landcover and biophysical estimation approach given the objective, semi-automated nature of MFMd-Scale physical scene modeling compared to subjective, user-driven methods such as ECM.

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