Large area forest classification and biophysical parameter estimation using the 5-Scale canopy reflectance model in Multiple-Forward-Mode

Abstract

The Multiple-Forward-Mode approach to running the 5-Scale geometric-optical reflectance model (MFM-5-Scale) provides an inversion modeling capability for powerful but non-invertible models, and yields both landcover classification and forest biophysical–structural information that is important in global change projects such as BOREAS. Unlike regular forward mode, MFM-5-Scale does not require exact physical stand descriptors, but instead requires only input ranges and model increments that are easily obtained, with results determined by matching satellite image data with modeled reflectance values as stored in a look-up table. In this work, MFM-5-Scale was applied to a mosaic of seven multi-year (1991–1998) Landsat TM scenes covering the BOREAS region in western Canada, with results validated against field data and also 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 MFM-5-Scale, but without the subjectivity of ECM. Classifications of 13 forest classes including deciduous and species-specific coniferous classes subdivided into low, medium and high crown densities as well as a mixed forest class were evaluated against 136 field checked validation sites. Overall classification accuracies were 91% for ECM and 85% for MFM-5-Scale. Additional assessments of agreement between the MFM-5-Scale and ECM products were also performed over larger sample areas. MFM-5-Scale classification of a full set of 25 forest and non-forest classes adhering to Global Observation of Forest Cover (GOFC) specifications was in 80% agreement with the ECM product ( n=11,442). This is viewed as a significant result given the ambitious and detailed nature of the classes considered (21 forest classes comprising 12 species and density specific conifer and deciduous classes, four mixed forest classes and five burnt forest classes, as well as four non-forest classes). The two products were also in 94% agreement for 6 forest density classes ( n=3730). MFM-5-Scale biophysical analysis of 63 BOREAS plots showed LAI was estimated within ±0.53 LAI compared with ground-based TRAC LAI 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 MFM-5-Scale physical scene modeling compared to subjective, user-driven methods such as ECM.