Operational monitoring of green biomass change for forest management

Forest managers are in constant need of accurate, up-to-date resource infor mation. A first attempt towards an operational, quantitative, remote-sensin g-based change detection system is described. The change information derive d from this system can then be used either to "flag" those areas that requi re additional detailed investigation, or to monitor conditions to determine if changes occur as expected. The digital change defection system described is based on standardized diff erences of Kauth-Thomas transformations. Minimum-distance, maximum-likeliho od, and Mahalanobis-distance classifiers were tested with field data and co mpared. The maximum-likelihood and Mahalanobis-distance classifiers produce d the more accurate results. They were able to detect small amounts of chan ge resulting from forest thinnings, which are the most difficult to discrim inate. Overall results of this work demonstrated the high potential value o f an operational, digital, quantitative change detection system to support forest management decisions across large geographic extents.