Procedures for predicting habitat and structural attributes in eucalypt forests using high spatial resolution remotely sensed imagery

SUMMARY Forest resource information is increasingly needed at fine spatial scales for operational and strategic applications including monitoring indicators of ecologically sustainable forest management, planning of harvesting operations and implementation of silvicultural prescriptions, and the maintenance of biodiversity and ecological sustainability. High resolution remotely sensed imagery is one data source that can provide cost effective information for forest management. This paper presents two methodologies that allow these data to be modelled to predict forest structure in eucalypt forests. One method emphasises the tree crown as the primary indicator of forest structure and utilises algorithms which automatically delineate tree canopies in high spatial resolution data. The second method investigates the spectral variability of the forest in relation to its habitat quality (biomass of tree canopy, shrubs, ground cover and litter) for ground-dwelling fauna. Both methods utilised the near infrared (...