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

Abstract

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 (NIR) region of the electromagnetic spectrum as it provides an indication of photosynthetic activity of the canopy and describes most spectral and spatial variation in the scene. Application of these methodologies indicated significant relationships between forest structure (measured as either habitat quality or canopy characteristics) and the NIR variance. In the case of automated tree crown delineation there were good relationships between the variation within the delineated crowns and field estimated canopy characteristics. A significant relationship was also found between the habitat complexity scores estimated at each plot and the maximum level of variance in the near infrared channel at each plot. These preliminary results indicate that high spatial resolution imagery can be used to predict forest structure and allow predictions to be spatially extrapolated. Ultimately the ecologically sustainable management of Australia's forest resource will depend on the availability of high resolution predictions of the diversity and extent of forest structure and habitat over large areas of high productivity forests.