Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters

Forests are the most widely distributed ecosystem on the earth, affecting t he Lives of most humans daily, either as an economic good or an environment al regulator. As forests are a complex and widely distributed ecosystem, re mote sensing provides a valuable means of monitoring them. Remote-sensing i nstruments allow for the collection of digital data through a range of scal es in a synoptic and timely manner. Accordingly, a variety of image-process ing techniques have been developed for the estimation of forest inventory a nd biophysical parameters from remotely sensed images. The use of remotely sensed images allows for the mapping of large areas efficiently and in a di gital manner that allows for accuracy assessment and integration with geogr aphic information systems. This article provides a summary of the image-pro cessing methods which may be applied to remotely sensed data for the estima tion of forest structural parameters while also acknowledging the various l imitations that are presented. Current advancements in remote-sensor techno logy are increasing the information content of remotely sensed data and res ulting in a need for new analysis techniques. These advances in sensor tech nology are occurring concurrently with changes in forest management practic es, requiring detailed measurements intended to enable ecosystem-level mana gement in a sustainable manner. This review of remote-sensing image analysis techniques, with reference to forest structural parameters, illustrates the dependence between spatial re solution to the level of detail of the parameters which may be extracted fr om remotely sensed imagery. As a result, the scope of a particular investig ation will influence the type of imagery required and the Limits to the det ail of the parameters that may be estimated. The complexity of parameters t hat may be extracted can be increased through combinations of image-process ing techniques. For example, multitemporal analysis of image radiance value s or multispectral image classification maps may be analysed to undertake t he assessment of such forest characteristics as area of forest disturbances , forest succession and development, or sustainability of forest management practices. Further, the combination of spectral and spatial information ex traction techniques shows promise for increasing the accuracy of estimates of forest inventory and biophysical parameters.