High-spatial-resolution remote sensing

Recent developments in high-spatial-resolution remote sensing have created a wide array of potential new forestry applications. High spatial resolution imagery allows a tree-scale of analysis, in which individual trees and their attributes are the focus of interest. This tree-scale remote sensing contrasts with the traditional community-scale remote sensing of medium resolution sensors such as Landsat. A variety of approaches have been developed to identify individual trees and delineate their boundaries, including the association of tree tops with local image maxima, delineating edges of trees by focusing on the darker, shadowed areas, recognizing the brighter regions as image segments, matching image chips, or templates, to the individual trees, and mapping the tree shapes in three dimensions. Attributes used in assigning each tree polygon to a single species may include spectral or spatial features. Forest health and mortality can be quantified on the basis of the impact on individual trees, thus supporting improved monitoring and management of forests. Tree information identified in high resolution imagery can also be used to scale up to the stand level, and stand boundaries and attributes can be predicted with high levels of accuracy. As the underlying imaging and analysis technology improves, high-spatial-resolution remote sensing is likely to become a core component of digital forestry.