Lacunarity analysis to determine optimum extents for sample-based spatial information extraction from high-resolution forest imagery
Lacunarity analysis was evaluated as a means to determine multiple pattern scales that are inherent in high-resolution imagery of forests and to specify an optimal spatial extent for spatial image information extraction. A series of 0.5 m pixel images of temperate hardwood and mixed boreal forests were analysed using lacunarity distributions calculated for spatial extents ranging from 7 m to 40 m. The optimal extent was taken as that which displayed the greatest number of distinct pattern scales. For the temperate hardwood forest dataset, 12-14 m extents were found to be optimal, detecting three pattern scales. For the boreal forest dataset, optimal extents were 14-18 m for five of six plots, detecting two or three pattern scales in each plot. The detected pattern scales ranged from 8m to 14m and showed some correspondence to tree crown size, but also responded to clusters of understorey and overstorey trees or to partially exposed tree crowns. The method can aid in determination of the sample extent that best captures the pattern scales present in the imagery. More generally, it can be useful in exploratory analysis of any spatial data for which the fundamental patterns are not known.
|Journal||International Journal of Remote Sensing|
Butson, C.R., & King, D. (2006). Lacunarity analysis to determine optimum extents for sample-based spatial information extraction from high-resolution forest imagery. International Journal of Remote Sensing, 27(1), 105–120. doi:10.1080/01431160500238844