Spatial Wavelet Analysis (SWA)

Summary:

Spatial Wavelet Analysis (SWA: Falkowski et al., 2006) is a powerful image-processing technique that has considerable potential to quantify spatial landscape and plant patterns at multiple scales and across large areas.

Wavelets have been used across a wide range of scientific disciplines from medical imaging to astronomy, to identify the size, shape, and location of individual features of interest. However, though wavelets hold tremendous promise for objectively and automatically quantifying ecological features in remotely sensed imagery, this application has gone largely unexplored. 

Our group is currently invloved within several projects to demonstrate and assess how Spatial Wavelet Analysis of aerial photography, lidar imagery, and satellite imagery can help provide spatial plant information. Accurate estimates of spatial plant patterns across large areas will allow ecologists to infer regarding relationships between landscape patterns and underlying ecological and environmental processes.

Example Results:

1. Spatial Wavelet Analysis applied to aerial photography of a Juniper plants within a sagebrush steppe landscape in central Idaho.

The dark points in the left image are the junipers. The green circles in the right image represents the projected 'size' of each juniper. Notice that the method sucessfully identifies these points regardless of whether the surronding matrix (i.e. background) is dark or light.

This application is further highlighted in Strand et al (2005, 2006).



2. Spatial Wavelet Analysis applied to a LiDAR digital canopy height model (CHM) of various trees within an open-canopy mixed conifer forest stand in northern Idaho.

In this case, SWA is used to identify light (tall height) objects on a dark (low height) background.

This application is further highlighted in Falkowski et al (2006) as detailed below.



Publications so far...

Garrity, S.R., Vierling, L.A., Smith, A.M.S. and Hann, D.B., Suitability of Spatial Wavelet Analysis (SWA) for the automatic assessment of shrubs within an arid environment, International Journal of Remote Sensing, in review.

Strand, E., Smith A.M.S., Bunting, S.C., Vierling, L.A., Hann, D.B. and Gessler, P.E., (2006) Wavelet estimation of vegetation spatial patterns in multi-temporal aerial photography, International Journal of Remote Sensing, 27, 9-10, 2049-2054. (PDF)

Falkowski, M.J., Smith, A.M.S., Hudak, A.T., Gessler, P.E., Vierling, L.A. and Crookston, N.L., (2006). Automated estimation of individual conifer tree height and crown diameter via Two-dimensional spatial wavelet analysis of lidar data, Canadian Journal of Remote Sensing, 32, 1, 153-161  (Link to PDF)

Falkowski, M.J., Hudak, A.T., Smith, A.M.S. and Gessler, P.E., (2005) A comparison of four individual tree height prediction methods for forest inventory, 26th Canadian Symposium on Remote Sensing, Wolfville, Nova Scotia, Canada, 14-16 Jun 2005

Garrity, S.R., Vierling, L.A., and Smith. A.M.S. (2006) ‘Automated detection of individual shrub location and crown area using aerial imagery and 2-D wavelet analysis’ SRM 59th Annual Meeting, Vancouver, B.C., Canada, 12-17 February 2006.

Strand, E.K., Vierling L.A., Smith, A.M.S., Bunting, S.C., Hann, D.B. and Gessler, P.E., (2005) Wavelet estimation of plant spatial patterns in multi-temporal aerial photography, Eos transactions, American Geophysical Union, 86, 52, Fall Meeting, Supplemental, B43B-0287.