Extracting tree crown areas from remote sensing data (visual images and LiDAR)

I am looking for a method to process a remote sensing image and extract the crown areas of the individual trees from the image.

I have both visual wavelength areal imagery, and lidar data from the area. The location in question is a desert area, so the tree cover isn't as dense as a forest area. The resolution of the aerial imagery is 0.5 feet by 0.5 feet. The lidar resolution is approximately 1 x 1 feet. Both the visual data and the lidar come from a Pima County, Arizona dataset. A sample of the type of aerial imagery I have is at the end of this post.

This question Single Tree detection in ArcMap? seems to be the same issue, but there does not seem to be a good answer there.

I can obtain a reasonable classification of the vegetation types (and information about the overall percent cover) in the area by using the Iso Cluster classification in Arcmap, but this provides little information on individual trees. The closest I have to what I want is the results of passing the output of the isocluster classification through the Raster to Polygon feature in Arcmap. The problem is that this method merges near by trees into a single polygon.

Edit: I probably should have included some more detail about what I have. The raw datasets I have are:

• Full las data, and a tiff raster generated from it.


• Visual imagery (like the sample image shown, but covering a much wider area)


• Manual direct measurements of a subset of the trees in the area.

From these I have generated:

1. The ground/vegetation classifications.


2. The DEM/DSM rasters.

Answer

There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with:

Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Canadian Journal of Remote Sensing 34(2):338-350.

Smith A.M.S., E.K. Strand, C.M. Steele, D.B. Hann, S.R. Garrity, M.J. Falkowski, J.S. Evans (2008) Production of vegetation spatial-structure maps by per-object analysis of juniper encroachment in multi-temporal aerial photographs. Canadian Journal Remote Sensing 34(2):268-285

If you are interested in the Wavelet method (Smith et al., 2008), I have it coded in Python but, it is very slow. If you have Matlab experience, this is where it is implemented in production mode. We have two papers where we identified ~6 million acres of juniper encroachment in eastern Oregon using the wavelet method with NAIP RGB-NIR imagery so, it is well proven.

Baruch-Mordo, S., J.S. Evans, J. Severson, J. D. Naugle, J. Kiesecker, J. Maestas, and M.J. Falkowski (2013) Saving sage-grouse from the trees: A proactive solution to reducing a key threat to a candidate species Biological Conservation 167:233-241

Poznanovic, A.J., M.J. Falkowski, A.L. Maclean, and J.S. Evans (2014) An Accuracy Assessment of Tree Detection Algorithms in Juniper Woodlands. Photogrammetric Engineering & Remote Sensing 80(5):627–637

There are some interesting approaches, in general object decomposition, from the applied mathematics state space literature using multiresolution Gaussian processes to decompose object characteristics across scale. I use these types of models to describe multi-scale process in ecological models but it could be adapted to decompose image object characteristics. Fun, but a bit esoteric.

Gramacy, R.B., and H.K.H. Lee (2008) Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association, 103(483):1119–1130

Kim, H.M., B.K. Mallick, and C.C. Holmes (2005) Analyzing nonstationary spatial data using piecewise Gaussian processes. Journal of the American Statistical Association, 100(470):653–668