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

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

To create a DHM subtract the DEM from the DEM, this can be done in Esri Raster Calculator or GDAL_CALC. This will put all your elevations on a 'level playing field'.

Syntax (Substitute full paths for DEM, DSM & DHM):

GDAL_CALC.py -A DSM -B DEM --outfile=DHM --CALC "A-B"

The DHM will be mostly 0 (or near enough), which you make your nodata value. With Raster Calculator or GDAL_CALC you can extract values more than an arbitrary value based on the amount of noise you observe in the DHM. The object of this is to reduce noise and highlight just the crowns of vegetation - in the instance where two 'trees' are adjacent this should split into two distinct blobs.

Syntax (Substitute full paths for Binary & DHM and observed value for Value):

GDAL_CALC.py -A DHM --outfile=Binary --calc "A*(A>Value)"

Now with either GDAL_CALC or Esri IsNull create a binary raster, which can be polygonized with GDAL_Polygonize or Esri Raster to Polygon.

To refine the polygons remove excessively small polygons and then compare them to the RGB bands looking for signatures, in Esri the Zonal Statistics tool will help. Then you can discard the polygons that clearly don't have the right statistics (based on experimentation and your data, I can't give you the values).

This should get you to about 80% accuracy at plotting individual crowns.

eCognition is the best software for that, I did that using other software but eCognition its better. Here is the reference to literature on the subject:

Karlson, M., Reese, H., & Ostwald, M. (2014). Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis. Sensors, 14(12), 22643-22669.

e.g. http://www.mdpi.com/1424-8220/14/12/22643

Additionally:

Zagalikis, G., Cameron, A. D., & Miller, D. R. (2005). The application of digital photogrammetry and image analysis techniques to derive tree and stand characteristics. Canadian journal of forest research, 35(5), 1224-1237.

e.g. http://www.nrcresearchpress.com/doi/abs/10.1139/x05-030#.VJmMb14gAA