Canopy Quantification via Image Classification

Premise:
Supervised classification of 2005 NAIP imagery (1 meter resolution) into 3 classes (grass, tree, shade), by the SMAP[1-3] algorithm. Tree heights are small compared to horizontal extent, and therefore "shadow" pixels can probably be interpreted as "tree" pixels. Field verification will be needed to check actual percent coverage of classes, and fractionation of tree species within the "tree" pixel class. Still looking up the specific date/time the source imagery was taken. Since the shadows appear to be falling north-ward, the photo was probably taken in the morning. The color of the grass suggests that it was probably summer. In these conditions there should be no shading of this region by local terrain features.

 
2005 NAIP Image:
http://archive.casil.ucdavis.edu/casil/remote_sensing/naip_2005/doqqs/39121/n39121c3se.tif

 
References:

  1. C. Bouman and M. Shapiro, "Multispectral Image Segmentation using a Multiscale Image Model", Proc. of IEEE Int'l Conf. on Acoust., Speech and Sig. Proc., pp. III-565 - III-568, San Francisco, California, March 23-26, 1992.
  2. C. Bouman and M. Shapiro 1994, "A Multiscale Random Field Model for Bayesian Image Segmentation", IEEE Trans. on Image Processing., 3(2), 162-177"
  3. McCauley, J.D. and B.A. Engel 1995, "Comparison of Scene Segmentations: SMAP, ECHO and Maximum Likelyhood", IEEE Trans. on Geoscience and Remote Sensing, 33(6): 1313-1316.

naip.png
NAIP Imagery

 

grass_tree.png
Classified Image: Yellow regions are grass, light green are tree canopy, dark green are shadows.

example.png
Combined Image: NAIP red channel (image intensity), with classes superimposed (as image hue).

Area Report



    
    

Example Procedure