Dear all,

I would like to perform a classification of a large lidar data points
acquired over a tropical forest plot network (500 km2).
I computed the mean canopy(tree) height by 5m x 5m cell (pixel) and 3 others
parameters (max height, height variation over 2 years, mean height in a 50m
x 50 m neighborhood)
I did most of this using the raster package.
Now, I have a kind of multispectral image with 4 layers and I would like to
perform a large-scale classification of the data based on these 4
parameters. My aim is to find out "homogeneous" canopy regions, for
instance: high canopy with low change over the 2 years, canopy gaps, areas
of recruitment, etc.

I tried to perfom a standard cluster analysis (hclust), but I could not
compute the dissimilarity matrix (dist) on such a big data set (80'000 rows
and 4 variables), even with a 64-bits PC.
The k-means (kmeans{}) classification works, but return me strange results
(4 main clusters north/south/east/west). I have seen that the biOps package
allowed to do isodata classification. However isodata{} required an image
and I don't know how to compute a 4-layer image (if possible).

Does anyone have any suggestion? Shall I turn me to GIS software?
I am trying to do it with SAGA at the moment, but find it difficult too :)

Thank you for any help. Best regards,
Ervan
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Ervan Rutishauser
Tropical forest ecology and dynamics
Montpellier

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