[GRASS-user] High-resolution agricultural land cover from satellite imagery

[Luigi Ponti]

Dear GRASS users,

(This is kind of new topic to me.)

After reading this paper that addresses the mixed-pixel issue via neural 
networks using Landsat Thematic Mapper (TM) data:

Tatem, A. J., Lewis, H. G., Nixon, M. S. and Atkinson, P. (2003) 
Increasing the spatial resolution of agricultural land cover maps using 
a Hopfield neural network. Int Journal of Geographic Information 
Sciences, 17, (7), 647-672. 
<http://eprints.soton.ac.uk/260104/1/tatem_tgis.pdf>

I have searched for GRASS documentation on image classification, 
particularly on land cover. The starting point is the wiki page on image 
classification <http://grass.osgeo.org/wiki/Image_classification> as 
well as section 8.6 of the GRASS book ("Thematic classification of 
satellite data"). They both give good basic reference info, but 
additional pointers a welcome.

Also found some neural network work on the topic done with GRASS 
<http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/muttiah_ranjan/muttiah.html>, 
which seems relevant but implemented in GRASS 4.1, and hence I am unsure 
it survived inside a GRASS module to date (at least, I could not find it).

We are targeting an agricultural area in southern Italy (several 
thousands hectares) for which we have full orthophoto coverage (0.5 
meters resolution), and Landsat TM data can apparently be downloaded 
freely from <http://glcf.umd.edu/data/landsat/>. High-resolution 
agricultural land cover might seem overkill, but the area is highly 
fragmented and hence standard CORINE land cover data tend to classify 
most of the land as mixed types (not very helpful).

I would like to ask a general recommendation on the best way to approach 
an agricultural land cover task such as the one outlined above, together 
with possible info on previous implementation of increasing spatial 
resolution of agricultural land cover maps in GRASS via neural networks 
or other approaches.

Kind regards, thanks in advance and apologies for a long post,

Luigi