Hill, R.A., 1999. Image segmentation for humid tropical forest classification in Landsat TM data. International Journal of Remote Sensing, 20 (5), pp. 1039-1044.
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Official URL: http://www.informaworld.com/smpp/content~content=a...
Humid tropical forest types have low spectral separability in Landsat TM data due to highly textured reflectance patterns at the 30m spatial resolution. Two methods of reducing local spectral variation, low-pass spatial filtering and image segmentation, were examined for supervised classification of 10 forest types in TM data of Peruvian Amazonia. The number of forest classes identified at over 90% accuracy increased from one in raw imagery to three in filtered imagery, and six in segmented imagery. The ability to derive less generalised tropical forest classes may allow greater use of classified imagery in ecology and conservation planning.
|Subjects:||Geography and Environmental Studies|
Science > Earth Sciences
|Group:||School of Applied Sciences > Centre for Conservation, Ecology and Environmental Change|
|Deposited By:||Dr Ross Hill|
|Deposited On:||07 Apr 2009 19:18|
|Last Modified:||07 Mar 2013 15:07|
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