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Open AccessArticle

Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery

1
Division of Environmental Science and Ecological Engineering, Korea University, Seoul 136-701, Korea
2
Department of Environmental Science, Policy and Management, University of California at Berkeley, Mulford Hall, Berkeley, CA 94720, USA
3
Division of Forest Resources Information, Korean Forest Research Institute, Seoul 136-012, Korea
*
Author to whom correspondence should be addressed.
Sensors 2011, 11(2), 1943-1958; https://doi.org/10.3390/s110201943
Received: 20 December 2010 / Revised: 28 January 2011 / Accepted: 30 January 2011 / Published: 1 February 2011
(This article belongs to the Section Remote Sensors)
This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images. View Full-Text
Keywords: digital forest cover map; high resolution; satellite image; pixel-based classification; segment-based classification digital forest cover map; high resolution; satellite image; pixel-based classification; segment-based classification
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MDPI and ACS Style

Kim, S.-R.; Lee, W.-K.; Kwak, D.-A.; Biging, G.S.; Gong, P.; Lee, J.-H.; Cho, H.-K. Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery. Sensors 2011, 11, 1943-1958.

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