Abstract
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.
Original language | English |
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Pages (from-to) | 1943-1958 |
Number of pages | 16 |
Journal | Sensors |
Volume | 11 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2011 Feb |
Keywords
- Digital forest cover map
- High resolution
- Pixel-based classification
- Satellite image
- Segment-based classification
ASJC Scopus subject areas
- Analytical Chemistry
- Biochemistry
- Atomic and Molecular Physics, and Optics
- Instrumentation
- Electrical and Electronic Engineering