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Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery.
- Source :
- European Journal of Remote Sensing; 2016, Vol. 49 Issue 1, p239-259, 21p
- Publication Year :
- 2016
-
Abstract
- Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE sensing
IMAGE processing
TAIGA ecology
Subjects
Details
- Language :
- English
- ISSN :
- 22797254
- Volume :
- 49
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- European Journal of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 117340583
- Full Text :
- https://doi.org/10.5721/EuJRS20164914