期刊
REMOTE SENSING OF ENVIRONMENT
卷 117, 期 -, 页码 135-145出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2011.07.009
关键词
Geographic object-based image analysis (GEOBIA); Wetland classification; Geographic object-based texture (GEOTEX); Multiscale; SPOT 5
资金
- Natural Sciences and Engineering Research Council of Canada
- Alberta Informatics Circle of Research Excellence
- University of Calgary
It is estimated that Canada comprises approximately 28% of the world's wetlands (similar to 1.5 million km(2)) providing essential ecological services such as purifying water, nutrient cycling, reducing flooding, recharging ground water supplies, and protecting shorelines. In order to better understand how wetland type and area differ over a range of spatial and thematic scales, this paper introduces a multi-scale geographic object-based image analysis (GEOBIA) approach that incorporates new object-based texture measures (geotex) and a decision-tree classifier (See5), to assess wetland differences through five common spatial resolutions (5, 10, 15. 20 and 30 m) and two different thematic classification schemes. Themes are based on (i) a Ducks Unlimited (DU: 15 class) regional classification system for wetlands in the Boreal Plain Ecosystem and (ii) the Canadian Wetland Inventory (CWI: 5 classes). Results reveal that the highest overall accuracies (67.9% and 82.2%) were achieved at the 10 m spatial resolution for both the DU and CWI classification schemes respectively. It was also found that the DU wetland types experienced greater area differences through scale with the largest differences for both classification schemes occurring in classes with a large treed component. Results further show that the inclusion of geotex channels (generated from dynamically sized and shaped window that measures the spatial variability of the wetland components composing a scene, rather than of individual pixels within a fixed sized window) improved wetland classification. (C) 2011 Elsevier Inc. All rights reserved.
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