Journal
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
Volume 28, Issue -, Pages 220-229Publisher
ELSEVIER
DOI: 10.1016/j.jag.2013.12.014
Keywords
Wetland mapping; Wetland-dynamics; Land use/land cover (LULC); Landsat ETM; Multivariate reflectance analysis; Random forest
Categories
Funding
- Eiselen Stiftung
- German Aerospace Center (DLR)
Ask authors/readers for more resources
Wetlands rank among the most diverse ecosystems on earth and function as important ecosystem service providers. Pressures on wetland ecosystems caused by human activities, such as land use transformations or agricultural intensification, lead to strong wetland degradation. Satellite-based wetland mapping still bears the most uncertainties compared to other land cover types mapping. Image classification techniques have to better adapt to specific wetland characteristics, such as spatial heterogeneity, seasonal dynamics and fuzzy transitions between different land cover classes. For this purpose, a pixel-based method for wetland delineation based on multi-temporal Landsat data in West Turkey was developed and analyzed. In addition to common vegetation indices and texture measures, the usefulness of seasonal indices was tested. Multi-temporal Landsat imagery was combined with high resolution satellite data to extract subpixel information of coastal and inland wetland classes based on a random forest regression algorithm. The classification achieved an overall accuracy of 79.02%. In addition to the hard wetland classification the mapping framework provides a map of fractional cover information of different wetland classes including information about fuzzy spatial transitions of highly heterogeneous distribution patterns of wetland habitats and related intra-annual seasonal dynamics. Mapping spatio-temporal wetland dynamics at continuous field scales increases the applicability of Landsat-derived maps for local-scale ecosystem monitoring and environmental management on habitat level. (C) 2014 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available