The effect of atmospheric and topographic correction on pixel-based image composites: Improved forest cover detection in mountain environments |
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| Institution: | 1. First Department of Cardiology, AHEPA University Hospital, Aristotle University Medical School, Thessaloniki, Greece;2. Department of Cardiology, Heart Failure Care Group, Royal Brompton Hospital, London, UK;3. Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;4. Laboratory of Medical Informatics, Aristotle University Medical School, Thessaloniki, Greece;5. Department of Cardiology, Electrophysiology Unit, Royal Brompton Hospital, London, UK;6. Institute of Applied Biosciences, Center for Research and Technology Hellas, Thessaloniki, Greece;7. Third Department of Cardiology, Hippokration University Hospital, Aristotle University Medical School, Thessaloniki, Greece;1. Department of Electrical Engineering, University of California, Los Angeles, United States;2. School of Engineering, Ecole Polytechnique Federale de Lausanne, Switzerland;1. Department of Electronics and Communication Engineering, National Institute of Technology, Faramagudi, Ponda Goa, 403401, India;2. Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India;3. Soft Computing Laboratory, Department of Computer Science, Yonsei University, Seoul 120-749, South Korea;4. Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India |
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| Abstract: | Quantification of forest cover is essential as a tool to stimulate forest management and conservation. Image compositing techniques that sample the most suited pixel from multi-temporal image acquisitions, provide an important tool for forest cover detection as they provide alternatives for missing data due to cloud cover and data discontinuities. At present, however, it is not clear to which extent forest cover detection based on compositing can be improved if the source imagery is firstly corrected for topographic distortions on a pixel-basis. In this study, the results of a pixel compositing algorithm with and without preprocessing topographic correction are compared for a study area covering 9 Landsat footprints in the Romanian Carpathians based on two different classifiers: Maximum Likelihood (ML) and Support Vector Machine (SVM). Results show that classifier selection has a stronger impact on the classification accuracy than topographic correction. Finally, application of the optimal method (SVM classifier with topographic correction) on the Romanian Carpathian Ecoregion between 1985, 1995 and 2010 shows a steady greening due to more afforestation than deforestation. |
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| Keywords: | Forest cover mapping Classification accuracy assessment Topographic correction Landsat Pixel-based compositing Mountain areas |
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