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Tree species classification using Sentinel-2 imagery and Bayesian inference
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- Axelsson, Arvid (author)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management
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- Lindberg, Eva (author)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management
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- Reese, Heather, 1964 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences
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- Elsevier BV, 2021
- 2021
- English.
- In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 0303-2434. ; 100
- Journal article (peer-reviewed)
Abstract
Subject headings
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- The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58°27′18.35″ N, 13°39′8.03″ E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen’s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Remote Sensing (hsv//eng)
Keyword
- Multi-temporal Satellite Bayesian Land cover Classification Maximum likelihood Sequential
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