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Tree Species Classification with Multi-Temporal Sentinel-2 Data
Linnaeus University, Faculty of Technology, Department of Forestry and Wood Technology.ORCID iD: 0000-0002-5811-1462
Swedish University of Agricultural Sciences.
University of Gothenburg.
2018 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 11, article id 1794Article in journal (Refereed) Published
Abstract [en]

The Sentinel-2 program provides the opportunity to monitor terrestrial ecosystems with a high temporal and spectral resolution. In this study, a multi-temporal Sentinel-2 data set was used to classify common tree species over a mature forest in central Sweden. The tree species to be classified were Norway spruce (Picea abies), Scots pine (Pinus silvestris), Hybrid larch (Larix x marschlinsii), Birch (Betula sp.) and Pedunculate oak (Quercus robur). Four Sentinel-2 images from spring (7 April and 27 May), summer (9 July) and fall (19 October) of 2017 were used along with the Random Forest (RF) classifier. A variable selection approach was implemented to find fewer and uncorrelated bands resulting in the best model for tree species identification. The final model resulting in the highest overall accuracy (88.2%) came from using all bands from the four image dates. The single image that gave the most accurate classification result (80.5%) was the late spring image (27 May); the 27 May image was always included in subsequent image combinations that gave the highest overall accuracy. The five tree species were classified with a user's accuracy ranging from 70.9% to 95.6%. Thirteen of the 40 bands were selected in a variable selection procedure and resulted in a model with only slightly lower accuracy (86.3%) than that using all bands. Among the highest ranked bands were the red edge bands 2 and 3 as well as the narrow NIR (near-infrared) band 8a, all from the 27 May image, and SWIR (short-wave infrared) bands from all four image dates. This study shows that the red-edge bands and SWIR bands from Sentinel-2 are of importance, and confirms that spring and/or fall images capturing phenological differences between the species are most useful to tree species classification.

Place, publisher, year, edition, pages
MDPI, 2018. Vol. 10, no 11, article id 1794
Keywords [en]
tree species classification, Sentinel-2, multi-temporal, Random Forest, variable selection, phenology, boreo-nemoral
National Category
Forest Science
Research subject
Technology (byts ev till Engineering), Forestry and Wood Technology
Identifiers
URN: urn:nbn:se:lnu:diva-79613DOI: 10.3390/rs10111794ISI: 000451733800125Scopus ID: 2-s2.0-85057097174OAI: oai:DiVA.org:lnu-79613DiVA, id: diva2:1279960
Available from: 2019-01-17 Created: 2019-01-17 Last updated: 2019-08-29Bibliographically approved

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Persson, Magnus

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