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Tree species classification using Sentinel-2 imagery and Bayesian inference

Axelsson, Arvid and Lindberg, Eva and Reese, Heather and Olsson, Håkan (2021). Tree species classification using Sentinel-2 imagery and Bayesian inference. International Journal of Applied Earth Observation and Geoinformation. 100 , 102318
[Research article]

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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.

Authors/Creators:Axelsson, Arvid and Lindberg, Eva and Reese, Heather and Olsson, Håkan
Title:Tree species classification using Sentinel-2 imagery and Bayesian inference
Series Name/Journal:International Journal of Applied Earth Observation and Geoinformation
Year of publishing :2021
Article number:102318
Number of Pages:7
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
Keywords:Multi-temporal, Satellite, Bayesian, Land cover, Classification, Maximum likelihood, Sequential
Permanent URL:
Additional ID:
Type of IDID
Web of Science (WoS)000647796800002
ID Code:24386
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:03 Jun 2021 13:23
Metadata Last Modified:03 Jun 2021 13:31

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