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Tree Species Classification with Multi-Temporal Sentinel-2 Data

Persson, Magnus and Lindberg, Eva and Reese, Heather (2018). Tree Species Classification with Multi-Temporal Sentinel-2 Data. Remote Sensing. 10 , 1794
[Research article]

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Abstract

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.

Authors/Creators:Persson, Magnus and Lindberg, Eva and Reese, Heather
Title:Tree Species Classification with Multi-Temporal Sentinel-2 Data
Series Name/Journal:Remote Sensing
Year of publishing :2018
Volume:10
Article number:1794
Number of Pages:17
Publisher:MDPI
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:tree species classification, Sentinel-2, multi-temporal, Random Forest, variable selection, phenology, boreo-nemoral
URN:NBN:urn:nbn:se:slu:epsilon-p-97667
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-97667
Additional ID:
Type of IDID
DOI10.3390/rs10111794
Web of Science (WoS)000451733800125
ID Code:23359
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:27 Apr 2021 11:26
Metadata Last Modified:27 Apr 2021 11:31

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