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Article

Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification

1
Department of Earth Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
2
Instituut Fysieke Veiligheid, 6816 RW Arnhem, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3710; https://doi.org/10.3390/rs12223710
Received: 7 October 2020 / Revised: 9 November 2020 / Accepted: 10 November 2020 / Published: 12 November 2020
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
Detailed information about tree species composition is critical to forest managers and ecologists. In this study, we used Sentinel-2 imagery in combination with a canopy height model (CHM) derived from airborne laser scanning (ALS) to map individual tree crowns and identify them to species level. Our study area covered 140 km2 of a mainly mixed temperate forest in the Veluwe area in The Netherlands. Ground truth data on tree species were acquired for 2460 trees. Tree crowns were automatically delineated from the CHM model. We identified the delineated tree crowns to species and phylum level (angiosperm vs. gymnosperm) using a random forest (RF) classification. The RF model used multitemporal spectral variables from Sentinel-2 and crown structural variables from the CHM and was validated using an independent dataset. Different combinations of variables were tested. After feature reduction from 25 to 15 features, the RF model identified tree crowns with an overall accuracy of 78.5% (Kappa value 0.75) for tree species and 84.5% (Kappa value 0.73) for tree phyla whilst using the combination of all variables. Adding crown structural and multitemporal spectral information improved the RF classification compared to using only a Sentinel image from one season as input data. The producer’s accuracies varied between 43.8% for Norway spruce (Picea abies) to 95.3% for Douglas fir (Pseudotsuga menziesii). The RF model was extrapolated to generate a tree species map over a study area (140 km2). The map showed high abundances of common oak (Quercus robur; 35.5%) and Scots pine (Pinus sylvestris; 22.8%) and low abundances of Norway spruce (Picea abies; 1.7%) and Douglas fir (Pseudotsuga menziesii; 2.8%). Our results indicate a high potential for individual tree classification based on Sentinel-2 imagery and automatically derived tree crowns from canopy height models. View Full-Text
Keywords: tree species classification; multitemporal; object-based; Sentinel-2; airborne laser scanning; random forest tree species classification; multitemporal; object-based; Sentinel-2; airborne laser scanning; random forest
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MDPI and ACS Style

Plakman, V.; Janssen, T.; Brouwer, N.; Veraverbeke, S. Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification. Remote Sens. 2020, 12, 3710. https://doi.org/10.3390/rs12223710

AMA Style

Plakman V, Janssen T, Brouwer N, Veraverbeke S. Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification. Remote Sensing. 2020; 12(22):3710. https://doi.org/10.3390/rs12223710

Chicago/Turabian Style

Plakman, Veerle, Thomas Janssen, Nienke Brouwer, and Sander Veraverbeke. 2020. "Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification" Remote Sensing 12, no. 22: 3710. https://doi.org/10.3390/rs12223710

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