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Open AccessArticle

Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

1
Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland
2
Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland
3
Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland
4
Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland
5
Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2610; https://doi.org/10.3390/rs12162610
Received: 13 July 2020 / Revised: 11 August 2020 / Accepted: 12 August 2020 / Published: 13 August 2020
Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests. View Full-Text
Keywords: hyperspectral imaging; airborne laser scanning; machine learning; tree species classification; European aspen; boreal forest hyperspectral imaging; airborne laser scanning; machine learning; tree species classification; European aspen; boreal forest
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MDPI and ACS Style

Viinikka, A.; Hurskainen, P.; Keski-Saari, S.; Kivinen, S.; Tanhuanpää, T.; Mäyrä, J.; Poikolainen, L.; Vihervaara, P.; Kumpula, T. Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data. Remote Sens. 2020, 12, 2610. https://doi.org/10.3390/rs12162610

AMA Style

Viinikka A, Hurskainen P, Keski-Saari S, Kivinen S, Tanhuanpää T, Mäyrä J, Poikolainen L, Vihervaara P, Kumpula T. Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data. Remote Sensing. 2020; 12(16):2610. https://doi.org/10.3390/rs12162610

Chicago/Turabian Style

Viinikka, Arto; Hurskainen, Pekka; Keski-Saari, Sarita; Kivinen, Sonja; Tanhuanpää, Topi; Mäyrä, Janne; Poikolainen, Laura; Vihervaara, Petteri; Kumpula, Timo. 2020. "Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data" Remote Sens. 12, no. 16: 2610. https://doi.org/10.3390/rs12162610

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