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

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Viinikka , A , Hurskainen , P , Keski-Saari , S , Kivinen , S , Tanhuanpää , T , Mäyrä , J , Poikolainen , L , Vihervaara , P & Kumpula , T 2020 , ' Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data ' , Remote Sensing , vol. 12 , no. 16 , 2610 .

Title: Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data
Author: Viinikka, Arto; Hurskainen, Pekka; Keski-Saari, Sarita; Kivinen, Sonja; Tanhuanpää, Topi; Mäyrä, Janne; Poikolainen, Laura; Vihervaara, Petteri; Kumpula, Timo
Contributor: University of Helsinki, Earth Change Observation Laboratory (ECHOLAB)
University of Helsinki, Forest Health Group
Date: 2020-08-13
Language: eng
Number of pages: 27
Belongs to series: Remote Sensing
ISSN: 2072-4292
Abstract: 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 tremulaL.) 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.
Subject: 1172 Environmental sciences
european aspen
boreal forest
4112 Forestry
1171 Geosciences
hyperspectral imaging
Airborne Laser Scanning
tree species classification
113 Computer and information sciences
machine learning
hyperspectral imaging
airborne laser scanning
machine learning
tree species classification
European aspen
boreal forest

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