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

被引:17
|
作者
Viinikka, Arto [1 ]
Hurskainen, Pekka [1 ,2 ]
Keski-Saari, Sarita [3 ,4 ]
Kivinen, Sonja [1 ,3 ]
Tanhuanpaa, Topi [3 ,5 ]
Mayra, Janne [1 ]
Poikolainen, Laura [3 ]
Vihervaara, Petteri [1 ]
Kumpula, Timo [3 ]
机构
[1] Finnish Environm Inst, Latokartanonkaari 11, Helsinki 00790, Finland
[2] Univ Helsinki, Dept Geosci & Geog, Earth Change Observat Lab, POB 64, FI-00014 Helsinki, Finland
[3] Univ Eastern Finland, Dept Geog & Hist Studies, POB 111, FI-80101 Joensuu, Finland
[4] Univ Eastern Finland, Dept Environm & Biol Sci, POB 111, FI-80101 Joensuu, Finland
[5] Univ Helsinki, Dept Forest Sci, FI-00014 Helsinki, Finland
基金
欧盟地平线“2020”; 芬兰科学院;
关键词
hyperspectral imaging; airborne laser scanning; machine learning; tree species classification; European aspen; boreal forest; TREE SPECIES CLASSIFICATION; IMAGING SPECTROMETRY DATA; LIDAR DATA; SPATIAL-RESOLUTION; LEAF; BIODIVERSITY; CHLOROPHYLL; INVENTORIES; DISCRIMINATION; REFLECTANCE;
D O I
10.3390/rs12162610
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:27
相关论文
共 50 条
  • [21] A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing
    Kivinen, Sonja
    Koivisto, Elina
    Keski-Saari, Sarita
    Poikolainen, Laura
    Tanhuanpaa, Topi
    Kuzmin, Anton
    Viinikka, Arto
    Heikkinen, Risto K.
    Pykala, Juha
    Virkkala, Raimo
    Vihervaara, Petteri
    Kumpula, Timo
    FOREST ECOLOGY AND MANAGEMENT, 2020, 462
  • [22] Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
    Hardenbol, Alwin A.
    Korhonen, Lauri
    Kukkonen, Mikko
    Maltamo, Matti
    METHODS IN ECOLOGY AND EVOLUTION, 2023, 14 (07): : 1610 - 1622
  • [23] Monitoring of coastal processes by using airborne laser scanning data
    Gruenthal, Erkko
    Gruno, Anti
    Ellmann, Artu
    9TH INTERNATIONAL CONFERENCE ENVIRONMENTAL ENGINEERING (9TH ICEE) - SELECTED PAPERS, 2014,
  • [24] Enhancement of bioenergy estimations within forests using airborne laser scanning and multispectral line scanner data
    Straub, Christoph
    Koch, Barbara
    BIOMASS & BIOENERGY, 2011, 35 (08) : 3561 - 3574
  • [25] BUILDING A NETWORK OF OVERMATURE FORESTS USING AIRBORNE LASER SCANNING AND LANDSCAPE GRAPHS
    Lalechere, Etienne
    Berges, Laurent
    Vacher, Julie
    Monnet, Jean-Matthieu
    Fuhr, Marc
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 5901 - 5904
  • [26] A comparative assessment of the vertical distribution of forest components using full-waveform airborne, discrete airborne and discrete terrestrial laser scanning data
    Crespo-Peremarch, Pablo
    Fournier, Richard A.
    Nguyen, Van-Tho
    van Lier, Olivier R.
    Angel Ruiz, Luis
    FOREST ECOLOGY AND MANAGEMENT, 2020, 473
  • [27] Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm
    Sterenczak, Krzysztof
    Kraszewski, Bartlomiej
    Mielcarek, Milosz
    Piasecka, Zaneta
    Lisiewicz, Maciej
    Heurich, Marco
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2020, 93
  • [28] Predicting stand age in managed forests using National Forest Inventory field data and airborne laser scanning
    Maltamo, Matti
    Kinnunen, Hermanni
    Kangas, Annika
    Korhonen, Lauri
    FOREST ECOSYSTEMS, 2020, 7 (01)
  • [29] Modeling Mediterranean forest structure using airborne laser scanning data
    Bottalico, Francesca
    Chirici, Gherardo
    Giannini, Raffaello
    Mele, Salvatore
    Mura, Matteo
    Puxeddu, Michele
    McRobert, Ronald E.
    Valbuena, Ruben
    Travaglini, Davide
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2017, 57 : 145 - 153
  • [30] Detection of snow disturbances in boreal forests using unitemporal airborne lidar data and aerial images
    Raty, Janne
    Kukkonen, Mikko
    Melin, Markus
    Maltamo, Matti
    Packalen, Petteri
    FORESTRY, 2024,