Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Olli Nevalainen (Corresponding Author), Eija Honkavaara, Sakari Tuominen, Niko Viljanen, Teemu Hakala, Xiaowei Yu, Juha Hyyppä, Heikki Saari, Ilkka Pölönen, Nilton N. Imai, Antonio M.G. Tommaselli

    Research output: Contribution to journalArticleScientificpeer-review

    96 Citations (Scopus)

    Abstract

    Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.
    Original languageEnglish
    Article number185
    JournalRemote Sensing
    Volume9
    Issue number3
    DOIs
    Publication statusPublished - 1 Mar 2017
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    remote sensing
    image resolution
    photogrammetry
    developmental stage
    boreal forest
    irradiance
    detection
    vehicle
    flight
    sensor
    market
    experiment
    method

    Keywords

    • classification
    • forest
    • hyperspectral
    • photogrammetry
    • point cloud
    • radiometry
    • UAV

    Cite this

    Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., ... Tommaselli, A. M. G. (2017). Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sensing, 9(3), [185]. https://doi.org/10.3390/rs9030185
    Nevalainen, Olli ; Honkavaara, Eija ; Tuominen, Sakari ; Viljanen, Niko ; Hakala, Teemu ; Yu, Xiaowei ; Hyyppä, Juha ; Saari, Heikki ; Pölönen, Ilkka ; Imai, Nilton N. ; Tommaselli, Antonio M.G. / Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. In: Remote Sensing. 2017 ; Vol. 9, No. 3.
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    abstract = "Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95{\%} overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40{\%} and 95{\%}, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.",
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    Nevalainen, O, Honkavaara, E, Tuominen, S, Viljanen, N, Hakala, T, Yu, X, Hyyppä, J, Saari, H, Pölönen, I, Imai, NN & Tommaselli, AMG 2017, 'Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging', Remote Sensing, vol. 9, no. 3, 185. https://doi.org/10.3390/rs9030185

    Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. / Nevalainen, Olli (Corresponding Author); Honkavaara, Eija; Tuominen, Sakari; Viljanen, Niko; Hakala, Teemu; Yu, Xiaowei; Hyyppä, Juha; Saari, Heikki; Pölönen, Ilkka; Imai, Nilton N.; Tommaselli, Antonio M.G.

    In: Remote Sensing, Vol. 9, No. 3, 185, 01.03.2017.

    Research output: Contribution to journalArticleScientificpeer-review

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    AU - Viljanen, Niko

    AU - Hakala, Teemu

    AU - Yu, Xiaowei

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