Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R2 relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation.


    Access

    Download


    Export, share and cite



    Title :

    Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets


    Contributors:


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

    I-Kuai Hung / Daniel Unger / David Kulhavy et al. | DOAJ | 2019

    Free access

    System and methods for semi-automated editing of orthomosaics built from remotely-sensed imagery

    TANG MATTHEW / THIEL JOHN / NECKELS DAVID | European Patent Office | 2019

    Free access

    Bell Caribou

    Engineering Index Backfile | 1941


    Assessing Reef-Island Shoreline Change Using UAV-Derived Orthomosaics and Digital Surface Models

    Meagan K. Lowe / Farrah Anis Fazliatul Adnan / Sarah M. Hamylton et al. | DOAJ | 2019

    Free access

    Bell Caribou

    Engineering Index Backfile | 1941