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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Terrain Model Reconstruction from Terrestrial LiDAR Data Using Radial Basis Functions.

Jules Morel, Alexandra Bac, Cedric Vega

    IEEE Computer Graphics and Applications
    |September 26, 2017
    PubMed
    Summary

    This study presents a new algorithm for creating accurate digital terrain models (DTMs) from forest laser scanning data. The method effectively handles occlusions and integrates ground-level data for improved terrain mapping.

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    Area of Science:

    • Geomatics
    • Forestry
    • Geomorphology

    Background:

    • Terrestrial laser scanning (TLS) in forests suffers from occlusions due to vegetation and complex topography.
    • This leads to data gaps at ground level and potential integration of above-ground objects in digital terrain models (DTMs).

    Purpose of the Study:

    • To introduce a novel surface-approximation algorithm for extracting DTMs from TLS data in forested environments.
    • To address challenges of data gaps and object misclassification inherent in forest TLS data.

    Main Methods:

    • A quadtree subdivision approach guided by local data density and distribution.
    • Surface modeling using radial basis functions as partitions of unity.
    • Merging of local quadratic approximating patches to create a continuous terrain surface.

    Main Results:

    • The algorithm effectively extracts DTMs from TLS data in challenging forest conditions.
    • Demonstrates improved handling of occlusions and data gaps compared to existing methods.
    • Successfully differentiates between ground-level terrain and above-ground objects.

    Conclusions:

    • The proposed algorithm provides a robust solution for DTM generation from forest TLS data.
    • Enhances the accuracy and reliability of terrain mapping in vegetated areas.
    • Offers a valuable tool for ecological and geomorphological studies in forests.