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THE SIGNIFICANCE OF USING RAW DATA: A CASE STUDY WITH CANOPY HEIGHT MODELS OF SHRUBS

Ondřej Lagner

Abstract

The quality of spatial data plays a crucial role in environmental modelling and management, especially in local scale studies needing a detail mapping of vegetation elements in a mosaic, near-natural landscape. One of the sources of spatial data for such modelling is airborne LiDAR. Although LiDAR-based vegetation and terrain models are often considered accurate, their quality is dependent on the density of the original raw point clouds and the computation algorithm. The aim of this study was to answer a question of how the method of LiDAR raw data processing affects the accuracy of the resulting canopy height models of shrubs in the mosaic landscape consisting of herbaceous plants and shrub formations. We hypothesize that using raw LiDAR data in conjunction with a suitable algorithm, we can obtain a more accurate shrub model than that acquired from the same raw LiDAR data through a general all-purpose processing used for computation of nationwide digital surface models. The comparison of vertical accuracy of individual models with reference field data showed that combining raw LiDAR data with an algorithm suitable for the studied area could lead to creating better shrub vegetation models than those available from the governmental products. Besides, our results also imply that even data with relatively low point cloud density that are not primarily intended for creating digital models of vegetation can yield a good canopy height model eligible for shrub detection if processed in a suitable way.

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DOI resolver: doi.org/10.5593/sgem2019/2.1/s08.141

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