Friborg, Thomas

Trepekli, Katerina

Westergaard-Nielsen, Andreas

English

Copenhagen University Research Information System

u n i ve r s i t y  o f  co pe n h ag e n  Københavns UniversitetUtilization of UAV-Lidar to infer surface roughness and leaf area index of aheterogeneous wooded areaTrepekli, Katerina; Westergaard-Nielsen, Andreas; Friborg, ThomasPublished in:Geophysical Research AbstractsPublication date:2019Document versionPublisher's PDF, also known as Version of recordDocument license:CC BYCitation for published version (APA):Trepekli, K., Westergaard-Nielsen, A., & Friborg, T. (2019). Utilization of UAV-Lidar to infer surface roughnessand leaf area index of a heterogeneous wooded area. Geophysical Research Abstracts, 21.Download date: 03. Feb. 2020Geophysical Research AbstractsVol. 21, EGU2019-5928-1, 2019EGU General Assembly 2019© Author(s) 2019. CC Attribution 4.0 license.Utilization of UAV-Lidar to infer surface roughness and leaf area index ofa heterogeneous wooded areaAikaterini Trepekli, Andreas Westergaard-Nielsen, and Thomas FriborgDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350Copenhagen, Denmark (atr@ign.ku.dk)The newly developed unmanned aerial vehicle (UAV)-based Light Detection and Ranging (Lidar) scanners mayprovide a research tool to yield a fine-grained analysis of surface processes and patterns for hydrological modelingby generating the 3-d structure of a surface in both high spatial and temporal resolution. To describe interactionsbetween water and vegetation dynamics the aerodynamic roughness length (zo) and zero-plane displacement (d)are usually estimated based on micrometeorological observations which are confined to a specific footprint, or onmorphometric surface roughness models which rely on the demarcation of surface objects. By linking the Lidar-derived geometry with surface roughness models, zo and d may be extracted without requiring any other datasources, such as aerial imagery or GIS databases. Compared to traditional remote sensing, Lidar may also moreaccurately measure vegetation structure with no apparent sign of saturation in estimating leaf area index (LAI).This conceptual approach aims to evaluate the potential for UAV borne Lidar in estimation of surface roughnessand LAI in a challenging heterogeneous field (3.5 x 5 km) in rural Switzerland, populated by deciduous trees of arange of species and heights and some unwooded grassy areas. To accomplish that, the study is focused on:i) evaluating the performance of different filtering algorithms (e.g., maximum local slope, adaptive triangular ir-regular network, progressive morphology and object based classification) to separate Lidar data into terrain andoff-terrain datasets, thus generating high-resolution digital terrain model and the necessary geometry for a mor-phometric model;ii) investigating whether UAV-Lidar derived geometry of a surface (e.g., total plan area, percentage of area coveredwith vegetation, average height of features, number of trees) can be integrated effectively to a morphometric modelin order to estimate the areal zo and d for subsequent use in resistance equations;iii) exploring the effectiveness of various canopy Lidar metrics including canopy density, mean, maximum, andpercentile height to estimate LAI over the heterogeneous field by incorporating them into a stepwise regressionprocedure to predict field-based measurements of LAI.This technique may advance the establishment of more precise spatial representation of potentially nonlinear re-lationships between canopy structural characteristics and surface water dynamics across heterogeneous land covertypes.

Utilization of UAV-Lidar to infer surface roughness and leaf area index of a heterogeneous wooded area

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Utilization of UAV-Lidar to infer surface roughness and leaf area index of a heterogeneous wooded area

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