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Detecting shrub encroachment in seminatural grasslands using UAS LiDAR

Citation

Madsen, B and Treier, UA and Zlinszky, A and Lucieer, A and Normand, S, Detecting shrub encroachment in seminatural grasslands using UAS LiDAR, Ecology and Evolution, 10, (11) pp. 4876-4902. ISSN 2045-7758 (2020) [Refereed Article]


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Copyright Statement

© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/) which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

DOI: doi:10.1002/ece3.6240

Abstract

  1. Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of Cytisus scoparius homogenizes the landscape negatively affecting local plant diversity. Detecting structural change (e.g., biomass) is essential for assessing negative impacts of encroachment. Hence, exploring new monitoring tools to achieve this task is important for effectively capturing change and evaluating management activities.
  2. This study combines traditional field-based measurements with novel Light Detection and Ranging (LiDAR) observations from an Unmanned Aircraft System (UAS). We investigate the accuracy of mapping C. scoparius in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh-density point cloud data (>1,000 pts/m2). Presence–absence of 12 shrub or tree genera was recorded across a 6.7 ha seminatural grassland area in Denmark. Furthermore, 10 individuals of C. scoparius were harvested for biomass measurements. With a UAS LiDAR system, we collected ultrahigh-density spatial data across the area in October 2017 (leaf-on) and April 2018 (leaf-off). We utilized a 3D point-based classification to distinguish shrub genera based on their structural appearance (i.e., density, light penetration, and surface roughness).
  3. From the identified C. scoparius individuals, we related different volume metrics (mean, max, and range) to measured biomass and quantified spatial variation in biomass change from 2017 to 2018. We obtained overall classification accuracies above 86% from point clouds of both seasons. Maximum volume explained 77.4% of the variation in biomass.
  4. The spatial patterns revealed landscape-scale variation in biomass change between autumn 2017 and spring 2018, with a notable decrease in some areas. Further studies are needed to disentangle the causes of the observed decrease, for example, recent winter grazing and/or frost events.
  5. Synthesis and applications: We present a workflow for processing ultrahigh-density spatial data obtained from a UAS LiDAR system to detect change in C. scoparius. We demonstrate that UAS LiDAR is a promising tool to map and monitor grassland shrub dynamics at the landscape scale with the accuracy needed for effective nature management. It is a new tool for standardized and nonbiased evaluation of management activities initiated to prevent shrub encroachment.

Item Details

Item Type:Refereed Article
Keywords:biomass, grassland dynamics, remote sensing, scotch broom, shrub encroachment, UAS LiDAR
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Assessment and management of terrestrial ecosystems
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:148019
Year Published:2020
Web of Science® Times Cited:12
Deposited By:Geography and Spatial Science
Deposited On:2021-11-30
Last Modified:2022-01-18
Downloads:2 View Download Statistics

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