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Detecting shrub encroachment in seminatural grasslands using UAS LiDAR.

Bjarke Madsen¹, Urs A Treier¹, András Zlinszky^1,2

¹Section for Ecoinformatics & Biodiversity Center for Biodiversity Dynamics in a Changing World Department of Biology Aarhus University Aarhus C Denmark.

Ecology and Evolution

|June 20, 2020

Summary

Unmanned Aircraft System (UAS) Light Detection and Ranging (LiDAR) effectively maps shrub encroachment in grasslands. This technology accurately measures structural changes, aiding biodiversity management and evaluating conservation efforts.

Keywords:

UAS LiDAR biomass grassland dynamics remote sensing scotch broom shrub encroachment

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Published on: September 12, 2017

Area of Science:

Ecology
Remote Sensing
Conservation Biology

Background:

Shrub encroachment in seminatural grasslands poses a significant threat to local biodiversity.
Dense vegetation, such as *Cytisus scoparius*, can homogenize landscapes and negatively impact plant diversity.
Effective management strategies require accurate methods for detecting structural changes like biomass variations.

Purpose of the Study:

To investigate the accuracy of mapping *Cytisus scoparius* in three dimensions (3D) using Unmanned Aircraft System (UAS) Light Detection and Ranging (LiDAR) data.
To assess the capability of ultrahigh-density point cloud data in deriving structural change metrics, specifically biomass.
To evaluate UAS LiDAR as a tool for monitoring shrub dynamics and assessing the effectiveness of management interventions in grasslands.

Main Methods:

Combined traditional field measurements with novel UAS LiDAR observations over a 6.7 ha seminatural grassland.
Collected ultrahigh-density point cloud data (over 1,000 pts/m²) in both leaf-on (October 2017) and leaf-off (April 2018) seasons.
Utilized 3D point-based classification to distinguish shrub genera and related LiDAR-derived volume metrics to measured biomass.

Main Results:

Achieved overall classification accuracies exceeding 86% for shrub mapping from point cloud data across both seasons.
Found that maximum volume metrics explained up to 77.4% of the variation in *C. scoparius* biomass.
Quantified landscape-scale variations in biomass change between autumn 2017 and spring 2018, indicating a decrease in certain areas.

Conclusions:

UAS LiDAR is a highly promising tool for mapping and monitoring grassland shrub dynamics at the landscape scale.
The developed workflow enables accurate, standardized, and non-biased evaluation of management activities aimed at controlling shrub encroachment.
Further research is needed to determine the specific drivers (e.g., grazing, frost) of observed biomass changes.