Detecting shrub encroachment in seminatural grasslands using UAS LiDAR.

Abstract

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.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/m 2 ). 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).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.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. 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.

Plant phenotyping relevance

UAS LiDARによる植物群落・低木の3D構造とバイオマスを推定する処理ワークフローを開発・精度評価しており、植物形質取得が研究の中心である。

Code and data availability

The paper's UAS LiDAR point clouds, field presence-absence records, and harvested shrub biomass measurements are explicitly deposited in the Dryad Data Repository with a public DOI link, making the paper-specific phenotyping data directly actionable.