Madsen Bjarke, Treier Urs A, Zlinszky András, Lucieer Arko, Normand Signe
Section for Ecoinformatics & Biodiversity Center for Biodiversity Dynamics in a Changing World Department of Biology Aarhus University Aarhus C Denmark.
MTA Centre for Ecological Research Tihany Hungary.
Ecol Evol. 2020 May 6;10(11):4876-4902. doi: 10.1002/ece3.6240. eCollection 2020 Jun.
Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of 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 in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh-density point cloud data (>1,000 pts/m). Presence-absence of 12 shrub or tree genera was recorded across a 6.7 ha seminatural grassland area in Denmark. Furthermore, 10 individuals of 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 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. We present a workflow for processing ultrahigh-density spatial data obtained from a UAS LiDAR system to detect change in . 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.
除非采取管理措施降低灌木密度，否则半天然草原中的灌木入侵会威胁当地生物多样性。茂密的植被会使景观同质化，对当地植物多样性产生负面影响。检测结构变化（如生物量）对于评估入侵的负面影响至关重要。因此，探索新的监测工具来完成这项任务对于有效捕捉变化和评估管理活动非常重要。本研究将传统的实地测量与来自无人机系统（UAS）的新型激光雷达（LiDAR）观测相结合。我们研究了从超高密度点云数据（>1,000个点/平方米）得出的三维（3D）映射精度和结构变化指标（即生物量）。在丹麦一个6.7公顷的半天然草原区域记录了12种灌木或乔木属的存在与否。此外，采集了10株[具体植物名称未给出]的样本用于生物量测量。使用无人机激光雷达系统，我们在2017年10月（有叶）和2018年4月（无叶）收集了该区域的超高密度空间数据。我们利用基于3D点的分类方法，根据灌木属的结构外观（即密度、光穿透率和表面粗糙度）来区分它们。从识别出的[具体植物名称未给出]个体中，我们将不同的体积指标（平均值、最大值和范围）与测量的生物量相关联，并量化了2017年至2018年生物量变化的空间差异。我们从两个季节的点云数据中获得了总体分类准确率超过86%的结果。最大体积解释了生物量变化的77.4%。空间模式揭示了2017年秋季至2018年春季生物量变化的景观尺度差异，一些区域有显著下降。需要进一步研究来厘清观察到的下降原因，例如近期的冬季放牧和/或霜冻事件。我们提出了一个处理从无人机激光雷达系统获得的超高密度空间数据以检测[具体植物名称未给出]变化的工作流程。我们证明，无人机激光雷达是一种有前景的工具，能够以有效自然管理所需的精度在景观尺度上绘制和监测草原灌木动态。它是一种用于对为防止灌木入侵而启动的管理活动进行标准化和无偏评估的新工具。
