Shrub encroachment in semi-natural 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 ultra-high density point-cloud data (>1000 pts/m2). We utilized a 3D point-based classification to distinguish shrub genera based on their structural appearance (i.e. density, light penetration and surface roughness) in a semi-natural grassland in Denmark. 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, e.g., recent winter grazing and/or frost events.
We present a workflow for processing ultra-high 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 non-biased evaluation of management activities initiated to prevent shrub encroachment.
- All data are associated to the processing of UAS-LiDAR point clouds in Mols with the following dates and dataset ID’s:
- Groundtruth GNSS points are available as point shapefiles
- Biomass measurements from harvested Cytisus scoparius individuals are available as spreadsheet.
- Associated scripts for the Opals processing are available as batch files
Processing: The LiDAR data were processed until step 1 following the workflow in Fig. 1 (described in Madsen et al. (2019)). Hence the data uploaded as .las files are already post-processed, georeferenced and filtered for noisy points.
The full data overview are presented in the document "MolsLiDAR17-18_processed.doc" and if necessary, the raw data or other levels of processing can be made available upon request.