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/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). 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.

Riparian Vegetation Density Mapping of an Extremely Densely Vegetated Confined Floodplain

“…The presented method could be used to accurately identify land cover types (e.g., forest, meadow) and their sub-classes (e.g., forest types). In our case, the field validation (at 72 points) reflects 83% classification accuracy, which is higher than the accuracy of the classification made by Saarinen et al [54] or Michez et al [55], and slightly lower than obtained by Madsen et al [56].…”

“…For example, in floodplain forests, Saarinen et al [54] achieved an accuracy of 72.6% when classifying mobile laser scanner data, while Michez et al [55] obtained an accuracy of 79.5-84.1% when classifying drone-derived point clouds. Madsen et al [56] achieved 86.9-95.2% classification accuracy when classifying aerial LiDAR data in a bushy area. The classification accuracy of open surfaces (75%) is the lowest because some vegetated plots were also defined as open surfaces.…”

“…The invasive Amorpha fruticosa bushes form almost homogeneous stands on fallow lands or in the area of forest clearances. The Amorpha is light-demanding and overgrows under favorable conditions [56]. The average height of the Amorpha thickets is only 4-5 m (Figure 9).…”

Presence of a dominant native shrub is associated with minor shifts in the function and composition of grassland communities in a northern savannah

“…Hesperostipa curtesita is another dominant grass in this system ( Lamb 2008 ), yet showed a significant negative association with wolf-willow. While many studies report richness effects associated with shrub presence ( Anthelme et al 2007 ; Price and Morgan 2008 ; Ratajczak et al 2012 ; Madsen et al 2020 ), our study shows that important community changes can occur even in the absence of a richness effect. Future studies should consider incorporating architectural or abundance measures (like our wolf-willow score) as well as looking at other components of biodiversity.…”

Woody plant encroachment enhanced global vegetation greening and ecosystem water‐use efficiency

“…Combined with field measurements and machine learning algorithms, high-resolution (≤ 30 m) satellite imagery and aerial photographs are widely used to observe small-scale woody expansion and can obtain accurate, detailed local information, such as encroaching species and plant height (Madsen et al, 2020;Marston et al, 2017); however, the limited spatial coverage and the lack of effective upscaling methods have hindered the application in large-scale research. Yang et al (2020) found that 250-500 m resolutions were suitable for the observation of potential woody vegetation coverage in a savanna ecosystem, suggesting that medium-resolution remote sensing might help to detect where WPE is distributed globally.…”