Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning

Abstract

Mapping 3D vegetation structure in wetlands is important for conservation and monitoring. Openly accessible country‐wide Airborne Laser Scanning (ALS) data—using light detection and ranging (lidar) technology—are increasingly becoming available and allow us to quantify 3D vegetation structures at fine resolution and across broad spatial extents. Here, we develop a new, open‐source workflow for classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide ALS data. We developed a case study in the Netherlands with a workflow consisting of four routines: (1) pre‐processing of ALS data, (2) calculation of lidar metrics (i.e. 31 features representing cover, 3D shape, vertical variability, horizontal variability and height of vegetation as well as microtopography), (3) assessing feature importance of lidar metrics for classifying wetland habitats, and (4) applying a Random Forest algorithm for mapping and prediction. We used an expert‐based vegetation map for annotation and generated 100, 500 and 1000 annotation points for each class. Using a three‐level hierarchical approach, we differentiated at level 1 planar surfaces (e.g. roads and agricultural fields) from wetland vegetation with 82% mean overall accuracy, using predominantly height and horizontal variability metrics. At level 2, we classified wetland vegetation into four land cover types (forest, grassland, reedbeds, shrubs) with 71% mean overall accuracy, using lidar metrics related to vegetation height and horizontal and vertical variability. At level 3, we differentiated two types of land reed as well as water reed with 78% mean overall accuracy, using predominantly vertical variability metrics. Our results demonstrate that lidar metrics (related to vegetation height, cover, vertical and horizontal variability) derived from country‐wide ALS data can differentiate land cover types and habitats within wetlands at high resolution. Given appropriate annotation data, our workflow can be up‐scaled to a country‐wide extent to allow the comprehensive mapping and monitoring of wetlands at national scales.

Identifying fine‐scale habitat preferences of threatened butterflies using airborne laser scanning

“…As most LiDAR studies have focussed on forests and woody habitats (Bakx et al., 2019; Davies & Asner, 2014), it remains open to what extent LiDAR can capture vegetation structure of low‐stature habitats such as grasslands, dunes and wetlands. Some previous studies show promising results for measuring 3D vegetation structure in grasslands and wetlands (e.g., Alexander et al., 2015; Koma et al., 2020; Zlinszky et al., 2014). However, country‐wide LiDAR surveys are often conducted in the leaf‐off season to optimize terrain mapping (Reutebuch et al., 2005).…”

Niche separation of wetland birds revealed from airborne laser scanning

“…into metrics which statistically aggregate the 3D point cloud information within raster cells (Davies and Asner 2014, Bakx et al 2019). LiDAR metrics can then be used to map animal habitats (Lucas et al 2019, Koma et al 2020) or to model the geographical distribution of animals such as birds, mammals and invertebrates (Zellweger et al 2013, 2014, Bakx et al 2019). Most applications have shown that the distribution and abundance of birds and other taxa are related to the vertical and horizontal heterogeneity of the vegetation as measured by various LiDAR metrics (Davies and Asner 2014).…”

“…However, recent studies have shown that ALS has also the potential to be used in non‐forested habitats such as wetlands, e.g. for quantifying vegetation height (Hladik and Alber 2012, Luo et al 2015, Nie et al 2018), for mapping the density and biomass of reed beds (Corti Meneses et al 2017, Luo et al 2017), or for classifying wetland‐related habitat types (Koma et al 2020). This is remarkable because wetland habitats such as marshes, reedbeds, swamps or peatlands predominantly consist of low vegetation which limits the detection of vegetation structure with LiDAR.…”

Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds

“…Moreover, a high proportion of swamp derived from the land cover map (landcover_propswamp) indicated a low probability of occurrence of that species because it tends to avoid swamp vegetation for breeding (Cramp, 1992). The vegetation cover metric derived from LiDAR (lidar_C_ppr) indicates the density of reed, where the Savi's warbler prefers more open reed vegetation such as land reed (Koma, Seijmonsbergen, et al, 2021). Overall, the results of the Savi's warbler encourage the synergetic use of different RS products for modelling the habitat suitability of wetland birds.…”

“…Close to the water edge, reed vegetation (water reed) grows taller and thicker than in reedbeds that are located in the drier parts of a wetland (i.e. land reed) (Graveland, 1998; Koma, Seijmonsbergen, et al, 2021).…”