Abstract
Abstract. Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as light detection and ranging (lidar). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote sensing knowledge. We processed Denmark's publicly available national airborne laser scanning (ALS) data set acquired in 2014/15, together with the accompanying elevation model, to compute 70 rasterised descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than 40 000 km2 covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small (∼94 GB, compressed 16.8 GB), and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark's national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterised data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond. The full data set is available on Zenodo: https://doi.org/10.5281/zenodo.4756556 (Assmann et al., 2021); a 5 MB teaser subset is also available: https://doi.org/10.5281/zenodo.6035188 (Assmann et al., 2022a).
Highlights
Over the last decades, airborne laser scanning (ALS) has become an established data source for providing fine-resolution measures of terrain and vegetation structure in ecological research (Moeslund et al, 2019; Guo et al, 2017; Zellweger et al, 2016)
We believe that to realise the full potential of ALS-derived data such as EcoDes-DK15 these data sets are ideally combined with other data sources including climate, field data and remote sensing observations
Finegrain optical imagery could provide proxies for horizontal vegetation structure in grasslands where the vegetation is too small to be captured by the Danish elevation model (DHM)/Point-cloud density (e.g. Malmstrom et al, 2017; Pazúr et al, 2021), and satellitederived time series can provide unique temporal perspectives that describe parameters of seasonality (e.g. Boelman et al, 2016) and the historical context on disturbances and landcover change not captured in the single time-point ALS data (e.g. Senf et al, 2017; Pekel et al, 2016)
Summary
Airborne laser scanning (ALS) has become an established data source for providing fine-resolution measures of terrain and vegetation structure in ecological research (Moeslund et al, 2019; Guo et al, 2017; Zellweger et al, 2016). Numerous biodiversity studies have successfully deployed ALS to study organisms like plants (Mao et al, 2018; Lopatin et al, 2016; Zellweger et al, 2016; Ceballos et al, 2015; Moeslund et al, 2013; Leutner et al, 2012), fungi (Peura et al, 2016; Thers et al, 2017), bryophytes, lichens (Moeslund et al, 2019), mammals (Tweedy et al, 2019; Froidevaux et al, 2016) and birds (see Bakx et al, 2019, for a comprehensive review) both in open landscapes and in forests These studies have all emphasised the value of ALS for representing fine-scale (∼ 10 m resolution) terrain or vegetation structural variation important to local biodiversity patterns. We hope that ease of access and thorough documentation of the EcoDes-DK15 data set will encourage uptake and facilitate the development of future versions of similar data sets in Denmark and beyond
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