Publications

This dataset provides a standardized collection of rasterized Light Detection And Ranging (LiDAR) metrics in GeoTIFF format, derived from country-wide airborne laser scanning (ALS) data across seven … demonstration sites in five European countries: Mols Bjerge National Park (Denmark), Reserve Naturelle Nationale du Bagnas (France), Oostvaardersplassen (Netherlands), Salisbury Plain (United Kingdom), Knepp Estate (United Kingdom), Monks Wood (United Kingdom), and the island of Comino (Malta). The sites range in areal size from 0.08 km2 to 54 km2 and include habitat types such as forests, broadleaf and conifer woodlands, small plantations, dry and wet grasslands, marshes, reedbeds, arable fields, farmland, scrublands and mediterranean garigue. A total of 35 LiDAR metrics were calculated, of which 28 represent vegetation structural attributes. These include vegetation height (seven metrics), vegetation cover (fourteen metrics), and vegetation vertical variability (seven metrics). Additionally, seven metrics describe point density (one metric), eigenvalues (three metrics), and normal vectors (three metrics). The rasterized LiDAR metrics have a spatial resolution of 10 m, with coverage and extent defined by shapefiles corresponding to each demonstration site. The raw ALS point clouds were clipped to the site boundaries and processed with the 'Laserfarm' workflow, a standardized computational workflow that includes modular pipelines for re-tiling, normalization, feature extraction, and rasterization. Laserfarm employs the feature extraction module of the open-source ‘Laserchicken’ software to compute the LiDAR metrics. The workflow was implemented using the IT services of the Dutch national facility for information and communication technology, SURF. The clipped LiDAR point clouds are available through a public repository, except for the LiDAR point clouds from Comino, Malta, which are not publicly available. The 35 rasterized LiDAR metrics (GeoTIFF files, 10 m resolution) from all sites, including Comino, as well as the corresponding site boundary shapefiles (geospatial vector format), are provided in a Zenodo repository. Additionally, the Jupyter Notebooks with Python code for executing the Laserfarm workflow are available to facilitate reproducibility and further computational applications. Users should note that the rasterized LiDAR metrics may contain zero or NA values, particularly over water surfaces, with the pulse penetration ratio metric potentially indicating false high vegetation cover over water. Users may reclassify or mask areas with zero values accordingly. Some pixels exhibit abnormal vegetation height values, which can be filtered before analysis. Certain striping patterns, likely resulting from overlapping flight lines and increased point density, are present in some metrics, though their overall impact appears minimal. This dataset enables diverse applications, including canopy height measurements, mapping of hedgerows, treelines, and forest patches, as well as characterizing vegetation density, vertical stratification, and habitat openness. It supports landscape-scale habitat analysis and contributes to the standardization of vegetation metrics from ALS data for site-specific ecological monitoring (e.g., Natura 2000). Moreover, the dataset demonstrates the automated execution of LiDAR data processing workflows, which is crucial for establishing a transnational and multi-site biodiversity and ecosystem observation network.

Indicators of habitat condition are essential for tracking conservation progress, but measuring biotic, abiotic and landscape characteristics at fine resolution over large spatial extents remains … challenging. In this viewpoint article, we provide a comprehensive synthesis of the challenges and solutions for consistently measuring and monitoring habitat condition with remote sensing using airborne Light Detection and Ranging (LiDAR) and affordable Unmanned Aerial Vehicles (UAVs) over multiple sites and transnational or continental extents. Key challenges include variability in sensor characteristics and survey designs, non-transparent pre-processing workflows, heterogeneous and complex data, issues with the robustness of metrics and indices, limited model generalizability and transferability across sites, and difficulties in handling big data, such as managing large volumes and utilizing parallel or distributed computing. We suggest that a collaborative cloud virtual research environment (VRE) for habitat condition research and monitoring could provide solutions, including tools for data discovery, access, and data standardization, as well as geospatial processing workflows for airborne LiDAR and UAV data. A VRE would also improve data management, metadata standardization, workflow reproducibility, and transferability of structure-from-motion algorithms and machine learning models such as random forests and convolutional neural networks. Along with best practices for data collection and adopting FAIR (findability, accessibility, interoperability, reusability) principles and open science practices, a VRE could enable more consistent and transparent data processing and metric retrieval, e.g., for Natura 2000 habitats. Ultimately, these improvements would support the development of more reliable habitat condition indicators, helping prevent habitat degradation and promoting the sustainable use of natural resources.