Towards consistently measuring and monitoring habitat condition with airborne laser scanning and unmanned aerial vehicles
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BiBTeXIndicators 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.
SEEK ID: https://workflowhub.eu/publications/39
DOI: 10.1016/j.ecolind.2024.112970
Teams: Laserfarm applications to European demonstration sites
Publication type: Journal
Journal: Ecological Indicators
Citation: Ecological Indicators 169:112970
Date Published: 1st Dec 2024
Registered Mode: by DOI
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Created: 7th Feb 2025 at 08:44
Last updated: 24th Apr 2025 at 15:53
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Teams: Snakemake-Workflows, IBISBA Workflows
Organizations: CMB genomics
https://orcid.org/0009-0005-2800-4112
Ecologist with an interest in the geospatial analysis and modelling of biodiversity and ecosystems (https://www.danielkissling.de/). Working as an Associate Professor of Quantitative Biodiversity at the Institute for Biodiversity and Ecosystem Dynamics (IBED) of the University of Amsterdam (UvA), The Netherlands.
Teams: LiDAR, Laserfarm applications to European demonstration sites
Organizations: Unspecified, University of Amsterdam
Teams: Laserfarm applications to European demonstration sites
Web page: Not specified
Examples of applying the Laserfarm workflow to calculate LiDAR vegetation metrics across European demonstration sites
Space: EcoLiDAR - ecological applications of LiDAR point clouds
Public web page: https://laserfarm.readthedocs.io/en/latest/
Laserfarm (https://doi.org/10.1016/j.ecoinf.2022.101836) is a high-throughput workflow for generating geospatial data products of ecosystem structure using LiDAR point clouds from national or regional airborne laser scanning (ALS) surveys. The workflow example here shows the application of the Laserfarm workflow to the 7.5 km2 large Reserve Naturelle Nationale du Bagnas ('Bagnas') in France (3.514360 E, 43.314332 N). The work has been performed in the context of the EU project MAMBO (Modern ...
Type: Jupyter
Creators: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi
Submitter: W. Daniel Kissling
Laserfarm (https://doi.org/10.1016/j.ecoinf.2022.101836) is a high-throughput workflow for generating geospatial data products of ecosystem structure using LiDAR point clouds from national or regional airborne laser scanning (ALS) surveys. The workflow example here shows the application of the Laserfarm workflow to the 3.5 km2 large island of Comino in Malta (36.0113 E, 14.3362 N). The work has been performed in the context of the EU project MAMBO (Modern Approaches to the Monitoring of Biоdiversity, ...
Type: Jupyter
Creators: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi
Submitter: W. Daniel Kissling
Laserfarm (https://doi.org/10.1016/j.ecoinf.2022.101836) is a high-throughput workflow for generating geospatial data products of ecosystem structure using LiDAR point clouds from national or regional airborne laser scanning (ALS) surveys. The workflow example here shows the application of the Laserfarm workflow to the 5.55 km2 large Knepp Estate in the United Kingdom (0.3758 W, 50.969859 N). The work has been performed in the context of the EU project MAMBO (Modern Approaches to the Monitoring ...
Type: Jupyter
Creators: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi
Submitter: W. Daniel Kissling
Laserfarm (https://doi.org/10.1016/j.ecoinf.2022.101836) is a high-throughput workflow for generating geospatial data products of ecosystem structure using LiDAR point clouds from national or regional airborne laser scanning (ALS) surveys. The workflow example here shows the application of the Laserfarm workflow to the 1.29 km2 large Mols Bjerge National Park in Denmark (10.478393 E, 56.289050 N). The work has been performed in the context of the EU project MAMBO (Modern Approaches to the Monitoring ...
Type: Jupyter
Creators: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi
Submitter: W. Daniel Kissling
Laserfarm (https://doi.org/10.1016/j.ecoinf.2022.101836) is a high-throughput workflow for generating geospatial data products of ecosystem structure using LiDAR point clouds from national or regional airborne laser scanning (ALS) surveys. The workflow example here shows the application of the Laserfarm workflow to the 0.08 km2 large Monks Wood in the United Kingdom (10.478393 E, 56.289050 N). The work has been performed in the context of the EU project MAMBO (Modern Approaches to the Monitoring ...
Type: Jupyter
Creators: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi
Submitter: W. Daniel Kissling
Laserfarm (https://doi.org/10.1016/j.ecoinf.2022.101836) is a high-throughput workflow for generating geospatial data products of ecosystem structure using LiDAR point clouds from national or regional airborne laser scanning (ALS) surveys. The workflow example here shows the application of the Laserfarm workflow to the 54 km2 large Oostvaardersplassen in the Netherlands (5.418842 E, 52.456870 N). The work has been performed in the context of the EU project MAMBO (Modern Approaches to the Monitoring ...
Type: Jupyter
Creators: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi
Submitter: W. Daniel Kissling
Laserfarm (https://doi.org/10.1016/j.ecoinf.2022.101836) is a high-throughput workflow for generating geospatial data products of ecosystem structure using LiDAR point clouds from national or regional airborne laser scanning (ALS) surveys. The workflow example here shows the application of the Laserfarm workflow to the 7.95 km2 large Salisbury Plain in the United Kingdom (1.866189 W, 51.220814 N). The work has been performed in the context of the EU project MAMBO (Modern Approaches to the Monitoring ...
Type: Jupyter
Creators: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi
Submitter: W. Daniel Kissling
