Towards consistently measuring and monitoring habitat condition with airborne laser scanning and unmanned aerial vehicles

Kissling, W. Daniel; Shi, Yifang; Wang, Jinhu; Walicka, Agata; George, Charles; Moeslund, Jesper E.; Gerard, France. 2024 Towards consistently measuring and monitoring habitat condition with airborne laser scanning and unmanned aerial vehicles. Ecological Indicators, 169, 112970. 19, pp. 10.1016/j.ecolind.2024.112970

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.ecolind.2024.112970
UKCEH and CEH Sections/Science Areas:	Hydro-climate Risks (Science Area 2017-)
ISSN:	1470-160X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	biodiversity monitoring, conservation management, deep learning, drone remote sensing, geospatial data, photogrammetry pipeline, vegetation mapping
NORA Subject Terms:	Ecology and Environment Electronics, Engineering and Technology Data and Information
Related URLs:	Dataset
Date made live:	11 Dec 2024 09:51 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538540

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