Publications

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6 Publications visible to you, out of a total of 6

Abstract (Expand)

The third Dutch national airborne laser scanning flight campaign (AHN3, Actueel Hoogtebestand Nederland) conducted between 2014 and 2019 during the leaf-off season (October–April) across the whole Netherlands provides a free and open-access, country-wide dataset with ∼700 billion points and a point density of ∼10(–20) points/m2. The AHN3 point cloud was obtained with Light Detection And Ranging (LiDAR) technology and contains for each point the x, y, z coordinates and additional characteristics (e.g. return number, intensity value, scan angle rank and GPS time). Moreover, the point cloud has been pre-processed by ‘Rijkswaterstraat’ (the executive agency of the Dutch Ministry of Infrastructure and Water Management), comes with a Digital Terrain Model (DTM) and a Digital Surface Model (DSM), and is delivered with a pre-classification of each point into one of six classes (0: Never Classified, 1: Unclassified, 2: Ground, 6: Building, 9: Water, 26: Reserved [bridges etc.]). However, no detailed information on vegetation structure is available from the AHN3 point cloud. We processed the AHN3 point cloud (∼16 TB uncompressed data volume) into 10 m resolution raster layers of ecosystem structure at a national extent, using a novel high-throughput workflow called ‘Laserfarm’ and a cluster of virtual machines with fast central processing units, high memory nodes and associated big data storage for managing the large amount of files. The raster layers (available as GeoTIFF files) capture 25 LiDAR metrics of vegetation structure, including ecosystem height (e.g. 95th percentiles of normalized z), ecosystem cover (e.g. pulse penetration ratio, canopy cover, and density of vegetation points within defined height layers), and ecosystem structural complexity (e.g. skewness and variability of vertical vegetation point distribution). The raster layers make use of the Dutch projected coordinate system (EPSG:28992 Amersfoort / RD New), are each ∼1 GB in size, and can be readily used by ecologists in a geographic information system (GIS) or analytical open-source software such as R and Python. Even though the class ‘1: Unclassified’ mainly includes vegetation points, other objects such as cars, fences, and boats can also be present in this class, introducing potential biases in the derived data products. We therefore validated the raster layers of ecosystem structure using >180,000 hand-labelled LiDAR points in 100 randomly selected sample plots (10 m × 10 m each) across the Netherlands. Besides vegetation, objects such as boats, fences, and cars were identified in the sampled plots. However, the misclassification rate of vegetation points (i.e. non-vegetation points that were assumed to be vegetation) was low (∼0.05) and the accuracy of the 25 LiDAR metrics derived from the AHN3 point cloud was high (∼90%). To minimize existing inaccuracies in this country-wide data product (e.g. ships on water bodies, chimneys on roofs, or cars on roads that might be incorrectly used as vegetation points), we provide an additional mask that captures water bodies, buildings and roads generated from the Dutch cadaster dataset. This newly generated country-wide ecosystem structure data product provides new opportunities for ecology and biodiversity science, e.g. for mapping the 3D vegetation structure of a variety of ecosystems or for modelling biodiversity, species distributions, abundance and ecological niches of animals and their habitats.

Authors: W. Daniel Kissling, Yifang Shi, Zsófia Koma, Christiaan Meijer, Ou Ku, Francesco Nattino, Arie C. Seijmonsbergen, Meiert W. Grootes

Date Published: 1st Feb 2023

Publication Type: Journal

Abstract (Expand)

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.

Authors: W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi

Date Published: 1st Jun 2025

Publication Type: Journal

Abstract (Expand)

Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields.

Authors: C. Meijer, M.W. Grootes, Z. Koma, Y. Dzigan, R. Gonçalves, B. Andela, G. van den Oord, E. Ranguelova, N. Renaud, W.D. Kissling

Date Published: 1st Jul 2020

Publication Type: Journal

Abstract (Expand)

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km2 with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.

Authors: W. Daniel Kissling, Yifang Shi, Zsófia Koma, Christiaan Meijer, Ou Ku, Francesco Nattino, Arie C. Seijmonsbergen, Meiert W. Grootes

Date Published: 1st Dec 2022

Publication Type: Journal

Abstract (Expand)

EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.

Authors: Toke Høye, Tom August, Mario V Balzan, Koos Biesmeijer, Pierre Bonnet, Tom Breeze, Christophe Dominik, France Gerard, Alexis Joly, Vincent Kalkman, W. Daniel Kissling, Teodor Metodiev, Jesper Moeslund, Simon Potts, David Roy, Oliver Schweiger, Deepa Senapathi, Josef Settele, Pavel Stoev, Dan Stowell

Date Published: 7th Dec 2023

Publication Type: Journal

Abstract (Expand)

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.

Authors: W. Daniel Kissling, Yifang Shi, Jinhu Wang, Agata Walicka, Charles George, Jesper E. Moeslund, France Gerard

Date Published: 1st Dec 2024

Publication Type: Journal

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