@article{MTMT:34562370,
	title = {Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach},
	url = {https://m2.mtmt.hu/api/publication/34562370},
	author = {Torresani, M. and Rocchini, D. and Ceola, G. and de, Vries J.P.R. and Feilhauer, H. and Moudrý, V. and Bartholomeus, H. and Perrone, M. and Anderle, M. and Gamper, H.A. and Chieffallo, L. and Guatelli, E. and Gatti, R.C. and Kleijn, D.},
	doi = {10.1038/s41598-023-50308-9},
	journal-iso = {SCI REP},
	journal = {SCIENTIFIC REPORTS},
	volume = {14},
	unique-id = {34562370},
	issn = {2045-2322},
	year = {2024},
	eissn = {2045-2322}
}

@article{MTMT:34042004,
	title = {Where are we now with European forest multi-taxon biodiversity and where can we head to?},
	url = {https://m2.mtmt.hu/api/publication/34042004},
	author = {Burrascano, Sabina and Chianucci, Francesco and Trentanovi, Giovanni and Kepfer-Rojas, Sebastian and Sitzia, Tommaso and Tinya, Flóra and Doerfler, Inken and Paillet, Yoan and Nagel, Thomas Andrew and Mitic, Bozena and Morillas, Lourdes and Munzi, Silvana and Van der Sluis, Theo and Alterio, Edoardo and Balducci, Lorenzo and de Andrade, Rafael Barreto and Bouget, Christophe and Giordani, Paolo and Lachat, Thibault and Matosevic, Dinka and Napoleone, Francesca and Nascimbene, Juri and Paniccia, Chiara and Roth, Nicolas and Aszalós, Réka and Brazaitis, Gediminas and Cutini, Andrea and D'Andrea, Ettore and De Smedt, Pallieter and Heilmann-Clausen, Jacob and Janssen, Philippe and Kozák, Daniel and Mårell, Anders and Mikoláš, Martin and Nordén, Björn and Matula, Radim and Schall, Peter and Svoboda, Miroslav and Ujhazyova, Mariana and Vandekerkhove, Kris and Wohlwend, Michael and Xystrakis, Fotios and Aleffi, Michele and Ammer, Christian and Archaux, Frederic and Asbeck, Thomas and Avtzis, Dimitrios and Ayasse, Manfred and Bagella, Simonetta and Balestrieri, Rosario and Barbati, Anna and Basile, Marco and Bergamini, Ariel and Bertini, Giada and Biscaccianti, Alessandro Bruno and Boch, Steffen and Bölöni, János and Bombi, Pierluigi and Boscardin, Yves and Brunialti, Giorgio and Bruun, Hans Henrik and Buscot, François and Byriel, David Bille and Campagnaro, Thomas and Campanaro, Alessandro and Chauvat, Matthieu and Ciach, Michał and Čiliak, Marek and Cistrone, Luca and Pereira, Joao Manuel Cordeiro and Daniel, Rolf and De Cinti, Bruno and De Filippo, Gabriele and Dekoninck, Wouter and Di Salvatore, Umberto and Dumas, Yann and Elek, Zoltán and Ferretti, Fabrizio and Fotakis, Dimitrios and Frank, Tamás and Frey, Julian and Giancola, Carmen and Gomoryová, Erika and Gosselin, Marion and Gosselin, Frederic and Gossner, Martin M. and Götmark, Frank and Haeler, Elena and Hansen, Aslak Kappel and Hertzog, Lionel and Hofmeister, Jeňýk and Hošek, Jan and Johannsen, Vivian Kvist and Justensen, Mathias Just and Korboulewsky, Nathalie and Kovács, Bence and Lakatos, Ferenc and Landivar, Carlos Miguel and Lens, Luc and Lingua, Emanuele and Lombardi, Fabio and Máliš, František and Marchino, Luca and Marozas, Vitas and Matteucci, Giorgio and Mattioli, Walter and Møller, Peter Friis and Müller, Jörg and Németh, Csaba and Ónodi, Gábor and Parisi, Francesco and Perot, Thomas and Perret, Sandrine and Persiani, Anna Maria and Portaccio, Alessia and Posillico, Mario and Preikša, Žydrūnas and Rahbek, Carsten and Rappa, Nolan James and Ravera, Sonia and Romano, Antonio and Samu, Ferenc and Scheidegger, Christoph and Schmidt, Inger Kappel and Schwegmann, Sebastian and Sicuriello, Flavia and Spinu, Andreea Petronela and Spyroglou, Gavriil and Stillhard, Jonas and Topalidou, Eleni and Tøttrup, Anders P. and Ujházy, Karol and Veres, Katalin and Verheyen, Kris and Weisser, Wolfgang W. and Zapponi, Livia and Ódor, Péter},
	doi = {10.1016/j.biocon.2023.110176},
	journal-iso = {BIOL CONSERV},
	journal = {BIOLOGICAL CONSERVATION},
	volume = {284},
	unique-id = {34042004},
	issn = {0006-3207},
	year = {2023},
	eissn = {1873-2917},
	orcid-numbers = {Tinya, Flóra/0000-0002-4271-9676; Kovács, Bence/0000-0002-8045-8489; Ódor, Péter/0000-0003-1729-8897}
}

@article{MTMT:34330027,
	title = {Vegetation structure from LiDAR explains the local richness of birds across Denmark},
	url = {https://m2.mtmt.hu/api/publication/34330027},
	author = {Davison, Charles W. and Assmann, Jakob J. and Normand, Signe and Rahbek, Carsten and Morueta-Holme, Naia},
	doi = {10.1111/1365-2656.13945},
	journal-iso = {J ANIM ECOL},
	journal = {JOURNAL OF ANIMAL ECOLOGY},
	volume = {92},
	unique-id = {34330027},
	issn = {0021-8790},
	abstract = {Classic ecological research into the determinants of biodiversity patterns emphasised the important role of three-dimensional (3D) vegetation heterogeneity. Yet, measuring vegetation structure across large areas has historically been difficult. A growing focus on large-scale research questions has caused local vegetation heterogeneity to be overlooked compared with more readily accessible habitat metrics from, for example, land cover maps.Using newly available 3D vegetation data, we investigated the relative importance of habitat and vegetation heterogeneity for explaining patterns of bird species richness and composition across Denmark (42,394 km(2)).We used standardised, repeated point counts of birds conducted by volunteers across Denmark alongside metrics of habitat availability from land-cover maps and vegetation structure from rasterised LiDAR data (10 m resolution). We used random forest models to relate species richness to environmental features and considered trait-specific responses by grouping species by nesting behaviour, habitat preference and primary lifestyle. Finally, we evaluated the role of habitat and vegetation heterogeneity metrics in explaining local bird assemblage composition.Overall, vegetation structure was equally as important as habitat availability for explaining bird richness patterns. However, we did not find a consistent positive relationship between species richness and habitat or vegetation heterogeneity; instead, functional groups displayed individual responses to habitat features. Meanwhile, habitat availability had the strongest correlation with the patterns of bird assemblage composition.Our results show how LiDAR and land cover data complement one another to provide insights into different facets of biodiversity patterns and demonstrate the potential of combining remote sensing and structured citizen science programmes for biodiversity research. With the growing coverage of LiDAR surveys, we are witnessing a revolution of highly detailed 3D data that will allow us to integrate vegetation heterogeneity into studies at large spatial extents and advance our understanding of species' physical niches.},
	keywords = {HETEROGENEITY; remote sensing; LIDAR; vegetation structure; land cover; citizen science; habitat availability; Bird diversity},
	year = {2023},
	eissn = {1365-2656},
	pages = {1332-1344},
	orcid-numbers = {Normand, Signe/0000-0002-8782-4154; Rahbek, Carsten/0000-0003-4585-0300; Morueta-Holme, Naia/0000-0002-0776-4092}
}

@article{MTMT:34330028,
	title = {Estimation of boreal forest floor lichen cover using hyperspectral airborne and field data},
	url = {https://m2.mtmt.hu/api/publication/34330028},
	author = {Kuusinen, Nea and Hovi, Aarne and Rautiainen, Miina},
	doi = {10.14214/sf.22014},
	journal-iso = {SILVA FENN},
	journal = {SILVA FENNICA},
	volume = {57},
	unique-id = {34330028},
	issn = {0037-5330},
	abstract = {Lichens are sensitive to competition from vascular plants, intensive silviculture, pollution and reindeer and caribou grazing, and can therefore serve as indicators of environmental changes. Hyperspectral remote sensing data has been proved promising for estimation of plant diversity, but its potential for forest floor lichen cover estimation has not yet been studied. In this study, we investigated the use of hyperspectral data in estimating ground lichen cover in boreal forest stands in Finland. We acquired airborne and in situ hyperspectral data of lichen-covered forest plots, and applied multiple endmember spectral mixture analysis to estimate the fractional cover of ground lichens in these plots. Estimation of lichen cover based on in situ spectral data was very accurate (coefficient of determination (r2) 0.95, root mean square error (RMSE) 6.2). Estimation of lichen cover based on airborne data, on the other hand, was fairly good (r2 0.77, RMSE 11.7), but depended on the choice of spectral bands. When the hyperspectral data were resampled to the spectral resolution of Sentinel-2, slightly weaker results were obtained. Tree canopy cover near the flight plots was weakly related to the difference between estimated and measured lichen cover. The results also implied that the presence of dwarf shrubs could influence the lichen cover estimates.},
	keywords = {SPECTROSCOPY; remote sensing; Cladonia},
	year = {2023},
	eissn = {2242-4075}
}

@article{MTMT:33940536,
	title = {Spaceborne LiDAR for characterizing forest structure across scales in the European Alps},
	url = {https://m2.mtmt.hu/api/publication/33940536},
	author = {Mandl, Lisa and Stritih, Ana and Seidl, Rupert and Ginzler, Christian and Senf, Cornelius},
	doi = {10.1002/rse2.330},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {33940536},
	abstract = {The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height-related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability-particularly in topographically complex terrain-remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape-scale, we evaluated the ability of GEDIs sample-based approach to characterize complex mountain landscapes by comparing it to wall-to-wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision, carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R-2 from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape-level, however, the agreement between GEDI and ALS was generally high, with R-2 values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape-scale analyses in the context of ecosystem dynamics and management.},
	keywords = {remote sensing; LIDAR; ALS; Mountain forests; Forest structure; GEDI},
	year = {2023},
	eissn = {2056-3485},
	orcid-numbers = {Mandl, Lisa/0000-0002-2448-5087}
}

@article{MTMT:33883696,
	title = {Finding the Green Grass in the Haystack? Integrated National Assessment of Ecosystem Services and Condition in Hungary, in Support of Conservation and Planning},
	url = {https://m2.mtmt.hu/api/publication/33883696},
	author = {Tanács, Eszter and Vári, Ágnes and Bede-Fazekas, Ákos and Báldi, András and Csákvári, Edina and Endrédi, Anett and Fabók, Veronika and Kisné Fodor, Lívia and Kiss, Márton and Koncz, Péter and Kovács-Hostyánszki, Anikó and Mészáros, János and Pásztor, László and Rezneki, Rita and Standovár, Tibor and Zsembery, Zita and Török, Katalin},
	doi = {10.3390/su15118489},
	journal-iso = {SUSTAINABILITY-BASEL},
	journal = {SUSTAINABILITY},
	volume = {15},
	unique-id = {33883696},
	abstract = {Human well-being needs healthy ecosystems, providing multiple ecosystem services. Therefore, the assessment of ecosystems on large scales is a priority action. In Hungary, this work (MAES-HU) took place between 2016 and 2022. Twelve ecosystem services (ES) were mapped and assessed along with several ecosystem condition (EC) indicators. Their integrated spatial analysis aimed to identify patterns of ES multifunctionality, reveal relationships between EC and ES and delineate ES bundles. The results show outstanding multifunctionality of natural ecosystem types compared with the more artificial types, emphasizing the importance of natural areas in order to fulfil human needs. Native forests provide the most varied range of services, which underlines the importance of forest management to consider multiple services. There is a positive correlation between condition and multifunctionality in forests; areas in better condition (in terms of species composition and structure) provide more services at an outstanding level. ES bundles mainly reflect the major ecosystem types, topography and forest condition. Our analysis represents an example of synthesizing national MAES results with a combination of methods. Finding ES hotspots on a national scale and connecting them with an assessment of EC may help in finding optimal strategies to balance conservation targets and competing land uses.},
	year = {2023},
	eissn = {2071-1050},
	orcid-numbers = {Tanács, Eszter/0000-0003-1953-9340; Vári, Ágnes/0000-0001-5285-847X; Bede-Fazekas, Ákos/0000-0002-2905-338X; Báldi, András/0000-0001-6063-3721; Kiss, Márton/0000-0002-5621-7976; Mészáros, János/0000-0003-2604-3052; Pásztor, László/0000-0002-1605-4412; Standovár, Tibor/0000-0002-4686-3456}
}

@article{MTMT:34273429,
	title = {Assessing biodiversity using forest structure indicators based on airborne laser scanning data},
	url = {https://m2.mtmt.hu/api/publication/34273429},
	author = {Toivonen, Janne and Kangas, Annika and Maltamo, Matti and Kukkonen, Mikko and Packalen, Petteri},
	doi = {10.1016/j.foreco.2023.121376},
	journal-iso = {FOREST ECOL MANAG},
	journal = {FOREST ECOLOGY AND MANAGEMENT},
	volume = {546},
	unique-id = {34273429},
	issn = {0378-1127},
	abstract = {The role of forests in biodiversity assessment and planning is substantial as these ecosystems support approxi-mately 80% of the world's terrestrial biodiversity. Forests provide food, shelter, and nesting environments for numerous species, and deliver multiple ecosystem services. It has been widely recognised that forest vegetation structure and its complexity influence local variations in biodiversity. As forests are facing threats globally caused by human activities, there is a need to map the biodiversity of these ecosystems. The main objective of this review was to summarise the use of airborne laser scanning (ALS) data in biodiversity-related assessment of forests. We draw attention to topics related to animal ecology, structural diversity, dead wood, fragmentation and forest habitat classification. After conducting a thorough literature search, we categorised scientific articles based on their topics, which served as the basis for the section division in this paper. The majority of the research was found to be conducted in Europe and North America, only a small fraction of the study areas was located elsewhere. Topics that have received the most attention were related to animal ecology (namely richness and diversity of forest fauna), assessment of dead trees and tree species diversity measures. Not all studies used ALS data only, as it were often fused with other remote sensing data - especially with aerial or satellite images. The fusion of spectral information from optical images and the structural information provided by ALS was highly advantageous in studies where tree species were considered. Relevant ALS variables were found to be case -specific, so variables varied widely between forest biodiversity studies. We found that there was a lack of research in geographical areas and forest types other than temperate and boreal forests. Also, topics that considered functional diversity, community composition and the effect of spatial resolution at which ALS data and field information are linked, were covered to much lesser extent.},
	keywords = {Biodiversity; FRAGMENTATION; Dead wood; Species richness; species diversity; Airborne Laser Scanning; Structural diversity; Forest structure},
	year = {2023},
	eissn = {1872-7042}
}

@article{MTMT:33431405,
	title = {EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set},
	url = {https://m2.mtmt.hu/api/publication/33431405},
	author = {Assmann, Jakob J. and Moeslund, Jesper E. and Treier, Urs A. and Normand, Signe},
	doi = {10.5194/essd-14-823-2022},
	journal-iso = {EARTH SYST SCI DATA},
	journal = {EARTH SYSTEM SCIENCE DATA},
	volume = {14},
	unique-id = {33431405},
	issn = {1866-3508},
	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 km(2) covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small (similar to 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 5MB teaser subset is also available: https://doi.org/10.5281/zenodo.6035188 (Assmann et al., 2022a).},
	year = {2022},
	eissn = {1866-3516},
	pages = {823-844}
}

@article{MTMT:33431404,
	title = {Predictive mapping of bryophyte diversity associated with mature forests using LiDAR-derived indices in a strongly managed landscape},
	url = {https://m2.mtmt.hu/api/publication/33431404},
	author = {Bourgouin, Maurane and Valeria, Osvaldo and Fenton, Nicole J.},
	doi = {10.1016/j.ecolind.2022.108585},
	journal-iso = {ECOL INDIC},
	journal = {ECOLOGICAL INDICATORS},
	volume = {136},
	unique-id = {33431404},
	issn = {1470-160X},
	abstract = {Recovery of bryophyte diversity following silvicultural treatments depends upon the reestablishment of favorable microhabitats and microclimatic conditions. Without sources of propagules (reproductive structures) within the managed landscape, however, even optimal habitat conditions would not be sufficient to ensure bryophyte diversity. To identify sources of propagules and ensure their protection, we used indices that were derived from a Digital Elevation Model (DEMs) and an airborne point cloud (LiDAR; Light Detection and Ranging) as explanatory variables to predict bryophyte biodiversity. Bryophytes were collected in the intensively managed Black Brook District of New Brunswick, Canada, in eight mature managed and unmanaged forest types (n = 38). Our results show a strong bryophyte community gradient between wetter stands (Cedar, riparian zone and SpruceFir) and drier stands (Tolerant Harwood and Plantation) forming two distinctive groups. Indices explaining bryophyte composition and richness were related to moisture (closest distance to a stream), canopy (canopy relief ratio, canopy closure and density) and microtopography (Topographic Position Index). Models obtained from these indices explained 75% of bryophyte composition and predicted composition with a certainty of 71% The predominance of the closest distance to a stream in our model reinforces the great importance of buffer along the hydrological network. Buffers represent a substantial propagule source for the landscape and notably increase its ecological connectivity. Although wetter sites had greater richness, the completely different composition find at drier sites suggest that biodiversity management efforts to maintain bryophytes should not be restricted to wetter stands. Our model demonstrates the potential of airborne LiDAR-derived indices as surrogates for field data in estimating and mapping bryophyte compositions to understand the variation in diversity across the managed landscape. This model can be used as a dynamic tool to target areas that represent the overall bryophyte diversity of the managed landscape to ensure protection of propagule sources and favors reestablishment.},
	keywords = {Biodiversity; LIDAR; Forest management; BRYOPHYTE; mature forests; Predictive mapping},
	year = {2022},
	eissn = {1872-7034}
}

@article{MTMT:33212288,
	title = {Scrub encroachment promotes biodiversity in temperate European wetlands under eutrophic conditions},
	url = {https://m2.mtmt.hu/api/publication/33212288},
	author = {Brunbjerg, Ane Kirstine and Fløjgaard, Camilla and Frøslev, Tobias Guldberg and Andersen, Dagmar Kappel and Bruun, Hans Henrik and Dalby, Lars and Goldberg, Irina and Lehmann, Louise Juhl and Moeslund, Jesper Erenskjold and Ejrnæs, Rasmus},
	doi = {10.1002/ece3.9445},
	journal-iso = {ECOL EVOL},
	journal = {ECOLOGY AND EVOLUTION},
	volume = {12},
	unique-id = {33212288},
	issn = {2045-7758},
	year = {2022},
	eissn = {2045-7758},
	orcid-numbers = {Brunbjerg, Ane Kirstine/0000-0003-0666-6535; Fløjgaard, Camilla/0000-0002-5829-8503; Frøslev, Tobias Guldberg/0000-0002-3530-013X; Bruun, Hans Henrik/0000-0003-0674-2577; Dalby, Lars/0000-0002-7270-6999; Moeslund, Jesper Erenskjold/0000-0001-8591-7149; Ejrnæs, Rasmus/0000-0003-2538-8606}
}

@article{MTMT:33431402,
	title = {Laserfarm - A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds},
	url = {https://m2.mtmt.hu/api/publication/33431402},
	author = {Kissling, W. Daniel and Shi, Yifang and Koma, Zsofia and Meijer, Christiaan and Ku, Ou and Nattino, Francesco and Seijmonsbergen, Arie C. and Grootes, Meiert W.},
	doi = {10.1016/j.ecoinf.2022.101836},
	journal-iso = {ECOL INFORM},
	journal = {ECOLOGICAL INFORMATICS},
	volume = {72},
	unique-id = {33431402},
	issn = {1574-9541},
	abstract = {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 con-sisting 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 in-frastructures, 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.},
	keywords = {Macroecology; big data; Python; computing architectures; essential biodiversity variable; Ecosystem morphological traits},
	year = {2022},
	eissn = {1878-0512}
}

@article{MTMT:32616460,
	title = {Unveil the unseen: Using LiDAR to capture time‐lag dynamics in the herbaceous layer of European temperate forests},
	url = {https://m2.mtmt.hu/api/publication/32616460},
	author = {Lenoir, Jonathan and Gril, Eva and Durrieu, Sylvie and Horen, Hélène and Laslier, Marianne and Lembrechts, Jonas and Zellweger, Florian and Alleaume, Samuel and Brasseur, Boris and Buridant, Jérôme and Dayal, Karun and De Frenne, Pieter and Gallet‐Moron, Emilie and Marrec, Ronan and Meeussen, Camille and Rocchini, Duccio and Van Meerbeek, Koenraad and Decocq, Guillaume},
	doi = {10.1111/1365-2745.13837},
	journal-iso = {J ECOL},
	journal = {JOURNAL OF ECOLOGY},
	volume = {110},
	unique-id = {32616460},
	issn = {0022-0477},
	year = {2022},
	eissn = {1365-2745},
	pages = {282-300}
}

@article{MTMT:33230295,
	title = {Using airborne lidar to characterize North European terrestrial high-dark-diversity habitats},
	url = {https://m2.mtmt.hu/api/publication/33230295},
	author = {Moeslund, Jesper Erenskjold and Clausen, Kevin Kuhlmann and Dalby, Lars and Flojgaard, Camilla and Partel, Meelis and Pfeifer, Norbert and Hollaus, Markus and Brunbjerg, Ane Kirstine and Disney, Mat and Zhang, Jian},
	doi = {10.1002/rse2.314},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {33230295},
	abstract = {A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with >40 000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity-that is, absence of suitable species-at national extent (>43 000 km(2)). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with seven different field-based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high-dark-diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation, and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High-dark-diversity habitats in forests had relatively tall vegetation, few natural-forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain- and soil-related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and possibly also open forests with old-growth elements such as dead wood and rot attacks.},
	keywords = {LIDAR; vegetation structure; PLANT DIVERSITY; Dark diversity; vegetation ecology; terrain structure},
	year = {2022},
	eissn = {2056-3485},
	orcid-numbers = {Moeslund, Jesper Erenskjold/0000-0001-8591-7149; Dalby, Lars/0000-0002-7270-6999; Flojgaard, Camilla/0000-0002-5829-8503; Pfeifer, Norbert/0000-0002-2348-7929; Hollaus, Markus/0000-0001-6063-7239; Brunbjerg, Ane Kirstine/0000-0003-0666-6535}
}

@article{MTMT:32329451,
	title = {Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests},
	url = {https://m2.mtmt.hu/api/publication/32329451},
	author = {Anderson, C. T. and Dietz, S. L. and Pokswinski, S. M. and Jenkins, A. M. and Kaeser, M. J. and Hiers, J. K. and Pelc, B. D.},
	doi = {10.1016/j.foreco.2021.119118},
	journal-iso = {FOREST ECOL MANAG},
	journal = {FOREST ECOLOGY AND MANAGEMENT},
	volume = {491},
	unique-id = {32329451},
	issn = {0378-1127},
	abstract = {Terrestrial LiDAR is a promising tool for providing accurate and consistent measurements of forest structure at fine scales and has the potential to address some of the drawbacks associated with traditional vegetation monitoring methods. To compare terrestrial LiDAR to traditional methods, we conducted vegetation surveys using common methods of estimating cover and structure, and scanned surveyed areas using a terrestrial LiDAR device, the Leica BLK360. We developed simple methods for using point cloud data to make approximations of complex forest structure metrics and compared the ability of both data collection types to predict species richness. Hybrid models accurately predicted total, herb, and shrub richness in southern pine forests using combinations of metrics collected from terrestrial LiDAR and traditional field-based sampling methodology. Our findings indicate terrestrial LiDAR data may be used to accurately predict species richness in community types where structure and richness are related. In addition, our results suggest terrestrial LiDAR technology has the potential to address the limitations of traditional methods used to quantify vegetation structure and improve our ability for studying forest structure-richness relationships.},
	keywords = {Biodiversity; terrestrial laser scanning; Forest monitoring; vegetation monitoring; ground-based LiDAR; Community richness; Daubenmire},
	year = {2021},
	eissn = {1872-7042}
}

@article{MTMT:33431406,
	title = {Predicting bilberry and cowberry yields using airborne laser scanning and other auxiliary data combined with National Forest Inventory field plot data},
	url = {https://m2.mtmt.hu/api/publication/33431406},
	author = {Bohlin, Inka and Maltamo, Matti and Hedenas, Henrik and Lamas, Tomas and Dahlgren, Jonas and Mehtatalo, Lauri},
	doi = {10.1016/j.foreco.2021.119737},
	journal-iso = {FOREST ECOL MANAG},
	journal = {FOREST ECOLOGY AND MANAGEMENT},
	volume = {502},
	unique-id = {33431406},
	issn = {0378-1127},
	abstract = {The increasing availability of wall-to-wall remote sensing datasets in combination with accurate field data enables the mapping of different ecosystem services more accurately and over larger areas than before. The provision of wild berries is an essential ecosystem service, and berries are the most used non-wood forest products in Nordic countries. The aim of the study was to 1) develop general prediction models for bilberry and cowberry yield based on metrics derived from airborne laser scanning (ALS) data and other existing wall-to-wall data and 2) to identify laser-based structural features of forests that can be linked to locations of the highest berry yields. We used the indirect approach where the correlation between forest structure described by the ALS data and the berry yields are utilized. Berry data collected in the Swedish National Forest Inventory (NFI) 2007-2016 were used for training the models and ALS data from 2009 to 2014 from the national ALS campaign of Sweden. Berry yields were modelled using generalised linear mixed models (GLMMs), and forest structural differences were demonstrated in histograms of presence/absence data. The ALS-based canopy cover was an important variable both in bilberry and cowberry models. Other significant variables were ALS-based height variance, shrub cover, height above sea level, slope, soil wetness and terrain ruggedness, satellite-based species-specific volume and percentage, seasonality of temperature and precipitation and annual precipitation, inventory year, soil type and land use class. In addition, the time difference between the inventory day and the Julian day when berries were expected to be ripe showed a 1.5% decrease for bilberry and a 1.1% decrease for cowberry yield per day during the season. The highest bilberry yield was identified in forests with a canopy cover of 50% and the highest cowberry yield in forests with a canopy cover close to zero. The canopy height of 15 m reflected the highest bilberry yield, whereas a canopy height close to 0 m resulted in the highest cowberry yield. The shrub cover was close to zero both with highest bilberry and cowberry yields. This is the first study combining ALS metrics with other wall-to-wall variables and NFI field data to model bilberry and cowberry yields. Prediction models can be used to produce maps showing the most potential locations for berry picking. Further, the models may, in the future, be imported into forest planning systems to obtain stand-level prognoses of berry yield development under different forest management strategies.},
	keywords = {remote sensing; LIDAR; Vaccinium myrtillus; MIXED MODELS; Forest structure; NFI; Vaccinium vitis-idaea L; Berry yield},
	year = {2021},
	eissn = {1872-7042}
}

@article{MTMT:32077627,
	title = {Quantifying 3D vegetation structure in wetlands using differently measured airborne laser scanning data},
	url = {https://m2.mtmt.hu/api/publication/32077627},
	author = {Koma, Zsófia and Zlinszky, András and Bekő, László and Burai, Péter and Seijmonsbergen, Arie C. and Kissling, W. Daniel},
	doi = {10.1016/j.ecolind.2021.107752},
	journal-iso = {ECOL INDIC},
	journal = {ECOLOGICAL INDICATORS},
	volume = {127},
	unique-id = {32077627},
	issn = {1470-160X},
	abstract = {Mapping and quantifying 3D vegetation structure is essential for assessing and monitoring ecosystem structure and function within wetlands. Airborne Laser Scanning (ALS) is a promising data source for developing indicators of 3D vegetation structure, but derived metrics are often not compared with 3D structural field measurements and the acquisition of ALS data is rarely standardized across different remote sensing surveys. Here, we compare a set of Light Detection And Ranging (LiDAR) metrics derived from ALS datasets with varying characteristics to a standardized set of field measurements of vegetation height, biomass and Leaf Area Index (LAI) across three Hungarian lakes (Lake Balaton, Lake Fert.o and Lake Tisza). The ALS datasets differed in whether the recording type was full waveform (FWF) or discrete return, and in their point density (4 pt/m(2) and 21 pt/m(2)). A total of eight LiDAR metrics captured radiometric information as well as descriptors of vegetation cover, height and vertical variability. Multivariate regression models with field-based measurements of vegetation height, biomass or LAI as response variable and LiDAR metrics as predictors showed major differences between ALS recording types, and were affected by differences in spatial resolution, temporal offset and seasonality between field and ALS data acquisition. Vegetation height could be estimated with high to intermediate accuracy (FWF ALS data only: R-2 = 0.84; combination of ALS datasets: R-2 = 0.67), demonstrating its potential as a robust indicator of 3D vegetation structure across different ALS datasets. In contrast, the estimation of biomass and LAI in these wetlands was sensitive to variation in ALS characteristics and to the discrepancies between field and ALS data in terms of spatial resolution, temporal offset and seasonality (biomass: R-2 = 0.20-0.22; LAI: R-2 = 0.08-0.30). We recommend the use of FWF ALS data within wetlands because it captures more vegetation structural details in dense reed and marshland vegetation. We further suggest that ecologists and remote sensing scientist should better coordinate the simultaneous and standardized acquisition of field and ALS data for testing the robustness of quantitative descriptors of vegetation cover, height and vertical variability within wetlands. This is important for establishing operational and spatially contiguous ALS-based indicators of 3D ecosystem structure across wetlands.},
	keywords = {HEIGHT; BIOMASS; reedbeds; LAI; Full waveform ALS; Discrete return ALS; Marshlands},
	year = {2021},
	eissn = {1872-7034},
	orcid-numbers = {Zlinszky, András/0000-0002-9717-0043}
}

@article{MTMT:32329453,
	title = {Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition},
	url = {https://m2.mtmt.hu/api/publication/32329453},
	author = {Kopecky, Martin and Macek, Martin and Wild, Jan},
	doi = {10.1016/j.scitotenv.2020.143785},
	journal-iso = {SCI TOTAL ENVIRON},
	journal = {SCIENCE OF THE TOTAL ENVIRONMENT},
	volume = {757},
	unique-id = {32329453},
	issn = {0048-9697},
	abstract = {Soil moisture controls environmental processes and spedes distributions, but it is difficult to measure and interpolate across space. Topographic Wetness Index (TWI) derived from digital elevation model is therefore often used as a proxy for soil moisture. However, different algorithms can be used to calculate TWI and this potentially affects TWI relationship with soil moisture and species assemblages.To disentangle insufficiently-known effects of different algorithms on TWI relation with soil moisture and plant species composition, we measured the root-zone soil moisture throughout a growing season and recorded vascular plants and bryophytes in 45 temperate forest plots. For each plot, we calculated 26 TWI variants from a LiDAR-based digital terrain model and related these TWI variants to the measured soil moisture and moisture-controlled species assemblages of vascular plants and bryophytes.A flow accumulation algorithm determined the ability of the TWI to predict soil moisture, while the flow width and slope algorithms had only a small effects. The TWI calculated with the most often used single-flow D8 algorithm explained less than half of the variation in soil moisture and species composition explained by the TWI calculated with the multiple-flow FD8 algorithm. Flow dispersion used in the 1D8 algorithm strongly affected the TWI performance, and a flow dispersion dose to 1.0 resulted in the TWI best related to the soil moisture and spedes assemblages. Using downslope gradient instead of the local slope gradient can strongly decrease TWI performance.Our results clearly showed that the method used to calculate TWI affects study conclusion. However, TMI calculation is often not specified and thus impossible to reproduce and compare among studies. We therefore provide guidelines for TWI calculation and recommend the FD8 flow algorithm with a flow dispersion close to 1.0, flow width equal to the raster cell size and local slope gradient for TWI calculation. (C) 2020 Elsevier B.V. All rights reserved.},
	keywords = {Volumetric water content; Compound topographic index; FD8 flow routing algorithm; Forest bryophytes; SAGA wetness index; TMS miaoclimate logger},
	year = {2021},
	eissn = {1879-1026},
	orcid-numbers = {Kopecky, Martin/0000-0002-1018-9316; Macek, Martin/0000-0002-5609-5921; Wild, Jan/0000-0003-3007-4070}
}

@article{MTMT:31824274,
	title = {Lidar-derived environmental drivers of epiphytic bryophyte biomass in tropical montane cloud forests},
	url = {https://m2.mtmt.hu/api/publication/31824274},
	author = {Lai, G.-Y. and Liu, H.-C. and Chung, C.-H. and Wang, C.-K. and Huang, C.-Y.},
	doi = {10.1016/j.rse.2020.112166},
	journal-iso = {REMOTE SENS ENVIRON},
	journal = {REMOTE SENSING OF ENVIRONMENT},
	volume = {253},
	unique-id = {31824274},
	issn = {0034-4257},
	year = {2021},
	eissn = {1879-0704}
}

@article{MTMT:31999550,
	title = {Relationships between macro-fungal dark diversity and habitat parameters using LiDAR},
	url = {https://m2.mtmt.hu/api/publication/31999550},
	author = {Valdez, J.W. and Brunbjerg, A.K. and Fløjgaard, C. and Dalby, L. and Clausen, K.K. and Pärtel, M. and Pfeifer, N. and Hollaus, M. and Wimmer, M.H. and Ejrnæs, R. and Moeslund, J.E.},
	doi = {10.1016/j.funeco.2021.101054},
	journal-iso = {FUNGAL ECOL},
	journal = {FUNGAL ECOLOGY},
	volume = {51},
	unique-id = {31999550},
	issn = {1754-5048},
	year = {2021},
	eissn = {1878-0083}
}

@article{MTMT:31956636,
	title = {Identifying fine‐scale habitat preferences of threatened butterflies using airborne laser scanning},
	url = {https://m2.mtmt.hu/api/publication/31956636},
	author = {Vries, Jan Peter Reinier and Koma, Zsófia and WallisDeVries, Michiel F. and Kissling, W. Daniel and Tingley, Reid},
	doi = {10.1111/ddi.13272},
	journal-iso = {DIVERS DISTRIB},
	journal = {DIVERSITY AND DISTRIBUTIONS},
	volume = {27},
	unique-id = {31956636},
	issn = {1366-9516},
	year = {2021},
	eissn = {1472-4642},
	orcid-numbers = {Kissling, W. Daniel/0000-0002-7274-6755}
}

@article{MTMT:31523270,
	title = {Multi-taxon inventory reveals highly consistent biodiversity responses to ecospace variation},
	url = {https://m2.mtmt.hu/api/publication/31523270},
	author = {Brunbjerg, Ane Kirstine and Bruun, Hans Henrik and Dalby, Lars and Classen, Aimee T. and Flojgaard, Camilla and Froslev, Tobias G. and Hansen, Oskar Liset Pryds and Hoye, Toke Thomas and Moeslund, Jesper Erenskjold and Svenning, Jens-Christian and Ejrnaes, Rasmus},
	doi = {10.1111/oik.07145},
	journal-iso = {OIKOS},
	journal = {OIKOS},
	volume = {129},
	unique-id = {31523270},
	issn = {0030-1299},
	abstract = {Amidst the global biodiversity crisis, identifying general principles for variation of biodiversity remains a key challenge. Scientific consensus is limited to a few macroecological rules, such as species richness increasing with area, which provide limited guidance for conservation. In fact, few agreed ecological principles apply at the scale of sites or reserve management, partly because most community-level studies are restricted to single habitat types and species groups. We used the recently proposed ecospace framework and a comprehensive data set for aggregating environmental variation to predict multi-taxon diversity. We studied richness of plants, fungi and arthropods in 130 sites representing the major terrestrial habitat types in Denmark. We found the abiotic environment (ecospace position) to be pivotal for the richness of primary producers (vascular plants, mosses and lichens) and, more surprisingly, little support for ecospace continuity as a driver. A peak in richness at intermediate productivity adds new empirical evidence to a long-standing debate over biodiversity responses to productivity. Finally, we discovered a dominant and positive response of fungi and insect richness to organic matter accumulation and diversification (ecospace expansion). Two simple models of producer and consumer richness accounted for 77% of the variation in multi-taxon species richness suggesting a significant potential for generalization beyond individual species responses. Our study widens the traditional conservation focus on vegetation and vertebrate populations unravelling the importance of diversification of carbon resources for diverse heterotrophs, such as fungi and insects.},
	keywords = {Environmental DNA; Abiotic environment; Primary producers; carbon resources; heterotrophs; taxonomic aggregation},
	year = {2020},
	eissn = {1600-0706},
	pages = {1381-1392},
	orcid-numbers = {Svenning, Jens-Christian/0000-0002-3415-0862}
}

@article{MTMT:31773035,
	title = {Predictive mapping of bryophyte richness patterns in boreal forests using species distribution models and remote sensing data},
	url = {https://m2.mtmt.hu/api/publication/31773035},
	author = {Cerrejon, Carlos and Valeria, Osvaldo and Mansuy, Nicolas and Barbe, Marion and Fenton, Nicole J.},
	doi = {10.1016/j.ecolind.2020.106826},
	journal-iso = {ECOL INDIC},
	journal = {ECOLOGICAL INDICATORS},
	volume = {119},
	unique-id = {31773035},
	issn = {1470-160X},
	abstract = {Bryophytes represent an essential component of global biodiversity and play a significant role in many ecosystems, including boreal forests. In Canadian boreal forests, industrial exploitation of natural resources threatens bryophyte species and the ecological processes and services they support. However, the consideration of bryophytes in conservation issues is limited by current knowledge gaps on their distribution and diversity patterns. This is mainly due to the ineffectiveness of traditional field surveys to acquire information over large areas. Using remote sensing data in combination with species distribution models (SDMs), we aim to predict and map diversity patterns (in terms of richness) of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) in 28,436 km(2) of boreal forests of Quebec (Canada). A bryophyte presence/absence database was used to develop four response variables: total bryophyte richness, moss richness, liverwort richness and sphagna richness. We pre-selected a group of 38 environmental predictors including climate, topography, soil moisture and drainage as well as vegetation. Then a final set of predictors was selected individually for each response variable through a two-step selection procedure. The Random Forest (RF) algorithm was used to develop spatially explicit regression models and to generate predictive cartography at 30 m resolution for the study area. Predictive mapping-associated uncertainty statistics were provided. Our models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), outperforming models from previous studies. Vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze-thaw events) consistently appeared among the most important predictors for all bryophyte groups modeled. However, guild-level models identified differences in important factors determining the richness of each of the guilds and, therefore, in their predicted richness patterns. For example, the predictor number of days > 30 degrees C was especially relevant for liverworts, while drainage class, topographic position index and PALSAR HH-polarized L-band were identified among the most important predictors for sphagna. These differences have important implications for management and conservation strategies for bryophytes. This study provides evidence of the potential of remote sensing for assessing and making predictions on bryophyte diversity across the landscape.},
	keywords = {INDICATORS; machine learning; CONSERVATION; digital mapping; Predictive modeling; BLACK SPRUCE FORESTS},
	year = {2020},
	eissn = {1872-7034}
}

@article{MTMT:31349502,
	title = {Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning},
	url = {https://m2.mtmt.hu/api/publication/31349502},
	author = {Koma, Zsófia and Seijmonsbergen, Arie C. and Kissling, W. Daniel},
	doi = {10.1002/rse2.170},
	editor = {Pettorelli, Nathalie and Disney, Mat},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	volume = {6},
	unique-id = {31349502},
	year = {2020},
	eissn = {2056-3485},
	orcid-numbers = {Koma, Zsófia/0000-0002-0003-8258}
}

@article{MTMT:31523269,
	title = {Primary productivity and climate control mushroom yields in Mediterranean pine forests},
	url = {https://m2.mtmt.hu/api/publication/31523269},
	author = {Miguel Olano, Jose and Martinez-Rodrigo, Raquel and Miguel Altelarrea, Jose and Agreda, Teresa and Fernandez-Toiran, Marina and Garcia-Cervigon, Ana I and Rodriguez-Puerta, Francisco and Agueda, Beatriz},
	doi = {10.1016/j.agrformet.2020.108015},
	journal-iso = {AGR FOREST METEOROL},
	journal = {AGRICULTURAL AND FOREST METEOROLOGY},
	volume = {288},
	unique-id = {31523269},
	issn = {0168-1923},
	abstract = {Mushrooms play a provisioning ecosystem service as wild food. The abundance of this resource shows high annual and interannual variability, particularly in Mediterranean ecosystems. Climate conditions have been considered the main factor promoting mushroom production variability, but several evidences suggest that forest composition, age and growth play also a role.Long-term mushroom production datasets are critical to understand the factors behind mushroom productivity. We used 22 and 24 year-long time series of mushroom production in Pinus pinaster and Pinus sylvestris forests in Central Spain to evaluate the effect of climate and forest productivity on mushroom yield. We combined climatic data (precipitation and temperature) and remote sensing data (soil moisture and the Normalized Difference Vegetation Index, NDVI, a surrogate of primary productivity) to model mushroom yields for each forest and for the main edible species of economic interest ( Boletus edulis and Lactarius deliciosus).We hypothesized that mushroom yield would be related to (i) forest primary productivity inferred from NDVI affects mushroom yields, that (ii) soil moisture inferred from remote sensors will equal the predictive power precipitation data, and that (iii) combining climatic and remote sensing will improve mushroom yield models.We found that (i) previous year NDVI correlated (r = 0.41-0.6) with mushroom yields; (ii) soil moisture from remote sensors rivaled the predictive power of precipitation (r = 0.63-0.72); and (iii) primary production and climate variances were independent, thus the combination of climatic and remote sensing data improved models with mean R-adj(2) as high as 0.629.On the light of these results, we propose as a working hypothesis that mushroom production might be modelled as a two step process. Previous year primary productivity would favour resource accumulation at tree level, potentially increasing resources for mycelia growth, climatic conditions during the fruiting season control the ability of mycelia to transform available resources into fruiting bodies.},
	keywords = {Soil moisture; NDVI; mushroom yield; Boletus edulis; Forest fungi; Lactarius deliciosus},
	year = {2020},
	eissn = {1873-2240},
	orcid-numbers = {Martinez-Rodrigo, Raquel/0000-0002-4277-398X; Rodriguez-Puerta, Francisco/0000-0002-4844-1759}
}

@article{MTMT:31519782,
	title = {Standardizing Ecosystem Morphological Traits from 3D Information Sources},
	url = {https://m2.mtmt.hu/api/publication/31519782},
	author = {Valbuena, R. and O'Connor, B. and Zellweger, F. and Simonson, W. and Vihervaara, P. and Maltamo, M. and Silva, C. A. and Almeida, D. R. A. and Danks, F. and Morsdorf, F. and Chirici, G. and Lucas, R. and Coomes, D. A. and Coops, N. C.},
	doi = {10.1016/j.tree.2020.03.006},
	journal-iso = {TRENDS ECOL EVOL},
	journal = {TRENDS IN ECOLOGY & EVOLUTION},
	volume = {35},
	unique-id = {31519782},
	issn = {0169-5347},
	abstract = {3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits - height, cover, and structural complexity - that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources - satellites, aircrafts, drones, or ground data - allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported.},
	year = {2020},
	eissn = {1872-8383},
	pages = {656-667}
}