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- W3025652605 abstract "3D-imaging data acquired from a variety of platforms have become critical for ecological and environmental management. However, the use of disparate information sources to produce comprehensive and standardized global products is hindered by a lack of harmonization and terminology around ecosystem structure. We propose a sensor- and platform-independent framework which effectively distils the wealth of 3D information into concise ecosystem morphological traits – height, cover, and structural complexity – easy to conceptualize by ecologists and conservation stakeholders lacking remote sensing background. The conceptual disaggregation of ecosystem structure would contribute to defining and monitoring essential biodiversity variables obtained from 3D imaging that can be used to inform progress towards the UN 2030 Sustainable Development Goals and other international policy targets. 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. 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. also known as 3D RS, the concept includes any RS method that detects 3D positions of ecosystem structural elements. LIDAR, SAR, and digital photogrammetry are specific types of 3D-imaging data sources. airborne LIDAR systems fire discrete pulses of green and infrared light from the height of a flying aircraft, so that the beam widens to about 0.3–0.5 m in diameter upon reaching the surface. When targeted on vegetation, only a portion of the laser pulse is backscattered from the upper crowns, while other components return off leaves and branches further down the canopy, understory vegetation, and the ground (see Figure I in Box 1). Thus, multiple returns backscattered off the different elements of the targeted ecosystem are obtained from a single pulse, resulting in an informative 3D point cloud of scanned LIDAR returns. 3D information from stereoscopic restitution of two or more images acquired from an aerial platform. While digital photogrammetry can be obtained from a variety of platforms (close range on the ground, or airborne/satellite imagery), the recent spread use of drones has popularized structure-from-motion methods which deliver dense DAP data. percentage of a fixed area covered by the vertical projection of the ecosystem structural elements. Common terms employed for vegetation are plant area index [13.Schneider F.D. et al.Mapping functional diversity from remotely sensed morphological and physiological forest traits.Nat. Comm. 2017; 8: 1441Crossref PubMed Scopus (106) Google Scholar,34.Marselis S.M. et al.Distinguishing vegetation types with airborne waveform Lidar data in a tropical forest-savanna mosaic: a case study in Lopé National Park, Gabon.Remote Sens. Environ. 2018; 216: 626-634Crossref Scopus (17) Google Scholar], or colony cover for corals [16.Calders K. et al.3D imaging insights into forests and coral reefs.Trends Ecol. Evol. 2019; 35: 6-9Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar]. average height of the highest ecosystem structural elements. Common terms employed are top of canopy height in forests [40.Asner G.P. Mascaro J. Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric.Remote Sens. Environ. 2014; 140: 614-624Crossref Scopus (197) Google Scholar] or reef elevation for corals [25.Duvall M.S. et al.Collapsing complexity: quantifying multiscale properties of reef topography.J. Geophys. Res. Oceans. 2019; 124: 5021-5038Crossref Scopus (8) Google Scholar]. variability in height and/or cover of the ecosystem structural elements. Standard deviation and coefficient of variation are common measures of ecosystem complexity [25.Duvall M.S. et al.Collapsing complexity: quantifying multiscale properties of reef topography.J. Geophys. Res. Oceans. 2019; 124: 5021-5038Crossref Scopus (8) Google Scholar,35.Valbuena R. et al.Key structural features of boreal forests may be detected directly using L-moments from airborne lidar data.Remote Sens. Environ. 2017; 194: 437-446Crossref Scopus (24) Google Scholar,39.Coops N.C. et al.A forest structure habitat index based on airborne laser scanning data.Ecol. Ind. 2016; 67: 346-357Crossref Scopus (41) Google Scholar]. Rugosity is a common term employed for both forest canopies and benthic habitats [53.Ferrari R. et al.Habitat structural complexity metrics improve predictions of fish abundance and distribution.Ecography. 2018; 41: 1077-1091Crossref Scopus (40) Google Scholar]. Measurements required to report the status and monitor trends in biodiversity change globally, to inform decision makers in management and policy [7.Navarro L.M. et al.Monitoring biodiversity change through effective global coordination.Curr. Opin. Environ. Sust. 2017; 29: 158-169Crossref Scopus (58) Google Scholar,24.Geijzendorffer I.R. et al.How can global conventions for biodiversity and ecosystem services guide local conservation actions?.Curr. Opin. Environ. Sust. 2017; 29: 145-150Crossref Scopus (12) Google Scholar]. LIDAR systems scan targeted surfaces by emitting laser pulses and detecting their reflection. Ground-based platforms are used to get an informative 3D cloud of scanned LIDAR returns over individual samples or transects. Airborne platforms obtain similar information over continuous swaths of land, with a trade-off between the density of 3D information and its coverage: drones obtain denser data over limited extents and aircrafts acquire sparser data covering whole regions. LIDAR pulses emitted from satellites cover an entire plant community, thus delivering a whole waveform instead (see Figure I in Box 1). Nonetheless, the information can be similarly utilized and the main difference is that satellite LIDAR provides global coverages but only at discrete samples (i.e., not spatially continuous). Methods acquiring information from ecosystems at a distance. RS may involve a variety of sensors (e.g., spectral cameras, lasers, radar) on a variety of platforms: ground-based, drones, airborne or spaceborne. The type of data collected depends on the sensor/platform combination, 3D-imaging is one specific type of RS in which the output information is 3D positions of objects. sessile biological entities constituting the biophysical environment of an ecosystem (e.g., plants or corals). an extremely large antenna would be needed in order to detect objects through very long distances using radar wavelengths. To avoid this, SAR simulates a long aperture through the flight path of a moving side-looking platform, airborne or spaceborne. The outcome products provide 3D structure information of the targets, at 1–5-m spatial resolutions. SAR can penetrate clouds, which makes it a useful technique in rainforests and mountainous regions. Depending on the wavelength (e.g., C-band or L-band) different ecological features can be recognized." @default.
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- W3025652605 date "2020-08-01" @default.
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- W3025652605 title "Standardizing Ecosystem Morphological Traits from 3D Information Sources" @default.
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- W3025652605 doi "https://doi.org/10.1016/j.tree.2020.03.006" @default.
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