Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-2989-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2989-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
05 Jul 2022
Data description paper |
| 05 Jul 2022
| 05 Jul 2022
Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests
3DGeo Research Group, Institute of Geography, Heidelberg University, Heidelberg, Germany
Jannika Schäfer
Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Karlsruhe, Germany
Lukas Winiwarter
3DGeo Research Group, Institute of Geography, Heidelberg University, Heidelberg, Germany
Nina Krašovec
3DGeo Research Group, Institute of Geography, Heidelberg University, Heidelberg, Germany
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
Fabian E. Fassnacht
Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Karlsruhe, Germany
Remote Sensing and Geoinformatics, Freie Universität Berlin, Berlin, Germany
Bernhard Höfle
3DGeo Research Group, Institute of Geography, Heidelberg University, Heidelberg, Germany
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
Related authors
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M. Potůčková, J. Albrechtová, K. Anders, L. Červená, J. Dvořák, K. Gryguc, B. Höfle, L. Hunt, Z. Lhotáková, A. Marcinkowska-Ochtyra, A. Mayr, E. Neuwirthová, A. Ochtyra, M. Rutzinger, A. Šedová, A. Šrollerů, and L. Kupková
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 989–996, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-989-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-989-2023, 2023
Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle
Earth Surf. Dynam., 11, 593–613, https://doi.org/10.5194/esurf-11-593-2023, https://doi.org/10.5194/esurf-11-593-2023, 2023
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We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
Lea Hartl, Thomas Zieher, Magnus Bremer, Martin Stocker-Waldhuber, Vivien Zahs, Bernhard Höfle, Christoph Klug, and Alessandro Cicoira
Earth Surf. Dynam., 11, 117–147, https://doi.org/10.5194/esurf-11-117-2023, https://doi.org/10.5194/esurf-11-117-2023, 2023
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The rock glacier in Äußeres Hochebenkar (Austria) moved faster in 2021–2022 than it has in about 70 years of monitoring. It is currently destabilizing. Using a combination of different data types and methods, we show that there have been two cycles of destabilization at Hochebenkar and provide a detailed analysis of velocity and surface changes. Because our time series are very long and show repeated destabilization, this helps us better understand the processes of rock glacier destabilization.
D. Hulskemper, K. Anders, J. A. Á. Antolínez, M. Kuschnerus, B. Höfle, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 53–60, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, 2022
K. Anders, L. Winiwarter, D. Schröder, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 973–980, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022, 2022
V. Zahs, L. Winiwarter, K. Anders, M. Bremer, M. Rutzinger, M. Potůčková, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1109–1116, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, 2022
L. Winiwarter, K. Anders, D. Schröder, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 79–86, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, 2022
K. Anders, L. Winiwarter, H. Mara, R. C. Lindenbergh, S. E. Vos, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 137–144, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, 2021
Veit Ulrich, Jack G. Williams, Vivien Zahs, Katharina Anders, Stefan Hecht, and Bernhard Höfle
Earth Surf. Dynam., 9, 19–28, https://doi.org/10.5194/esurf-9-19-2021, https://doi.org/10.5194/esurf-9-19-2021, 2021
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In this work, we use 3D point clouds to detect topographic changes across the surface of a rock glacier. These changes are presented as the relative contribution of surface change during a 3-week period to the annual surface change. By comparing these different time periods and looking at change in different directions, we provide estimates showing that different directions of surface change are dominant at different times of the year. This demonstrates the benefit of frequent monitoring.
M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020
L. Winiwarter, K. Anders, D. Wujanz, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 789–796, https://doi.org/10.5194/isprs-annals-V-2-2020-789-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-789-2020, 2020
K. Anders, R. C. Lindenbergh, S. E. Vos, H. Mara, S. de Vries, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 317–324, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, 2019
A. Kumar, K. Anders, L Winiwarter, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 373–380, https://doi.org/10.5194/isprs-annals-IV-2-W5-373-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-373-2019, 2019
S. Crommelinck, B. Höfle, M. N. Koeva, M. Y. Yang, and G. Vosselman
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 81–88, https://doi.org/10.5194/isprs-annals-IV-2-81-2018, https://doi.org/10.5194/isprs-annals-IV-2-81-2018, 2018
M. Scaioni, B. Höfle, A. P. Baungarten Kersting, L. Barazzetti, M. Previtali, and D. Wujanz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1503–1510, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, 2018
M. Hämmerle, N. Lukač, K.-C. Chen, Zs. Koma, C.-K. Wang, K. Anders, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 59–65, https://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017, 2017
Sabrina Marx, Katharina Anders, Sofia Antonova, Inga Beck, Julia Boike, Philip Marsh, Moritz Langer, and Bernhard Höfle
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2017-49, https://doi.org/10.5194/esurf-2017-49, 2017
Revised manuscript has not been submitted
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Global climate warming causes permafrost to warm and thaw, and, consequently, to release the carbon into the atmosphere. Terrestrial laser scanning is evaluated and current methods are extended in the context of monitoring subsidence in Arctic permafrost regions. The extracted information is important to gain a deeper understanding of permafrost-related subsidence processes and provides highly accurate ground-truth data which is necessary for further developing area-wide monitoring methods.
Luisa Griesbaum, Sabrina Marx, and Bernhard Höfle
Nat. Hazards Earth Syst. Sci., 17, 1191–1201, https://doi.org/10.5194/nhess-17-1191-2017, https://doi.org/10.5194/nhess-17-1191-2017, 2017
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This study provides a new method for flood documentation based on user-generated flood images. We demonstrate how flood elevation and building inundation depth can be derived from photographs by means of 3-D reconstruction of the scene. With an accuracy of 0.13 m ± 0.10 m, the derived building inundation depth can be used to facilitate damage assessment.
S. Bechtold and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 161–168, https://doi.org/10.5194/isprs-annals-III-3-161-2016, https://doi.org/10.5194/isprs-annals-III-3-161-2016, 2016
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Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
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Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
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Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
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Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
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Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
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The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
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We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
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This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
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We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
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This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
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Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
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This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-364, https://doi.org/10.5194/essd-2022-364, 2023
Revised manuscript accepted for ESSD
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Modern and fossil pollen data contain precious information to reconstruct pas climate and environment. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
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The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
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The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
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We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
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Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
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This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
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The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
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Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
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We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
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Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
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We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
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Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
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Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
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Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Quandi Niu, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, and Wenping Yuan
Earth Syst. Sci. Data, 14, 2851–2864, https://doi.org/10.5194/essd-14-2851-2022, https://doi.org/10.5194/essd-14-2851-2022, 2022
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In this paper we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using Landsat images. The derived phenological indicators agree with in situ observations and provide more spatial details than moderate resolution phenology products. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future.
Alexandre Castagna, Luz Amadei Martínez, Margarita Bogorad, Ilse Daveloose, Renaat Dasseville, Heidi Melita Dierssen, Matthew Beck, Jonas Mortelmans, Héloïse Lavigne, Ana Dogliotti, David Doxaran, Kevin Ruddick, Wim Vyverman, and Koen Sabbe
Earth Syst. Sci. Data, 14, 2697–2719, https://doi.org/10.5194/essd-14-2697-2022, https://doi.org/10.5194/essd-14-2697-2022, 2022
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Here we describe a dataset of optical measurements paired with the concentration and composition of dissolved and particulate components of water systems in Belgium. Sampling was performed over eight lakes, a coastal lagoon, an estuary, and coastal waters, covering the period of 2017 to 2019. The data cover a broad range of conditions and can be useful for development and evaluation of hyperspectral methods in hydrology optics and remote sensing.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
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In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Esther Githumbi, Ralph Fyfe, Marie-Jose Gaillard, Anna-Kari Trondman, Florence Mazier, Anne-Birgitte Nielsen, Anneli Poska, Shinya Sugita, Jessie Woodbridge, Julien Azuara, Angelica Feurdean, Roxana Grindean, Vincent Lebreton, Laurent Marquer, Nathalie Nebout-Combourieu, Miglė Stančikaitė, Ioan Tanţău, Spassimir Tonkov, Lyudmila Shumilovskikh, and LandClimII data contributors
Earth Syst. Sci. Data, 14, 1581–1619, https://doi.org/10.5194/essd-14-1581-2022, https://doi.org/10.5194/essd-14-1581-2022, 2022
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Reconstruction of past land cover is necessary for the study of past climate–land cover interactions and the evaluation of climate models and land-use scenarios. We used 1128 available pollen records from across Europe covering the last 11 700 years in the REVEALS model to calculate percentage cover and associated standard errors for 31 taxa, 12 plant functional types and 3 land-cover types. REVEALS results are reliant on the quality of the input datasets.
Russell Doughty, Thomas P. Kurosu, Nicholas Parazoo, Philipp Köhler, Yujie Wang, Ying Sun, and Christian Frankenberg
Earth Syst. Sci. Data, 14, 1513–1529, https://doi.org/10.5194/essd-14-1513-2022, https://doi.org/10.5194/essd-14-1513-2022, 2022
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We describe and compare solar-induced chlorophyll fluorescence data produced by NASA from the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) and OCO-3 platforms.
Sandy P. Harrison, Roberto Villegas-Diaz, Esmeralda Cruz-Silva, Daniel Gallagher, David Kesner, Paul Lincoln, Yicheng Shen, Luke Sweeney, Daniele Colombaroli, Adam Ali, Chéïma Barhoumi, Yves Bergeron, Tatiana Blyakharchuk, Přemysl Bobek, Richard Bradshaw, Jennifer L. Clear, Sambor Czerwiński, Anne-Laure Daniau, John Dodson, Kevin J. Edwards, Mary E. Edwards, Angelica Feurdean, David Foster, Konrad Gajewski, Mariusz Gałka, Michelle Garneau, Thomas Giesecke, Graciela Gil Romera, Martin P. Girardin, Dana Hoefer, Kangyou Huang, Jun Inoue, Eva Jamrichová, Nauris Jasiunas, Wenying Jiang, Gonzalo Jiménez-Moreno, Monika Karpińska-Kołaczek, Piotr Kołaczek, Niina Kuosmanen, Mariusz Lamentowicz, Martin Lavoie, Fang Li, Jianyong Li, Olga Lisitsyna, José Antonio López-Sáez, Reyes Luelmo-Lautenschlaeger, Gabriel Magnan, Eniko Katalin Magyari, Alekss Maksims, Katarzyna Marcisz, Elena Marinova, Jenn Marlon, Scott Mensing, Joanna Miroslaw-Grabowska, Wyatt Oswald, Sebastián Pérez-Díaz, Ramón Pérez-Obiol, Sanna Piilo, Anneli Poska, Xiaoguang Qin, Cécile C. Remy, Pierre J. H. Richard, Sakari Salonen, Naoko Sasaki, Hieke Schneider, William Shotyk, Migle Stancikaite, Dace Šteinberga, Normunds Stivrins, Hikaru Takahara, Zhihai Tan, Liva Trasune, Charles E. Umbanhowar, Minna Väliranta, Jüri Vassiljev, Xiayun Xiao, Qinghai Xu, Xin Xu, Edyta Zawisza, Yan Zhao, Zheng Zhou, and Jordan Paillard
Earth Syst. Sci. Data, 14, 1109–1124, https://doi.org/10.5194/essd-14-1109-2022, https://doi.org/10.5194/essd-14-1109-2022, 2022
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We provide a new global data set of charcoal preserved in sediments that can be used to examine how fire regimes have changed during past millennia and to investigate what caused these changes. The individual records have been standardised, and new age models have been constructed to allow better comparison across sites. The data set contains 1681 records from 1477 sites worldwide.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
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Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Jakob J. Assmann, Jesper E. Moeslund, Urs A. Treier, and Signe Normand
Earth Syst. Sci. Data, 14, 823–844, https://doi.org/10.5194/essd-14-823-2022, https://doi.org/10.5194/essd-14-823-2022, 2022
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In 2014 and 2015, the Danish government scanned the whole of Denmark using laser scanners on planes. The information can help biologists learn more about Denmark's natural environment. To make it easier to access the outputs from the scan, we divided the country into 10 m x 10 m squares and summed up the information most relevant to biologists for each square. The result is a set of 70 maps describing the three-dimensional architecture of the Danish landscape and vegetation.
Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, Kathleen Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, Darcy Peter, Christina Minions, Julia Nojeim, Roisin Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrén López-Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans-Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret-Harte, Sigrid Dengel, Han Dolman, Colin W. Edgar, Bo Elberling, Eugenie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang-Jong Park, Roman Petrov, Anatoly S. Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva-Stiina Tuittila, Juha-Pekka Tuovinen, William Quinton, Andrej Varlagin, Donatella Zona, and Viacheslav I. Zyryanov
Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, https://doi.org/10.5194/essd-14-179-2022, 2022
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The effects of climate warming on carbon cycling across the Arctic–boreal zone (ABZ) remain poorly understood due to the relatively limited distribution of ABZ flux sites. Fortunately, this flux network is constantly increasing, but new measurements are published in various platforms, making it challenging to understand the ABZ carbon cycle as a whole. Here, we compiled a new database of Arctic–boreal CO2 fluxes to help facilitate large-scale assessments of the ABZ carbon cycle.
Arial J. Shogren, Jay P. Zarnetske, Benjamin W. Abbott, Samuel Bratsman, Brian Brown, Michael P. Carey, Randy Fulweber, Heather E. Greaves, Emma Haines, Frances Iannucci, Joshua C. Koch, Alexander Medvedeff, Jonathan A. O'Donnell, Leika Patch, Brett A. Poulin, Tanner J. Williamson, and William B. Bowden
Earth Syst. Sci. Data, 14, 95–116, https://doi.org/10.5194/essd-14-95-2022, https://doi.org/10.5194/essd-14-95-2022, 2022
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Rapidly sampling multiple points in an entire river network provides a high-resolution snapshot in time that can reveal where nutrients and carbon are being taken up and released. Here, we describe two such datasets of river network chemistry in six Arctic watersheds in northern Alaska. We describe how these repeated snapshots can be used as an indicator of ecosystem response to climate change and to improve predictions of future release of carbon, nutrient, and other solutes.
Lore T. Verryckt, Sara Vicca, Leandro Van Langenhove, Clément Stahl, Dolores Asensio, Ifigenia Urbina, Romà Ogaya, Joan Llusià, Oriol Grau, Guille Peguero, Albert Gargallo-Garriga, Elodie A. Courtois, Olga Margalef, Miguel Portillo-Estrada, Philippe Ciais, Michael Obersteiner, Lucia Fuchslueger, Laynara F. Lugli, Pere-Roc Fernandez-Garberí, Helena Vallicrosa, Melanie Verlinden, Christian Ranits, Pieter Vermeir, Sabrina Coste, Erik Verbruggen, Laëtitia Bréchet, Jordi Sardans, Jérôme Chave, Josep Peñuelas, and Ivan A. Janssens
Earth Syst. Sci. Data, 14, 5–18, https://doi.org/10.5194/essd-14-5-2022, https://doi.org/10.5194/essd-14-5-2022, 2022
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We provide a comprehensive dataset of vertical profiles of photosynthesis and important leaf traits, including leaf N and P concentrations, from two 3-year, large-scale nutrient addition experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N and P, and other leaf nutrients, in photosynthesis in tropical forests.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
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Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
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Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Yi-Wei Zhang, Yanpei Guo, Zhiyao Tang, Yuhao Feng, Xinrong Zhu, Wenting Xu, Yongfei Bai, Guoyi Zhou, Zongqiang Xie, and Jingyun Fang
Earth Syst. Sci. Data, 13, 5337–5351, https://doi.org/10.5194/essd-13-5337-2021, https://doi.org/10.5194/essd-13-5337-2021, 2021
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Nitrogen (N) and phosphorus (P) are limiting nutrients for ecosystem productivity. For the first time, we mapped N and P densities of living plants, litter, and soil in forest, shrubland, and grassland ecosystems across China using random forest models based on a dataset of 4868 field sites. Our results depicted the spatial distribution pattern, the total pool, and the allocation among ecosystem components of N and P, which could benefit a more precise prediction of the carbon cycle.
McKenzie A. Kuhn, Ruth K. Varner, David Bastviken, Patrick Crill, Sally MacIntyre, Merritt Turetsky, Katey Walter Anthony, Anthony D. McGuire, and David Olefeldt
Earth Syst. Sci. Data, 13, 5151–5189, https://doi.org/10.5194/essd-13-5151-2021, https://doi.org/10.5194/essd-13-5151-2021, 2021
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Methane (CH4) emissions from the boreal–Arctic region are globally significant, but the current magnitude of annual emissions is not well defined. Here we present a dataset of surface CH4 fluxes from northern wetlands, lakes, and uplands that was built alongside a compatible land cover dataset, sharing the same classifications. We show CH4 fluxes can be split by broad land cover characteristics. The dataset is useful for comparison against new field data and model parameterization or validation.
Cited articles
Applanix Corporation: POSPAC MMS 8,
https://www.applanix.com/downloads/products/specs/POSPac_MMS_8_Infosheet.pdf (last access: 24 June 2022),
2018. a
Arumäe, T. and Lang, M.: Estimation of Canopy Cover in Dense Mixed-Species
Forests Using Airborne Lidar Data, Eur. J. Remote Sens., 51,
132–141, https://doi.org/10.1080/22797254.2017.1411169, 2018. a
ASPRS: LAS Specification Version 1.2,
http://www.asprs.org/a/society/committees/standards/asprs_las_format_v12.pdf
(last access: 27 July 2021), 2008. a
Barber, C. B., Dobkin, D. P., and Huhdanpaa, H.: The Quickhull algorithm for
convex hulls, ACM T. Math. Software, 22, 469–483, 1996. a
Boudon, F.: PlantScan3D, GitHub [code],
https://github.com/fredboudon/plantscan3d (last access: 24 June 2022), 2021. a
Bournez, E., Landes, T., Saudreau, M., Kastendeuch, P., and Najjar, G.: From TLS Point Clouds to 3D Models of Trees: a Comparison of Existing Algorithms for 3D Tree Reconstruction, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 113–120, https://doi.org/10.5194/isprs-archives-XLII-2-W3-113-2017, 2017. a
Bouvier, M., Durrieu, S., Fournier, R. A., and Renaud, J.-P.: Generalizing
Predictive Models of Forest Inventory Attributes Using an Area-Based Approach
with Airborne LiDAR Data, Remote Sens. Environ., 156, 322–334,
https://doi.org/10.1016/j.rse.2014.10.004, 2015. a
Bruggisser, M., Hollaus, M., Otepka, J., and Pfeifer, N.: Influence of ULS
acquisition characteristics on tree stem parameter estimation, ISPRS J. Photogramm., 168, 28–40,
https://doi.org/10.1016/j.isprsjprs.2020.08.002, 2020. a
Calders, K., Lewis, P., Disney, M., Verbesselt, J., and Herold, M.:
Investigating assumptions of crown archetypes for modelling LiDAR returns,
Remote Sens. Environ., 134, 39–49, https://doi.org/10.1016/j.rse.2013.02.018,
2013. a
Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M.,
Culvenor, D., Avitabile, V., Disney, M., Armston, J., and Kaasalainen, M.:
Nondestructive estimates of above-ground biomass using terrestrial laser
scanning, Methods Ecol. Evol., 6, 198–208,
https://doi.org/10.1111/2041-210X.12301, 2015. a
Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley,
L. P., Chave, J., Danson, F. M., Demol, M., Disney, M., Gaulton, R., Krishna
Moorthy, S. M., Levick, S. R., Saarinen, N., Schaaf, C., Stovall, A.,
Terryn, L., Wilkes, P., and Verbeeck, H.: Terrestrial laser scanning in
forest ecology: Expanding the horizon, Remote Sens. Environ., 251,
112102, https://doi.org/10.1016/j.rse.2020.112102, 2020. a
CloudCompare: CloudCompare, version 2.10.2, GitHub [code], https://github.com/CloudCompare/CloudCompare/releases/tag/v2.10.2, last access: 3 April 2019. a
Computree Group: Computree, http://computree.onf.fr/?page_id=589, last access: 24 June 2022. a
Craig, A.: The Concave Hull of a Set of Points, CodeProject [code],
https://www.codeproject.com/Articles/1201438/The-Concave-Hull-of-a-Set-of-Points
(last access: 1 December 2020), 2017. a
Dassot, M., Colin, A., Santenoise, P., Fournier, M., and Constant, T.:
Terrestrial laser scanning for measuring the solid wood volume, including
branches, of adult standing trees in the forest environment, Comput.
Electron. Agr., 89, 86–93, https://doi.org/10.1016/j.compag.2012.08.005,
2012. a
Disney, M. I., Kalogirou, V., Lewis, P., Prieto-Blanco, A., Hancock, S., and
Pfeifer, M.: Simulating the impact of discrete-return lidar system and
survey characteristics over young conifer and broadleaf forests, Remote
Sens. Environ., 114, 1546–1560, https://doi.org/10.1016/j.rse.2010.02.009,
2010. a, b
Disney, M. I., Boni Vicari, M., Burt, A., Calders, K., Lewis, S. L., Raumonen,
P., and Wilkes, P.: Weighing trees with lasers: advances, challenges and
opportunities, Interface Focus, 8, 20170048, https://doi.org/10.1098/rsfs.2017.0048,
2018. a, b
DJI: MATRICE 600 PRO – User Manual,
https://dl.djicdn.com/downloads/m600 pro/1208EN/Matrice_600_Pro_User_Manual_v1.0_EN_1208.pdf
(last access: 17 May 2021), 2018. a
Du, S., Lindenbergh, R., Ledoux, H., Stoter, J., and Nan, L.: AdTree:
Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees, Remote
Sensing, 11, 2074, https://doi.org/10.3390/rs11182074, 2019. a
Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S.,
Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H.,
Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P. L., Qi,
W., and Silva, C.: The Global Ecosystem Dynamics Investigation:
High-resolution laser ranging of the Earth's forests and topography,
Sci. Remote Sens., 1, 100002, https://doi.org/10.1016/j.srs.2020.100002,
2020. a
Fu, H., Li, H., Dong, Y., Xu, F., and Chen, F.: Segmenting Individual Tree from
TLS Point Clouds Using Improved DBSCAN, Forests, 13, 566, https://doi.org/10.3390/f13040566,
2022. a
Gastellu-Etchegorry, J. P., Martin, E., and Gascon, F.: DART: a 3D model
for simulating satellite images and studying surface radiation budget,
Int. J. Remote Sens., 25, 73–96,
https://doi.org/10.1080/0143116031000115166, 2004. a
Gastellu-Etchegorry, J.-P., Yin, T., Lauret, N., Cajgfinger, T., Gregoire, T.,
Grau, E., Feret, J.-B., Lopes, M., Guilleux, J., Dedieu, G., Malenovský, Z.,
Cook, B. D., Morton, D., Rubio, J., Durrieu, S., Cazanave, G., Martin, E.,
and Ristorcelli, T.: Discrete Anisotropic Radiative Transfer (DART 5) for
Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of
Natural and Urban Landscapes, Remote Sensing, 7, 1667–1701,
https://doi.org/10.3390/rs70201667, 2015. a
Hackenberg, J.: SimpleTree Plugin, version Beta 4.33.06, https://rdinnovation.onf.fr/projects/computree-simpletree-beta-version/files
(last access: 21 May 2019), 2017. a
Hackenberg, J.: SimpleForest: A tree modelling software,
https://simpleforest.org/ (last access: 24 June 2022), 2021. a
Hackenberg, J., Spiecker, H., Calders, K., Disney, M., and Raumonen, P.:
SimpleTree – An Efficient Open Source Tool to Build Tree Models from TLS
Clouds, Forests, 6, 4245–4294, https://doi.org/10.3390/f6114245, 2015. a, b
Höfle, B., Qu, J., Winiwarter, L., Weiser, H., Zahs, V., Schäfer, J., and Fassnacht, F. E.: pytreedb: library for point clouds of tree vegetation objects, GitHub [code], https://github.com/3dgeo-heidelberg/pytreedb, last access: 24 June 2022. a
Holopainen, M., Vastaranta, M., and Hyyppä, J.: Outlook for the Next
Generation's Precision Forestry in Finland, Forests, 5, 1682–1694,
https://doi.org/10.3390/f5071682, 2014. a
Krishna Moorthy, S. M., Calders, K., Vicari, M. B., and Verbeeck, H.: Improved
Supervised Learning-Based Approach for Leaf and Wood Classification From
LiDAR Point Clouds of Forests, IEEE T. Geosci. Remote
Sens., 58, 3057–3070, https://doi.org/10.1109/TGRS.2019.2947198, 2020. a
Latifi, H., Fassnacht, F. E., Müller, J., Tharani, A., Dech, S., and Heurich,
M.: Forest inventories by LiDAR data: A comparison of single tree
segmentation and metric-based methods for inventories of a heterogeneous
temperate forest, Int. J. Appl. Earth Obs., 42, 162–174, https://doi.org/10.1016/j.jag.2015.06.008, 2015. a
Lewis, P.: Three-dimensional plant modelling for remote sensing simulation
studies using the Botanical Plant Modelling System, Agronomie, 19, 185–210,
https://doi.org/10.1051/agro:19990302, 1999. a
Lewis, P. and Muller, J.-P.: The Advanced Radiometric Ray-Tracer (ARARAT) for plant canopy reflectance simulation, Int. Arch. Photgramm. Rem. Sens., (Commission VII(B7)) 29, 26–34, https://www.isprs.org/proceedings/XXIX/congress/part7/26_XXIX-part7.pdf (last access: 24 June 2022), 1992. a
Liu, G., Wang, J., Dong, P., Chen, Y., and Liu, Z.: Estimating Individual Tree
Height and Diameter at Breast Height (DBH) from Terrestrial Laser Scanning
(TLS) Data at Plot Level, Forests, 9, 398, https://doi.org/10.3390/f9070398, 2018. a
Maltamo, M., Eerikäinen, K., Pitkänen, J., Hyyppä, J., and Vehmas, M.:
Estimation of timber volume and stem density based on scanning laser
altimetry and expected tree size distribution functions, Remote Sens.
Environ., 90, 319–330, https://doi.org/10.1016/j.rse.2004.01.006, 2004. a
Maltamo, M., Næsset, E., and Vauhkonen, J. (Eds.): Forestry Applications of
Airborne Laser Scanning, Vol. 27, Springer Netherlands, Dordrecht, ISBN 978-94-017-8663-8, 2014. a
Montaghi, A., Corona, P., Dalponte, M., Gianelle, D., Chirici, G., and Olsson,
H.: Airborne laser scanning of forest resources: An overview of research in
Italy as a commentary case study, Int. J. Appl. Earth
Obs., 23, 288–300,
https://doi.org/10.1016/j.jag.2012.10.002, 2013. a
Moreira, A. and Yasmina Santos, M.: Concave hull: A k-nearest neighbours
approach for the computation of the region occupied by a set of points, in:
Proceedings of the Second International Conference on Computer Graphics
Theory and Applications – Volume 2: GRAPP, 61–68, INSTICC, SciTePress,
https://doi.org/10.5220/0002080800610068, 2007. a
Morsdorf, F., Eck, C., Zgraggen, C., Imbach, B., Schneider, F. D., and
Kükenbrink, D.: UAV-based LiDAR acquisition for the derivation of
high-resolution forest and ground information, Leading Edge, 36,
566–570, https://doi.org/10.1190/tle36070566.1, 2017. a
Nan, L., Messal, L., Du, S., and Yang, Z.: AdTree, GitHub [code],
https://github.com/tudelft3d/adtree (last access: 24 June 2022), 2021. a
Næsset, E.: Predicting Forest Stand Characteristics with Airborne Scanning
Laser Using a Practical Two-Stage Procedure and Field Data, Remote Sens.
Environ., 80, 88–99, https://doi.org/10.1016/S0034-4257(01)00290-5, 2002. a
Næsset, E., Gobakken, T., Solberg, S., Gregoire, T. G., Nelson, R.,
Ståhl, G., and Weydahl, D.: Model-Assisted Regional Forest Biomass
Estimation Using LiDAR and InSAR as Auxiliary Data: A Case Study from
a Boreal Forest Area, Remote Sens. Environ., 115, 3599–3614,
https://doi.org/10.1016/j.rse.2011.08.021, 2011. a
Pearse, G. D., Watt, M. S., Dash, J. P., Stone, C., and Caccamo, G.: Comparison
of Models Describing Forest Inventory Attributes Using Standard and
Voxel-Based Lidar Predictors across a Range of Pulse Densities, Int.
J. Appl. Earth Obs., 78, 341–351,
https://doi.org/10.1016/j.jag.2018.10.008, 2019. a, b, c
Pfeifer, N., Mandlburger, G., Otepka, J., and Karel, W.: OPALS – A framework
for Airborne Laser Scanning data analysis, Comput. Environ. Urban, 45, 125–136, https://doi.org/10.1016/j.compenvurbsys.2013.11.002, 2014. a, b, c
Pfennigbauer, M. and Ullrich, A.: Improving quality of laser scanning data
acquisition through calibrated amplitude and pulse deviation measurement,
in: Laser Radar Technology and Applications XV, edited by: Turner, M. D. and
Kamerman, G. W., Vol. 7684, 463–472, International Society for Optics
and Photonics, SPIE, https://doi.org/10.1117/12.849641, 2010. a, b, c
Raumonen, P.: TreeQSM: Reconstruction of quantitative structure models of
trees from point cloud data, Zenodo [code], https://doi.org/10.5281/zenodo.844625, 2020. a
Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H.,
Vastaranta, M., Holopainen, M., Disney, M., and Lewis, P.: Fast Automatic
Precision Tree Models from Terrestrial Laser Scanner Data, Remote Sens., 5,
491–520, https://doi.org/10.3390/rs5020491, 2013. a, b
RIEGL Laser Measurement Systems: Data Processing Software RiPROCESS for
RIEGL Scan Data,
http://www.riegl.com/uploads/tx_pxpriegldownloads/RiProcess_Datasheet_2020-08-20_01.pdf
(last access: 23 March 2021), 2020a. a
RIEGL Laser Measurement Systems: Operating & Processing Software RiSCAN PRO for RIEGL 3D Laser Scanners, http://www.riegl.com/uploads/tx_pxpriegldownloads/RiSCAN-PRO_DataSheet_2020-10-07.pdf (last access: 4 July 2022),
2020b. a
Roberts, O., Bunting, P., Hardy, A., and McInerney, D.: Sensitivity Analysis
of the DART Model for Forest Mensuration with Airborne Laser Scanning,
Remote Sens., 12, 247, https://doi.org/10.3390/rs12020247, 2020. a
Roussel, J.-R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R., Meador,
A. S., Bourdon, J.-F., de Boissieu, F., and Achim, A.: lidR: An R package
for analysis of Airborne Laser Scanning (ALS) data, Remote Sens.
Environ., 251, 112061, https://doi.org/10.1016/j.rse.2020.112061, 2020. a
Rusu, R. B. and Cousins, S.: 3D is here: Point Cloud Library (PCL), in: IEEE
International Conference on Robotics and Automation (ICRA), Shanghai, China, https://doi.org/10.1109/ICRA.2011.5980567,
2011. a
Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., and Beetz, M.: Towards 3D
Point cloud based object maps for household environments, Robot.
Auton. Syst., 56, 927–941, https://doi.org/10.1016/j.robot.2008.08.005, 2008. a
Scion: Forest Science, Brochure,
https://www.scionresearch.com/__data/assets/pdf_file/0003/51645/Forest_Science_Brochure.pdf
(last access: 25 April 2022), 2021. a
Sinoquet, H., Le Roux, X., Adam, B., Ameglio, T., and Daudet, F. A.: RATP: a
model for simulating the spatial distribution of radiation absorption,
transpiration and photosynthesis within canopies: application to an isolated
tree crown, Plant Cell Environ., 24, 395–406,
https://doi.org/10.1046/j.1365-3040.2001.00694.x, 2001. a
Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Evans, J. S., Robinson, A. P.,
and Steele, C. M.: A Cross-Comparison of Field, Spectral, and Lidar Estimates
of Forest Canopy Cover, Can. J. Remote Sens., 35, 447–459,
https://doi.org/10.5589/m09-038, 2009. a
Vicari, M. B.: TLSeparation, GitHub [code], https://github.com/TLSeparation (last access: 24 June 2022),
2021. a
Vicari, M. B., Disney, M., Wilkes, P., Burt, A., Calders, K., and Woodgate, W.:
Leaf and wood classification framework for terrestrial LiDAR point clouds,
Methods Ecol. Evol., 10, 680–694,
https://doi.org/10.1111/2041-210X.13144, 2019. a
Vincent, G., Antin, C., Laurans, M., Heurtebize, J., Durrieu, S., Lavalley, C.,
and Dauzat, J.: Mapping plant area index of tropical evergreen forest by
airborne laser scanning. A cross-validation study using LAI2200 optical
sensor, Remote Sens. Environ., 198, 254–266,
https://doi.org/10.1016/j.rse.2017.05.034, 2017. a
Wallace, L., Musk, R., and Lucieer, A.: An Assessment of the Repeatability of
Automatic Forest Inventory Metrics Derived From UAV-Borne Laser Scanning
Data, IEEE T. Geosci. Remote Sens., 52, 7160–7169,
https://doi.org/10.1109/TGRS.2014.2308208, 2014. a
Wang, D.: LeWoS, Zenodo [code], https://doi.org/10.5281/zenodo.3516856, 2020. a
Wang, D., Momo Takoudjou, S., and Casella, E.: LeWoS: A universal leaf-wood
classification method to facilitate the 3D modelling of large tropical trees
using terrestrial LiDAR, Methods Ecol. Evol., 11, 376–389,
https://doi.org/10.1111/2041-210X.13342, 2020. a
Wang, Y., Lehtomäki, M., Liang, X., Pyörälä, J., Kukko, A., Jaakkola, A.,
Liu, J., Feng, Z., Chen, R., and Hyyppä, J.: Is field-measured tree height
as reliable as believed – A comparison study of tree height estimates from
field measurement, airborne laser scanning and terrestrial laser scanning in
a boreal forest, ISPRS J. Photogramm., 147,
132–145, https://doi.org/10.1016/j.isprsjprs.2018.11.008, 2019. a
Weiser, H.: 3dgeo-heidelberg/syssifoss: Version 1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.6759913, 2022. a
Weiser, H., Winiwarter, L., Anders, K., Fassnacht, F. E., and Höfle, B.:
Opaque voxel-based tree models for virtual laser scanning in forestry
applications, Remote Sens. Environ., 265, 112641,
https://doi.org/10.1016/j.rse.2021.112641, 2021. a, b, c
Weiser, H., Schäfer, J., Winiwarter, L., Krašovec, N., Seitz,
C., Schimka, M., Anders, K., Baete, D., Braz, A. S., Brand, J.,
Debroize, D., Kuss, P., Martin, L. L., Mayer, A., Schrempp, T.,
Schwarz, L.-M., Ulrich, V., Fassnacht, F. E., and Höfle, B.:
Terrestrial, UAV-borne, and airborne laser scanning point clouds of central
European forest plots, Germany, with extracted individual trees and manual
forest inventory measurements, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.942856,
2022a. a, b
Weiser, H., Schäfer, J., Winiwarter, L., Krašovec, N., Seitz,
C., Schimka, M., Anders, K., Baete, D., Braz, A. S., Brand, J.,
Debroize, D., Kuss, P., Martin, L. L., Mayer, A., Schrempp, T.,
Schwarz, L.-M., Ulrich, V., Fassnacht, F. E., and Höfle, B.:
Terrestrial, UAV-borne, and airborne laser scanning point clouds of central
European forest plots, Germany, with extracted individual trees and manual
forest inventory measurements – Metadata Documentation,
https://download.pangaea.de/reference/109167/attachments/SYSSIFOSS_2019-2020_meta_4.pdf (last access: 24 June 2022),
2022b. a, b, c, d, e, f, g
White, J. C., Tompalski, P., Vastaranta, M., Wulder, M. A., Saarinen, N.,
Stepper, C., and Coops, N. C.: A model development and application guide for
generating an enhanced forest inventory using airborne laser scanning data
and an area-based approach, CWFC Information Report FI-X-018, Natural
Resources Canada, Victoria, BC, Canada,
https://cfs.nrcan.gc.ca/pubwarehouse/pdfs/38945.pdf (last access: 24 June 2022), 2017. a
Widlowski, J.-L., Lavergne, T., Pinty, B., Verstraete, M., and Gobron, N.:
Rayspread: A Virtual Laboratory for Rapid BRF Simulations Over 3-D Plant
Canopies, in: Computational Methods in Transport, edited by: Graziani, F.,
211–231, Springer, Berlin, Heidelberg, 211–231, https://doi.org/10.1007/3-540-28125-8_10, 2006. a
Widlowski, J.-L., Mio, C., Disney, M., Adams, J., Andredakis, I., Atzberger,
C., Brennan, J., Busetto, L., Chelle, M., Ceccherini, G., Colombo, R.,
Côté, J.-F., Eenmäe, A., Essery, R., Gastellu-Etchegorry, J.-P., Gobron,
N., Grau, E., Haverd, V., Homolová, L., Huang, H., Hunt, L., Kobayashi, H.,
Koetz, B., Kuusk, A., Kuusk, J., Lang, M., Lewis, P. E., Lovell, J. L.,
Malenovský, Z., Meroni, M., Morsdorf, F., Mõttus, M., Ni-Meister, W.,
Pinty, B., Rautiainen, M., Schlerf, M., Somers, B., Stuckens, J., Verstraete,
M. M., Yang, W., Zhao, F., and Zenone, T.: The fourth phase of the radiative
transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and
conformity testing, Remote Sens. Environ., 169, 418–437,
https://doi.org/10.1016/j.rse.2015.08.016, 2015.
a
Winiwarter, L., Pena, A. M. E., Weiser, H., Anders, K., Sanches, J. M., Searle,
M., and Höfle, B.: 3dgeo-heidelberg/helios, Zenodo [code], https://doi.org/10.5281/zenodo.4452870,
2021. a
Winiwarter, L., Esmorís Pena, A. M., Weiser, H., Anders, K., Martínez
Sánchez, J., Searle, M., and Höfle, B.: Virtual laser scanning with
HELIOS++: A novel take on ray tracing-based simulation of topographic
full-waveform 3D laser scanning, Remote Sens. Environ., 269, 112772,
https://doi.org/10.1016/j.rse.2021.112772, 2022. a, b
Yun, T., An, F., Li, W., Sun, Y., Cao, L., and Xue, L.: A Novel Approach for
Retrieving Tree Leaf Area from Ground-Based LiDAR, Remote Sens., 8, 942,
https://doi.org/10.3390/rs8110942, 2016. a
Zhou, J., Wei, H., Zhou, G., and Song, L.: Separating Leaf and Wood Points in
Terrestrial Laser Scanning Data Using Multiple Optimal Scales, Sensors, 19, 1852,
https://doi.org/10.3390/s19081852, 2019. a
Short summary
3D point clouds, acquired by laser scanning, allow us to retrieve information about forest structure and individual tree properties. We conducted airborne, UAV-borne and terrestrial laser scanning in German mixed forests, resulting in overlapping point clouds with different characteristics. From these, we generated a comprehensive database of individual tree point clouds and corresponding tree metrics. Our dataset may serve as a benchmark dataset for algorithms in forestry research.
3D point clouds, acquired by laser scanning, allow us to retrieve information about forest...
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