Articles | Volume 14, issue 11
https://doi.org/10.5194/essd-14-4967-2022
https://doi.org/10.5194/essd-14-4967-2022
Data description paper
 | 
11 Nov 2022
Data description paper |  | 11 Nov 2022

SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches

Femke van Geffen, Birgit Heim, Frederic Brieger, Rongwei Geng, Iuliia A. Shevtsova, Luise Schulte, Simone M. Stuenzi, Nadine Bernhardt, Elena I. Troeva, Luidmila A. Pestryakova, Evgenii S. Zakharov, Bringfried Pflug, Ulrike Herzschuh, and Stefan Kruse

Related authors

Hydroclimatic anomalies detected by a sub-decadal diatom oxygen isotope record of the last 220 years from Lake Khamra, Siberia
Amelie Stieg, Boris K. Biskaborn, Ulrike Herzschuh, Jens Strauss, Luidmila Pestryakova, and Hanno Meyer
Clim. Past, 20, 909–933, https://doi.org/10.5194/cp-20-909-2024,https://doi.org/10.5194/cp-20-909-2024, 2024
Short summary
LegacyVegetation 1.0: Global reconstruction of vegetation composition and forest cover from pollen archives of the last 50 ka
Laura Schild, Peter Ewald, Chenzhi Li, Raphaël Hébert, Thomas Laepple, and Ulrike Herzschuh
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-486,https://doi.org/10.5194/essd-2023-486, 2024
Preprint under review for ESSD
Short summary
A global compilation of diatom silica oxygen isotope records from lake sediment – trends and implications for climate reconstruction
Philip Meister, Anne Alexandre, Hannah Bailey, Philip Barker, Boris K. Biskaborn, Ellie Broadman, Rosine Cartier, Bernhard Chapligin, Martine Couapel, Jonathan R. Dean, Bernhard Diekmann, Poppy Harding, Andrew C. G. Henderson, Armand Hernandez, Ulrike Herzschuh, Svetlana S. Kostrova, Jack Lacey, Melanie J. Leng, Andreas Lücke, Anson W. Mackay, Eniko Katalin Magyari, Biljana Narancic, Cécile Porchier, Gunhild Rosqvist, Aldo Shemesh, Corinne Sonzogni, George E. A. Swann, Florence Sylvestre, and Hanno Meyer
Clim. Past, 20, 363–392, https://doi.org/10.5194/cp-20-363-2024,https://doi.org/10.5194/cp-20-363-2024, 2024
Short summary
The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model
Moritz Langer, Jan Nitzbon, Brian Groenke, Lisa-Marie Assmann, Thomas Schneider von Deimling, Simone Maria Stuenzi, and Sebastian Westermann
The Cryosphere, 18, 363–385, https://doi.org/10.5194/tc-18-363-2024,https://doi.org/10.5194/tc-18-363-2024, 2024
Short summary
Circumarctic landcover diversity considering wetness gradients
Annett Bartsch, Aleksandra Efimova, Barbara Widhalm, Xaver Muri, Clemens von Baeckmann, Helena Bergstedt, Ksenia Ermokhina, Gustaf Hugelius, Birgit Heim, and Marina Leibmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2295,https://doi.org/10.5194/egusphere-2023-2295, 2023
Short summary

Related subject area

Domain: ESSD – Land | Subject: Land Cover and Land Use
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024,https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024,https://doi.org/10.5194/essd-16-1623-2024, 2024
Short summary
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024,https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024,https://doi.org/10.5194/essd-16-1333-2024, 2024
Short summary
A 2020 forest age map for China with 30 m resolution
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024,https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary

Cited articles

Abdi, A. M.: Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data, GISci Remote Sens., 57, 1–20, https://doi.org/10.1080/15481603.2019.1650447, 2020. 
ABoVE Science Definition Team: A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment, ORNL DAAC, Oak Ridge, Tennessee, USA, [data set], https://doi.org/10.3334/ORNLDAAC/1617, 2014. 
Agisoft LLC: Agisoft PhotoScan Professional, Version 1.4.3; Agisoft LLC: St. Petersburg, Russia, 2018. 
Alexander, H., Paulson, A., DeMarco, J., Hewitt, R., Lichstein, J., Loranty, M., Mack, M., McEwan, R., Borth, E., Frankenberg, S., and Robinson, S.: Fire influences on forest recovery and associated climate feedbacks in Siberian Larch Forests, Russia, 2018–2019, Arctic Data Center, https://doi.org/10.18739/A2XG9FB90, 2020. 
Astola, H., Seitsonen, L., Halme, E., Molinier, M., and Lönnqvist, A.: Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest, Remote Sens.-Basel, 13, 2392, https://doi.org/10.3390/rs13122392, 2021. 
Download
Short summary
SiDroForest is an attempt to remedy data scarcity regarding vegetation data in the circumpolar region, whilst providing adjusted and labeled data for machine learning and upscaling practices. SiDroForest contains four datasets that include SfM point clouds, individually labeled trees, synthetic tree crowns and labeled Sentinel-2 patches that provide insights into the vegetation composition and forest structure of two important vegetation transition zones in Siberia, Russia.
Altmetrics
Final-revised paper
Preprint