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

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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. 
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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.
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