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UAV-Based Synchronous Observation Dataset of Greenhouse Gases and Wind Fields over a Subtropical Plantation at Qianyanzhou Ecosystem Station (2023-2024)


SUN Haijiong1LI Shaomeng*2WANG Yifei3GUO Junfei4GAO Xiangyu4
1 Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China2 College of Environmental Sciences and Engineering,Peking University,Beijing 100101,China3 Inner Mongolia Institute of Metrology and Testing,Hohhot 010050,China4 Beijing WeisiDe Technology Co.,Ltd.,Beijing 100070,China

DOI:10.3974/geodb.2026.08.07.V1

Published:Aug. 2026

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Key Words:

forest ecosystem,carbon dioxide,unmanned aerial vehicle,Qianyanzhou

Abstract:

Synchronous observations of greenhouse gas concentrations, wind fields, and spatial position information above forest canopies provide an important data basis for investigating vegetation-atmosphere exchange processes in forest ecosystems over complex terrain and for quantitatively assessing forest carbon sources and sinks. From August 2023 to December 2024, the authors conducted multi-period and multi-season atmospheric observations above the forest canopy of the Qianyanzhou subtropical plantation using an unmanned aerial vehicle (UAV) observation platform equipped with a gas analyzer, a three-dimensional ultrasonic anemometer, and a high-precision positioning system. Based on these observations, the UAV-Based Synchronous Observation Dataset of Greenhouse Gases and Wind Fields over a Subtropical Plantation at Qianyanzhou Ecosystem Station (2023–2024) was developed. Using UAV flight time as the common temporal axis, the dataset synchronously integrates flight trajectory, meteorological, and gas concentration observations. It mainly includes: (1) UAV flight time and location; (2) ambient temperature data acquired synchronously during flight, wind direction and wind speed observations above the forest canopy; (3) carbon dioxide, water vapor, and nitrous oxide concentration data observed using gas analyzer. The dataset is archived in .xlsx format, and consists of 3 data files with data size of 21.4 MB (compressed into 1 file with 20.3 MB).

Foundation Item:

Ministry of Science and Technology of P. R. China (2024YFF0618500)

Data Citation:

SUN Haijiong, LI Shaomeng*, WANG Yifei, GUO Junfei, GAO Xiangyu. UAV-Based Synchronous Observation Dataset of Greenhouse Gases and Wind Fields over a Subtropical Plantation at Qianyanzhou Ecosystem Station (2023-2024)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.08.07.V1.
.

References:


     [1] Pan, Y., Birdsey, R. A., Fang J., et al. A large and persistent carbon sink in the world's forests[J]. Science, 2011, 333(6045): 988-993.
     [2] Baldocchi, D., Falge, E., Gu, L., et al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities [J]. Bulletin of the American Meteorological Society, 2001, 82(11): 2415-2434.
     [3] Baldocchi, D. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future [J]. Global Change Biology, 2003, 9(4): 479-492.
     [4] Feigenwinter, C., Bernhofer, C., Vogt, R. The influence of advection on the short term CO2-budget in and above a forest canopy [J]. Boundary-Layer Meteorology, 2004, 113(2): 201-224.
     [5] Etzold, S., Buchmann, N., Eugster, W. Contribution of advection to the carbon budget measured by eddy covariance at a steep mountain slope forest in Switzerland[J]. Biogeosciences, 2010, 7(8): 2461-2475.
     [6] Bolek, A., Heimann, M., Göckede, M. UAV-based in situ measurements of CO2 and CH4 fluxes over complex natural ecosystems [J]. Atmospheric Measurement Techniques, 2024, 17(18): 5619-5636.
     [7] Sun, Y., Ma, J., Sude, B., et al. A UAV-based eddy covariance system for measurement of mass and energy exchange of the ecosystem: preliminary results [J]. Sensors, 2021, 21(2): 403.
     [8] Liu, Y. F., Yu, G. R., Wen, X. F., et al. Seasonal dynamics of CO2 fluxes from subtropical plantation coniferous ecosystem [J]. Science in China Series D-Earth Sciences, 2006, 49: 99-109.
     [9] Xu, M. J., Wang, H. M., Wen, X. F., et al. The full annual carbon balance of a subtropical coniferous plantation is highly sensitive to autumn precipitation [J]. Scientific Reports, 2017, 7.
     [10] Watai, T., Machida, T., Ishizaki, N., et al. A lightweight observation system for atmospheric carbon dioxide concentration using a small unmanned aerial vehicle [J]. Journal of Atmospheric and Oceanic Technology, 2006, 23(5): 700-710.
     [11] Kunz, M., Lavric, J. V., Gerbig, C., et al. COCAP: a carbon dioxide analyser for small unmanned aircraft systems [J]. Atmospheric Measurement Techniques, 2018, 11: 1833-1849.
     [12] Yang, Y. R., Zhang, Y. H., Han, T. R., et al. A correction algorithm for rotor-induced airflow and flight attitude changes during three-dimensional wind speed measurements made from a rotary unoccupied aerial vehicle [J]. Atmospheric Measurement Techniques, 2025, 18(13): 3035-3050.
     [13] Han, T. R., Xie, C. H., Liu, Y. Y., et al. Development of a continuous UAV-mounted air sampler and application to the quantification of CO2 and CH4 emissions from a major coking plant [J]. Atmospheric Measurement Techniques, 2024, 17(2): 677-691.
     [14] Vickers, D., Mahrt, L. Quality control and flux sampling problems for tower and aircraft data [J]. Journal of Atmospheric and Oceanic Technology, 1997, 14(3): 512-526.
     [15] Hojstrup, J. A statistical data screening procedure [J]. Measurement Science and Technology, 1993, 4(2): 153-157.
     [16] Foken, T., Wichura, B. Tools for quality assessment of surface-based flux measurements [J]. Agricultural and Forest Meteorology, 1996, 78(1-2): 83-105.

Data Product:

ID Data Name Data Size Operation
1 QYZ_UAV_GHG_Wind_2023_2024.rar 20851.99KB
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