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Data Details

Experimental Dataset for Rapid Generation of Grassland Key Parameters from UAV Images


WANG Dongliang1LI Yuzhe1ZHANG Aochong1
1 Key Laboratory of Land Surface Pattern and Simulation，Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China

DOI:10.3974/geodb.2024.02.03.V1

Published:Feb. 2024

Visitors：3607       Data Files Downloaded：15      
Data Downloaded：3.48 MB      Citations：

Key Words：

UAV imagery,VDVI,FVC,AGB

Abstract：

On July 19, 2023, the authors used a fixed-wing UAV to quickly take aerial photographs of the pasture, and calculated the key parameters of grassland including FVC, AGB, etc. The dataset includes: (1) FVC data; (2) AGB data. The dataset is archived in .shp data format, and consists of 16 data files with data size of 1.23 MB (Compressed to 1 file with data size of 237 KB).Browse

Foundation Item：

Ministry of Science and Technology of P. R. China (2021YFD1300501); Chinese Academy of Sciences (XDA23100200)

Data Citation：

WANG Dongliang, LI Yuzhe, ZHANG Aochong. Experimental Dataset for Rapid Generation of Grassland Key Parameters from UAV Images[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.02.03.V1.

WANG Dongliang. A quick generation method for key parameters of grassland at the hourly scale in ranch scale [J]. Journal of Global Change Data & Discovery, 2023, 7(4): 399-405.

References：


     [1] Li, B. The Rangeland Degradation in North China and Its Preventive Strategy [J]. Scientia Agricultura Sinica, 1997, 30(6): 2-10.
     [2] Tang, G. J., Bao, Q. D., Nomadic civilization: research on the wisdom of survival and development and its ecological dimension [J]. Heilongjiang National Series, 2023, (1): 137-143.
     [3] Shen, H. H., Zhu, Y. K., Zhao, X., et al. Analysis on the current situation of grassland resources in China [J]. Chinese Science Bulletin, 2016, 61(2): 139-154.
     [4] Wang, D., Xin, X., Shao, Q., et al. Modeling aboveground biomass in Hulunber grassland ecosystem by using unmanned aerial vehicle discrete lidar [J]. Sensors, 2017, 17(1): 180.
     [5] Wang, D., Liao, X. H., Zhang, Y. J., et al. Grassland livestock real-time detection and weight estimation based on unmanned aircraft system video streams [J]. Chinese Journal of Ecology, 2021, 40(12): 4099-4108.
     [6] Wang, D., Song, Q., Liao, X. H., et al. Integrating satellite and unmanned aircraft system (UAS) imagery to model livestock population dynamics in the Longbao wetland national nature reserve, china [J]. Science of the Total Environment, 2020, 746: 140327.
     [7] Wang, X., Zuo, X. Q., Modeling and visualization of drone oblique photographic data based on ODM and Cesium [J]. Computer Engineering & Software, 2020, 41(4): 124-129.
     [8] Wang, X. Q., Wang, M. M., Wang, S. Q., et al. Extraction of vegetation information from visible unmanned aerial vehicle images [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 152-158.
     [9] Du, M. M., Noboru, N., Atsushi, I., et al. Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle [J]. International Journal of Agricultural and Biological Engineering, 2017, 10(5): 1-13.
     [10] Zhou, J., Zhang, K., Du, T. Research on vegetation cover variations in reservoir areas based on satellite remote sensing: A case study of Sanhekou Reservoir Area [J]. Water Resources and Hydropower Engineering, 2023, 1(1): 1-12.
     [11] Wang, Y., Ma, L., Wang, Q. et al. A lightweight and high-accuracy deep learning method for grassland grazing livestock detection using UAV imagery [J]. Remote Sensing, 2023, 15(6): 1593.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_UAV_AGB_FVC.pdf4023.00kbDownLoad
1 UAV_AGB_FVC.rar 237.31KB
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