Dataset List

Vol.|Area

No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
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：716       Data Files Downloaded：9      
Data Downloaded：2.09 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).

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.

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
1 UAV_AGB_FVC.rar 237.31KB
Co-Sponsors
Superintend