Dataset List

Vol.|Area

No.11 Vol.11,2024
No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
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

Farmland Distribution Dataset in the Yaluzangbu River, Nianchu River and Lhasa River Region of the Tibetan Plateau (2020)


SANG Yiming1,2LU Yahan1,2WANG Xue*1XIN Liangjie*1
1 Key Laboratory of Land Surface Pattern and Simulation，Institute of Geographic Sciences and Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 University of Chinese Academy of Sciences，Beijing 100049，China

DOI:10.3974/geodb.2022.10.04.V1

Published:Oct. 2022

Visitors：7008       Data Files Downloaded：119      
Data Downloaded：1850.87 MB      Citations：

Key Words：

Tibetan Plateau,the Yaluzangbu River, Nianchu River and Lhasa River Region, farmland,2020,Google Earthn

Abstract：

Based on the high spatial resolution remote sensing images of Google Earth (2 m) in 2020, the authors interpreted and developed the farmland distribution dataset in the Yaluzangbu River, Nianchu River and Lhasa River Region of the Tibetan Plateau (2020) (YLN-F2020). Then geostatistical analysis method was taken to analyze the spatial distribution pattern of farmland. The dataset includes the following data in the study area: (1) boundary data; (2) spatial vector data of farmland; (3) spatial raster data of farmland; (4) verification points data of farmland. The results show that the total area of farmland of YLN-F2020 product is 2356.15 km², and the overall accuracy is 95.2%. The farmland in this area is mainly distributed along rivers, and more farmland in the east than in the west, more in the south than in the north. The farmland was mainly distributed in the southwestern area and the eastern area. The dataset is archived in the .shp, and .tif formats, and consists of 30 data files with data size of 568 MB (Compressed into one single file with 15.5 MB).Browse

Foundation Item：

Ministry of Science and Technology of the People's Republic of China (2019QZKK0603)

Data Citation：

SANG Yiming, LU Yahan, WANG Xue*, XIN Liangjie*. Farmland Distribution Dataset in the Yaluzangbu River, Nianchu River and Lhasa River Region of the Tibetan Plateau (2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.10.04.V1.

SANG Yiming, LU Yahan, WANG Xue, et al. Farmland distribution dataset of the Yarlung Zangbo-Lhasa-Nyangqu River region of the Tibetan Plateau [J]. Journal of Global Change Data & Discovery, 2022, 6(4): 619-630.

References：

[1] Yu, L., Wang, J., Clinton, N., et al. FROM-GC: 30 m global cropland extent derived through multisource data integration [J]. International Journal of Digital Earth, 2013, 6(6): 521-533.
     [2] Lv, T. T., Liu, C. Extraction of information of cultivated land using time-series MODIS data in Thailand [J]. Transactions of the CSAE, 2010, 26(2): 244-250.
     [3] Aparna, R. P., Mutlu, O. Large area cropland extent mapping with Landsat data and a generalized classifier [J]. Remote Sensing of Environment, 2018, 219: 180-195.
     [4] Pouliot, D., Latifovic, R., Zabcic, N., et al. Development and assessment of a 250 m spatial resolution MODIS annual land cover time series (2000-2011) for the forest region of Canada derived from change-based updating [J]. Remote Sensing of Environment, 2014, 140: 731-743.
     [5] Han, K. S., Champeaux, J. L., Roujean, J. L. A land cover classification product over France at 1 km resolution using SPOT4/ VEGETATION data [J]. Remote Sensing of Environment, 2004, 92(1): 52-66.
     [6] Zhang, M., Wu, B. F., Yu, M. Z., et al. Concepts and implementation of monthly monitoring of uncropped arable land: A case study in Argentina [J]. Journal of Remote Sensing, 2015, 19(4): 550-559.
     [7] Liu, J. Y., Liu, M. L., Tian, H. Q., et al. Spatial and temporal patterns of China's cropland during 1990-2000: An analysis based on Landsat TM data [J]. Remote Sensing of Environment, 2005, 98: 442-456.
     [8] Zhang, Z., Wang, X., Zhao, X., et al. A 2010 update of National Land Use/ Cover Database of China at 1: 100000 scale using medium spatial resolution satellite images [J]. Remote Sensing of Environment, 2014, 149: 142-154.
     [9] Zhang, Y. L., Liu, L. S., Wang, Z. F., et al. Spatial and temporal characteristics of land use and cover changes in the Tibetan Plateau [J]. Chinese Science Bulletin, 2019, 64(27): 2865-2875.
     [10] Wei, H., Lv, C. H., Yang, K. J., et al. Spatial distribution dataset for facility agriculture in the Tibetan Plateau and two typical regions [J]. Journal of Global Change Data & Discovery, 2019, 3(4): 364-369+471-476.
     [11] Chen, F., Chen, J., Wu, H., et al. A landscape shape index-based sampling approach for land cover accuracy assessment [J]. Science China Earth Sciences, 2016, 59(12): 2263-2274.
     [12] Chen, X. Y., Lin, Y., Zhang, M., et al. Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China [J]. Journal of Integrative Agriculture, 2017, 16(2): 298-311.
     [13] Chen, J., Cao, X., Peng, S., et al. Analysis and applications of GlobeLand30: A review [J]. ISPRS International Journal of Geo-Information, 2017, 6(8): 230.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_YLN-F2020.pdf9059.00kbDownLoad
1 YLN-F2020.rar 15926.82KB
Co-Sponsors
Superintend