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

30 m Grid Dataset of Potential Forestation Land and Its Climatic Potential Productivity in China


XU Jinyong1
1 Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100101，China

DOI:10.3974/geodb.2023.03.02.V1

Published:Mar. 2023

Visitors：6151       Data Files Downloaded：584      
Data Downloaded：430544.43 MB      Citations：

Key Words：

Potential forestation land,Climatic potential productivity,China

Abstract：

Based on integrating analysis to the GLC_FCS30 and GlobeLand30 in 2020 and the Miami climatic potential productivity, the 30 m Grid Dataset of Potential Forestation Land and Its Climatic Potential Productivity in China was developed. The dataset includes the following data: (1) spatial distribution of potential forestation land; (2) Miami climatic potential productivity; (3) spatial distribution of potential afforestation land and reforestation land; (4) areas above the timberline; (5) land sources for potential forestation land; (6) change trends of climatic potential productivity; (7) climatic conditions classification; (8) slope conditions; (9) path distance to the nearest road; (10) the potential forestation land area in each province of China; (11) statistics of forest area (30 m grid number) changes with slope in China. The dataset is archived in .tif, .xlsx and .txt formats, and consists of 68 data files with data size of 7.52 GB (Compressed into 7 files with 4.77 GB). The analysis paper based on the dataset will be published in coordination with Acta Geographica Sinica, Vol. 78, No. 3, 2023.

Foundation Item：

Chinese Academy of Sciences (XDA19090119)

Data Citation：

XU Jinyong. 30 m Grid Dataset of Potential Forestation Land and Its Climatic Potential Productivity in China[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.03.02.V1.

References：

[1] Chen, J., Ban, Y. F., Li, S. China: Open access to earth land-cover map [J]. Nature, 2014, 514(7523): 434-434.
     [2] Esterby, S. R. Review of methods for the detection and estimation of trends with emphasis on water quality applications [J]. Hydrological Processes, 1996, 10: 127-149.
     [3] Holdridge, L. R. Determination of world plant formations from simple climatic data [J]. Science, 1947, 105: 367-368.
     [4] Kendall, M. G. Rank Correlation Methods [M]. 4th ed. London: Charles Griffin, 1975.
     [5] Mann, H. B. Non-parametric tests against trend [J]. Econometrica, 1945, 13(3): 245-259.
     [6] Lieth, H. Primary production: Terrestrial ecosystems [J]. Human Ecology, 1973, 1(4):303-332.
     [7] Peng, S. Z., Ding, Y. X., Liu, W. Z., et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017 [J]. Earth System Science Data, 2019, 11(4): 1931-1946.
     [8] Shadmani, M., Marofi, S., Roknian, M. Trend analysis in reference evapotranspiration using Mann-Kendall and spearman's rho tests in arid regions of Iran [J]. Water Resources Management, 2012, 26(1): 211-224.
     [9] Zhang, X., Liu, L. Y., Chen, X. D., et al. GLC_FCS30: Global land- cover product with fine classification system at 30 m using time-series Landsat imagery [J]. Earth System Science Data, 2021, 13(6): 2753-2776.
     [10] Zhang, X., Liu, L., Wu, C., et al. Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform [J]. Earth System Science Data, 2020, 12(3): 1625-1648.
     [11] Li, Z. J., Duan, C. C., Jin, L. L., et al. Spatial and temporal variability of climatic potential productivity in Yunnan Province, China [J]. Chinese Journal of Applied Ecology, 2019, 30(7): 2181-2190.
     [12] Guo, X. Q., Liu. M. C., Qian, L., et al. Evolution regularity of precipitation in the Shiyang River Basin on Mann-Kendall features [J]. Arid Land Geography, 2010, 33(4): 593-599.
     [13] Guo, X. C., Wu, G. Q. A GIS-based Method for Identifying and Extracting Micro-topography Factors [J]. Electric Power Survey & Design, 2019, S1: 207-209+217.
     [14] Qiu, C. T., Li, D. H. The calculation algorithms for average wind direction and their comparison [J]. Plateau Meteorology, 1997, 16(1): 95-99.
     [15] Wang, X. P., Zhang, L. & Fang, J. Y. Geographical Differences in Alpine Timberline and its Climatic Interpretation in China [J]. Acta Geographic Sinica, 2004, 59(6): 871-879.
     [16] Zhou, G. Y., Xia, J., Zhou, P., et al. Improper vegetation restoration leads to reduction of water resources [J]. Scientia Sinica Terrae, 2021, 51(2):175-182.
     [17] Zhou, G. S., Zheng, Y. R., Chen, S. Q., et al. NPP Model of Natural Vegetation and its Application in China [J]. Scientia Silvae Sinicae, 1998, 34(5): 4-13.
     

Data Product:

ID Data Name Data Size Operation
1 1_Fig3-Potential_forestation_land.rar 664700.39KB
2 2_Fig5-MIAMI_MEAN_for_potential_forestation_land.rar 1713647.20KB
3 3_4_Fig4_Fig2a-Potential_afforestation_reforestation-Above_treeline.rar 453293.19KB
4 5_Fig2b-Land_source.rar 705528.34KB
5 6_7_Fig2c_Fig2d-MIAMI_change_trend-Climate_condition.rar 440790.77KB
6 8_Fig2e-SRTMv3_slpoe_reclass5.rar 875923.05KB
7 9_10_11_Fig2f_Tab1_Fig6.rar 136475.24KB
Co-Sponsors
Superintend