Dataset on Changes in National Borders Forests on the African Continent and the Contribution of Their Major Factors
TANG Mengya1LI Peng*2,3LI Xia1CHEN Shengmei4Jeffrey Chiwuikem CHIAKA5,6
1 School of Land Engineering，Chang'an University，Xi'an 710054，China2 Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China3 College of Resources and Environment，University of Chinese Academy of Sciences，Beijing 100049，China4 School of Geosciences，Yangtze University，Wuhan 430100，China5 State Key Joint Laboratory of Environmental Simulation and Pollution Control，School of Environment，Beijing Normal University，Beijing 100875，China6 Anambra-Imo River Basin Development Authority，Owerri 1301，Nigeria
DOI:10.3974/geodb.2025.07.01.V1
Published:July 2025
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Key Words：
Africa Continent,border land use,forest loss,active fire
Abstract：
Based on the data of land cover, active fires, population density and armed conflicts from 2017-2022, the authors use Random Forest Regression and Correlation Analysis to define the factors influencing forest change and quantify their main causes and contributions along the borders of the African Continent. The dataset includes: (1) boundary of study area; (2) percentage of main land cover types converted within the African international borders of African Continent during 2017-2022; (3) changes in the number of grids of forest degraded areas with active fire zones at country borders on the African Continent during 2017-2022; (4) inter-monthly changes in the number of correlated grids between forest change and active fire occurrence within the international borders of continental Africa during 2017-2022; (5) percentage statistics on varied levels of forest change and active fire disturbance within the international borders of continental Africa during 2017-2022; (6) percentage of importance of factors driving change in border forests in African countries; (7) percentage of active fire contribution to forest change in the borders of African countries; and (8) spatial distribution of the correlation between forest changes along the African Continent’s border and the occurrence of active fires, population density, and armed conflicts from 2017 to 2022. The dataset is archived in .shp, .xlsx and .tif formats, and consists of 108 data files with total size of 33.7 MB (compressed into 1 file, 11.5 MB). The analysis paper based on the dataset was published in Acta Geographica Sinica, Vol. 80, No. 5, 2025.
Foundation Item：
National Natural Science Foundation of China (42371282, 42130508)
Data Citation：
TANG Mengya, LI Peng*, LI Xia, CHEN Shengmei, Jeffrey Chiwuikem CHIAKA.Dataset on Changes in National Borders Forests on the African Continent and the Contribution of Their Major Factors[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.07.01.V1.
References：
[1] Karra, K., Kontgis, C., Statman-Weil, Z., et al. Global land use / land cover with Sentinel 2 and deep learning [C]. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021.
[2] United Nations Department of Economic and Social Affairs - Population Division. Definition of regions [EB/OL]. World Population Prospects 2024. https://population.un.org/wpp/definition-of-regions.
[3] Schroeder, W., Oliva, P., Giglio, L., et al. The New VIIRS 375m active fire detection data product: algorithm description and initial assessment [J]. Remote Sensing of Environment, 2014, 143(4): 85-96.
[4] Li, P., Liu, Y., Shi, D., et al. Comparison of the consistency and discrepancy of three remotely-sensed active fire products (MODIS C6, VIIRS V1 and VIIRS J1) [J]. Geographical Research, 2022, 41(5): 1481-1495.
[5] Bright, E. A., Coleman, P. R., Dobson, J. E. LandScan: a global population database for estimating populations at risk [J]. Photogrammetric Engineering and Remote Sensing, 2000, 66(7): 849-858.
[6] Davies, S., Pettersson, T., Öberg, M. Organized violence 1989–2022, and the return of conflict between states [J]. Journal of Peace Research, 2023, 60(4): 691-708.
[7] Chen, S. M., Li, P., Feng, Z. M., et al. Forest disturbance process caused by the expansion of agricultural and construction lands in the border zones of Mainland Southeast Asian countries and their neighbors [J]. Progress in Geography, 2024, 43(4): 741-754.
[8] Hu, T., Peng, J., Dong, J. Q., et al. Forest definitions collaboration based on global remote sensing data products [J]. Acta Geographica Sinica, 2024, 79(5): 1115-1128.
[9] Du, S. K., Zhang, J., Han, Z. J., et al. Armed conflict risk prediction and influencing factors analysis based on the random forest model at the grid-month scale: A case study of Indochina Peninsula. Journal of Geo-Information Science, 2023, 25(10): 2026-2038.
[10] Breiman, L. Random forests [J]. Machine Learning, 2001, 45(1): 5-32.
Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 1 |
AfricaForestChange&Factors2017-2022.rar |
11826.36KB |
|