Machine Learning-Based Forest Type Mapping from Multi-Temporal Remote Sensing Data: Performance and Comparative Analysis
Author:
Affiliation:
1. Department of Computer Engineering, Ahmadu Bello University, Zaria 810211, Nigeria
2. Department of Electrical and Electronics Engineering, Kaduna Polytechnic, Kaduna 800282, Nigeria
Publisher
MDPI
Link
https://www.mdpi.com/2673-4931/29/1/9/pdf
Reference12 articles.
1. Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms;Shang;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2013
2. Tree species mapping with Airborne hyper-spectral MIVIS data: The Ticino Park study case;Boschetti;Int. J. Remote Sens.,2007
3. Joshi, C., De Leeuw, J., and van Duren, I.C. (2004, January 12–23). Remote sensing and GIS applications for mapping and spatial modelling of invasive species. Proceedings of the ISPRS, Istanbul, Turkey.
4. Biswas, S., Huang, Q., Anand, A., Mon, M.S., Arnold, F.-E., and Leimgruber, P. (2020). A multi sensor approach to forest type mapping for advancing monitoring of sustainable development goals (SDG) in Myanmar. Remote Sens., 12.
5. Stability of soil organic carbon pools affected by land use and land cover changes in forests of eastern Himalayan region, India;Ahirwal;Catena,2022
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