A systematic review of remote sensing and machine learning approaches for accurate carbon storage estimation in natural forests
Author:
Affiliation:
1. Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
Funder
DST_NRF SARchi Landuse Planning and Management UKZN
Publisher
National Inquiry Services Center (NISC)
Subject
Forestry
Link
https://www.tandfonline.com/doi/pdf/10.2989/20702620.2023.2251946
Reference143 articles.
1. Mapping mangrove extents on the Red Sea coastline in Egypt using polarimetric SAR and high resolution optical remote sensing data;Abdel-Hami A;Sustainability,2018
2. Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review
3. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm
4. Quantifying forest carbon stocks by integrating satellite images and forest inventory data;Ali A;Austrian Journal of Forest Science,2018
5. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
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