Estimation of above-ground biomass using machine learning approaches with InSAR and LiDAR data in tropical peat swamp forest of Brunei Darussalam
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Italian Society of Sivilculture and Forest Ecology (SISEF)
Reference41 articles.
1. ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2
2. Shorea albida Sym. does not regenerate in the Badas peat swamp forest, Brunei Darussalam – An assessment using remote sensing technology
3. Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass
4. Improved allometric models to estimate the aboveground biomass of tropical trees
5. Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery
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