Data mining applied to feature selection methods for aboveground carbon stock modelling
نویسندگان
چکیده
Abstract The objective of this work was to apply the random forest (RF) algorithm modelling aboveground carbon (AGC) stock a tropical by testing three feature selection procedures – recursive removal and uniobjective multiobjective genetic algorithms (GAs). used database covered 1,007 plots sampled in Rio Grande watershed, state Minas Gerais state, Brazil, 114 environmental variables (climatic, edaphic, geographic, terrain, spectral). best strategy RF with GA reaches minor root-square error 17.75 Mg ha-1 only four spectral normalized difference moisture index, burnratio 2 correlation text ure, treecover, latent heat flux –, which represents reduction 96.5% size database. Feature strategies assist obtaining better performance, improving accuracy reducing volume data. Although showed similar performance as strategies, latter presents smallest subset variables, highest accuracy. findings study highlight importance using near infrared, short wavelengths, derived vegetation indices for remote-sense-based estimation AGC. MODIS products show significant relationship AGC should be further explored scientific community stock.
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ژورنال
عنوان ژورنال: Pesquisa Agropecuaria Brasileira
سال: 2022
ISSN: ['1678-3921', '0100-204X']
DOI: https://doi.org/10.1590/s1678-3921.pab2022.v57.03015