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Feature selection from Multi-Angular TECIS data for forest biomass estimation using a Model-driven sensitivity analysis approach
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DOI：10.1016/j.jag.2026.105519.png)
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Abstract
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• A Copula–Shapley sensitivity framework was proposed to account for dependencies among forest structural parameters in feature assessment. • An allometric equation-based projection operator was developed to map input sensitivities to the AGB domain. • The proposed framework identified 17 AGB-sensitive features from TECIS DMC multi-angle data, providing a reliable basis for biomass estimation.
Keywords:
TECIS
Multi-angular remote sensing
AGB
Copula-Shapley
Feature selection
Sensitivity analysis
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