Comprehensive measurements of global forest aboveground biomass (AGB) are crucial information to promote the sustainable management of forests to mitigate climate change and preserve the multiple ecosystem services provided by forests. Optical and radar sensors are available at different spatial, spectral, and temporal scales. The integration of multi-sources sensor data with field measurements, using appropriated algorithms to identify the relationship between remote sensing predictors and reference measurements, is important to improve forest AGB estimation. This chapter aims to present different types of predicted variables derived from multi-sources sensors, such as original spectral bands, transformed images, vegetation indices, textural features, and different regression algorithms used (parametric and non-parametric) that contribute to a more robust, practical, and cost-effective approach for forest AGB estimation at different levels.
Part of the book: Forest Biomass