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Modeling aboveground biomass in São Paulo State Atlantic Forest: an upscalling approach

Grant number: 23/00241-0
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): September 01, 2023
Effective date (End): February 28, 2026
Field of knowledge:Biological Sciences - Ecology - Ecosystems Ecology
Principal Investigator:Paulo Guilherme Molin
Grantee:Giulio Brossi Santoro
Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Associated research grant:21/11940-0 - Restoration of native vegetation in the Atlantic Forest through the strategic combination of mandatory measures and voluntary commitments - CCD-EMA, AP.CCD


Recently, as a consequence of climate change, the growing demand for environmental awareness and ecosystem restoration have become evident. Furthermore, there is also the need to proper understand and assess the carbon stocks in tropical forests. The use of data from active remote sensors such as LiDAR (Light Detection and Ranging) has great potential for measuring forest attributes - such as Above Ground Biomass (AGB) - due to its ability to characterize the vertical structure of the forest, which also allows the creation of predictive models for the evaluated attributes. The GEDI sensor ("Global Ecosystem Dynamics Investigation") is one of the newest and most promising technologies used to monitor vegetation worldwide. It is an orbital LiDAR sensor specifically designed to characterize and describe the structure of tropical forests. In this context, this research seeks to explore the potential of combining multiscale spatial data to estimate the AGB of forest vegetation in the state of São Paulo in the best and most accurate way possible. The study goal is to develop statistical models to estimate the AGB of the state forest vegetation using the correlation between field estimates (forest inventory) and airborne LiDAR data and later with GEDI data (orbital LiDAR), allowing predictions with gain of scale anchored by reliable field estimates. It is expected that the developed models present statistical consistency with high values for the coefficient of determination (R²) and acceptable values for evaluation metrics (Root Mean Squared Error - RMSE - and Mean Squared Error - MSE). Therefore, the models will deliver accurate estimates that can guide policymaking at local and regional scales, pointing out regions with a higher importance for conservation, while also identifying locations that lack dense vegetation capable of stocking/sequestering carbon, therefore contributing to ecological restoration initiatives and climate control. (AU)

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