High technology to measure forest carbon stocks:
How can advanced remote sensing improve biomass quantification in the Brazilian Amazon?
Remote sensing technology has advanced rapidly, making it possible to scan a forest in 3D and obtain hundreds of images in radiation bands that go beyond what our eyes can see. A large amount of highly detailed information about forests opens a possibility to improve carbon stock estimates, but it also creates a challenge to find the best modeling strategies.
The accurate estimation of aboveground biomass (AGB) is paramount for effective strategies in ecosystem conservation and climate change mitigation. However, estimating AGB at regional and local scales remains a challenge. The good news is that we have powerful tools at our disposal: advanced remote sensing technologies like Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI). These groundbreaking tools offer us a unique perspective on the diverse structure and functionality of forests, capturing details at a sub-meter resolution. By harnessing their synergistic potential, we can improve AGB estimates and contribute to a sustainable future.
In this case study, we will embark on a journey into the Brazilian Amazon, where we explore the potential of airborne LiDAR, HSI, and their synergistic combination. Through experiments with different regression methods, we seek to improve our predictions of AGB in this remarkable rainforest region.
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