In the realm of energy and climate research, a team of scientists from the University of Alberta, Canada, led by Nelson Mattié, Arturo Sanchez-Azofeifa, Pablo Crespo-Peremarch, and Juan-Ygnacio López-Hernández, has been working on improving methods to estimate aboveground biomass in tropical dry forests. Their work, published in the journal Remote Sensing of Environment, is crucial for understanding and reporting on carbon emissions and absorption, key factors in the global fight against climate change.
Tropical dry forests are among the least understood tropical forest environments, making accurate estimations of their carbon pools a significant challenge. The researchers employed a comparative analysis using different laser scanning datasets—Airborne Laser Scanning (ALS), Unmanned Laser Scanning (ULS), and Space Laser Scanning (SLS)—to estimate aboveground biomass (AGB). These datasets were used in conjunction with Ordinary Least Squares and Bayesian approaches, specifically Support Vector Machine (SVM) regressions, to extract forest metrics.
The study identified six key variables primarily related to tree height that are important for AGB estimation using ALS and ULS. These variables include Elevminimum, ElevL3, ElevMADmode, Elevmode, ElevMADmedian, and Elevskewness. For SLS, the most vital variables were Leaf Area Index, canopy coverage and height, terrain elevation, and full-waveform signal energy. The researchers applied variable selection, SVM regression tuning, and cross-validation to account for overfitting and underfitting, ensuring the robustness of their models.
The AGB values estimated from ten permanent tropical dry forest plots in Costa Rica’s Guanacaste province ranged from 26.02 Mg/ha to 175.43 Mg/ha. The SVM regressions demonstrated an average error of 17.89 across all laser scanning systems, with the SLSF W system showing the lowest error of 17.07 in estimating total biomass per plot.
For the energy sector, accurate estimation of aboveground biomass is crucial for understanding carbon sequestration potential in forest ecosystems. This research provides a more precise method for assessing carbon stocks, which can inform policies and practices aimed at reducing greenhouse gas emissions. By improving the accuracy of carbon reporting, this study supports the energy industry’s efforts to meet the obligations set out in the Paris Climate Change Agreement. The practical applications of this research extend to forest management, carbon trading, and the development of renewable energy projects that rely on sustainable forest resources.
The researchers’ work highlights the importance of advanced remote sensing techniques in enhancing our understanding of forest ecosystems and their role in mitigating climate change. As the energy sector continues to seek sustainable solutions, the ability to accurately estimate and monitor carbon stocks will be increasingly vital. This study represents a significant step forward in achieving these goals.
This article is based on research available at arXiv.