In the heart of the Indian subcontinent, a groundbreaking study led by S. Mamgain of the Indian Institute of Remote Sensing, Indian Space Research Organization (ISRO), is revolutionizing our understanding of aboveground biomass (AGB) distribution. The research, published in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, evaluates three machine learning models—Random Forest (RF), Gradient Tree Boosting (GTB), and Classification and Regression Trees (CART)—to predict AGB across the region. This isn’t just about trees; it’s about carbon stocks, ecosystem dynamics, and the future of energy and environmental management.
The study uses a range of vegetation and topographic layers as predictors, including the Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and Fraction of Photosynthetically Active Radiation, along with land cover and various topographic features. These predictors are crucial for capturing the ecological and topographical characteristics that influence biomass distribution. The models were evaluated using the coefficient of determination (R²) and Pearson’s correlation coefficient (r) to assess predictive accuracy.
The results are striking. The Random Forest model emerged as the most accurate, with an R² value of 0.834 and an r value of 0.913. This model effectively captures the spatial variability in AGB across the subcontinent’s diverse ecosystems. “The Random Forest model’s ability to handle complex interactions between variables makes it particularly well-suited for this type of analysis,” Mamgain explains. “It allows us to predict AGB with a high degree of accuracy, which is essential for effective forest management and carbon accounting.”
The predictions for 2023 reveal significant spatial variation in biomass density, reflecting the region’s diverse ecological zones and land-use patterns. In India, high biomass densities are found in the Himalayan foothills, northeastern states, and Western Ghats, while arid regions like Rajasthan and Gujarat have lower values. Pakistan generally exhibits low biomass densities, with higher values near the northern border with India. Nepal and Bhutan show high densities in their forested regions, particularly in the mid-hills, high mountains, and Eastern Himalaya. Bangladesh has moderate to low biomass densities. In Sri Lanka, the central highlands and southwestern rainforests have the highest biomass densities, while the more arid northern and eastern regions exhibit lower values.
This research has profound implications for the energy sector. Accurate predictions of AGB are crucial for carbon accounting and can inform policies aimed at reducing carbon emissions. “Understanding the distribution of biomass is not just an academic exercise,” Mamgain notes. “It has real-world applications in forest management, carbon accounting, and biodiversity conservation. By using robust machine learning models, we can make more informed decisions about how to manage our forests and mitigate the impacts of climate change.”
The study highlights the importance of using advanced machine learning techniques to accurately capture spatial patterns of biomass distribution. This could shape future developments in the field by providing a more nuanced understanding of ecosystem dynamics and carbon stocks. As we move towards a more sustainable future, the ability to predict and manage biomass distribution will be increasingly important. This research is a significant step in that direction, offering a roadmap for how machine learning can be used to inform environmental policy and practice.
