Forests have been providing essential benefits to humanity long before the concept of forest bathing became popularized. These ecosystems serve as significant carbon sinks and offer a variety of ecosystem services, including timber, non-timber products, recreation, and climate regulation. To comprehend carbon storage and promote sustainable forest management, precise assessments of forest biomass are critical. However, due to their vast three-dimensional nature, studying forests has traditionally posed considerable challenges.
Measuring the height of individual trees has historically been a labor-intensive endeavor, particularly in remote or steep terrains, making large-scale forest assessments difficult. Consequently, researchers have faced limitations in formulating accurate biomass estimation equations. Recently, advancements in drone technology, specifically LiDAR (Light Detection and Ranging), have emerged as powerful tools for researchers, facilitating more efficient measurements of forest biomass and tree structures.
A research team from Kyoto University harnessed the potential of LiDAR to conduct an extensive forest survey across Japan. They assessed the crown structures of 4,326 canopy trees, representing 149 different species, across 23 distinct forest census plots, ranging from sub-boreal forests in Hokkaido to subtropical forests in Okinawa. By integrating data obtained from drones with detailed ground-based measurements, the team developed species-specific equations for estimating tree biomass based on crown structural characteristics.
“To our knowledge, this represents the most comprehensive study to date estimating biomass for such a large number of tree species using drone-based data,” stated first author Kyaw Kyaw Htoo. The analysis demonstrated that a model relying solely on tree height and crown area could explain 72% of the variation in biomass. This explanatory power increased to 79% when considering functional types, such as conifer, deciduous broadleaf, and evergreen broadleaf trees, and rose to 83% when species-level information was integrated.
Despite drones being unable to directly capture data on understory trees, the study indicated that canopy trees generally account for around 75% of the total forest biomass across various forest types. Team leader Yusuke Onoda remarked, “These results provide a foundation for estimating total forest biomass, including the understory.” This innovative approach allows for more accurate biomass estimation in species-rich natural forests by offering a repeatable, scalable, and data-driven tool for long-term monitoring and evaluation of forest ecosystems.
Additionally, this methodology holds the potential to enhance the accuracy of carbon credit assessments and biodiversity monitoring efforts. “By effectively utilizing these tools, we aim to improve the efficiency of forest resource assessments and contribute to both scientific advancement and the promotion of sustainable forestry practices,” Htoo emphasized.
