There are an estimated three trillion trees on the planet that researchers hope to measure and map. Though this will take some time, investigators at the University of New Hampshire have found a way to improve tree mapping capabilities and find trees with LiDAR.
Researchers are looking at factors influencing the ability to effectively apply automated tree crown delineation methods in temperate forests. This approach uses LiDAR to segment the forest canopy into individual trees, thus allowing researchers to derive meaningful information such as estimates of tree size, species identity, or carbon stocks. This information is helpful to further manage natural resources, track biodiversity and improve climate models. Testing different demarcation methods across plots of different physical structures and tree species helped isolate factors that affect the accuracy of each method. The results show strong environmental control for the success of automated LiDAR crown depiction.
LiDAR’s data collection
The study had a wide range of forest types as it took place at one of the Smithsonian ForestGEO MegaPlots at the Harvard Forest. This allowed the researchers to simulate how methods would work across forests throughout the region. The study highlighted LiDAR’s strengths for delineating conifer-dominated forests. Deciduous trees, on the other hand, were more difficult to demarcate due to their irregular crown shapes. Researchers relied on LiDAR data collected by NASA’s G-LiHT airborne imager and evaluated automated crown delineations from high-resolution UAV imagery collected by a DJI Phantom 4 Pro.
The future of this study will revolve around the integration of other methods to help in the delineation of broadleaf trees which are rich in information missed by LiDAR data. Ultimately, understanding the environmental factors that influence crown delineation will help researchers improve their methods and reach the goal of mapping the world’s forests.