Utilizing Semivariance to Understand Forest Structure and Biomass in Boreal and Tropical Forests

Boreal and tropical forests comprise two of the three major forest types, spanning globally between 50° and 70° northern latitude and 23° and -23° latitude across the equator, respectively, and together cover nearly a third of the Earth's land surface. Although geographically distant, both are important contributors to many terrestrial biosystems, such as local and global carbon cycles, and are highly susceptible to disturbance events such as fire, drought, and deforestation. As climate change and human activity places forests at increasing vulnerability to such disruptions, it is necessary to measure present forest health and further develop our understanding of forest dynamics. Since boreal and tropical forests are considerable in spatial extent, remote sensing techniques offer efficient means of surveying large areas while effectively studying large-scale attributes, such as forest structure and biomass. This research utilized data from aircrafts equipped with Light Detection and Ranging (LiDAR) instruments to investigate horizontal structure attributes of these forests and relate them to forest biomass. A semivariance algorithm, used to quantify the relationship of spatially separated objects, was created and its ability to estimate biomass from horizontal structure data assessed. The algorithm analyzed LiDAR images of boreal and tropical forests to investigate how changes in structural attributes, including tree characteristic distributions and canopy dimensions, affect biomass changes and large-scale forest dynamics. Vertical forest structure metrics were extracted from flight data over selected forests, and Fast Fourier Transforms (FFT) were used to further examine characteristics of forests' horizontal structure through the creation of vegetation profiles. From vegetation profiles we related forest structure to known values of biomass from field data with the development of statistical models and regression techniques. The algorithm's utility for researching known fire disturbances in remote geographies was also investigated. A deeper knowledge of forest structure trends will grant insight into the likely future of boreal and tropical forest ecosystems and connected global processes, helping to guide economic and conservation policy alike.