Large-scale characterization of forest structure and complexity from remote sensing optical images

Abstract

Forest structure complexity is an essential variable in forest management and conservation, as it has a direct impact on ecosystem processes and functions. Previous studies have primarily focused on tree cover as a proxy, which often falls short in providing comprehensive information on the structural complexity of forests. Sub-meter resolution remote sensing data and tree crown segmentation techniques hold promise in offering detailed information that can support the characterization of forest structure and complexity. In this study, we generated a dataset with over 5 billion tree crowns, and developed an Overstory Complexity Index (OCI) to characterize forest structure complexity from a horizontal perspective, by analyzing spatial relationships among neighboring trees from remote sensing optical images. We first extracted the location and crown size of overstory trees from optical satellite and aerial imagery at sub-meter resolution. We subsequently calculated the distance between tree crown centers, their angles, the crown size and crown spacing and linked this information with individual trees. We then used Principal Component Analysis (PCA) to condense the structural information into the OCI and tested it in China’s Guangxi province, Rwanda, and Denmark. In addition, we conducted a comparative analysis of OCI between protected and unprotected areas and among different forest types across these regions. Finally, we explored the relationships of terrain slope, distance to settlement and aboveground biomass with the OCI. Our result showed that the distribution of OCI values varies across the different bioclimatic regions, closely related to their respective forest characteristics. Higher OCI values were observed in protected areas as compared to unprotected areas, and OCI showed a positive correlation with terrain slope, distance to settlement and aboveground biomass. The proposed OCI is derived directly from standard tree-level attributes and supports a deeper understanding on forest structure and complexity in diverse ecosystems as compared to existing proxies.