Structural biodiversity monitoring in savanna ecosystems: Integrating LiDAR and high resolution imagery through object-based image analysis

Abstract

Savannas are heterogeneous systems characterized by the coexistence of grasses and woody trees. Growing recognition of the importance of the structural component of biodiversity has highlighted the need to understand the spatial distribution and temporal dynamics of woody plant structural diversity. Advances in LiDAR technology have enabled three dimensional information of vegetation to be obtained remotely over large areas. Whilst the use of LiDAR has gained considerable momentum in forested areas there has been limited application to savanna systems. We explore the applicability of LiDAR and object-based image analysis to the monitoring of woody structural diversity in a savanna system. We demonstrate how an object-based approach to image analysis significantly improves the accuracy of woody layer classification form in a heterogeneous landscape. Furthermore we illustrate how standard approaches to LiDAR derived canopy models suffer from interpolation artifacts in savannas, due to the heterogeneity of the woody layer. By integrating LiDAR with high resolution aerial photography, through object-based analysis, these artifacts can be removed to produce a robust canopy model. The object-based integration of LiDAR with aerial imagery holds immense potential for structural diversity monitoring in savannas.

Publication
Object-Based Image Analysis