Lidar and Obect-based Image Analysis as Tools for Monitoring the Structural Diversity of Savanna Vegetation

Savannas are heterogeneous systems characterized by the coexistence of grasses and woody trees. Riparian zones are dynamic parts of savanna landscapes with structurally diverse woody vegetation. 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 structural diversity. Most studies of temporal change in savannas have employed the use of satellite imagery or black and white aerial photography. These techniques are useful for examining changes in woody cover over time, but cannot portray the threedimensional structure of vegetation. 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. LiDAR and colour aerial photography were acquired for the riparian and adjacent upland zones of four main rivers of the Shingwedzi Catchment Kruger Park, South Africa. The resulting imagery, ground elevation and tree canopy elevation data covered an area of 635km. The tree canopy surface and colour imagery were segmented at two scales with object-based image analysis algorithms (eCognition v4.0) to construct a patch hierarchy. A classification hierarchy was constructed with fuzzy logic membership rules derived from field data. Height, elevation and spectral data were used to distinguish different vegetation types from bare ground. Results suggest that LiDAR can reliably return vegetation height and crown diameter data in savanna systems. The inclusion of object-based image analysis is an important step in fusing height data with colour imagery and generating multi-scale outputs. This approach provides a means for monitoring spatio-temporal changes in savanna woody vegetation structure at multiple scales.