Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

• Single trees were classified using Quickbird, LiDAR and object-based methods.
• Random Forest was used for identifying importance feature variables.
• Support Vector Machine was used for classification.
• The combining of LiDAR and spectral data improved classification for single trees.

There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.