The GEOgraphic Object-Based Image Analysis (GEOBIA) paradigm continues to prove its efficacy in remote sensing image analysis by providing tools which emulate human perception and combine analyst's experience with meaningful image-objects. However, challenges remain in the evolution of this new paradigm as sophisticated methods attempt to deliver on the goal of automated geo-intelligence (i.e., geospatial content within context) from geospatial sources. In order to generate geo-intelligence from a forest scene, this article introduces a GEOBIA framework to estimate canopy height, above-ground biomass (AGB) and volume by combining lidar (light detection and ranging) transects, Quickbird imagery and machine learning algorithms. This framework is comprised three main components: (i) image-object extraction, (ii) lidar transect selection, and (iii) forest parameter generalization. The rational for integrating these methods is to provide a semi-automatic GEOBIA approach from which detailed forest information is obtained at the individual tree crown or small tree cluster level (i.e., mean object size of 0.04 ha); while also dramatically reducing airborne lidar data acquisition costs. Analysis is performed over a 16,330 ha forested study site in Quebec, Canada. Forest parameter estimation results derived from our GEOBIA framework demonstrate a strong relationship with those using the full lidar cover; where the highest estimates for canopy height (R = 0.85; RMSE = 3.37 m), AGB (R = 0.85; RMSE = 39.48 Mg/ha) and volume (R = 0.85; RMSE = 52.59 m3/ha) were achieved using a lidar transect sample representing only 7.6% of the total study area.
['Gang Chen', 'Geoffrey J. Hay', 'Benoît St-Onge']
A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada