Estimation of forest structure metrics relevant to hydrologic modelling using coordinate transformation of airborne laser scanning data
- 1Department of Forest Resources Management, University of British Columbia, 2231-2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
- 2Department of Geography, University of Victoria, 3800 Finnerty Road, Victoria, BC, V8P 5C2, Canada
- 3BC Ministry of Forests, Lands, and Natural Resource Operations, 200-640 Borland Street, Williams Lake, BC, V2G 4T1, Canada
Abstract. An accurate characterisation of the complex and heterogeneous forest architecture is necessary to parameterise physically-based hydrologic models that simulate precipitation interception, energy fluxes and water dynamics. While hemispherical photography has become a popular method to obtain a number of forest canopy structure metrics relevant to these processes, image acquisition is field-intensive and, therefore, difficult to apply across the landscape. In contrast, airborne laser scanning (ALS) is a remote-sensing technique increasingly used to acquire detailed information on the spatial structure of forest canopies over large, continuous areas. This study presents a novel methodology to calibrate ALS data with in situ optical hemispherical camera images to obtain traditional forest structure and solar radiation metrics. The approach minimises geometrical differences between these two techniques by transforming the Cartesian coordinates of ALS data to generate synthetic images with a polar projection directly comparable to optical photography. We demonstrate how these new coordinate-transformed ALS metrics, along with additional standard ALS variables, can be used as predictors in multiple linear regression approaches to estimate forest structure and solar radiation indices at any individual location within the extent of an ALS transect. We expect this approach to substantially reduce fieldwork costs, broaden sampling design possibilities, and improve the spatial representation of forest structure metrics directly relevant to parameterising fully-distributed hydrologic models.