Brandon Heung

Brandon Heung is an Assistant Professor in Geospatial Informatics, where his primary research interests are in the field of digital soil mapping (DSM) – the intersection of Geospatial Information Systems (GIS), spatial analysis, and soil science. In 2017, he received his Ph.D. from the Department of Geography at Simon Fraser University, where his research proposed a suite of machine-learning techniques to produce DSMs across Southern British Columbia. His research interests are focussed around these themes: (1) the use of DSM techniques for mapping soils at local-, regional-, and national-scales; (2) the development of digital soil assessment techniques to support land management decisions and policy; and (3) the development of spatiotemporal approaches for modelling soil processes across a landscape.

Current collaborators include the BC Ministry of Forests, Lands, and Natural Resource Operations; and Agriculture and Agri-Food Canada to produce provincial-scale DSMs. In addition, he is pursuing ongoing collaborations with the University of Saskatchewan to develop a suite of DSM techniques that are suitable for precision agriculture purposes. With the Canadian Forest Service, Brandon is also involved with developing DSM approaches for enhanced forest resource inventory in Ontario. Currently, he is a member of the Canadian Digital Soil Mapping Working Group (a network of digital soil mappers from academic, provincial, and federal government agencies) and the Scientific & Technical Subcommittee where they are tasked with overseeing the development and distribution of national-scale DSM products for Canada.

• Heung, B., Hodúl, M., and Schmidt, M.G., 2017. Comparing the use of legacy soil pits and soil survey polygons as training data for mapping soil classes. Geoderma 290: 51-68.

• Freeland, T., Heung, B., Burley, D.V., Clark, G., and Knudby, A., 2016. Using airborne LiDAR for prospection and analysis of monumental architecture and settlement patterns in the Kingdom of Tonga. Journal of Archaeological Science 69: 64-74.

• Bulmer, C. E., Schmidt, M.G., Heung, B., Scarpone, C., Zhang, J., Filatow, D., Finvers, M., Berch, S., and Smith, C.A.S., 2016. Improved soil mapping in British Columbia, Canada with legacy soil data and Random Forest. In Digital Soil Mapping Across Paradigms, Scales and Boundaries. Springer Environmental Science and Engineering, pp. 291-303.

• Heung, B., Zhang, J., Ho, H.C., Knudby, A., Bulmer, C.E., and Schmidt, M.G., 2016. An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma 265: 62-77.

• Heung, B., Bulmer, C.E., and Schmidt, M.G., 2014. Predictive soil parent material mapping at a regional-scale: A Random Forest approach. Geoderma 214-215: 141-154.

• Heung, B., Bakker, L., Schmidt, M.G., and Dragićević, S., 2013. Modelling the dynamics of soil redistribution induced by sheet erosion using the Universal Soil Loss Equation and cellular automata. Geoderma 202-203: 112-125.