Predicting Frost-Prone and Cold Air Accumulation Sites from DEMs

Important spatial information including slope, curvature, and surface roughness were derived from a digital elevation model, and combined with data for the biogeoclimatic ecosystem classification zones, old growth forests, and annual precipitation for the study area. Each dataset in the model was reclassified into a binary raster, based factors contributing to frost potential, and then combined into a single frost-prone dataset.

The final results provided excellent insight into how frost may develop on the landscape, and achieved an overall accuracy of 73% when tested against ground-truthing data. The results and easily-reproducible code written for this analysis can help better-inform reforestation efforts in British Columbia, and with further data acquisition of additional variables, as well as more ground-truthing data, the study can be expanded to predict frost potential for all of British Columbia.