Polygonization of discontinuous raster classes from machine-learning predictive ecosystem mapping (PEM)

Biogeoclimatic Ecosystem Classification (BEC) has been applied extensively in characterizing forested ecosystems in British Columbia. With a lack of qualified vectorization method used for BEC data transformation, the main goal of this research is to polygonize discontinuous BEC raster classes into vector map with better overall effectiveness and efficiency especially regarding the linear areas. The original data input for analysis is a machine-learning BEC zone raster map of Deception Study Area located in middle BC near Telkwa, with a resolution of 5m*5m. A comprehensive comparison between vectorization algorithms in GIS applications was conducted, including different filtering, simplifying and smoothing algorithms. Since we have the original predicted BEC raster map as the performance measurement, accuracy was directly measured as the percentage of correctly classified pixels when rasterizing the polygons. The evaluation criteria include visual effect, number of polygons, linear patches accuracy processing time. We found an appropriate vectorization routine to polygonize the classification raster maps. The polygonal map using Scenario D has overall satisfactory effectiveness and efficiency with a 46% linear patch accuracy and 62,014 polygons. The method also provides good approximations of the areas with moderate processing time. This is partly because we allow vertices to be located anywhere and not just exactly on the boundary of the original raster zones. We can promote this polygonization method in future predicted ecosystem mapping (PEM) product with similar linear and discontinuous areas. Priority of several key BEC zone classification with importance level regarding to the ecosystem condition related to endangered species can be further explored and added to the algorithms to better polygonize those areas in future studies.