Gregory Greene

Background

About Gregory Greene

I am a geospatially-minded fire ecologist with over 20 years of experience in GIS and wildfire science. I hold degrees in Fire Ecology (PhD, MSc), GIS (BSc), Interdisciplinary GIS Applications (Minor) and Anthropology (Minor). Work Focus: • Assess (a) the impacts of fires on forests, and (b) the post-fire response of forests • Assess fuel treatment efficacy • Model forest fuels and fire behavior • Assess the risk of fires to forests, communities, mines and utilities • Develop bespoke software for a broad range of applications Specialties: Fire ecology, Wildfire modelling, Forest fuels modelling, Fuel treatment efficacy assessment, Python programming, GIS application development, Project development, Work-flow analysis, GIS database management Common Python Packages: numpy, pandas, rasterio/fiona/shapely, geopandas, xarray, dask, arcpy, matplotlib, sklearn, scipy, multiprocessing, subprocess, requests, PySimpleGUI, RPy2, win32com.client Custom Python Code Development: • Built a Python package (cffdrs) with two raster-capable modules:(1) cffwis - model fire weather variables with the Canadian Forest Fire Weather Index System; and (2) cffbps - model fire behavior with the Canadian Forest Fire Behavior Prediction System • Built a Python module to model cumulative radiative and convective heat flux (transfer) from a moving fire front to objects at any height above the ground and distance from the fire • Built a Python module to model flame characteristics, including mid-flame wind speed, and flame length, tilt, depth, residence time, and height • Built Python modules to automate (a) the generation of FBFM datasets from LiDAR data,(b) the generation of LCP files, and (c) fire spread modelling with Missoula Fire Lab CLI apps (FlamMap/MTT/Farsite) • Built a Python Class to automate WindNinja modelling through the CLI • Built a Python package (ProcessGeospatial) with two modules:(1) ProcessRasters - use Python geospatial libraries to process raster data; and (2) ProcessFeatures - use Python geospatial libraries to process feature data. The modules are intended to avoid the need for ArcGIS (save $), use function names that are similar to those used in arcpy (add familiarity), and simplify the function I/O process.