Guillermo is a Spanish Forest Engineer (MSc from the Polytechnic University of Madrid, 1990) who specialized in remote sensing (PhD from UPM, 2003) thanks to a fellowship in the European Space Agency (ESA, 1999-2000). He moved to Canada in 2006 as a post-doctoral fellow at the Department of Geography of the UofC, where he remained working in different positions, including that of Adjunct professor (that he still holds), until he joined the Edmonton lab of the Canadian Forest Service in 2014 as a research scientist. Guillermo integrates geospatial technologies to map and monitor land cover, forest structure and composition, and natural and anthropogenic disturbances, and develops theories, methods and tools to automatize the production of geographic information from remote sensing imagery from local to regional scales.
Characterizing Vegetation Structure on Anthropogenic Features in Alberta’s Boreal Forest with UAV (unmanned aerial vehicle)
Characterizing vegetation structure is an important component for understanding ecological recovery on seismic lines and other non-permanent human footprint features (NPHF). Structural metrics provide an important baseline upon which to build a monitoring program, and a mechanism for comparing NPHF sites to un-disturbed reference locations. Accurate estimation of vegetation structural parameters provides a quantitative assessment of the vegetation status on and besides seismic lines, which is a prerequisite for studying vegetation recovery. However, current approaches to measuring vegetation structure rely on detailed field protocols that are costly and difficult to scale. UAVs (unmanned aerial vehicles) have shown great promise in characterizing vegetation structure in more cost-saving and effective way, compared to traditional field protocols. This project will evaluate the abilities of photogrammetric data from UAVs for characterizing vegetation structure on seismic lines. This study will also give conclusions and suggestions of optimal conditions, processing procedure and analysis method for obtaining the most accurate estimations of vegetation structural parameters. The project will contribute to establish repeatable, cost-effective, and final scale vegetation and ecological monitoring strategies on human disturbed features.