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.
Remote Sensing Analyst
Canadian Forest Service
UAV photogrammetry to examine restoration success along linear disturbances in northern Alberta
Michelle joined the remote sensing team at the Canadian Forest Service in 2006 bringing her LiDAR, remote sensing, database and programming experience. Her skills have lent itself to projects such as: the multi-source vegetation inventory which scales ground measurements using airborne and spaceborne LiDAR to generate forest structure attributes in the NWT; national forest fire mapping and production of the National Burn Area Composite; multi-temporal change detection of spruce budworm defoliation; investigating spectral unmixing techniques of optical data for aspen dieback mapping; and most recently the processing and analysis of UAV photogrammetry to examine restoration success along linear disturbances in northern Alberta.
Upscaling plot-scale methane fluxes across disturbed boreal peatlands using UAV imagery/Impact of seismic lines on soil compaction
I am a postdoctoral fellow at the University of Waterloo, working with Dr. Maria Strack within the Wetland Soil and Greenhouse Gas Exchange Lab.
Broadly, my research interests include boreal peatland disturbances and restoration. Within the BERA project, I am working on two aspects of disturbance from seismic lines. The goal of this project is to use Unmanned Aerial Vehicle (UAV) imagery to map vegetation communities in order to upscale plot level methane emissions across seismic lines and adjacent undisturbed areas to the landscape scale. I am also looking at soil compaction across these linear disturbances.
My previous research has looked at combining field observations of greenhouse gas fluxes with high resolution vegetation maps created using a combination of field spectroscopy and multi-spectral satellite imagery across a variety of arctic tundra ecosystems. I hope to use similar methodologies within my postdoctoral work to map boreal peatland vegetation at a fine scale.
Ludwig-Maximilian University Munich
Quantifying human impact on the boreal forest of northern Alberta using LiDAR and photogrammetry
I am a Graduate Student at the University of Munich. The research for my thesis focuses on the evaluation of different techniques for quantifying forest disturbances in the boreal forest affected by natural resource extraction in northern Alberta. This past summer I spent two months at the University of Alberta to conduct my in field data collection. This data will be used to validate the three techniques involving Light Detection and Radar and Stereophotogrammetry for delineating the location and dimension of linear as well as non-linear disturbances. The goal of my research is to identify the best method to sustainably, reliably and accurately monitor forest disturbance and regeneration by comparing the point clouds derived from LiDAR, photogrammetry and a combination of the two, with regards to their accuracy as well as individual advantages and drawbacks. This will support provincial environmental protection plans as well as collect important information on the structure and inventory of the Canadian boreal forest.
Remote Sensing of Coarse Woody Debris for Caribou Habitat Restoration in Alberta’s Boreal Forest
The focus of my research is the development of an automated technique for Coarse Woody Debris (CWD) detection and measurement in seismic lines of Alberta’s Boreal Forest. The presence and density of CWD in seismic lines are important aspects of the environmental restoration framework set by the Government of Alberta to promote caribou habitat restoration. I will test different feature-extraction algorithms: machine-learning based classification versus more traditional pattern recognition. The results from different methods will be compared against ground-truth data for CWD collected at the test sites. By analyzing the results of this study, I will select a favoured method and adapt it to best fit the theoretical and practical challenges posed by this specific environment.
Semisupervized Object Detection in UAV Images
Michael is a Computer Scientist at the Ludwig-Maximilian University of Munich. His master thesis investigated the effectiveness of machine learning algorithms for automatic detection of coniferous seedling data along Boreal seismic lines. Since the seismic lines cover a length of more than 10,000 km, an automated solution is necessary. He used convolutional neural networks as a feature extractor on the images. Subsequently, they trained an object detector to learn the model to detect seedlings. After the training, the model can be used on unseen data to predict the location of the trees. In this work he also evaluated the accuracy of modern object detectors such as Faster R-CNN with regard to remote sensing capacity of conifer seedlings. Michael further investigated the problem by doing several experiments which focused on the special environmental variables in nature, including seasons and flight height of the drone. They also conducted experiments on the amount of data needed to achieve high accuracy and we also studied the influence of pre-trained networks on the object detector.
Michael’s project was completed in summer 2018 with a Masters thesis on Semisupervized Object Detection in UAV Images.
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.
Shijuan’s project was completed in summer 2017 with a Masters thesis and a journal publication on “Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry”.
UAV Workflows for Assessment of Vegetation Structure
I am an MGIS student and Geospatial Technician for the BERA project and responsible for operating our fleet of unmanned aerial vehicles (UAVs) for the Remote Sensing Team at the University of Calgary. This summer I will be collecting aerial data for BERA and managing sensor integration, platform modifications and contributing to best practices for in field data collection. I have been providing support for several projects focused on vegetation analysis, but my personal research focus is to develop an automated workflow for rapid assessment of small replanted coniferous seedlings (<10 cm). UAVs are likely to decrease the need for onsite manual surveys from personnel for a variety of resource industries in Canada. The objective of my research is to create a scale invariant, sample-based algorithm that is stable across different study areas. Results of classified pine density estimates are accurate enough to suggest that UAVs can soon be used in an operational role for monitoring silviculture activities. My research also aims to prove that sample based flight plans are an effective method of extending UAV flight range for vegetation assessment tasks.