Monitoring vegetation regeneration using multiple remotely piloted aircraft system sensors and methodologies

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dc.contributor.authorPearse, Ben
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorHopkinson, Christopher
dc.contributor.supervisorChasmer, Laura
dc.date.accessioned2024-11-20T20:24:18Z
dc.date.available2024-11-20T20:24:18Z
dc.date.issued2024
dc.degree.levelMasters
dc.description.abstractEffective restoration of vegetation following mining or other anthropogenic disturbances requires the ability to accurately measure indicators of progress towards benchmarks and the desired restoration end points. In this thesis, remotely piloted aircraft systems (RPAS) were used to collect data for estimating ecosystem proxies for productivity and measures of vegetation diversity at reference and reclamation sites in the Yukon and Alberta, with a focus on developing or confirming existing methods and testing their use on an operational level. In the first case study, the ability to estimate Leaf Area Index (LAI) and classify plant functional type using Object-Based Image Analysis in mixed species communities was evaluated. It was found that both conceptual and regression models were robust enough after two years of data collection to estimate LAI across the range of sites sampled (r2 ranging from 0.73 - 0.86 and RMSE from 0.29 - 0.38 m2/m2), showing spatiotemporal transferability of the models. Plant functional types (shrub, herbaceous, grass, and moss) were classified with high accuracy (F-scores ranging from 0.95 - 1.0). The second case study assessed the potential for lidar to be used as a stand-alone sensor to monitor vegetation regeneration of a post-wildfire study site by estimating biomass and LAI and classifying woody and herbaceous vegetation. Furthermore, the ability to classify vegetation species was evaluated using object-based image analysis, multi-temporal data, and a fusion of multiple sensor types. The results show that average height was best for estimating biomass (R2 = 0.76, RMSE = 254 g/m2) at 1m2 plots. Woody and herbaceous vegetation were poorly classified using the lidar point clouds, however, the addition of spectral (NDVI) and moisture information (distance to a stream) improved the classifications. Object-based image analysis using a single data acquisition during a period of maximum foliage was unable to comprehensively classify species. However, the addition of a second data acquisition during the fall capitalized on spectral diversity of different species during different phenophases and improved the classification. This research demonstrates the unique potential of RPAS to be used in restoration monitoring with its ability to utilize different sensors and collect datasets dependent on user needs. The methods developed here for estimating productivity and species diversity can potentially be incorporated into long-term industry-based monitoring programs and can help decision-makers learn from current restoration efforts and apply successes to new areas.
dc.description.sponsorshipMitacs, Integral Ecology Group
dc.embargoNo
dc.identifier.urihttps://hdl.handle.net/10133/6955
dc.language.isoen
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Geography and Environment
dc.publisher.departmentDepartment of Geography and Environment
dc.publisher.facultyArts and Science
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)
dc.subjectvegetation regeneration
dc.subjectRPAS remote sensing
dc.subjectremote sensing
dc.subjectstructure from motion
dc.subjectlidar remote sensing
dc.subjectmine reclamation
dc.subject.lcshDissertations, Academic
dc.subject.lcshVegetation surveys--Yukon
dc.subject.lcshVegetation surveys--Alberta
dc.subject.lcshOptical radar
dc.subject.lcshAbandoned mined lands reclamation--Research
dc.subject.lcshDrone aircraft--Scientific applications
dc.subject.lcshDrone aircraft in remote sensing--Research
dc.subject.lcshVegetation mapping--Yukon
dc.subject.lcshVegetation mapping--Alberta
dc.subject.lcshAerial photogrammetry
dc.subject.lcshRestoration monitoring (Ecology)--Yukon
dc.subject.lcshRestoration monitoring (Ecology)--Alberta
dc.titleMonitoring vegetation regeneration using multiple remotely piloted aircraft system sensors and methodologies
dc.typeThesis
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