OPUS: Open Ulethbridge Scholarship
Open ULeth Scholarship (OPUS) is the University of Lethbridge's open access research repository. It contains a collection of materials related to research and teaching produced by the academic community.
Self-archiving your research in OPUS is one way to meet Open Access policies of granting agencies. It is important to retain your final, post-peer-reviewed drafts for submission to OPUS, as this is often the only version publishers will allow to be archived. Click here for information on the U of L Open Access Policy.
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Benchmarking tree species classification from proximally sensed laser scanning data: introducing the FOR-species20K dataset
(Wiley, 2025) Pulti, Stefano; Lines, Emily R.; Mullerova, Jana; Frey, Julian; Schindler, Zoe; Straker, Adrian; Allen, Matthew J.; Winiwarter, Lukas; Rehush, Nataliia; Hristova, Hristina; Murray, Brent; Calders, Kim; Coops, Nicholas; Hofle, Bernhard; Irwin, Liam; Junttila, Samuli; Krucek, Martin; Krok, Grzegorz; Kral, Kamil; Levick, Shaun R.; Luck, Linda; Missarov, Azim; Mokros, Martin; Owen, Harry J. F.; Sterenczak, Krzysztof; Pitkanen, Timo P.; Puletti, Nicola; Saarinen, Ninni; Hopkinson, Christopher; Terryn, Louise; Torresan, Chiara; Tomelleri, Enrico; Weiser, Hannah; Astrup, Rasmus
1. Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single-tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification.
2. To address the above limitations, we compiled the FOR-species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR-species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP-Mixer, DGCNNs) and multi-view 2D-based methods (SimpleView, DetailView, YOLOv5).
3. 2D Image-based models had, on average, higher overall accuracy (0.77) than 3D point cloud-based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top-scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes.
4. The FOR-species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state-of-the-art of point cloud tree species classification methods.
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Applying remote sensing for large-landscape problems: inventorying and tracking habitat recovery for a broadly distributed Species At Risk
(Wiley, 2023) Dickie, Melanie; Hricko, Branislav; Hopkinson, Christopher; Tran, Victor; Kohler, Monica; Toni, Sydney; Serrouya, Robert; Kariyeva, Johan
1. Anthropogenic habitat alteration is leading to the reduction of global biodiversity. Consequently, there is an imminent need to understand the state and trend of habitat alteration across broad areas. In North America, habitat alteration has been linked to the decline of threatened woodland caribou. As such, habitat protection and restoration are critical measures to support recovery of self-sustaining caribou populations. Broad estimates of habitat change through time have set the stage for understanding the status of caribou habitat. However, the lack of updated and detailed data on post-disturbance vegetation recovery is an impediment to recovery planning and monitoring restoration effectiveness. Advances in remote sensing tools to collect high-resolution data at large spatial scales are beginning to enable ecological studies in new ways to support ecosystem-based and species-based management.
2. We used semi-automated and manual methodologies to fuse photogrammetry point clouds (PPC) from high-resolution aerial imagery with wide-area light detection and ranging (LiDAR) data to quantify vegetation structure (height, density, class) on disturbances associated with caribou declines. We also compared vegetation heights estimated from the semi-automated PPC-LiDAR fusion to heights estimated in the field, using stereoscopic interpretation, and using multi-channel TiTAN LiDAR.
3. Vegetation regrowth was occurring on many of the disturbance types, though there was local variability in the type, height and density of vegetation. Heights estimated using PPC-LiDAR fusion were highly correlated (r ≥ 0.87 in all cases) with heights estimated using stereomodels, TiTAN multi-channel LiDAR and field measurements.
4. We demonstrated that PPC-LiDAR fusion can be operationalized over large areas to collect comprehensive and consistent vegetation data across landscape levels, providing opportunities to link fine-resolution remote sensing to landscape-scale ecological studies. Crucially, these data can be used to estimate rates of habitat recovery at resolutions that are not feasible using more commonly used satellite-based sensors, bridging the gap between resolution and extent. Such data are needed to achieve effective and efficient habitat monitoring to support caribou recovery efforts, as well as a myriad of additional forest management needs.
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A bi-temporal airborne lidar shrub-to-tree aboveground biomass model for the taiga of western Canada
(Taylor & Francis, 2024) Flade, Linda; Hopkinson, Christopher; Chasmer, Laura
Monitoring aboveground biomass (AGB) is critical for carbon reporting and quantifying ecosystem change. AGB from field data can be scaled to the region using airborne lidar. However, lidar-based AGB products emphasize upland forests, which may not represent the conditions in rapidly changing peatland complexes in the southern Taiga of western Canada. In addition, to ensure that modeled AGB changes do not incorporate systematic error due to differences between older and newer lidar technologies, model transfer tests are required. The aim of this study was to develop one bi-temporal lidar-based AGB model applicable to (1) vegetation structures at varying vertical and horizontal continuity in this region and to (2) data collected with an earlier generation lidar system for which Canada-wide aerial coverage is available. Goodness-of-fit metrics show that AGB can be modeled with moderate (R2 = 48%–58% Taiga Shield, peatlands) to high accuracies (R2 = 83%–89% Taiga Plains, upland/permafrost plateau forests including ecotones) by using the point clouds average height and 90th height percentile within a weighted approach as function of modeled AGB and calibrating the earlier lidar data. These results are important for quantifying climate change effects on forest to peatland ecotones.
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Warmer air temperatures predicted to result in wetland drying in the Upper Columbia River Valley, British Columbia, Canada
(Elsevier, 2025) Rodrigues, Italo S.; Hopkinson, Christopher; Chasmer, Laura; MacDonald, Ryan J.; Bayley, Suzanne E.
Climatic warming is likely to affect the Canadian Rockies, leading to changes in the land cover (LC) and hydrological cycles. This study estimates climate-induced changes in LC (open water, marsh, wet meadow, and woody/shrub) in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada, from 1984 to 2040. An artificial Neural Network (ANN) approach was used with Landsat series archive data from 1984 to 2022 to project seasonal LC change from 2020s to 2040s. Concurrently, hydroclimatic-based models (using air temperature and precipitation to predict river discharge at the UCRW, 1984–2022) were developed (average Nash Sutcliffe: training 0.75 and validation of 0.70) to predict (1984–2040) river discharge forced by Representative Concentration Pathway (RCP) 4.5 and 8.5. The 1984–2022 regression between river discharge and UCRW open water area was forced by RCP scenario river discharge results, calculating open water area for both scenarios. ANN-predicted LC with a Kappa of 0.85 (average of all seasons) for 2020s reference and projected LC, and 0.82 for reference and projected LC change maps (2000s–2020s). From 2020s to 2040s, the ANN projected a reduction (−5 %) of open water areas during late summer (August to mid-September) in the UCRW, consistent with RCP 4.5 forecasts. The peak of the open water area in the UCRW is projected to shift from summer (late-May to July) to spring (April to mid-May) in both RCP scenarios. The projected changing hydrological conditions reduced the marsh area (−1 % to −12 %) and increased the wet meadow (+1 % to +4 %) mostly in the summer and late summer. Meanwhile, woody and shrubby vegetation on the floodplain increased (3 % to 5 %), indicating that the floodplain is projected to dry out.
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Using bi-temporal lidar to evaluate canopy structure and ecotone influence on Landsat vegetation index trends within a boreal wetland complex
(MDPI, 2025) Aslami, Farnoosh; Hopkinson, Christopher; Chasmer, Laura; Mahoney, Craig; Peters, Daniel L.
Wetland ecosystems are sensitive to climate variation, yet tracking vegetation type and structure changes through time remains a challenge. This study examines how Landsat-derived vegetation indices (NDVI and EVI) correspond with lidar-derived canopy height model (CHM) changes from 2000 to 2018 across the wetland landscape of the Peace–Athabasca Delta (PAD), Canada. By comparing CHM change and NDVI and EVI trends across woody and herbaceous land covers, this study fills a gap in understanding long-term vegetation responses in northern wetlands. Findings show that ~35% of the study area experienced canopy growth, while 2% saw a reduction in height. CHM change revealed 11% ecotonal expansion, where shrub and treed swamps encroached on meadow and marsh areas. NDVI and EVI correlated significantly (p < 0.001) with CHM, particularly in shrub swamps (r2 = 0.40, 0.35) and upland forests (NDVI r2 = 0.37). However, EVI trends aligned more strongly with canopy expansion, while NDVI captured mature tree height growth and wetland drying, indicated by rising land surface temperatures (LST). These results highlight the contrasting responses of NDVI and EVI—NDVI being more sensitive to moisture-related changes such as wetland drying, and EVI aligning more closely with canopy structural changes—emphasizing the value of combining lidar and satellite indices to monitor wetland ecosystems in a warming climate.