Chasmer, Laura
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- ItemA bi-temporal airborne lidar shrub-to-tree aboveground biomass model for the taiga of western Canada(Taylor & Francis, 2024) Flade, Linda; Hopkinson, Christopher; Chasmer, LauraMonitoring 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.
- ItemWarmer 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.
- ItemUsing 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.
- ItemWildfire as a major driver of recent permafrost thaw in boreal peatlands(Nature Portfolio, 2018) Gibson, Carolyn M.; Chasmer, Laura; Thompson, Dan; Quinton, William L.; Flannigan, Mike D.; Olefeldt, DavidPermafrost vulnerability to climate change may be underestimated unless effects of wildfire are considered. Here we assess impacts of wildfire on soil thermal regime and rate of thermokarst bog expansion resulting from complete permafrost thaw in western Canadian permafrost peatlands. Effects of wildfire on permafrost peatlands last for 30 years and include a warmer and deeper active layer, and spatial expansion of continuously thawed soil layers (taliks). These impacts on the soil thermal regime are associated with a tripled rate of thermokarst bog expansion along permafrost edges. Our results suggest that wildfire is directly responsible for 2200 ± 1500 km2 (95% CI) of thermokarst bog development in the study region over the last 30 years, representing ~25% of all thermokarst bog expansion during this period. With increasing fire frequency under a warming climate, this study emphasizes the need to consider wildfires when projecting future circumpolar permafrost thaw.
- ItemFiltering stems and branches from terrestrial laser scanning point clouds using deep 3-D fully convolutional networks(MDPI, 2018) Xi, Zhouxin; Hopkinson, Christopher; Chasmer, LauraTerrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.