Hopkinson, Christopher
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- ItemRemote sensing of wetlands in the prairie pothole region of North America(MDPI, 2021) Montgomery, Joshua; Mahoney, Craig; Brisco, Brian; Boychuk, Lyle; Cobbaert, Danielle; Hopkinson, ChristopherThe Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.
- ItemDelegate perspectives on transitioning the 41st Canadian Symposium on remote sensing to a virtual event due to the COVID-19 Pandemic(Taylor & Francis, 2022) Hopkinson, Christopher; Coburn, Craig A.The 41st Canadian Symposium on Remote Sensing (CSRS) was a unique event, originally planned to be hosted as an in-person event in Yellowknife, Northwest Territories but ultimately delivered 100% online due to the COVID-19 global pandemic. As the 41st CSRS represented an unprecedented departure from the CRSS-SCT’s long history of annual in-person symposia, this note summarizes the transition from an in-person to an online event. In particular, delegate feedback on some of the challenges encountered, as well as positive and negative perceptions of the event delivery. It is important that a record of these collective experiences is preserved and considered for future symposia, and our experience is shared with the global research community.
- ItemThe Multisource Vegetation Inventory (MVI): a satellite-based forest inventory for the Northwest Territories taiga plains(MDPI, 2022) Castilla, Guillermo; Hall, Ronald J.; Skakun, Rob; Filiatrault, Michelle; Beaudoin, André; Gartrell, Michael; Smith, Lisa; Groenewegen, Kathleen; Hopkinson, Christopher; van der Sluijs, JurjenSustainable forest management requires information on the spatial distribution, composition, and structure of forests. However, jurisdictions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, often lack detailed forest information across their land base. The goal of the Multisource Vegetation Inventory (MVI) project was to create a large area forest inventory (FI) map that could support strategic forest management in the NWT using optical, radar, and light detection and ranging (LiDAR) satellite remote sensing anchored on limited field plots and airborne LiDAR data. A new landcover map based on Landsat imagery was the first step to stratify forestland into broad forest types. A modelling chain linking FI plots to airborne and spaceborne LiDAR was then developed to circumvent the scarcity of field data in the region. The developed models allowed the estimation of forest attributes in thousands of surrogate FI plots corresponding to spaceborne LiDAR footprints distributed across the project area. The surrogate plots were used as a reference dataset for estimating each forest attribute in each 30 m forest cell within the project area. The estimation was based on the k-nearest neighbour (k-NN) algorithm, where the selection of the four most similar surrogate FI plots to each cell was based on satellite, topographic, and climatic data. Wall-to-wall 30 m raster maps of broad forest type, stand height, crown closure, stand volume, total volume, aboveground biomass, and stand age were created for a ~400,000 km2 area, validated with independent data, and generalized into a polygon GIS layer resembling a traditional FI map. The MVI project showed that a reasonably accurate FI map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost by combining limited field data with remote sensing data from multiple sources.
- Item3D graph-based individual-tree isolation (treeiso) from terrestrial laser scanning point clouds(MDPI, 2022) Xi, Zhouxin; Hopkinson, ChristopherUsing terrestrial laser scanning (TLS) technology, forests can be digitized at the centimeter-level to enable fine-scale forest management. However, there are technical barriers to converting point clouds into individual-tree features or objects aligned with forest inventory standards due to noise, redundancy, and geometric complexity. A practical model treeiso based on the cut-pursuit graph algorithm was proposed to isolate individual-tree points from plot-level TLS scans. The treeiso followed the local-to-global segmentation scheme, which grouped points into small clusters, large segments, and final trees in a hierarchical manner. Seven tree attributes were investigated to understand the underlying determinants of isolation accuracy. Sensitivity analysis based on the PAWN index was performed using 10,000 parameter combinations to understand the treeiso’s parameter importance and model robustness. With sixteen reference TLS plot scans from various species, an average of 86% of all trees were detected. The mean intersection-over-union (mIoU) between isolated trees and reference trees was 0.82, which increased to 0.92 within the detected trees. Sensitivity analysis showed that only three parameters were needed for treeiso optimization, and it was robust against parameter variations. This new treeiso method is operationally simple and addresses the growing need for practical 3D tree segmentation tools.
- ItemVulnerability assessment of peatland complexes in the Hudson Plains (Ontario, Canada) to permafrost-thaw-induced landcover and hydrological change using a multiscale approach(Wiley, 2023) Mack Mikhail; Quinton, William; McLaughlin, James; Hopkinson, ChristopherThe Hudson Plains, Canada, is one of the largest, undisturbed peatland regions (370,000 km2) in the world. Air temperature in the Hudson Plains is increasing rapidly leading to unprecedented permafrost thaw. The region's remoteness has hindered our knowledge of how permafrost thaw alters peatland land cover and hydrological response at multiple scales. To assess which landscapes in the Hudson Plains are vulnerable to such disturbances, we analysed latitudinal distributions of land cover over a 300-km transect spanning the sporadic (<30% areal) to continuous (>80% areal) permafrost zone in northern Ontario and quantified land cover changes over 40 years using multiple remote sensing datasets (lidar, air photographs, and high-resolution satellite imagery). We then evaluated these landscapes at a fundamental hydrological unit, the peatland complex, identified five peatland complex types, and conceptualized their potential hydrological response using circuitry analogues. Over four decades, we found that permafrost peatlands declined by 4%, 8.5%, and 2% areal in the sporadic, discontinuous, and continuous permafrost zones, respectively. Circuitry analogues partitioned peatland complexes into their component peatland forms (e.g., permafrost peatland, bog, and fen) and represented each component's hydrological function using an electrical equivalent (e.g., generator, switch, and conductor). When interpreted at the landscape scale, circuitry analogues demonstrated latitudinal patterns in landscape structure (i.e., circuitry wiring) and indicated where permafrost thaw will have the greatest impact on landscape structure (i.e., rewiring) and therefore hydrological response. Based on these analyses, we suggest a 60-km latitudinal segment (54.5°N to 54.9°N) where peatland complexes are most vulnerable to permafrost-thaw-induced land cover and hydrological change and should therefore be the focus of future research and monitoring efforts.