Conference Agenda

Mining activities constitute a major source of land disturbance in British Columbia and pose long-lasting risks to salmon-bearing watersheds through sedimentation, habitat fragmentation, and water quality degradation. However, existing mining inventories often lack spatial precision and consistency, limiting their usefulness for cumulative effects assessment. This study presents a new multi-stage remote sensing framework designed to systematically detect and map mining disturbances across the province using Landsat time series (1984–2023), Sentinel-2 imagery, and provincial mining databases.

The workflow integrates spectral–temporal change detection (LandTrendr), land cover and disturbance history from the Satellite-Based Forest Inventory, Sentinel-2 spectral clustering, and final visual interpretation using very high-resolution imagery. This approach effectively distinguishes mining disturbances from wildfires, harvesting, and other land surface changes common in BC’s diverse landscapes.

Applied province-wide, the framework identified 1,037 mining sites with a 92% thematic accuracy, producing the most spatially explicit and consistent inventory of mining disturbances currently available for British Columbia. Results highlight persistent mining hotspots and reveal that mineral mines—especially coal, gold, and silver—dominate the cumulative disturbance footprint, with peak activity occurring between 1970 and 1990.

The resulting dataset provides a critical foundation for evaluating the cumulative impacts of mining on salmon habitats and supports ongoing efforts toward transparent, data-driven land-use planning. The framework is scalable, updateable, and transferable to other regions where large-area monitoring of mining activity is needed.

The objective of this study was to investigate the application of RADARSAT Constellation Mission (RCM) CP data for the estimation of relative oil thickness. On July 25, 2020, the bulk carrier MV Wakashio ran aground off the coast of Mauritius with 1000 tonnes of oil was estimated to have spilled into the Indian Ocean. RCM CP data were acquired on August 9, 12, and 13, 2020. CP data entails the acquisition of two phase-preserving channels, CH and CV. A 5x5 polarimetric filter was applied and CP discriminators, Degree of Linear Polarization (DLP), Degree of Polarization (DOP), and Entropy (H), were extracted. For the three images, the DLP, DOP, and H were calculated for “thick” and “thin” oil, and oil-free regions. The performance of the DLP, DOP, and H was consistent with the expected results for both thin and thick oil and oil-free regions. The correlation between the thick, thin, and oil-free regions was calculated based on an Area-based Classification-by-Histogram (ACH). The results for H (August 13) show a strong negative correlation between thick oil/oil free, a small positive correlation between thin oil/oil-free, and a negative correlation between thick/thin oil. The results of the CP discriminators were consistent with theoretical expectations, with H providing the best overall performance. The results of the CP discriminators were consistent with theoretical expectations, with H providing the best overall performance. The results suggest that CP data is a viable option for the estimation of relative oil thickness.

Eelgrass (Zostera marina) is the primary native seagrass species in intertidal areas across North America and plays an important role in marine ecosystems. Current eelgrass mapping is primarily limited to localized areas using various field and remotely piloted aerial systems (RPAS) methods, resulting in limited coverage and update frequency. To support more frequent, wide area monitoring of eelgrass along Canada’s Pacific coast, we are developing Eelgrass Explorer (E2), an automated system to provide eelgrass distribution maps across British Columbia’s (BC) intertidal zones from either Sentinel-2 or Planet SuperDove multispectral data. The deep learning approach central to the system is based on a DenseNet architecture developed for seagrass detection elsewhere in the world, modified for BC conditions. Our proof of concept used training data across 6 sites along the BC coast and obtained 95% accuracy for test points within training sites, a 12% percent improvement over a Random Forest approach using the same data. Future work will include more rigorous validation in new sites, refining the model for even better generalization, and incorporating it into an automated processing pipeline. The resulting 10-meter eelgrass extent maps across BC’s intertidal zone will be made openly available to the research community.

Ensuring robust and precise navigation in GNSS-denied or degraded environments remains a core challenge for autonomous systems. The demand for precise, real-time positioning is critical across various applications, including fleet management, automotive, rail, pavement, and airport safety, particularly within GNSS-limited operational settings. This paper presents a novel approach to integrating Visual Odometry (VO) and Map-Based Localization (MBL) as external aiding sources for inertially-aided navigation. This integrated solution is specifically designed for land mobile mapping applications and leverages a high-precision inertially-aided GNSS solution inherent to the mobile mapping system.

This paper is structured as follows:

• Overview of VO and MBL Techniques: A detailed review of the theoretical principles underpinning the Visual Odometry (VO) and Map-Based Localization (MBL) techniques.

• Real-Time Deployment Strategies: Examination of the specific strategies required for real-time operational deployment, including handling delayed measurements, managing out-of-sequence updates, and implementing dynamic uncertainty adaptation.

• Kalman Filter Framework Design: Development of the Kalman filter framework to accommodate the delta pose data (derived from VO) and absolute pose data (derived from MBL) as distinct aiding sources. This includes modelling specific measurement errors and introducing dedicated state components.

• Theoretical and Practical Accuracy Analysis: Evaluation of the integrated system's effectiveness through a rigorous theoretical and practical accuracy analysis under a wide range of operational conditions, including the quantification of positioning performance enhancement when utilizing low-cost IMUs.

Over the last three decades, advancements in sensor and positioning technology have fundamentally transformed geospatial data acquisition, processing, and quality control, enabling surveyors and professionals to collect, interact with, and produce mapping products with unprecedented accuracy and resolution. Sensor Fusion concepts started at the academic level in the early 1990s (c.f., Schwarz et al., 1993; El-Sheimy, 1996; Mostafa and Schwarz, 1997; Ip et al., 2007; Ravi et al., 2018).

The fusion of LiDAR and photogrammetric sensors paired with GNSS, and inertial positioning systems has effectively supplanted many traditional mapping methods that relied heavily on high-accuracy positioning combined with significant data interpolation (c.f., Scherzinger et al., 2018)

Today, geospatial data acquisition is increasingly performed simultaneously using land mobile mapping systems, UAVs, and marine vessels all equipped with multiple LiDARs and diverse imaging sensors (e.g., panoramic, RGB, NIR, thermal, etc.), rapidly becoming the industry standard. These multi-stream datasets are now typically integrated and optimized within a post-processing environment. This paper will highlight the technology and workflows surrounding these synergistic systems, demonstrating how their fusion is yielding an unprecedented level of speed and quality hitherto unseen in the industry.

This contribution examines whether satellite derived crop condition signals can be translated into a practical market indicator for Prairie crops that do not trade on futures exchanges. In Canada, remote sensing programs such as the Crop Condition Assessment Program already provide in season crop monitoring and support official yield and production estimation. This study builds on that foundation, but asks a different question: how crop condition information is incorporated into prices in decentralized cash markets for non futures crops such as peas, lentils, and mustard.

Using Canada’s operational AVHRR and MODIS NDVI archives, the study outlines a simple method for aggregating weekly NDVI composites to key producing regions, deriving seasonal anomalies and phenological measures, and combining them into a normalized regional index for each week of the growing season. The purpose of this index is not to replace official crop condition or yield models, but to provide a transparent and interpretable signal that can be examined alongside observed cash market pricing behavior.

The empirical focus is on market linkage rather than agronomic prediction alone. Specifically, the study compares the relationship between the NDVI based index and weekly changes in benchmark futures prices with its relationship to posted bids for selected non futures crops. The working hypothesis is that crop condition information is incorporated relatively quickly into futures linked markets, while non futures cash bids respond more slowly and less directly. If confirmed, the index could serve as a public benchmark for price discovery in thin and fragmented specialty crop markets.

Accurate mobile mapping, a critical requirement for various applications, is frequently compromised in GNSS-denied environments, resulting in degraded final mapping products. This research investigates the efficacy of simultaneous optimization of mobile mapping system data, specifically encompassing the trajectory, system calibration, and LiDAR point cloud. The study explores the integration of inertially-aided GNSS data with LiDAR data to mitigate trajectory and point cloud errors and refine installation parameter calibration during GNSS outages. Utilizing datasets acquired with a Mobile Mapping System in a suburban setting in Richmond Hill, Ontario, Canada, the performance of this integrated approach was rigorously evaluated. The results demonstrate the capability of Simultaneous LiDAR & Trajectory Data Optimization to effectively and concurrently compensate for diverse error sources using LiDAR data, GNSS/Inertial measurements, and calibration parameters. This highlights the significant potential for achieving enhanced data accuracy in challenging land mobile mapping scenarios where GNSS availability is limited.