Canadian Journal of Remote Sensing

The convergence of advanced remote sensing technologies and analytical methodologies holds significant promise for bolstering the safety and sustainability of infrastructure development in geotechnical engineering. Rock mass characterization is a fundamental step for any rock-engineering project. However, surveying discontinuities in the rock masses is usually challenging and can be biased. This paper introduces novel approaches to leveraging remote sensing technology to analyze rock outcrops and rock slope cuts precisely. It delves into the specifics of two distinct portable techniques, metrology-grade laser scanner and SLAM-based laser scanner technology, and their respective efficacy in capturing engineering geological features, such as rock quality designation, discontinuity spacing, aperture, and surface roughness. The findings underscore the metrology-grade laser scanner’s superior ability to capture precise geological features compared to SLAM-based technology, which faces challenges related to uneven point distribution. While the metrology-grade laser scanner facilitates detailed analysis and accurate measurement at smaller scales, SLAM-based technology allows for swift data acquisition over larger areas with reduced processing time. A case study from Archer Point in British Columbia is presented to exemplify the practical implementation of these techniques.

Coastline change serves as a crucial indicator of environmental changes in coastal areas. By utilizing Landsat imagery time series from 2000 to 2022, we integrated the Modified Normalized Water Index, Support Vector Machine supervised classifier, and the Digital Coastline Analysis System to track coastline changes in Sydney's sandy beaches over 2000-2022, and analyzed the potential factors. Since the beginning of the 21st century, Sydney's sandy beaches exhibited an erosion trend of −0.17 m/a, resulting in a net coastline movement of −6.84 m, with over 80% of the coastline experiencing erosion. Before 2010, the sandy beaches, on average, accreted at a rate of 0.40 m/a. then, from 2010 to 2019, the average beach accretion slowed down (0.07 m/a), with some beaches showing an erosion trend. However, after 2019, sandy coastline erosion in Sydney has greatly accelerated, with an average rate of −4.81 m/a. the primary factors influencing the spatial-temporal patterns of Sydney's sandy coastline include sea level height, significant wave height, and storm events. this study provides valuable insights into the sustainable management and protection of sandy beaches, disaster response planning, and the sustainable development of coastal areas. RÉSUMÉ Le changement de la côte est un indicateur crucial des changements environnementaux dans les zones côtières. en utilisant les séries chronologiques d'images de Landsat de 2000 à 2022, nous avons intégré l'indice modifié normalisé de l'eau (≪Modified Normalized Water Index≫), le classificateur supervisé par machine vectorielle de soutien (≪Support Vector Machine≫) et le système numérique d'analyse du littoral (≪Digital Coastline Analysis System≫) pour suivre les changements du littoral sur les plages de Sydney au cours des années 2000-2022, et a analysé les facteurs potentiels. Depuis le début du 21e siècle, les plages de Sydney affichent une tendance à l'érosion de 0,17m/an, ce qui a entraîné un mouvement net de 6,84 m sur la côte, avec plus de 80 % de la côte en érosion. Avant 2010, les plages, en moyenne, ont augmenté à un taux de 0,40 m/an. De 2010 à 2019, l'accrétion moyenne des plages a ralenti (0,07 m/an), certaines plages montrant une tendance à l'érosion. Cependant, après 2019, l'érosion du littoral sablonneux à Sydney s'est fortement accélérée, avec un taux moyen de 4,81 m/an. Les principaux facteurs qui influencent les modèles spatio-temporels de la côte sablonneuse de Sydney comprenne la hauteur du niveau de la mer, la hauteur significative des vagues et les événements de tempête. Cette étude fournit des informations précieuses sur la gestion durable et la protection des plages, la planification de la réponse aux catastrophes et le développement durable des zones côtières.

Optical and radar remote sensing are both used to map surface water features. Since use cases range considerably in the literature between applications, a direct comparison is warranted to assess how well each perform in a wide range of geographic settings using a range of classification methods. Thus, surface water maps generated from Sentinel-1 Synthetic Aperture Radar (S1SAR) and Sentinel-2 Multispectral Instrument (S2MSI) imagery were compared across four machine learning techniques and eight diverse image areas in Canada. Additionally, the polarizations and multispectral bands were varied to understand their effect. The results were validated using high resolution satellite imagery, and analysis of variance was calculated. S2MSI consistently produced higher accuracy surface water maps compared to S1SAR. Contrary to previous understanding, the cross-polarization did not produce significantly more accurate surface water maps than like-polarization, and the same was true for dual and single polarization. The introduction of an additional band of multispectral imagery improved accuracy significantly. In flooded conditions, dual polarization produced the best results, and for the detection of ice, cross-polarization produced the best results. These findings will increase the quality and efficient generation of surface water maps for water resource management, climate change impact studies, and other disciplines.

Integrating airborne laser scanning (ALS) forest attributes with photo-interpreted forest stand age and species attributes can provide managers with the best information to drive estate planning, growth and yield projections, and forest operations. Photo-interpreted forest inventories provide certain forest attributes that are difficult to measure with ALS, yet are subjective and irregularly updated. ALS is objective and provides detailed estimates of forest structure attributes, but poorly estimates age and species composition. We used Generic Region Merging segmentation and k-nearest neighbor imputation to integrate photo-interpreted and ALS-derived forest attributes into a contemporary stand-based forest inventory. We first segmented gridded ALS attributes into forest stand polygons across a ∼630,000 ha managed forest in Ontario, Canada. We next applied imputation to a photo-interpreted inventory, assessing the influence of model parameters and imputed vs. observed values of age and species using leave-one-out cross-validation. Compared to the photo-interpreted inventory, the optimal imputation model estimated age with a mean absolute and mean bias difference of 16.06 and −0.14 years, and classified leading species with 65.46% accuracy. We lastly integrated imputed age and species attributes into the automatically segmented forest stand polygons, finding similar age and species distributions across the landscape when compared to the photo-interpreted inventory.

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.