Christopher Bone

Machine learning (ML) is prevalent in land change modeling for automating transition rule specification. However, aspatial strategies are typically used to mitigate the imbalances characterizing multitemporal land cover (LC) datasets. While spatialized cost‐sensitive learning strategies including spatial sample weights (SSWs) have demonstrated benefits for ML models, the spatial pattern variations linked to LC change events have not been considered. Therefore, the goal of this study is to enhance SSW strategies by regulating LC change sample importance with a novel patch weight (PW) term derived from patch‐level landscape metrics. An application example demonstrates Extreme Gradient Boosting Machine (XGB) models used to forecast LC change for the City of Kelowna, Canada, under various SSW configurations. The results showed numerous PW settings facilitated realistic quantities of change overall and for each LC class while reducing allocation errors compared to baseline configurations. This study advances spatialized cost‐sensitive learning strategies to enable land change modeling using ML.

The construction of hydroelectric dams leads to substantial land-cover alterations, particularly through the removal of vegetation in wetland and valley areas. This results in exposed sediment that is susceptible to erosion, potentially leading to dust storms. While the reintroduction of vegetation plays a crucial role in restoring these landscapes and mitigating erosion, such efforts incur substantial costs and require detailed information to help optimize vegetation densities that effectively reduce dust storm risk. This study evaluates the performance of drones for measuring the growth of introduced low-lying grasses on reservoir beaches. A set of test flights was conducted to compare LiDAR and photogrammetry data, assessing factors such as flight altitude, speed, and image side overlap. The results indicate that, for this specific vegetation type, photogrammetry at lower altitudes significantly enhanced the accuracy of vegetation classification, permitting effective quantitative assessments of vegetation densities for dust storm risk reduction.

Recreational activities have been shown to induce fear responses in carnivores, causing both spatial and temporal displacement that can have cascading effects on the wider ecological community. Cougars Puma concolor are one species that are particularly sensitive to human disturbances as they have been found to be impacted by various forms of recreational activities. This research aimed to quantify the spatial and temporal responses of cougars to human activities on southern Vancouver Island in British Columbia, Canada. Fifty camera traps were deployed in a systematic probabilistic sampling design and data from the summer season of 2022 was used for analyses. We modeled cougar weekly occurrence frequency in relation to human (hikers and bikers) occurrence frequency, vehicle occurrence frequency, and landscape features, using generalized linear mixed models. Temporal responses of cougars were investigated using activity pattern analysis to compare activity patterns within a recreation area and a restricted public access reserve. Cougars avoid occurring at sites where recreationalists frequently visited, but not sites frequented by vehicles on unpaved roads. We observed minimal influence of habitat features or prey availability on cougar occurrence frequency, as measured by information loss (AIC _c scores). Activity pattern analysis did not demonstrate significant differences in cougar daily activity patterns between the recreation area and no‐entry reserve, suggesting a lack of temporal response. These results suggest recreational activities on southern Vancouver Island are impacting cougar spatial – but not temporal – behavioral dynamics. Insights on how cougars respond to recreational activities can help inform landscape management and policies by providing information that can be used to mitigate negative impacts and thus ensure that recreational activities are remaining compatible with conservation efforts.

Forest management practices can increase climate change mitigation potential through applications focused on carbon budgets. One such application involves utilizing non-merchantable material (i.e., logging residues typically piled and burned) for bio-energy. However, limited remote sensing data is available for estimating wood residues until after timber has been harvested, at which point recovery of residual wood is of little financial interest. This research utilizes a hybrid method to develop models that provide pre-harvest estimates of the amount of merchantable and non-merchantable material that would result from harvesting and investigates the scalability and transferability of such measures to the harvest block level. Models were trained using 38 plots across two sites dominated by Douglas-fir, then expanded to ten harvest blocks, and transferred to eight blocks from two sites without training data before being compared against multiple independent block-level estimates. Model results showed root mean square errors of 35% and 38% for merchantable and non-merchantable volumes, respectively. Merchantable volume estimates in blocks with training had average absolute differences from the harvest scale (9–34%) similar to transferred blocks without training (15–20%). Non-merchantable model results were also similar in both trained and transferred harvest blocks, with the pre-harvest model results having lower differences from the post-harvest geospatial versus field surveys. The results from this study show promise for hybrid methods to improve estimates of merchantable wood volume compared to conventional forest cover data approaches, and provide the ability to predict non-merchantable volumes within the range of accuracy of post-harvest residue survey methods.

In this study, we investigate a novel application of the photogrammetric monoplotting technique for assessing wildfires. We demonstrate the use of the software program WSL Monoplotting Tool (MPT) to georeference operational oblique aerial wildfire photographs taken during airtanker response in the early stages of fire growth. We located the position of the fire front in georeferenced pairs of photos from five fires taken 31–118 min apart, and calculated the head fire spread distance and head fire rate of spread (HROS). Our example photos were taken 0.7 to 4.7 km from fire fronts, with camera angles of incidence from −19° to −50° to image centre. Using high quality images with detailed landscape features, it is possible to identify fire front positions with high precision; in our example data, the mean 3D error was 0.533 m and the maximum 3D error for individual fire runs was less than 3 m. This resulted in a maximum HROS error due to monoplotting of only ~0.5%. We then compared HROS estimates with predictions from the Canadian Fire Behavior Prediction System, with differences mainly attributed to model error or uncertainty in weather and fuel inputs. This method can be used to obtain observations to validate fire spread models or create new empirical relationships where databases of such wildfire photos exist. Our initial work suggests that monophotogrammetry can provide reproducible estimates of fire front position, spread distance and rate of spread with high accuracy, and could potentially be used to characterize other fire features such as flame and smoke plume dimensions and spotting.

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Cette étude évalue une approche particulière de la cartographie numérique, la cybercartographie, en tant qu’outil d’autodétermination autochtone. L’étude, réalisée auprès des Premières Nations du Canada, s’appuie sur les principes autochtones de propriété, de contrôle, d’accès et de possession pour reconnaitre les moyens précis par lesquels la cybercartographie peut aborder certains aspects de l’autodétermination. Les résultats montrent que les exigences en matière d’applications cybercartographiques sont propres à chaque communauté, et que ces applications peuvent faciliter l’autodétermination quand les communautés participent activement à la sélection de la technologie pendant le processus de recherche. L’étude de cas présentée ici révèle que la cybercartographie, et la cartographie numérique en général, peuvent véhiculer d’importants éléments culturels autochtones et servir à rehausser les épisodes éducatifs pendant lesquels se transmettent les connaissances entre les générations.

Forest fertilization is common in coastal British Columbia as a means to increase wood production and potentially enhance carbon sequestration. Generally, the effects of fertilization are determined by measuring sample plots pre- and post-treatment, resulting in fertilization effects being determined for a limited portion of the treatment area. Applications of remote sensing-based enhanced forest inventories have allowed for estimations to expand to the wider forested area. However, these applications have not focused on monitoring the effects of silvicultural treatments. The objective of this research was to examine if a multi-temporal application of the LiDAR area-based method can be used to detect the fertilization effects on volume, biomass, and height in a second-growth Douglas-fir (Pseudotsuga menziesii) stand. The study area on Vancouver Island was fertilized in January 2007, and sample plots were established in 2011. LiDAR acquisitions were made in 2004, prior to fertilization, and in 2008, 2011, and 2016, covering both treated and untreated areas. A total of 29 paired LiDAR blocks, comprised of four 20 m resolution raster cells, were selected on either side of the fertilization boundary for analysis of the effects across several different stand types differing in the percentage of Douglas-fir, site index, and age. Random forest (RF) plot-level models were developed to estimate total stem volume and total stem biomass for each year of LiDAR acquisition using an area-based approach. Plot level results showed an increase in stem volume by 13% fertilized over control from 2005 to 2011, which was similar to a 14% increase in above-ground carbon stocks estimated using a tree-ring stand reconstruction approach. Plot-level RF models showed R² values of 0.86 (volume) and 0.92 (biomass) with relative cross-validated root mean square errors of 12.5% (volume) and 11.9% (biomass). For both the sample plots and LiDAR blocks, statistical results indicated no significant differences in volume or biomass between treatments. However, significant differences in height increments were detected between treatments in LiDAR blocks. The results from this research highlight the promising potential for the use of enhanced forest inventory methods to rapidly expand the assessment of treatment effects beyond sample plots to the stand, block, or landscape level.

Climate change has resulted in the need for adaptation tools to provide stakeholders with the ability to respond to a broad range of potential impacts. Geovisualizations serve as powerful engagement tools due to their capacity in communicating complex climate data to various audiences. Studies have also shown a preference towards conveying climate data through geo-visual representations, to quickly present ideas rooted in geographical challenges and solutions. However, a rapid pace of technological advancements has paved the way for an abundance of geovisualization products that have eclipsed the necessary theoretical inquiry and knowledge required to establish effective visualization principles. This study addresses this research gap through a two-step process of (1) conducting a thorough review of the geovisualization for climate change literature, and (2) creating a conceptual framework that classifies existing geovisualization products into themes relating to visualization features, audiences, and the intended outcome or purpose of the visualization medium. The result is the Climate Visualizations for Adaptation Products (CVAP) framework, a tool for researchers and practitioners to use as a decision support system to discern an appropriate type of geovisualization product to implement within a specific use case or audience. Visualizations with more interactivity were favoured among expert user groups, to act as tools for knowledge discovery. This is contrary to the visualizations intended for communicating a known message to a user group, as those products often had a low level of interactivity associated with their use.

The Songgukri culture (c. 2900–2400 cal. BP) in the Geum River basin is often regarded as one of the earliest complex societies in the Korean peninsula, based on some evidence for an intensified agrarian economy and social differentiation. This study focuses on landscape visibility as a method of detecting settlement relationships of the Songgukri culture. Two measures of landscape visibility, viewshed size and shared-ness of viewshed (SoV), are examined in this study. Our results indicate that while Songgukri centers tend to have larger visibility of landscape than non-centers, both centers and non-centers share their visible landscape with other settlements at a remarkably high rate. We argue that landscape visibility at Songgukri settlements reflects a shared sense of cultural belonging among settlers, rather than sociopolitical inequality between the elites in centers and the non-elites in other settlements. This study highlights a long-term process, in which bottom-up cultural interactions of Songgukri residents may have contributed to the development of settlement organization and regional communal identities over time.