cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14288/1.0448482
title: Evaluating Riparian Function Using LiDAR-Derived Vegetation Intactness and Cattle Accessibility in the Cariboo Region, British Columbia

description: Riparian zones provide vital ecological services, such as maintaining water quality, supporting biodiversity, and stabilizing stream banks; however, these areas in British Columbia’s Cariboo Natural Resource Region are increasingly pressured by forestry practices, cattle grazing, and private land management. Traditional cumulative effects assessments rely predominantly on indirect indicators of disturbance potential, which may inaccurately represent actual conditions, making direct quantification of riparian vegetation intactness and cattle accessibility critical for effective management. This study utilized high-resolution Light Detection and Ranging (LiDAR) data and spatial datasets to directly assess riparian conditions across four watersheds (approximately 20 km² each), addressing three objectives: (1) quantifying vegetation intactness within riparian zones on private land and Indian Reserves compared to adjacent crown land; (2) modeling cattle accessibility to riparian areas within active range tenures based on slope and vegetation density; and (3) evaluating the effectiveness of vegetation retention within forestry cutblock buffers (harvested areas less than 40 years old). Results revealed significant disparities in vegetation intactness, with crown land riparian buffers showing substantially higher mean canopy heights (7.54 m) than private and Indian Reserve lands (2.78 m). Approximately 50–60% of riparian areas within range tenures were highly accessible to cattle, with an additional 30–40% classified as medium accessibility, underscoring potential ecological risks from grazing. While 79.7% of forestry riparian buffers retained vegetation, their average canopy height (5.80 m) was significantly lower than surrounding areas (7.82 m), suggesting compromised ecological integrity. These findings emphasize the importance of leveraging remote sensing data in cumulative effects assessments, providing precise management guidance to enhance riparian protection strategies and policy efficacy.
DOI: 10.14288/1.0448484
title: Energy Consumption Comparison Between Electric Vehicles and Internal Combustion Vehicles Traveling Between Horseshoe Bay and Whistler with BEVER Tool

description: The transportation sector is a major contributor to global greenhouse gas emissions, prompting a growing shift toward electric vehicles (EVs) as a climate mitigation strategy. EVs are known for their efficiency, but how they perform in hilly or mountainous terrain highway still needs closer study. This study investigates the energy consumption and cost efficiency of internal combustion vehicles (ICVs) and EVs along a mountainous corridor to evaluate the operational benefits of EVs in terrain where regenerative braking may offer significant advantages. Focusing on the Sea to Sky Highway between Horseshoe Bay and Whistler, British Columbia, the research compares a Volvo XC40 (ICV) and a Volvo EX40 (EV), two nearly identical vehicle models differing only in powertrain. The study area, known for steep and frequent elevation changes, provides an ideal landscape to assess the energy recovery potential of EVs. The analysis used data from Canada’s 2021 Road Network Census and LiDAR-derived digital elevation models. The road segment was digitized and processed in QGIS to extract elevation and road network attributes. Energy consumption was calculated using the Battery-Electric Vehicle Energy Routing (BEVER) tool, which incorporates the Bellman-Ford algorithm to capture both energy use and regenerative gains. Results showed that the EX40 consumed significantly less energy (under 120 kWh) than the XC40 (over 300 kWh) over the same route. Regenerative braking helped maintain a flatter energy curve. Financial analysis confirmed the EX40’s cost advantage, even when using Level 3 public charging. These findings underscore the operational and economic benefits of EVs in topographically varied landscapes and highlight the importance of charging infrastructure and policy support for accelerating EV adoption.
DOI: 10.14288/1.0448455
title: Assessing Baseline and Monitoring Change in Forest Carbon Projects

description: This study evaluates carbon stock changes in the Northern Scarp West Forest Reserve (project site) and Bumfuom Forest Reserve (control site) in Ghana, using remote sensing techniques and biomass estimation methodologies. The aim is to compare two carbon crediting frameworks Gold Standard V2.1 and Verra VM0047 in terms of their effectiveness in monitoring carbon sequestration in afforestation and reforestation projects. These frameworks are fundamental for ensuring accurate carbon credit issuance, but differ in their baseline approaches: the static Gold Standard provides a conservative, fixed baseline, while the dynamic Verra framework adapts to real-time sequestration trends, allowing for more flexible monitoring. Remote sensing is critical in this context, particularly for regions like Ghana where limited ground-based data and large, inaccessible forested areas make traditional monitoring methods challenging. The study integrates NDVI (Normalized Difference Vegetation Index) a satellite-derived metric that quantifies vegetation health by comparing red and near-infrared reflectance values along with performance benchmarks and Chloris above-ground biomass (AGB) products to estimate carbon stocks and vegetation cover changes. Results show a significant increase in carbon stocks at the project site, with a baseline carbon stock of 36.04 tCO₂/ha. Verra VM0047’s dynamic baseline approach outperforms Gold Standard V2.1 in reflecting real-time sequestration trends. This study highlights the importance of incorporating dynamic baselines in carbon crediting methodologies, especially in ecosystems undergoing frequent land-use changes and recovery, while emphasizing the role of remote sensing in scalable, cost-effective monitoring. The findings contribute to a deeper understanding of forest carbon accounting methodologies and their application in tropical regions for climate change mitigation
DOI: 10.14288/1.0448458
title: Combining Multi-Season Multispectral Imagery and Airborne Laser Scanning Data to Improve Predicted Quercus garryana Distribution

description: Garry oak ecosystems are one of North America's most distinctive and ecologically significant ecosystems. Currently, only 1–5% of their historical extent remains in near-natural condition. Effective conservation requires accurate information on Garry oak distribution and forest composition. Remote sensing and machine learning offer advantages over traditional methods in terms of resources and scalability. Spectral similarities with co-occurring species, rarity, and subcanopy growth present challenges in transitional forests. Previous studies have found incorporating light detection and ranging (LiDAR) and multi-season data to be advantageous in tree species classification compared to single season imagery. This research evaluates the effect of including multi-season LiDAR data and imagery on the accuracy of Garry oak identification in a random forest classification of a mixed broadleaf and coniferous forest on Vancouver Island, Canada. LiDAR improved overall accuracy by 5.21%, Garry oak producer accuracy by 19%, and user accuracy by 7.67% on average compared to imagery alone. The impact of seasonality was less clear. On average, classifications using leaf-on LiDAR outperformed multi-season and leaf-off LiDAR, while spring imagery, followed by multi-season imagery, performed best. However, that was not consistently true. Seasonality of inputs significantly impacted misclassification patterns and final proportion of predicted species. These findings highlight the benefits of integrating LiDAR data in classifications to identify Garry oaks. Further research on the impact of species composition and phenology could help optimize data acquisition timing.
DOI: 10.14288/1.0448463
title: The Impact of Tree Shade on Building Cooling Energy Demand During Summer

description: The upsurge of climate change has exacerbated the urban heat island (UHI) effect, leading to severe consequences for human health, urban livability, and energy demand. Rising temperatures significantly increase the demand for cooling energy, as the cooling load of buildings is heavily influenced by the amount of solar radiation reaching their surfaces. One of the most effective and sustainable strategies is urban greening. These interventions can take various forms, including street trees, urban parks, and vegetation surrounding buildings, all of which contribute to heat relief and improved urban resilience. Trees offer a wide range of ecosystem benefits that improve the climate and habitability of cities, including public spaces, by acting as natural shading and wind-shielding elements around buildings. By blocking solar radiation, trees help lower the energy needed for cooling, especially in buildings with poor window-to-wall ratios. This project contributes to the Neighborhood Climate Action Plan (NCAP) goals by quantifying tree shade on buildings on the University of British Columbia campus and analyzing its impact on building energy demand. Using high-resolution Using high-resolution Light Detection and Ranging (LiDAR) data and sun position data, a Digital Surface Model was created to represent campus elevation, and hillshade analysis was employed to simulate shade coverage at 15-minute intervals. Findings revealed that there is a negative correlation between shade coverage and cooling energy demand, highlighting the effectiveness of tree shading as a cooling strategy, as buildings with more shade tend to have lower cooling loads. Also, the analysis, although statistically significant, revealed that other confounding factors affect energy demand in a building, including the age of the building, occupancy level and behaviour, shape and orientation of the building. The findings highlight the importance of strategic tree planting in urban environments to mitigate the urban heat island effect and enhance energy efficiency. Integrating tree shading into urban planning policies is essential for fostering sustainable and climate-resilient communities. Future research should incorporate real-time occupancy and equipment usage data, quantify transpiration contributions alongside shading effects and how the age of buildings can influence their energy demand over time.
DOI: 10.14288/1.0448471
title: Exploring Social Interactions in Vancouver's Neighbourhood Parks Through Sentiment Analysis of Google Reviews

description: Understanding social cohesion in urban green spaces is essential for fostering inclusive and resilient communities. This study analyzes social sentiment in Vancouver parks using a transfer learning natural language processing (NLP) approach to classify and interpret visitor reviews. A self-attention deep learning model was trained to detect social cohesion–related sentiment and categorize it into five classes: strong negative, moderately negative, neutral, moderately positive, and strong positive. Google reviews for 180 Vancouver parks were collected and pre-processed, including tokenization and translation where needed. Binary classification was first applied to filter for reviews relevant to social cohesion, followed by fine-tuning RoBERTa, a robustly optimized BERT (Bidirectional Encoder Representations from Transformers) model that excels at understanding contextual word meaning within sentences, on a labeled dataset. This fine-tuning process adapted the model’s attention toward content associated with social cohesion themes such as “welcoming,” “family,” “crowded,” and “unsafe.” The average park sentiment score was then used as the independent variable in a generalized linear regression (GLR) model, followed by geographically weighted regression (GWR) to account for spatial variation. Twenty candidate socioeconomic variables from the 2021 Canadian census were tested as predictors of average park sentiment. Results showed that household size and median income were statistically significant positive predictors of higher park sentiment at the dissemination area (DA) scale. The findings highlight disparities in perceived social cohesion across Vancouver’s neighborhoods and reveal the social atmosphere of parks beyond Google star ratings alone, offering evidence-based insights for park planning and management. Key Words: Social cohesion, sentiment analysis, transfer learning, deep learning, geographically weighted regression, natural language processing
DOI: 10.14288/1.0448474
title: Historically Changed 3D University of British Columbia (UBC) Buildings and Landscapes

description: This study addresses the lack of comprehensive 3D documentation of the University of British Columbia (UBC) Vancouver campus's architectural and ecological evolution from 2015 to 2024. By integrating 2D polygon shapefiles, Light Detection and Ranging (LiDAR) data, Digital Elevation Models, and open-source geographical data, an interactive time-slider enabled 3D model was developed to capture the campus's historical transformation. The methodology leverages LiDAR point clouds to extract detailed information on buildings, vegetation, and terrain, supplemented by satellite imagery and vector data for validation and calibration. Results reveal distinct spatial development patterns: stability in the academic core around Main Mall with maturing tree canopy, contrasted with significant residential expansion in peripheral areas, particularly the Wesbrook neighborhood. The visualization demonstrates UBC's implementation of sustainability policies, including Leadership in Energy and Environmental Design (LEED) certification standards for new construction, while balancing growth with ecological preservation. This model provides insights into the interplay between development and environmental planning, supporting evidence-based decision-making for campus planning while offering an educational tool for understanding the historical landscape changes. The approach establishes a replicable framework for similar historical landscape modeling efforts at other institutions seeking to integrate sustainability into their development strategies.
DOI: 10.14288/1.0448481
title: Evaluating LiDAR technology to Augment Fuel Typing Classification by Enhancing accuracy of Vegetation Resource Indices in The Resort Municipality of Whistler

description: This study evaluates the integration of airborne LiDAR (Light Detection and Ranging) data with British Columbia’s Vegetation Resource Inventory (VRI) to identify opportunities for enhancement. LiDAR-derived forest structure metrics were compared against VRI estimates in the Resort Municipality of Whistler, revealing discrepancies and variable correlation strengths. Tree height showed a moderately strong correlation (r ≈ 0.66, p &lt; 0.001) between VRI-projected and LiDAR-measured values. LiDAR heights were on average ~0.86 m lower than VRI estimates, through sharing identical patterns and trends, indicating agreement in vertical structure. In contrast, canopy closure (percent cover) has only a weak correlation (r ≈ 0.27). LiDAR-derived canopy closure values were substantially higher, about 55.8% on average than VRI projections, suggesting that our LiDAR method overestimated due to methodological differences. Conifer vs. broadleaf compositions showed that LiDAR morphological metrics can partly predict species composition: percent conifer in VRI had a moderate negative correlation with LiDAR-derived canopy cover (r ≈ –0.41) and moderate positive correlation with tree shape aspect ratio (r ≈ 0.40) though showing some correlation this is too modest for any conclusive predictive power. Our methodology combined terrain-normalized LiDAR point clouds, individual tree segmentation, and extraction of metrics such as height, crown area, aspect ratio, and crown surface curvature. The results demonstrate that LiDAR can augment VRI by providing more objective measurements, higher spatial resolution, and improved estimates of key forest attributes. These enhancements could benefit applications including forest management, wildfire risk assessment, and carbon accounting.
DOI: 10.14288/1.0448483
title: Mapping Invasive Highbush Blueberry in Richmond Nature Park: A Multi-temporal Normalized Difference Vegetation Index (NDVI) and Spectral Analysis Approach for Peatland Conservation

description: This study demonstrates the application of multi-temporal Normalized Difference Vegetation Index (NDVI) analysis and red-band spectral signatures from Sentinel-2 imagery to map the distribution of invasive highbush blueberry (Vaccinium corymbosum) within Richmond Nature Park, British Columbia. Located in the Lulu Island Bog, this critical peat ecosystem faces significant ecological threats from invasive species that disrupt native vegetation communities and alter hydrological processes. By analyzing seasonal NDVI variations across multiple time periods (February-November 2023) and incorporating red-band dominance filters to capture characteristic fall senescence patterns. In this study a habitat suitability model is developed that effectively predicts blueberry presence within the study area. The methodology yielded a strong positive correlation between predicted suitability scores and field-verified blueberry coverage (r = 0.81, p &lt; 0.001), with the model explaining 65.5% of the variance in blueberry distribution patterns (adjusted R² = 0.64). September-October NDVI differences emerged as particularly powerful indicators of blueberry presence, capitalizing on distinctive fall coloration absent in native bog species. This cost-effective approach using freely available medium-resolution satellite imagery offers conservation managers a valuable tool for identifying priority intervention areas without requiring expensive high-resolution data sources. This study’s findings contribute to broader conservation efforts aimed at preserving endangered peat bog ecosystems and their critical carbon sequestration functions in the face of increasing climate change concerns.
DOI: 10.14288/1.0448425
title: Transitioning dairy cattle from parlor to automatic milking systems

description: This paper investigated behavioral responses associated with fear when cows first experienced milking in an automatic milking system (AMS), comparing cows that had experienced two different types of training: baseline training involved being moved into an inactive AMS unit to receive a grain reward for 3 days before milking; enhanced training used a grain reward to encourage cows to enter the AMS followed for 10 days before baseline training began. All 45 cows tested, regardless of training regime, showed evidence of fear when entering the AMS for milking, and fear responses declined over multiple milkings.
DOI: 10.14288/1.0448504
title: It’s not black and white: Perspectives of Western Canadian beef farmers on dairy-beef production.

description: This dataset contains the supplementary materials of the study: "It’s not black and white: Perspectives of Western Canadian beef farmers on dairy-beef production." Abstract: Non-replacement dairy calves (i.e., male calves and females not needed for milking herd replacement) can face multiple welfare challenges due to their lower economic value in the dairy and beef industries. Incorporating beef genetics into dairy herd breeding programs has become common on dairy farms to produce ‘beef-on-dairy’ calves that are better suited for beef production than pure dairy breed animals. This practice results in increased revenue from non-replacement dairy calves for dairy farmers, but little is known about the impact of dairy-beef production on beef farmers. This study aimed to investigate the attitudes of Canadian beef farmers toward dairy-beef production, with a focus on how beef-on-dairy breeding strategies by dairy may affect the beef industry. We conducted semi-structured interviews with 20 beef farmers in Western Canada, exploring their awareness, attitudes, and recommendations for the management of dairy-beef calves. Participants (11 male, 9 female) were recruited using snowball sampling and interviewed following a semi-structured interview guide. The audio-recorded interviews (averaging 44±15 min in duration) were transcribed verbatim and analyzed using inductive thematic analysis, resulting in three main themes: 1) the dairy and beef relationship, 2) attitudes to beef-on-dairy animals, and 3) a shared future. In the first theme, participants discussed the relationship between the dairy and beef industries, highlighting differences in Canadian market structures (dairy as supply-managed vs. beef as an open market), farming practices (beef farming as more extensive vs. dairy as more intensive) and public perceptions of the two systems. In the second theme, participants showed mixed attitudes toward dairy-beef production and discussed their views about beef-on-dairy calves compared to purebred dairy calves, the management practices used to raise them, and the potential impacts of dairy-beef production on the beef industry. In the third theme, participants reflected on the future of dairy-beef production, discussing who should be involved in shaping the future of this practice. Participants showed mixed feelings towards the use of beef genetics in dairy herds, with some perceiving this as an opportunity for the beef industry to meet consumer demand and others expressing concern about the over-saturation of the beef market and possible threats to traditional ways of rearing beef. Our study enhances the understanding of the relationship between the dairy and beef industries in Western Canada and suggests the need for communication and collaboration among producers and others in the supply chain.
DOI: 10.15468/dl.b934pn
title: Occurrence Download

description: A dataset containing 1399 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cerapus Say, 1817"
 ]
}

 The dataset includes 1399 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013035-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wfzjw3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((24.53152 -7.427288, 24.54633 -7.427288, 24.54633 -7.410363, 24.53152 -7.410363, 24.53152 -7.427288))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013037-250515123054153/datasets/export for details.
DOI: 10.15468/dl.5fayer
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Squalus nasutus Last, Marshall & White, 2007"
 ]
}

 The dataset includes 123 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002628-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/7dro2z
title: repository.zip

description: :unav
DOI: 10.57745/eas6jh
title: README.md

description: :unav
DOI: 10.15468/dl.pa9nbj
title: Occurrence Download

description: A dataset containing 44687 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "Country is China",
 "GadmGid is CHN.2_1",
 "Month is one of (April, September, October)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2024-2024"
 ]
}

 The dataset includes 44687 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029029-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.