cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp3/p3pjlb
title: Marine Debris Hotspot Analysis in Howe Sound, British Columbia

description: Marine debris poses significant threats to coastal ecosystems and infrastructure, especially in semi-enclosed regions where monitoring is limited by inaccessibility and uneven population distribution. In Howe Sound, British Columbia, this study integrates remote sensing and environmental modeling to predict debris accumulation zones. Sentinel-2 satellite imagery was selected due to its high spatial resolution, broad spectral range, and frequent revisit time, which make it well-suited for capturing detailed coastal features. A neural network algorithm was used to classify six landcover types with an overall accuracy of 0.98. The classification results showed that many known debris hotspots are located near urban shorelines and within semi-enclosed bays. To simulate debris transport, river discharge and seasonal wind direction were modeled as surface movement drivers. The study area was divided into three sections to account for spatial variation in debris driving forces contribution. Hourly wind data from four weather stations were used to construct wind rose diagrams that captured seasonal changes in wind direction. The simulation identified 49 predicted debris hotspot locations. Of these, 20 overlapped with known hotspots, while 10 of the 29 newly identified hotspots are in less populated and previously underreported areas, particularly along the western shoreline. These findings demonstrate that remote sensing, when combined with physically based modeling, can overcome limitations of traditional monitoring methods and improve the identification of marine debris accumulation. This approach provides a scalable and transferable framework for supporting more targeted and proactive coastal management strategies.
DOI: 10.5683/sp3/zta8sg
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: 10.5683/sp3/ea5gar
title: Detecting Change of Aboveground Biomass (AGB) in Kwahu South, Ghana
title: A Comparison Between AGB Model Derived from Landsat 9 and PlanetScope Data

description: Forest plays a vital role in regulating the global carbon cycle, yet their aboveground biomass (AGB) remains difficult to estimate over large areas due to limitations of field-based measurements. This study addresses the challenge by evaluating satellite-based approaches to estimate AGB in Kwahu South, Ghana. The objective was to determine which optical satellite imagery, when paired with a consistent reference dataset, offers the most accurate biomass prediction. Specifically, three datasets were compared: Landsat 9, PlanetScope, and rescaled PlanetScope. 

Global Ecosystem Dynamics Investigation (GEDI) L4A data, collected by a full waveform LiDAR instrument mounted on the International Space Station (ISS), was used as reference data for validation. GEDI provides high-resolution laser ranging observations that capture the 3D structure of Earth's forests. GEDI L4A specifically provides globally available aboveground biomass density (AGBD) estimates, serving as reliable reference data for training and validating remote sensing models and reducing reliance on labor-intensive field campaigns. 

Vegetation indices and topographic variables were extracted from each satellite dataset and used as explanatory variables in a Random Forest regression model trained using ArcGIS Pro. Results showed that rescaled PlanetScope imagery achieved the highest prediction accuracy with a model explaining 84% of variability in observed AGB and the lowest prediction error. Despite its higher spatial resolution, the original Planet model was less accurate compared to Landsat 9. These findings highlight the importance of matching the spatial scale of satellite data to that of reference observations. They also suggest that increasing resolution does not always improve model performance and that thoughtful data preprocessing can significantly enhance prediction accuracy. This research offers a scalable and cost-effective method for monitoring biomass in regions where ground measurements are limited, contributing to better-informed forest management and carbon accounting efforts.
description: ArcGIS Pro, 3.4.2
DOI: 10.5683/sp3/vtahfa
title: Other Effective area-based Conservation Measure (OECM) Identification in Átl'ka7tsem / Howe Sound Biosphere Region

description: To support Canada’s ambitious conservation commitments of protecting 25% of its terrestrial and marine ecosystems by 2025, and 30% by 2030, new and flexible conservation approaches are necessary. Other Effective Area-Based Conservation Measures (OECMs) represent an opportunity to achieve these targets by recognizing biodiversity conservation occurring indirectly through existing land uses rather than explicitly through protected area designation. This research developed a systematic methodology to identify candidate OECM parcels in Howe Sound Biosphere Region, British Columbia. By integrating ecological variables with governance and zoning data into a weighted suitability model, the study demonstrated a scalable framework for OECM candidate parcel selection. Results highlighted 260 candidate parcels across three regional districts, emphasizing parcels whose governance structures align well with long-term conservation goals. The developed methodology addresses critical limitations of traditional qualitative approaches, enhancing conservation planning efficiency and scalability. Ultimately, this study provides a replicable model to facilitate Canada’s OECM identification process, promoting biodiversity conservation and contributing meaningfully to the nation's climate and ecological objectives.
DOI: 10.5683/sp3/lcxq8w
title: Promoting human-wildlife coexistence through ecological connectivity modelling on the University of British Columbia Vancouver campus

description: Urban landscape networks of connected habitat nodes are expanding and play an important role in shaping the biosphere. Within these networks, humans and wildlife coexist, living and moving in close proximity with the potential for conflict. Remotely sensed data provides accurate knowledge of landscape cover. Circuit-based ecological connectivity models can simulate the movement of wildlife across complex urban landscapes. The resistance surfaces needed to perform this modelling were derived using random forest machine learning land cover classification, on combined Planet SkySat multispectral satellite imagery, and light detection and ranging-derived digital surface model datasets with 78.2% overall accuracy. By comparing movement models for wildlife species and humans, sites of overlapping movement were identified primarily along roads and between forested patches in the south of campus. Areas of particular interest were located at intersections where vehicle and pedestrian traffic is high, and at active construction sites both near the Museum of Anthropology and in Wesbrook Village. These findings are consistent with previous projects studying ecological connectivity on campus. Recommendations involve monitoring construction projects, roads, and neighbourhoods. These results support Campus Vision 2050 initiatives to increase green space to improve ecological connectivity in the centre of campus, design protected connectivity corridors, and discourage unnecessary single-passenger vehicle traffic. Future research may incorporate object-based classification, or consider timing in human and animal movement patterns.
description: ArcGIS Pro, 3.4.2
description: scikit-learn, 1.6.1
description: Omniscape, 0.6.2
DOI: 10.5683/sp3/gxtrzg
title: Visualize Six Years of Architectural Changes at the University of British Columbia Vancouver Campus: Impacts on Tree Growth, Green Connectivity, and Coyote Footprints

description: Urban development, such as campus expansion, alters land cover and disrupts ecological processes, but the full extent of these impacts is often underexamined at fine spatial and temporal scales. This study investigates how building expansion on the University of British Columbia (UBC) Vancouver campus from 2015 to 2021 has influenced tree dynamics and coyote movement by leveraging high-resolution Light Detection and Ranging (LiDAR) data and spatial analysis. A campus-wide analysis revealed an increase in building density from 0.91 to 1.03 buildings per hectare, with total building coverage expanding from 16.62% to 18.39%. Eight campus neighborhoods were analyzed in detail, with Wesbrook Place experiencing the most substantial change, adding 22 new primarily residential buildings. Beyond direct tree removal, construction activities indirectly hindered vegetation recovery by damaging root systems and compacting soil. Neighborhoods with lower construction intensity, such as East Campus and Hawthorn Place, showed more stable canopy structure and tree growth. Wildlife patterns were also affected; fragmentation of greenspace and reduction in canopy cover disrupted ecological corridors, influencing coyote movement across the campus. These findings highlight the critical role of remote sensing in tracking and visualizing land-use change, offering valuable insights for sustainable campus development and contributing to urban ecological planning in rapidly expanding metropolitan areas.
DOI: 10.5683/sp3/8zopaq
title: Mapping Equity Access to Green Spaces Across The University of British Columbia Vancouver Campus

description: Green spaces play an important role in providing ecosystem services such as air purification, temperature regulation, and recreational opportunities; however, their unequal distribution often highlights environmental inequities. This study examines the spatial distribution of green space coverage across the University of British Columbia (UBC) Vancouver campus to identify areas with limited access, termed Equity Initiative Zones (EIZs). A Geographic Information System (GIS)-based methodology was employed, integrating Light Detection and Ranging (LiDAR) data, green space data, a pedestrian walkway dataset, and a hexagonal grid framework to analyze pedestrian accessibility to green spaces using service area analysis, and to identify green space diversity and spatial equity disparities through statistical analysis. Results revealed that while the UBC campus has an average of 37.8% green space and 46.7% tree canopy coverage, approximately 6% of the campus falls within EIZs, primarily in northeast areas near academic buildings and parking lots—with mean green space coverage of approximately 1.8%. A moderate negative correlation between distance to green spaces and canopy coverage (r = −0.25, p = 1.96 × 10⁻⁷) suggested reduced canopy coverage in areas further away from green spaces. Additionally, green space diversity analysis showed that high-traffic areas like the academic zones are dominated by homogenized lawns, whereas peripheral areas such as those near the UBC Farm exhibit greater diversity. These findings highlight environmental inequities where EIZs may experience diminished access to ecosystem benefits, emphasizing the need for targeted interventions such as tree planting or the creation of pocket parks to promote a sustainable and inclusive campus environment.
description: ArcGIS Pro, 3.4
description: Python(NumPy, pandas, SciPy), 3.10
DOI: 10.5683/sp3/su2t7p
title: Modeling Climate Resilient Planting Locations of Culturally Significant Plants for Musqueam on the University of British Columbia's Vancouver Campus
title: Modeling Future Planting Locations of Salmonberry and Sword Fern on the University of British Columbia's Vancouver Campus

description: Urban greenspaces provide essential ecosystem services, enhance biodiversity, and hold cultural significance, particularly for Indigenous communities. However, these areas are increasingly threatened by climate change, urbanization, and invasive species. While greenspace planning and species distribution modeling are established tools for supporting ecological resilience, few studies integrate future climate suitability into site-specific planting guidelines. This represents a key gap in designing adaptive urban landscapes. Remote sensing offers a distinct advantage in this context by enabling fine-scale, spatially continuous assessment of environmental conditions, such as precipitation and shade, that are difficult to capture through ground-based methods alone.
This study addressed this gap by assessing habitat suitability and future climate impacts at a local scale. Specifically, it identified future habitat suitability and optimal planting locations for culturally significant plants of the Musqueam people on the University of British Columbia Vancouver campus. To achieve this, the study combined iNaturalist occurrence data, future climate projections from ClimateBC, and local environmental variables derived from light detection and ranging within a MaxEnt modeling framework. The analysis focused on two species: Salmonberry (Rubus spectabilis) and Sword Fern (Polystichum munitum), evaluating their suitability under different climate scenarios. The resulting suitability maps highlighted optimal planting locations based on projected climate conditions and environmental factors such as slope, aspect, and shade. Shade emerged as the most influential variable across all models, contributing between 74% and 92%. Other environmental variables had a limited effect on model outcomes, likely due to the study’s small geographic scope. Overall, the model outputs aligned closely with the known ecological preferences of the studied species. These findings can inform climate-resilient planting strategies for culturally significant species in urban greenspaces.
DOI: 10.5683/sp3/nzwkgx
title: Estimating Above-Ground Biomass Using Remote Sensing and Machine Learning in Kwahu South, Ghana

description: Tropical forests in regions like Kwahu South, Ghana, are vital for mitigating climate change by storing carbon in aboveground biomass (AGB), but deforestation and land use changes, driven by agriculture and mining, threaten their role as carbon sinks. Accurate AGB estimation is crucial for conservation and sustainable land management, yet traditional field methods are costly and impractical for large, remote areas. Remote sensing offers a solution by using satellite imagery to map forest biomass efficiently across vast regions, providing a practical tool for monitoring. This study assessed the effectiveness of Landsat 8 and Sentinel-2 satellite data for AGB estimation in Kwahu South, addressing the gap in understanding which satellite source and modeling approach best suits tropical forest environments. We applied Linear Regression and Random Forest (RF) machine learning models, using spectral bands and vegetation indices (NDVI, EVI, NDMI, SAVI) from both datasets, preprocessed to 30m resolution. The RF model with Landsat 8 data outperformed the Linear Regression model, achieving a 20% lower root mean square error (RMSE = 37.09 t/ha) and explaining 85% of the variance in AGB measurements (R² = 0.85). Spatial analysis revealed higher AGB (up to 475.3 t/ha) in dense forest zones in the southwest and lower values (down to 4.23 t/ha) in sparsely vegetated eastern areas, aligning with ecological patterns. These findings highlight that non-parametric machine learning, paired with Landsat 8 data, provides a reliable and cost-effective method for AGB estimation in tropical settings. This approach enhances carbon stock assessments and informs forest management strategies, offering a practical tool for researchers and policymakers working on climate resilience in regions facing environmental pressures.
DOI: 10.5683/sp3/0dizve
title: Generating Synthetic Remote Sensing Data with Deep Learning for Improved Wetland Classification

description: Wetlands in the Prairie Pothole region of Canada are critical ecosystems that support biodiversity and provide essential ecosystem services, yet they face increasing threats from agricultural expansion and climate change. Remote sensing offers a powerful tool for monitoring these landscapes over time, enabling large-scale, consistent land cover classification compared to conventional field-based methods. However, machine learning approaches often struggle with rare wetland classes, such as fens, due to limited training data. To address this challenge, a CycleGAN model, a generative adversarial network (GAN) designed for image-to-image translation, was used to generate synthetic four-band orthophoto imagery of fens from more readily available marsh imagery. The model was trained using classified wetland imagery from central Saskatchewan within the ArcGIS deep learning framework, and the resulting synthetic fen images were statistically compared to real fen images. A t-test (p &lt; 0.05) revealed significant differences in mean pixel intensity across all bands except blue, while Jensen-Shannon divergence values (Blue: 0.1288, Green: 0.2077, Red: 0.2339, IR: 0.1885) indicated relative similarity between real and synthetic histograms. Additionally, synthetic images exhibited significantly higher mean entropy values in all four bands (p &lt; 0.05), suggesting increased variability. These results demonstrate that CycleGAN-generated images retain key spectral characteristics of real fens while introducing additional diversity, offering a potential solution for improving wetland classification models.
DOI: 10.5683/sp3/lrl6tf
title: Mapping Mangroves Ecosystems Using Synthetic Aperture Radar (SAR)

description: Mangrove ecosystems provide essential ecological services, including shoreline protection, carbon 
storage, and habitat support. However, they are increasingly under threat from anthropogenic 
pressures such as agricultural encroachment, logging, and climate-induced sea-level rise. 
Monitoring mangrove extent and condition is therefore critical, yet challenging due to their 
location in remote intertidal zones and frequent cloud cover that limits field-based and optical 
observations. Remote sensing offers an effective solution by enabling consistent, large-scale 
monitoring of vegetation across time and space.
This study assessed the effectiveness of Sentinel-1 Synthetic Aperture Radar (SAR) data for 
mangrove mapping in Ambanja Bay, Madagascar, and evaluated whether fusing it with Sentinel-2 optical imagery enhances classification performance. Using a Random Forest classifier, both 
SAR-only and multi-sensor datasets were analyzed to classify land cover and assess changes 
between 2019 and 2024.
The SAR-only classification achieved 60% accuracy, with notable confusion between mangroves 
and water due to similar backscatter responses. In contrast, the fusion approach significantly 
improved classification, achieving an overall accuracy of 94%. Land cover change analysis 
revealed transitions from barren land to non-mangrove vegetation and localized expansion of 
mangrove cover.
These findings demonstrate that integrating SAR and optical data substantially improves 
classification accuracy, reinforcing the value of multi-sensor remote sensing for environmental 
monitoring and conservation planning in dynamic coastal ecosystems.
description: Google Earth Engine
description: ArcGIS Pro
description: Python
DOI: 10.5683/sp3/vz7khy
title: Pollinators utilize both traditional and forb-supplemented set-aside fields in agriculture

description: Raw dataset associated with the manuscript "Pollinators utilize both traditional and forb-supplemented set-aside fields in agriculture", published in Agriculture, Ecosystems & Environment (2025). This data was also used in the MSc thesis "Harnessing the potential for pollinator conservation in agriculture : semi-natural habitat enhancements in Delta, British Columbia" (2022).
DOI: 10.5683/sp3/dnjzx6
title: Applying Declassified Cold War Intelligence Imagery for Long-Term Forest Cover Change Analysis

description: Land cover change analysis using satellite imagery is constrained by the availability and range of historical data, with programs like Landsat offering data only from the 1980s onward. Furthermore, the 30-meter resolution of these datasets makes it challenging to distinguish fine-scale land cover variability in heterogeneous landscapes. This study addressed these limitations by utilizing panchromatic imagery that was captured by the United States’ intelligence agencies during the Cold War. The study applies declassified Hexagon (KH-9) imagery from 1979 to assess long-term forest extent and cover change in Tajikistan’s Pamir Mountains. A hybrid classification approach was developed, integrating image segmentation and masking based on per-segment mean pixel values to exclude some areas that are not forested and therefore create a more balanced dataset. Texture-based analysis was applied to the panchromatic imagery, testing different texture metric window sizes (3×3, 7×7, and 11×11) to optimize classification accuracy. A supervised Random Forest classifier was used to classify multispectral modern PlanetScope imagery from 2024 for comparison to the 1979 land cover classification. Results show that the masking process has a significant impact on reducing false positives and that classification accuracy increases with texture window size. There was a significant increase in forest cover over the study period. The study demonstrates the feasibility of classifying historic panchromatic imagery for long-term environmental monitoring and provides a scalable methodology for assessing land cover change in data-scarce regions.
description: R
description: ArcGIS Pro