cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14288/1.0448465
title: Modeling Tree Shade Coverage: Planting Recommendations for Optimizing Shade on the University of British Columbia Vancouver Campus

description: Tree shade in urban environments serves to improve human thermal comfort and building energy use during the summer months. Under future climates, tree shade becomes an even more important ecosystem service that promotes climate change resilience. Tree shade was modelled in order to optimize shade coverage on the University of British Columbia (UBC) Vancouver campus by providing recommendations for tree planting based on species and planting configuration. Using Light Detection And Ranging (LiDAR) point clouds of 463 individual trees from species considered resilient to climate change, median normalized shade area per species was modelled over the course of one day. Furthermore, the total tree shade for each neighbourhood on campus was modelled using the same LiDAR dataset in order to determine which planting configurations most effectively shaded buildings. The planting configurations of neighbourhoods with a higher percentage of total tree shade falling on buildings were examined in order to provide recommendations for future planting efforts. Deodar cedar and black pine were observed to provide the highest normalized median shaded area, and are recommended for planting in order to promote climate change resilience and shade cover on UBC campus. University Boulevard and Chancellor Place were found to be the neighbourhoods with the most efficient planting configurations for the purpose of shading buildings. This was due to trees planted in thin belts along the south-west and south-east faces of buildings, maximizing individual shade contributions from each tree along the sun-facing side of each building. This tree planting configuration is recommended for the optimal shading of important targets on UBC campus, especially when resources are limited and must be properly allocated.
DOI: 10.14288/1.0448467
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.
DOI: 10.14288/1.0448469
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: 10.14288/1.0448472
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: 10.14288/1.0448476
title: Monitoring Mangrove Recovery in the Bay of Assassins, Madagascar

description: Mangroves are unique plant species that support livelihoods and provide critical ecosystem services. Globally, mangroves have faced significant threats from deforestation, causing rapid decline; however, recent restoration efforts have made a positive impact. Effective restoration requires regular monitoring and evaluation to ensure its long-term success. This study aims to support mangrove restoration efforts in the Bay of Assassins, Madagascar by evaluating the utility of remote sensing technologies for short-term monitoring. Since 2015, restoration efforts in the area have achieved notable success making continued monitoring essential for sustainability. Blue Ventures’ Google Earth Mangrove Mapping Methodology (GEM), a cloud computing tool hosted on Google Earth Engine, along with Sentinel-2 satellite imagery, were leveraged to examine mangroves from 2019 to 2024. The objective was to assess and quantify mangrove growth and health dynamics, identifying trends in gain and loss over the five-year period. Previous GEM applications exclusively used Landsat imagery with each pixel representing 30 by 30 meters of ground area. This study aimed to build on this work by using the higher level of detail provided by Sentinel-2’s 10-meter resolution. The findings demonstrate that Sentinel-2 imagery, and GEM effectively captured changes in mangrove extent and health, revealing a net increase in total mangrove cover from 2019 to 2024. Mangrove health assessments using spectral indices indicated that many restored areas improved in health, while some exhibited stagnation or decline, suggesting the need for further investigation into site-specific conditions. With an average accuracy of 96% across all mangrove maps, this study successfully demonstrated the utility of the GEM tool in performing high-resolution analysis and assessing mangrove growth
DOI: 10.14288/1.0448470
title: Replication Data for: Evaluating Carbon Baselines in Kwahu South: A Comparison of VM0047 and Open Forest Protocol

description: Forests are essential for climate change mitigation, acting as carbon sinks by absorbing carbon dioxide. Remote sensing, particularly through Landsat 8’s 30m resolution imagery, facilitates consistent, large-scale monitoring of forest cover and biomass changes, crucial for precise carbon stock assessments in reforestation efforts. However, differences in baseline estimation methodologies complicate these evaluations. This study analyzes forest change and carbon dynamics in deforested areas of Kwahu South, Ghana, from 2014 to 2024, comparing Verra VM0047 and Open Forest Protocol (OFP) approaches. Landsat’s fine-scale resolution enabled accurate tracking of deforestation and regrowth patterns. VM0047 employs a dynamic performance benchmark, updated periodically, capturing biomass fluctuations (e.g., -1.5 in 2015 to 1.0 in 2023), while OFP uses a static, tree-focused baseline, yielding near-zero estimates due to initial low tree cover (4.56%). A t-test (p = 0.449) validated the control site’s suitability. Results highlight VM0047’s adaptability to ecological variability versus OFP’s conservative simplicity, informing reforestation strategies in deforestation-prone regions.
DOI: 10.14288/1.0448475
title: Modeling Climate Resilient Planting Locations of Culturally Significant Plants for Musqueam 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: 10.14288/1.0448460
title: Predicting Pacific Herring Spawn Events in the Howe Sound, British Columbia

description: Pacific herring (Clupea pallasii) are a vital forage fish, supporting marine food webs and coastal fisheries while holding deep cultural significance for Indigenous communities. The timing of Pacific herring spawning is influenced by various environmental factors, yet the specific cues driving this behavior remain unclear. This study investigates the role of lunar cycles, sea surface temperature, and photoperiod in determining the timing of Pacific herring spawning events in the Howe Sound, British Columbia. Using data collected from 2021 to 2024 by the Marine Stewardship Initiative, alongside satellite-derived sea surface temperature and the environmental variables lunar phase and photoperiod, the study examines correlations between these factors and observed Pacific herring spawning events. The findings reveal that lunar cycles, particularly the Waxing Crescent phase, are the primary driver of spawn timing, with spawning events peaking during this phase. Sea surface temperature and photoperiod showed no significant correlation with spawn timing, suggesting that tidal conditions rather than temperature or light levels are more critical for herring reproduction in this region. These results have important implications for conservation efforts, indicating that spatial protections and management strategies should be aligned with lunar cycles to maximize spawning success. This study contributes to the understanding of environmental cues influencing herring spawning and highlights the need for further research into site-specific factors and additional environmental drivers.
DOI: 10.14288/1.0448468
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.
DOI: 10.15468/dl.zk9m6f
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eremogone gypsophiloides"
 ]
}

 The dataset includes 240 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036683-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3cyaud
title: Occurrence Download

description: A dataset containing 50574 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-73.54352 -26.0528,-80.73791 -47.77564,-80.17912 -57.34486,-66.69839 -58.60213,-60.69143 -55.24941,-68.93353 -48.54397,-67.53657 -40.51141,-67.25717 -30.73264,-73.54352 -26.0528))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cinclodes patagonicus",
 "Year 1970-2025"
 ]
}

 The dataset includes 50574 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036622-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nc2vam
title: Occurrence Download

description: A dataset containing 490 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Marshallia trinervia"
 ]
}

 The dataset includes 490 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036686-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tz27fv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Litopenaeus vannamei",
 "Country is Saint Kitts and Nevis",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036685-250811113504898/datasets/export for details.
DOI: 10.15468/dl.fxtw73
title: Occurrence Download

description: A dataset containing 346 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Medicago medicaginoides"
 ]
}

 The dataset includes 346 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036687-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yb3jvv
title: Occurrence Download

description: A dataset containing 181 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorghum halepense",
 "Country is Croatia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 181 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036688-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.47ebhh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Litopenaeus vannamei",
 "Country is Thailand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036689-250811113504898/datasets/export for details.
DOI: 10.15468/dl.ef7w52
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorghum halepense",
 "Country is Cuba",
 "HasCoordinate is true"
 ]
}

 The dataset includes 40 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036690-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c9jn7b
title: Occurrence Download

description: A dataset containing 587 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Alouatta seniculus",
 "Year 2020-2025"
 ]
}

 The dataset includes 587 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036693-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y5f32j
title: Occurrence Download

description: A dataset listing the 27 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-26.55317 62.03616,-12.46405 62.03616,-12.46405 67.66116,-26.55317 67.66116,-26.55317 62.03616))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hygrocybe"
 ]
}

 The dataset's 27 records were derived from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036679-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nuwp3v
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorghum halepense",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 47 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036692-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2qjpt7
title: Occurrence Download

description: A dataset containing 1781 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arrhenatherum elatius sardoum"
 ]
}

 The dataset includes 1781 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036691-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wbshz6
title: Occurrence Download

description: A dataset containing 235 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ericameria obovata"
 ]
}

 The dataset includes 235 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036694-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9tuerm
title: Occurrence Download

description: A dataset containing 5423 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arrhenatherum album"
 ]
}

 The dataset includes 5423 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036695-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.