cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp3/duhlyv
title: Using LiDAR-based Inventories to Evaluate the Impact of Silvicultural Practices on Forest Biomass

description: Forests are crucial in absorbing and storing carbon in land-based ecosystems, and their proper management is essential for mitigating climate change. This study assesses the influence of silvicultural systems on the amount of forest carbon in the Boundary Timber Supply Area (TSA) located in BC. The evaluation is based on enhanced forest inventories conducted using LiDAR technology. The methodology utilizes Multiple Linear Regression (MLR) to evaluate historical biomass dynamics in nine different silvicultural systems (SSs) and their regeneration after harvesting. Results show an average annual biomass increase of approximately 0.51 tons per hectare, underscoring the influence of silvicultural management on forest regeneration. Findings reveal a general inverse relationship between retention rates and regeneration rates across the SSs, with implications for forest carbon mitigation efforts. The study demonstrates that LiDAR analysis can augment in-situ measurements, contributing valuable parameters to harvest retention and regeneration across different silviculture systems. Advancements in allometric equations and improved data consistency and fusion are recommended to refine carbon assessments and inform forest management planning.
DOI: 10.5683/sp3/xgmztd
title: Deriving airborne LiDAR metrics to estimate ladder fuel density in Whistler, British Columbia

description: With increasing wildfire frequency and severity, understanding ladder fuels—vegetation structures facilitating fire propagation from forest floors to canopies—is crucial for effective wildfire management. Active remote sensors, such as light detection and ranging (LiDAR), are able to penetrate the forest canopy and allow for the characterization of understory vegetation structures. This study investigates the use of airborne LiDAR technology for estimating ladder fuels in the Resort Municipality of Whistler, British Columbia, Canada. Normalized relative density (NRD) provides an understory LiDAR density metric by dividing the number of returns in a stratum of 
 interest by all returns within and below the stratum of interest. NRD of returns in the 1 – 4 m height stratum was compared between treated (NRD = 0.06 ± 0.09) and control (NRD = 0.31 ± 0.27) plots and was shown to be statistically different between treatments (p = 0.0002). A model was developed using LiDAR-derived metrics—NRD and percentage of returns below the 30th height percentile (ipcumzq30)—to estimate understory stems per hectare (stems/ha) as a proxy for ladder fuel density (Adjusted R2 = 0.37). Despite the model’s relatively low explanatory power, p-values for both input variables were &lt; 0.05, indicating significant contribution of the predictors in explaining variability in understory stem density. Estimating understory stems per hectare allows classifying ladder fuels into categories as described by the BC Wildfire Threat Assessment Guide, facilitating the identification and prioritization of areas for ladder fuel reduction treatments.
description: R, 4.3.1
description: lidR, 4.1.1
description: ArcGIS Pro, 3.2
DOI: 10.5683/sp3/swazyb
title: Comparing the Accuracy of Polarimetric Synthetic Aperture Radar (PolSAR) Canopy Height Models for Forests in the Pacific Maritime and Montane Cordillera Ecozones

description: Land management practices increasingly depend on detailed forest structure information, including canopy height, necessitating the adoption of advanced remote sensing methodologies. This research contributes to meeting this demand by leveraging polarimetric synthetic aperture radar (PolSAR) data, integrated with multispectral imagery, to model forest canopy height in the Pacific Maritime and the Montane Cordillera ecozones of British Columbia. PolSAR technology employs microwave wavelengths of vertical and/or horizontal orientations to map the Earth’s surface. SAR systems stand out in their global coverage, cost-effectiveness, and are uniquely capable of providing reliable observations under any weather condition. A distinctive aspect of the research is the comparative analysis of PolSAR data behavior across different ecosystem-specific characteristics. The approach takes an exploratory analysis of polarimetric parameter extraction from PolSAR data— a method not commonly outlined in literature for canopy height predictions using C-band wavelengths. This exploratory technique was adopted due to the challenges presented by the reduced canopy penetration of C-band data. The results of the study indicate notable accuracy in canopy height predictions, achieving a coefficient of determination (R2) of 0.79 and a root mean square error (RMSE) of 3.57 meters for the Pacific Maritime ecozone study site. For the Montane Cordillera ecozone study site, an R2 of 0.66 and an RMSE of 2.83 meters were achieved. These findings highlight the promise of integrating C-band PolSAR with multispectral data for developing accurate forest structure models. These results challenge the conventional reliance on light detecting and ranging (LiDAR) technology exclusively, enhancing accessibility and facilitating broader-scale applications.
DOI: 10.5683/sp3/0qumml
title: Hotspot analysis for mangrove restoration suitability

description: This research paper presents a globally replicable methodology for subnational hotspot analysis of mangrove restoration suitability. The study utilized Central America as a focal area and employed a two-phase workflow involving scripted analysis in RStudio and non-scripted application of QGIS geoprocessing tools and qualitative assessment. Approaches to spatially defining mangrove areas for analysis were examined, including global administrative zones, buffering around mangrove areas of loss, and manual boundary selection. Specific datasets for restoration suitability indicators such as mangrove loss, population distribution, poverty metrics, soil organic carbon, protected areas and others were evaluated for effectiveness. Key findings included high restoration suitability in Nicaragua and Honduras, consistent underestimation of mangrove loss to aquaculture conversion, and varying effectiveness of protected areas between countries and designation types. The discussion section expands on the effectiveness of different indicators, compares mangrove delineation methods from the literature, emphasizes the usefulness of screening processes, and suggests future directions for restoration hotspot analysis. Overall, this research presents a flexible hotspot analysis methodology suitable for restoration practitioners operating within common constraints such as open-source software and freely accessible data.
description: QGIS, 3.34
description: RStudio, 4.3.2
DOI: 10.5683/sp3/mjvw8x
title: Optimizing Seagrass Mapping with Remotely Piloted Aircraft Systems and Machine Learning
title: Exploring the application of multispectral imagery for seagrass detection

description: Seagrass meadows play a vital role in the protection of coastlines and support productive habitats for marine life, unfortunately, seagrasses are experiencing declines due to climate change and human activities. Gathering accurate data on their spatial extent is essential for coastal management, yet traditional field methods are costly and impractical over large areas. Remotely piloted aircraft systems (RPAS) offer a flexible and cost-effective alternative for collecting imagery, though a standardized method for detecting seagrass meadows in British Columbia (BC) has yet to be established. Coastal dynamics associated with seagrass habitat such as tidal conditions and growing season limits the extent to which seagrass can be accurately detected. In this study, I test a methodology for seagrass detection and evaluate how detection accuracy is impacted by tidal conditions, growing season, and the incorporation of multispectral sensors. Specifically, I compare classifications of two eelgrass (Zostera marina) sites in the Gulf Islands (Sidney and Moresby) in southwestern BC. I employed a Random Forest Classifier and compared the incorporation of near-infrared and red-edge wavelengths to traditional red-green-blue (RGB) imagery to explore potential improvements in eelgrass detection. This achieved overall accuracies up to 95%. RGB images captured at low and high tide in July and August performed better than images captured in June and at medium tide. Imagery captured at medium tide exhibited the most variability in accuracy. The addition of multispectral imagery did not significantly improve eelgrass detection for these two sites and should be further tested over a greater sample size. The results of this analysis intend to contribute to the development of a provincial seagrass mapping methodology, supporting eelgrass monitoring and conservation in BC.
description: ArcGIS Pro, 3.2.49742
description: Rstudio, 4.3.1
DOI: 10.5683/sp3/n3i17r
title: Comparing the Impact of Hurricane Rai on Two Mangrove Forests in the Philippines

description: Mangrove ecosystems are vital to coastal communities by providing a variety of crucial ecosystem services including the providing of fish stocks, building materials and firewood; the storing of large amounts of carbon; and the sheltering of coastal communities from tropical storms. For many decades, human activities have led to the degradation of these mangrove ecosystems. There are further concerns that increases in the severity of hurricanes caused by climate change may have severe negative impacts on the health of these important ecosystems. This study aims to determine whether mangroves degraded by humans will be more damaged by hurricanes than healthy, undisturbed mangroves. The impact of Hurricane Rai was measured on two different mangrove forests, found on Siargao Island in the Philippines: one in a protected national park and the other degraded by human activities for decades. The impact of the hurricane was compared using normalized vegetation index (NDVI) time-series analysis before and after the passing of the hurricane. The time series measured an NDVI decline of 0.0468 at the control site and a decline of 0.0539 at the impacted site. Furthermore, 16.3% of the total area at the control site showed a significant decrease in NDVI, while only 1.8% of the area at the impacted site showed a significant decline. Based on the results of this study, it could not be confirmed whether human degradation of mangroves has a negative impact on their resilience to hurricanes.
description: R studio, 4.3.1
description: ArcGIS Pro, 3.2.1
DOI: 10.5683/sp3/7r1ll0
title: Mangrove mapping in Saloum Delta, Senegal using Google Earth Engine Mangrove Mapping Methodology (GEEMMM)

description: Mangroves provide crucial biodiversity values to the ecosystem and the carbon dioxide sequestration capacity of mangrove forests helps mitigate global climate change. Given the diverse ecosystem and economic values, mapping how mangroves change over time is important for mangrove forest management and protection. The traditional mangrove mapping methodology using remote sensing requires a comprehensive understanding of mangrove ecology, remote sensing knowledge, and programming skills. The lack of specialists may stop the mapping of mangrove forests in many areas. In 2020, the Google Earth Engine Mangrove Mapping Methodology was introduced to accessibly map and monitor mangroves with random forest classifier and Landsat satellite imagery. This study applied Google Earth Engine Mangrove Mapping Methodology in Saloum Delta, Senegal for land cover classification with an overall accuracy of 96.45% in 2013 and 97.51% in 2023. Mangrove forests in Saloum Delta experienced heavy harvesting since 1950, and conservation projects have been conducted since 2004 by international organizations and the local government. The results suggest that 93.9% of mangrove forests remained unchanged, while 3.1% were lost and 2.9% saw an increase over the last decade. The results highlighted the mangrove loss areas that need more conservation attention and provided a valuable mangrove forest dynamic map to help the local communities in Saloum Delta with mangrove forest management. For the future development of Google Earth Engine Mangrove Mapping Methodology, the addition of more classifiers for land cover classification and higher resolution Sentinel satellite imagery should be considered. With a user-friendly interface and detailed guidance, Google Earth Engine Mangrove Mapping Methodology has a bright potential to help people with mangrove forest mapping for sustainable management globally in the future.
description: QGIS, 3.36.0
description: Google Earth Engine
description: ArcGIS Pro
description: R
description: Google Earth Pro
DOI: 10.5683/sp3/sin4ed
title: Monitoring mangrove reforestation in Madagascar with Sentinel 2 derived spectral indices

description: Mangroves play a crucial role in carbon sequestration, coastal protection, and biodiversity. The efficacy of mangrove reforestation projects is important for environmental management and conservation efforts around the world. This study evaluates the use of spectral indices derived from Sentinel-2 data to monitor mangrove reforestation. Specifically, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI-2), Red Edge Normalized Difference Vegetation Index (RedEdge NDVI), and Mangrove Vegetation Index (MVI) were analyzed across two study areas: Tsimipaika Bay and Assassin's Bays. Our findings indicate that all four indices were capable of detecting change in mangrove reflectance, but differed in their relative efficacy. The research underscores the complex interaction between mangrove growth, reforestation area size, and the effectiveness of different spectral indices in remote sensing. Recommendations for future studies include refining data extraction methods to enhance change detection and to consider seasonal variations to mitigate the influence of vegetation saturation. This study contributes to the optimization of monitoring strategies for mangrove reforestation, offering insights into the scalability and applicability of remote sensing techniques in conservation efforts.
DOI: 10.5683/sp3/iqik0d
title: Deriving LiDAR Metrics for Forest Structure Mapping in Wildfire Risk Assessment

description: The escalating incidents of wildfire pose a critical challenge in Canada, necessitating an understanding of forest structures for the effective implementation of fuel treatment strategies to mitigate wildfire risks. While previous research has explored forest structures and wildfire risk, a gap remains in translating these studies into operational solutions for foresters and land managers. This study addresses this critical gap by employing Light Detection and Ranging (LiDAR) technology for a preliminary assessment of wildfire risks in the Wildfire Urban Interface (WUI) of Whistler. Utilizing LiDAR’s ability to provide a detailed point cloud, the study introduces a simplified yet effective method for mapping crown fuel, ladder fuel and surface fuel. In assessing the crown fuel and ladder fuel, the project uses Canopy Height Models (CHM) and tree segmentation techniques to quantify forest structure. A notable result is the development of a tree volume index (TVI), which allows for a macro-scale evaluation of fuel volumes crucial for wildfire risk assessment. The analysis identifies a moderate positive correlation (0.55) between the LiDAR-derived TVI and data from the Canada Forest Satellite-Based Inventory 2020 (SBFI), demonstrating the approach’s validity and effectiveness in assessing wildfire risks. Furthermore, the study identifies potential surface fuel sources by analyzing pixel metrics, such as the percentage of vegetation returns between 1 and 2.5 m, thus offering insights into wildfire risk assessment. This pilot study exemplifies a novel approach to preliminary fuel mapping, facilitating the understanding of complex forest structures across extensive areas through LiDAR technology, and presents a practical methodology to improve wildfire risk management strategies.
DOI: 10.5683/sp3/ymtrv0
title: Using LiDAR to characterize urban forest structure and composition and locate hotspots based on derived individual tree attributes

description: This study provides urban forest tree inventories for the University Endowment Lands, Pacific Spirit Regional Park, Musqueam First Nation, and Southlands of British Columbia for 2015 and 2021. It describes urban forest structure and composition based on LiDAR-derived urban forest attributes including tree count, maximum height (zmax), crown area, and species type (coniferous/broadleaf). Hotspot analyses revealed spatially biased distributions of hot and cold spots, indicating heterogeneous urban forest structure and composition across the study area. Hot spots characterized by taller coniferous trees and higher mean crown areas were found across coastal, park & forest, or residential zones. Cold spots characterized by shorter trees and smaller mean crown areas were found in zones with more infrastructure. Overall, forest attributes differed between species type and inventory-year. Results suggest that anthropogenic drivers, such as urban development and land-use change, interact variably with tree attributes to influence the structure, composition, and distribution pattern of urban forests. The study highlights the applicability of using LiDAR-derived urban forest inventories to prioritize conservation and management strategies at the local level. By understanding the complex interactions between urban forest attributes and change drivers, communities can optimize green space utilization, sustainability, and equality within urban forest ecosystems.
description: R Studio
description: ArcGIS Pro
DOI: 10.5683/sp3/gcamiy
title: Pratiques et savoirs juridiques inuit au Nunavik

description: Cet ensemble de données a été produit dans le cadre du projet "Saimatsianiq : Documenter, mobiliser et valoriser les pratiques juridiques inuit pour préserver l’harmonie sociale au Nunavik", mené par la Chaire de recherche Sentinelle Nord sur les relations avec les sociétés inuit (Université Laval) entre janvier 2021 et mars 2024. Le projet avait trois objectifs: 1) Documenter les pratiques et les savoirs traditionnels et contemporains juridiques des Inuit du Nunavik ;  2) Mobiliser les connaissances acquises au sujet des pratiques et des savoirs formels et informels du droit inuit. ; 3) Promouvoir les pratiques et savoirs traditionnels inuit en lien avec la justice. Le projet a été réalisé en partenariat avec département de la Justice de la Société Makivvik et le Service juridique, socio judiciaire et de la gestion municipale de l'Administration régionale Kativik.
Les données ont été récoltées de trois façons:
1) Dix-huit entretiens semi-dirigés avec des membres du personnel inuit et non inuit du secteur de la justice;
2) Cinq ateliers de recherche sur les perspectives, les expériences et les préoccupations d'intervenants inuit du secteur de la justice: un atelier avec des interprètes de la Cour itinérante du Nunavik, deux ateliers avec des comités de justice, des travailleuses parajudiciaires et une agente de liaison correctionnelle, et deux ateliers avec agentes d'aide aux victimes et des agentes de réintégration communautaire.
3) Observation à la Cour itinérante.
Les données du sondage et de l'atelier ne sont pas directement accessibles, mais la manière de les obtenir est décrite dans un des documents déposés ("Accès aux données").
DOI: 10.5683/sp3/iju5x8
title: Egg production, egg quality, and fatty acids profiles in eggs and tissues in Lohmann LSL lite hens fed algal oils rich in docosahexaenoic acid (DHA)

description: Enriching eggs with omega-3 fatty acids (n-3 FA), such as docosahexaenoic acid (DHA), is a well-accepted practice that benefits the egg industry and consumers. However, issues around cost, sustainability, and product acceptance have necessitated the search for alternatives to feeding hens fish oil for DHA enrichment. The effects of feeding 2 algal oils on egg production and DHA enrichment in eggs and selected tissues were investigated. The algal oils were: 1) OmegaPro (OPAO) standardized algal oil for DHA content and 2) Crude algal oil (CAO). A total of 400, 46-wk-old Lohmann LSL lite hens were housed in enriched cages (10 birds/cage) and allocated 5 diets (n = 8) for a 12-wk trial. The iso-caloric and -nitrogenous diets were a standard corn and soybean meal diet, standard plus 0.25 or 0.76% OPAO and standard plus 0.23 or 0.69% CAO; algal oils diets supplied similar DHA at each level. Egg production indices (hen day egg production, feed intake, FCR, egg weight, egg mass, and eggshell quality) were monitored for 10 wk. Diet samples were analyzed for fatty acids (FA) on wk 1, 6, and 12 and eggs on wk 4, 5, 6, 9, and 12. At the end of the trial, one hen/cage was weighed and dissected for liver, breast and thigh for FA and long bones for ash content analyses. Concentration of omega-6 to omega-3 FA ratio was 12.9, 6.64, 3.48, 6.96, and 3.59 for standard, 0.23 and 0.76% OPAO, 0.25 and 0.69% CAO, respectively. Algal oils increased (P ≤ 0.046) eggshell thickness linearly. The concentration of DHA in the eggs from the birds fed the standard, 0.23 and 0.76% OPAO, 0.25 and 0.69% CAO was 84, 195, 286, 183, and 297 mg/100g egg, respectively, and algal oils enriched eggs with DHA linearly and quadratically (P ≤ 0.01). In conclusion, algal oils increased the concentration of DHA in eggs and had no adverse effects on egg production and eggshell quality.
DOI: 10.5683/sp3/o3tltn
title: Foliar Endophytic Fungi from Geum peckii in Canada

description: Foliar fungal endophytes isolated from rare and endangered Eastern Mountain Avens, Geum peckii from Canadian population on Digby Neck, NS. Sampled locations represented varying levels of observable changes to habitat conditions, varying from a minimally changed reference site to heavily changed sites. Reported species are the first known records from this species.