cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14288/1.0441413
title: Epidemiology of pediatric post-discharge mortality in Rwanda

description: &lt;br/&gt;&lt;strong&gt;Background:&lt;/strong&gt;In Sub-Saharan Africa, pediatric post-discharge death is increasingly recognized as an important contributor to mortality. To address morbidity and mortality during this period, it is critical to generate a representative evidence base throughout sub-Saharan Africa to inform resource prioritization, as well as policy and guideline development. To date, no studies have been conducted in Rwanda, limiting the understanding of the epidemiology of post-discharge mortality in this region. This study aims to describe the epidemiology of post-discharge mortality in a group of children admitted for suspected sepsis in Rwanda.&lt;br /&gt; &lt;br /&gt;&lt;strong&gt;Methods:&lt;/strong&gt; We prospectively recruited children aged 0-60 months admitted for suspected sepsis at two sites in Rwanda: Ruhengeri Referral Hospital in Musanze, Rwanda (rural) and University Hospital of Kigali in Kigali, Rwanda (urban) from May 2022 - February 2023. Clinical, laboratory and social variables were collected at admission. Following discharge, participants were followed up to 6 months to determine vital status and health-seeking. We analyzed data in two age-specific cohorts, defined a priori: 0-6m and 6-60m. Multivariate logistic regression was used to identify risk factors. Age-stratified Kaplan-Meier curves were used to estimate the cumulative hazard of 6-month post-discharge mortality.&lt;br /&gt; &lt;br /&gt;&lt;strong&gt;Findings:&lt;/strong&gt;Of 1218 children enrolled, 115 died (11%): 50% in-hospital (n=57) and 50% after discharge (n=58). Post-discharge mortality was higher in 0-6m cohort (n=28/274, 10%) than in those 6-60m (30/850, 4%), and in Kigali (n=37/413, 9%) vs Ruhengeri (n=21/805, 3%). Median time to post-discharge death was ~1 month (38d in 0-6m; 33d in 6-60m). In both cohorts, increased odds of post-discharge death were associated with weight-for-age z-score &lt;-3 (OR=3.16 (1.26-7.93), 0-6m; OR=7.44 (2.93-18.89), 6-60m) while higher maternal education was protective (OR=0.15 (0.03-0.85), 0-6m; OR=0.09 (0.02-0.75), 6-60m). Abnormal coma scale (OR=3.29 (1.47-7.38)), travel time of &gt;2h (OR=4.63 (1.40-15.22)) and being referred for higher level of care (OR=4.09 (1.04-16.12)) were significant in 6-60 months. Younger children were at highest risk of cumulative mortality.&lt;br /&gt; &lt;br /&gt;&lt;strong&gt;Ethics Declaration:&lt;/strong&gt; Ethical approval was obtained from the University of Rwanda College of Medicine and Health Sciences (No 411/CMHS IRB/2021); University Teaching Hospital of Kigali (EC/CHUK/005/2022), University of California San Francisco (381688) and the University of British Columbia (H21-02795).&lt;br /&gt;
DOI: 10.14288/1.0441525
title: Mapping Incremental Loss of Biodiversity Through Zoning

description: As urbanization escalates, reconciling human development with ecological preservation becomes imperative. This study addresses the challenge by assessing the incremental loss of biodiversity within the District of Sechelt, British Columbia, due to land use zoning. Focusing on a region renowned for its diverse ecosystems, the research evaluates current zoning practices, identifies potential threats to ecosystems and biodiversity, and proposes an integrated framework for sustainable urban development. Employing Geographic Information Systems (GIS) and remote sensing technology, a multi-criteria decision analysis (MCDA) framework is constructed, incorporating criteria like the Normalized Difference Vegetation Index (NDVI), land cover type, and proximity to sensitive ecosystems. Through this, land parcels are assigned ecological values, creating a composite map that visualizes high-priority conservation areas. The results reveal that areas zoned for Commercial, Industrial, Residential, and Residential-Multiple uses harbor lower ecological values and face greater risk of degradation. The research identified these zones as particularly vulnerable due to lenient regulations, resulting in habitat fragmentation and potential loss of biodiversity. High-value ecological lands within these zones were recognized as threatened ecosystems requiring conservation prioritization. The study proposes zoning modifications to enhance ecological conservation, suggesting that integrating ecological concerns into urban planning is not only feasible but also critical for the sustainability of urban centers. It underscores the importance of integrating ecological evaluation into urban planning processes, demonstrating a path forward for developing urban areas that prioritize ecological integrity alongside growth.
DOI: 10.14288/1.0441375
title: Comparative Analysis of Forest Canopy Height Estimation using Random Forest and Support Vector Machine Models with Synthetic Aperture Radar and Optical Imagery

description: In this study, the accuracy of forest canopy height estimation was assessed using Synthetic Aperture Radar (SAR), including backscatter and Polarimetric SAR (PolSAR), as well as optical indices derived from optical imagery, and Random Forest (RF) and Support Vector Machine (SVM) models were applied by using canopy heights derived from Light Detection and Ranging (LiDAR) as a reference for validation. Accurate measurement of canopy height is critical for effective forest management, biodiversity conservation, and climate change analysis, so this study attempted to address the challenges posed by traditional measurement methods, which are time-consuming and limited in scope. SAR with its all-weather, day and night imaging capability, has the distinct advantage of being able to continuously monitor forest canopy dynamics over a wide area, thus overcoming the spatial time and cost constraints of ground-based observations. Approaches in this study involved pre-processing of SAR and LiDAR data to reduce inherent data inaccuracies, as well as calculating optical indices to facilitate indirect estimation of canopy height. This study provided a comparative assessment of the performance of RF and SVM models using various data integrations, highlighted the higher accuracy was achieved through the synergistic combination of PolSAR and optical indices. The results showed that the data-integrated approach improved the accuracy of canopy height estimation, with the RF model performing slightly better than the SVM model in terms of prediction under the optimal data configurations of the two models in this study. These findings support the advanced application of incorporating remote sensing techniques, validated against LiDAR benchmarks, as a viable strategy for refining forest canopy height estimation, thereby providing insights for forest management and ecological modelling programs.
DOI: 10.14288/1.0441376
title: Comparing PolSAR, SAR and Optical Imagery Integration for Forest Canopy Height Estimation in Interior British Columbia, Canada

description: Forest canopy height is an important indicator for estimating forest carbon stock and biomass, which have implications for multiple environmental issues. In remote sensing, canopy height is most commonly measured using light detection and ranging (LiDAR), which comes with satisfactory accuracy but also high costs. This study looked at the potential of using synthetic aperture radar (SAR) and polarimetric synthetic aperture radar (PolSAR), together with optical imagery, to estimate canopy height in an interior coniferous forest in British Columbia, Canada. A machine learning approach was used to derive the canopy height values. SAR backscatter coefficients from Sentinel-1 and ALOS-2, as well as PolSAR parameters from Sentinel-1, were used as independent variables. Sentinel-2 optical indices and bands were fused as independent variables in some trials in an attempt to further enhance model performance. LiDAR-extracted canopy height values served as ground truth for both model training and validation. We compared between using SAR backscatter and PolSAR parameters, as well as between using SAR backscatter from Sentinel-1 and ALOS-2. Results show that PolSAR parameters outperforms SAR backscatter in predicting canopy height, while ALOS-2 achieved better accuracy in canopy height estimation than Sentinel-1. Optical indices and bands were capable of enhancing model accuracy in all scenarios. To conclude, Sentinel-1 PolSAR parameters were the best predictor variables due to PolSAR’s ability to capture different scattering mechanisms under different canopy heights. Future studies should examine the potential of polarimetric synthetic aperture radar interferometry (PolInSAR) and ALOS-2 PolSAR to yield more accurate canopy height estimation over interior coniferous forests.
DOI: 10.14288/1.0441377
title: Impact of Forest Age and Forest Type on the Accuracy of Canopy Height Estimation by Synthetic Aperture Radar and Optical Imagery

description: Forest canopy height measurement is critical for forest resources management, several remote sensing techniques have been applied to estimate the canopy height, including Light Detection and Ranging (LiDAR). However, the limited publicly available LiDAR data in British Columbia, limitation by clouds, and the cost create some challenges. To overcome this issue, this study employed publicly available Sentinel-1 SAR and Sentinel-2 Optical Imagery data with the support vector machine (SVM) model to estimate the canopy height in areas with different forest characteristics in British Columbia, analyzed the influence of tree species composition and forest age on the accuracy of canopy height estimation derived by SAR compared to LiDAR. The result indicates that each study area contained different estimation accuracies, and the accuracy level is affected by forest age and forest type. Overall, the young conifer area obtained the highest accuracy, suggesting that the application of SAR data may rely on a broad range of different forest characteristics within the area. In addition, the integration of SAR data with optical imagery data significantly increases the estimation accuracy, demonstrating the importance of data fusion in remote sensing applications. This research showed the potential of combining SAR and optical imagery in estimating canopy height, encouraging further research on improving the accuracy of the canopy height estimation model.
DOI: 10.14288/1.0441378
title: Detection and Monitoring of Wildfire-Induced Earthflows: InSAR Time Series Analysis in the Deadman River Watershed, British Columbia

description: This research investigates the efficiency of Interferometric Synthetic Aperture Radar (InSAR) in identifying and tracking changes in earthflow activity and movement post wildfire. The study was conducted in British Columbia's Deadman River Watershed, which was severely impacted by the Sparks Lake wildfire in 2021, increasing the possibility of landslide reactivation and movement. Using InSARderived time series data, earthflow ground deformation velocity was measured before and after the wildfire. Sentinel-1 satellite data was processed through the Alaska Satellite Facility’s Hybrid Pluggable Processing Pipeline (HyP3), R programming language, and the Miami InSAR Time-series software in Python (MintPy) to develop the time series. The analysis successfully identified two large earthflows near Snohoosh Lake and Castle Rock Hoodoos Provincial Park in the Northern region. Both earthflows accelerated in average velocity post-wildfire, with the highest average velocity increase recorded at 8.64 cm/year. The earthflows were located in areas classified as having medium to high burn severity. This research demonstrates the effectiveness of InSAR analysis in detecting and tracking slow-moving earthflows in the region and their potential hazards. The approach used in earthflow detection and monitoring provides valuable data to regional districts and land management agencies, who can implement risk mitigation strategies to protect local communities and infrastructure in the Deadman watershed. The study highlights the potential of InSAR as a cost-effective and efficient tool for post-wildfire environmental monitoring and geohazard analysis.
DOI: 10.14288/1.0441387
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: 10.14288/1.0441412
title: Mapping Species At Risk and Of Cultural Value

description: The escalating urbanization and human-induced land use changes have precipitated a global biodiversity crisis, imperiling indigenous ecosystems and cultural heritage. Nanaimo Regional District (NRD) in British Columbia, with its rich biodiversity and Indigenous lands, is facing the challenge of balancing urban development and species conservation. To provide better insights for urban planners and indigenous communities on protecting local biodiversity, the study aims to visualize the biodiversity of the identified species and evaluate the land protection levels by species richness and Land Cover Species Importance Score (LCSIS). The study assigns value to two criteria by importance level to gain the SIS of the identified species at risk and of cultural value. Integrating species occurrence data with land parcels and land cover data to illustrate spatial patterns of species richness and importance level. It reveals that larger rural parcels have higher species richness, primarily located in the eastern part of NRD. Smaller parcels around urban areas, particularly east coastal regions, have higher species richness density. LCSIS value varies across different land cover types. By reclassifying and combining the species richness and LCSIS, the spatial distribution of identified Protection Areas (PAs) is mapped and classified into three classes, high, median, and low. The study also explores the proximity of different classes of PAs to urban areas, to assess if further urban expansion would impact the identified PA. Implications for urban planning are profound. By delineating priority conservation areas and integrating them into land use plans, planners can mitigate the adverse impacts of urban expansion on biodiversity and cultural heritage. This proactive approach fosters sustainable development, preserves vital ecosystems, and honors Indigenous traditions.
DOI: 10.14288/1.0441523
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: 10.14288/1.0441524
title: Distribution of Berries in response to climate change

description: Climate change and greenhouse gas emissions are among the most serious issues worldwide, significantly impacting biodiversity and food security. In Canada, berries such as the Saskatoon berry and Red Huckleberry hold considerable cultural, economic, and ecological value. This study evaluates the potential impact of climate change on the future distribution of these two berry species across four time periods – 2021-2040, 2041-2060, 2061-2080 and, 2081-2100, focusing on the Lower Mainland and Vancouver Island as study area. This study is grounded on ecological relationships between each berry species and their specific tree species – Douglas fir for Saskatoon berry and Western Red Cedar for Red Huckleberry. Adopting, a novel approach, this study shifts from using traditional climate models towards species suitability models of respective tree species for species distribution modelling of berries through MaxEnt algorithm. Our findings indicate a poleward shift in the suitable habitats of both, under current emission scenarios, with Saskatoon berry showing greater adaptability to climate effects. This resilience is evidenced by a minimal predicted loss in habitat by the 2081-2100 period, in contrast to the significant decline observed for the Red Huckleberry. Each model was evaluated with area under the curve (AUC score) and all models showed a good fit, with Saskatoon berry models ranging from 0.878 (2081-2100) to 0.891 (2041-2060) while Red Huckleberry models ranging from 0.839 (2041-2060) to 0.843 (2061-2080). This research also highlights the influence of topography on species distribution. The insights gained are vital for informing ecological restoration and conservation strategies, ensuring the sustainability of these important berry species in the face of climate change.
DOI: 10.14288/1.0443775
title: Exploring the Feasibility of Individual Tree Segmentation Algorithms on Terrestrial LiDAR Data

description: Terrestrial Laser Scanning (TLS) offers complementary and non-destructive methods for capturing three-dimensional information on forest structure. ITS is an essential process for modern forestry and ecological studies as it enables the precise isolation and characterization of individual trees from surrounding forest, facilitating critical analyses such as biomass estimation and advanced forest structure assessment. This study explores the applicability of two established Individual Tree Segmentation (ITS) algorithms, dalponte2016 and watershed, to TLS data for the assessment of forest structure. Conducted within the David C. Lam Asian Garden of University of British Columbia, the research employed a Trimble TX08 scanner to collect high-density point clouds, aiming to improve urban forest management and sustainability practices. Despite the benefits of TLS’s high spatial resolution and rapid data collection, challenges such as over-segmentation, under-segmentation and occlusion by forest canopy impeded accurate tree stem and understory detection. Both algorithms, traditionally used in Airborne Laser Scanning (ALS), demonstrated limitations when applied to TLS data, highlighting the need for algorithm refinement and the incorporation of machine learning techniques. This study contributes to the literature by critically assessing the performance of ITS algorithms in TLS data processing, offering insights for future advancements in precise forestry inventory. The absence of ground truth data and inaccessibility of ground control points (GCPs) underscored the necessity for a more comprehensive approach with regards to data collection and the validation of tree segmentation methods.
DOI: 10.14288/1.0444066
title: Ecological Connectivity Through UBC – An Omniscape Approach

description: Human activities and rapid urbanization have led to habitat destruction and fragmentation, severely impacting biodiversity and ecosystem services globally. This study focuses on the UBC campus, surrounded by the Pacific Spirit Regional Park, to assess and enhance ecological connectivity through the urban landscape. Using advanced remote sensing techniques, including Planet SkySat satellite imagery and British Columbia 2022 LiDAR data, we classified the landscape and identified key habitats. Keystone species were selected based on habitat dependency, observation density, and conservation status, utilizing datasets from the Global Biodiversity Information Facility (GBIF) between 2010 to 2023. The study employed Omniscape, an implementation of Circuitscape 4.0, to model the randomized movement of animals across a resistance-weighted landscape, providing a nuanced understanding of how urban features influence ecological flows. Our findings indicate that urban areas, especially the central UBC campus, act as barriers and funnel species movement, particularly affecting those with small movement ranges and unique habitat requirements, such as the Pacific Tree Frog and Douglas's Squirrel. The analysis highlighted areas of highly channelized flows where predicted movement exceeds landscape capacity, leading from Pacific Spirit Regional Park to UBC's Central core. UBC’s Main Mall was predicted to have highly channelized flow across all species and is a critical corridor for conservation and enhancement. Other recommendations for UBC include creating an East West Ecological Corridor, replicating Main Mall’s design. The existing landscape should be intensified by integrating more water bodies and riparian areas to support amphibian species and interspersing shrub planting between trees to expand habitat availability.