cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp3/iiwcpf
title: Wolfville dikelands field data and model input files

description: The files in this dataset relate to the field data and model input files presented in the Water Resources Research paper LeRoux et al. (2023). See the 'Read me' file for more details.
DOI: 10.5683/sp3/lh1mdk
title: Supplemental data for: Impacts of changes in cell surface components and intraspecific competition on the interactions of Pseudomonas aeruginosa with cystic fibrosis bronchial epithelial cells

description: Time-lapse fluorescence microscopy videos for appendices 1 (Movies A1.1 - A1.9) and 2 (Movies A2.1 - A2.6) for PhD thesis titled, "Impacts of changes in cell surface components and intraspecific competition on the interactions of Pseudomonas aeruginosa with cystic fibrosis bronchial epithelial cells".
DOI: 10.5683/sp3/bzqgat
title: Metrics of Change: Informing Ecological Restoration by Quantifying Landscapes and Processes in Banff National Park

description: &lt;p&gt;In response to the growing need for broad-scale ecological
monitoring, recent efforts have been made to quantify important
landscape changes. The landscapes in the Rocky Mountain Cordillera
undergo constant change due to the intricate interplay of ecological
processes, with fire being the primary disturbance agent. Fire
suppression policies have impeded the ecological role of fire, resulting
in increased conifer forest dominance and declines in landscape
heterogeneity. The objective of this study was to develop a methodology
to accurately measure regional landscape changes resulting from fire
suppression and fire restoration in Banff National Park, with the goal
of supporting data-informed resource management.&lt;/p&gt;
&lt;p&gt;The use of remote sensing technologies provides consistent and
accurate data over large spatial and temporal scales, which would be
challenging with traditional field-based methods. A landscape metrics
approach was selected to investigate changes across 27 management units.
Four landscape metrics were calculated using annual land cover maps
derived from Virtual Land Cover Engine/Landsat-5/7 imagery spanning 34
years (1984-2019). Metrics were selected to capture conifer encroachment
(proportion of conifer class) and landscape heterogeneity (contagion,
edge density, Shannon’s diversity index) changes. Time-series analysis
and Thiel-Sen slope estimation were used to identify metric trends.&lt;/p&gt;
&lt;p&gt;The methodology was successful in discerning regional-scale aspects
of fire ecology using landscape metrics. Management units lacking fire
(or with &lt;10% area burned) are less heterogeneous and are trending
toward more conifer-dominated, less heterogeneous states. Reassuringly,
the opposite patterns are observed in management units with greater than
10% area burned, suggesting successful fire restoration efforts from a
landscape ecology standpoint.&lt;/p&gt;
&lt;p&gt;The study’s resulting metrics offer resource managers additional
landscape attributes to compare and monitor management units, which can
support decision-making, prioritize fire restoration and inform future
ecological studies.&lt;/p&gt;
description: ArcGISPro, Version 2.9.1
description: RStudio, Version 4.2.2
description: landscapemetrics R package, ersion 1.5.4
description: raster R package, Version 3.6.3
description: dplyr R package, Version 1.0.10
description: sf R package, Version 1.0.8
description: trend R package, Version 1.1.4
description: ggplot2 R package, Version 3.4.0
description: trend R package, Version 1.1.4
description: terra R package, Version 1.6.33
DOI: 10.5683/sp3/lyikh3
title: Leveraging machine learning and remote sensing to improve grassland inventory in British Columbia
title: A pilot study over the Rocky Mountain District

description: Machine learning algorithms have been widely adopted in the monitoring ecosystem. British Columbia suffers from grassland degradation but the province does not have an accurate spatial database for effective grassland management. Moreover, computational power and storage space remain two of the limiting factors in developing the database. In this study, we leverage supervised machine learning algorithms using the Google Earth Engine to better annual grassland inventory through an automated process. 
The pilot study was conducted over the Rocky Mountain district. We compared two different classification algorithms: the Random forest, and the Support vector machine. Training data was sampled through stratified and grided sampling. 19 predictor variables were chosen from Sentinel-1 and Sentinel-2 imageries and relevant topological derivatives, spectral indices, and textural indices using a wrapper-based feature selection method. The resultant map was post-processed to remove land features that were confounded with grasslands. 
Random forest was chosen as the prototype because the algorithm predicted features relevant to the project’s scope at relatively higher accuracy (67% - 86%) than its counterparts (50% - 76%). The prototype was good at delineating the boundaries between treed and non-treed areas and ferreting out opened patches among closed forests. These opened patches are usually disregarded by the VRI but they are deemed essential to grassland stewardship and wildlife ecologists. The prototype demonstrated the feasibility of automating grassland delineation by a Random forest classifier using the Google Earth Engine. Furthermore, grassland stewards can use the product to identify monitoring and restoration areas strategically in the future.
description: The model was developed with training data created within the extent of 1747697E,498250N: 1865494E, 577520N (EPSG:3005) so the model is only applicable within the study area. Further study is required to validate the model’s accuracy in applying it beyond the extent. We assumed spatial autocorrelation has been addressed by stratified and grided (5 km x 5 km) sampling. We highly recommended performing a Moran’s I test and semivariogram for each predictor variable before designing a sampling scheme when applying a similar model to the province.
DOI: 10.5683/sp3/fstouq
title: Rose Swanson Mountain Data Collation and Citizen Science

description: This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.
description: ArcGIS Pro, ArcGIS Dashboard, Survey123
DOI: 10.5683/sp3/iuulce
title: The Cooling Effects of Campus Greenery at the University of British Columbia Campus
title: How has urban greenery coverage affected Land Surface Temperature and Air Temperature?

description: Heat waves have threatened many people’s health and affected people’s thermal comfort on the West Coast of Canada in recent years. Urban greenery is crucial in mitigating climate change issues by influencing the temperature in cities. Land surface temperature is a metric to understand energy change at a landscape scale, while the air temperature is not representative of its surroundings. Land surface temperature can be measured and calculated by using remote sensing technology. This technology can capture surface temperature data from satellite imagery, allowing for accurate measurements of temperature changes over time. The paper studied how urban greenery coverage affected land surface temperature and air temperature respectively at the University of British Columbia in July and August 2022. The study calculated land surface temperature from Landsat images and collected air temperature data from available sensors. Two linear regression models were built to illustrate relationships between urban greenery coverage and land surface temperature and air temperature respectively. The results revealed that with the increasing percentage of urban greenery coverage, the land surface temperature reduced. Deciduous trees showed the strongest ability to reduce land surface temperature, followed by modified grass-herb and coniferous trees. Areas that are classified as barren had the highest land surface temperature. However, there was no significant relationship between urban greenery coverage and air temperature. This study provides valuable information for urban planners and policymakers to design and implement effective green space strategies that can address climate change and human thermal comfort issues.
description: RStudio
description: Arcgis Pro, 3.1.0
DOI: 10.5683/sp3/lvxf0v
title: Assessment of Green Roof Suitability by Active Remote Sensing of University of British Columbia Buildings

description: Green roofs are a promising mitigation tool against environmental concerns caused by increased urbanization. This study aims to address which of the University of British Columbia’s buildings are most suitable for a retrofitted green roof using active remote sensing. Through the assessment of six characteristics of buildings, a suitability score was given to all buildings within the area of interest on campus. The six suitability criteria are rooftop slope, rooftop area, building usage, building ownership, structural materials, and light intensity. The six suitability characteristics where selected based off literature on existing green roof retrofitting analyzing their structure, types and performance. This study is part of the Social Ecological Economic Development Studies (SEEDS) Sustainability Program to address sustainability policies and practices on UBC’s campus. The building suitability analysis resulted in the detection of five buildings with the greatest suitability score and their total rooftop area being 31,940.80 m^2. Each of the five buildings then was further investigated in terms of the vegetation health and density surrounding the buildings to isolate potential concerns of planting in the area. The mean NDVI of existing greenery surrounding the top five suitable buildings is 0.67, indicative of moderate to high density vegetation. Implementing green roof retrofitting on the suitable UBC buildings requires an additional accessibility analysis to maximize the positive social impacts of increasing campus greenery and make decisions on the green roof structure.
DOI: 10.5683/sp3/fw6ir9
title: Exploring the Potential of 3D Game Engines for Precise and Detailed Geo-Visualization in Forestry Education
title: A Study on Building Structures and Solar Radiation Analysis using Minetest and GIS Software

description: In response to the growing concern in geographic information science, which pertains to utilizing contemporary internet technology to communicate past information or knowledge for establishing foundations in geography. Recent studies have investigated geomatics solutions for historical city, and enhancing GIS skills through collaborative approach. In this study, we build upon prior research by exploring how the implementation of current technology can promote a cooperative learning environment, particularly within the realm of forestry education. Minetest, the 3D voxel game engine has high capability of modification, for visualizing natural environments and urban structures. The goal of this study was to investigate the potential of using the game engine for forestry education purposes. To meet this objective, we developed precise and detailed models of building structures and their surrounding environment. We also explored the visualization beyond 3D geospatial data, by generating analytical results of solar radiation on building roofs using GIS software. The visualization process was facilitated by the use of 3D light detection and ranging (LiDAR) data, provided by the UBC Campus + Community Planning department. The proposed approach proved to be effective in producing compatible geospatial data for visualization in the game engine. We also conducted exploratory statistical analysis to examine the relationship between building energy usage and solar radiation. The exploratory regression result of the solar radiation analysis has an R2adj of 0.19, which indicates its insignificant impact on building energy usage. Finally, the findings of this research provide a foundation for future studies that can continue to explore the potential of using 3D game engines. 

Keywords: 3D Geo-Visualization, Forestry Education, Remote Sensing, Light Detection and Ranging (LiDAR), Building Energy Usage, Solar Radiation Analysis
description: ArcGIS Pro, 3.0
description: R, version 4.2.2
description: Minetest, 5.6.1
DOI: 10.15468/dl.xs4rsb
title: Occurrence Download

description: A dataset containing 363805 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 363805 records from 219 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046670-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5683/sp3/8ko0wa
title: Assessing the Effect of Tree Stand Composition on Urban Cooling Effect on the UBC Vancouver Campus Using the 2021 Western North America Heat Wave

description: Rapid urbanization leads to the reduction of urban greenspaces, affecting the urban thermal environment and exacerbating the urban heat island effect. As urban forests are effective contributors to mitigating the urban heat island effect, understanding the composition of urban forests is important for assessing cooling effects. In response to growing concerns about the urban heat island effect, recent studies have used satellite imagery to investigate the cooling effects of urban forests’ characteristics, as remotely sensed data are flexible in spatial and temporal resolutions. This study determined the spatial patterns of land surface temperatures and tree stand compositions, as well as examined the cooling effects of different tree stand compositions at the UBC Vancouver campus during the 2021 Western North America Heat Wave. This study was done by retrieving land surface temperatures from Landsat 8 images and using grid-based analysis to classify tree stand composition in the following classification schemes: coniferous, deciduous, and mixed wood. A linear mixed effects model was also conducted to quantify the relationship between land surface temperature differences and tree stand compositions throughout the Heat Wave. The results showed that coniferous stand composition was positively correlated, but deciduous stand composition was negatively correlated with land surface temperature differences. The findings indicated that expanding forested areas could lower land surface temperatures, and deciduous stand composition has the greatest cooling effectiveness. Overall, this study would provide valuable insights for UBC campus planners seeking to implement new strategies to enhance the resilience of the campus climate.
description: ArcGIS Pro, 2.8.6
description: RStudio, 2023.03.0+386
DOI: 10.5683/sp3/hvcqji
title: Using high spatial resolution satellite imagery (PlanetScope) to count moored marine vessels in desolation sound marine provincial park

description: This study aims to assess the applicability of using high spatial resolution spaceborne imageries (PlanetScope with a 3-meter-pixel size) to monitor the number of marine vessels moored nearshore in Desolation Sound Marine Provincial Park, British Columbia, Canada during the summer and early fall of 2022. The purpose of this study is to understand the number of vessels moored near eelgrass habitat, a critical plant species in the park threatened by boating activities. Twelve trap cameras were set up to capture marine vessel activities during the summer, and filtering and manual counting were conducted to ensure the absolute accuracy of the data. Comparisons are made between the marine vessel counts from trap cameras and satellite imagery for each day throughout the period of study. The results indicate that the use of high spatial resolution spaceborne imageries to monitor the number of marine vessels is applicable and demonstrates 100% accuracy in the range of five undercounts or overcounts. However, during the peak boating season, precision fluctuates due to undercounts, which happen more frequently than precise count or overcounts. Further studies are recommended by ensuring the functionality and increasing the coverage area of trap cameras. Spaceborne imageries with higher-resolution can also be used if cost is not a concern.
DOI: 10.5683/sp3/vpxiey
title: Using LiDAR Data to Analyze the Habitat Suitability for Birds and Create the Minetest Digital Twin Model of UBC Botanical Garden

description: Urban green spaces are closely related to the abundance and biodiversity of birds by providing important habitats and together contribute to ecosystem health. This project aims to guide the University of British Columbia Botanical Garden to create Bird-friendly green spaces by using LiDAR data to analyze and map UBCBG's bird habitat suitability and create a 3D digital twin model of UBCBG in the open source game engine Minetest to increase 3D visualization and aid in landscape planning. By extracting the Canopy Height Model (CHM) using LiDAR data and performing individual tree segmentation, the derived metrics were used to identify trees with the highest bird habitat suitability index. The results showed that the suitability index ranges from -0.0016 to 0.5187, with a mean value of 0.2051. There are 68 trees with high suitability above the 0.4 intervals which have significance to bird populations and are worthy of being protected, accounting for only 3.38% of the total trees. They usually have a low vertical complexity index and foliage height diversity but are characterized by very tall trees with relatively large tree crowns. The Digital Elevation Model (DEM), Canopy Height Model (CHM) generated by LiDAR data were visualized in Minetest's UBCBG's 3D digital twin model using real terrain mod as topography and vegetation layers, while bird habitat suitability was used to symbolize the tree canopy layer. This study is highly relevant for landscape adaptation and planning in conjunction with other management considerations to support bird-friendly green spaces. The digital twin model can be used for educational and promotional purposes, and for landscape planning and aesthetic design with the consideration of bird conservation.
DOI: 10.5683/sp3/lfr4q0
title: Using Planet data to classify mangrove subtypes in Northwestern Madagascar in 2020 and 2022

description: In response to the growing concerns about mangrove deforestation, recent studies have used various remote sensing technology like satellite imagery to measure the mangrove extent. In this work, we investigated the mangrove distribution in Northwestern Madagascar by using fine spatial imagery with a pixel size as small as 3m and compared it with the result of the traditional method based on relatively coarser Landsat data. Mangroves are an essential biodiverse ecosystem found along tropical and subtropical intertidal beaches, providing critical goods and services to coastal communities, and supporting diverse organisms. However, anthropogenic
activities have caused the loss of mangroves in Madagascar, necessitating a new mapping approach utilizing the fine spatial resolution map from Planet data to create a map with advanced detail. The quantitative result central to this work is the new multi-date map of the Tsimipaika- Ampasindava-Ambaro Bays (TAB) from 2020 to 2022, which provides advanced detail and direct comparison with the shift in local mangrove species. The classification maps are based on Random Forest and Maximum Likelihood algorithms, and all of them have an overall accuracy of over 85%. The dynamics of mangrove forests from 2020 to 2022 are quantified, with a 12.6% loss in closed-canopy mangroves, and a 24.1% loss in open-canopy mangroves I am overestimated. Limitations regarding the classification model are also found in this study, including the overestimation of open canopy mangroves caused by the shadow and the seamline in the base map. This result shows the potential of using fine-resolution satellite imagery in supervised land cover classification, and the corresponding challenges raised by the smaller pixel size.
DOI: 10.5683/sp3/24i9kx
title: Managing Shifting Precipitation Regimes: Sustainable Stormwater Management at University of British Columbia

description: In response to growing concerns about climate change, recent studies have investigated the climate change impact on shifting precipitation and storm events. In this work, we expand on this body of work through various analyses using the data from the Environment and Climate Change Canada (ECCC) and UBC Abacus Data Network to understand the effects of shifting precipitation of climate change on green infrastructure management at the University of British Columbia (UBC) Point Grey campus in Vancouver, Canada. The study has important implications for policymakers, urban planners, and environmental managers, emphasizing the need for proactive planning and management strategies to mitigate potential risks from changing precipitation patterns. Results showed that there are increasing chances of storm events and the existing green infrastructure at UBC is not designed to handle large volumes of precipitation, leading to flooding and erosion issues with a small portion of permeable surfaces and green infrastructure, which highlights the need for sustainable stormwater management practices that take into account the unique characteristics of each location. Based on the findings, several recommendations can be made for UBC's green infrastructure planning and stormwater management, such as balancing permeable and impermeable surfaces, installing and upgrading green roofs and other green infrastructure, and recording and updating related climate data and performance.