cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp2/nnzjzb
title: Mapping the potential loss and migration of mangroves in Northwest Madagascar due to sea level rise

description: Madagascar’s mangroves comprise 2% of the world’s total, but are at risk of loss due to increasing sea level rise (SLR). Vertical elevation gain (VEC) via sediment accumulation has historically allowed mangroves to adjust to SLR, but currently in many parts of the world, SLR is outpacing VEC. This study focuses on the Ambanja-Ambaro Bays (AAB) region in Northwestern Madagascar, with a 7km buffer inland to capture the terrestrial mangrove data and satellite altimeter data for sea level in the Indian Ocean from 2000 to 2016. Rates of change were determined for both SLR based on altimeter data from the Topex/Poseidon and Jason-1, 2, and 3 satellites and VEC data collected from 2016 to 2017 by the NGO Blue Ventures. DEM (from ASTER satellite tiles) and slope were combined with existing land cover classification data from Jones et al. (2016) to determine the potential of mangroves to migrate inland in response to SLR.
DOI: 10.5683/sp2/6d3ows
title: Evaluating Landscape Metrics as Predictors of Area Burned by Wildfire and Monitoring Landscape Level Change in the Interior Douglas-Fir Zone in British Columbia

description: The Interior Douglas-fir zone of British Columbia’s Cariboo Region evolved alongside wildfire through millennia of repeat exposure. Resulting from these interactions were fire adapted species and landscape configurations that supported low to moderate severity wildfires. Removing the dominant disturbance agent of these dry forest ecosystems through systematic fire suppression has resulted in unforeseen repercussions. Presently, forests of the Interior Douglas-fir zone hold the potential for higher severity wildfire posing an increased threat to human life. This project was conducted to evaluate landscape metrics as predictors of area burned within the Interior Douglas-fir zone of the Cariboo Region. Six landscape metrics pertaining to the composition and configuration of landscape patches were calculated within 2500 hectare sampling cells. Additional work was conducted to quantify changing landscape metrics between 1985 and 2016. Key to this report are annual land cover maps generated following the Virtual Land Cover Engine framework applied to Landsat-5 TM and Landsat-7 ETM+ imagery. Results indicate that none of the landscape metrics involved in this project are effective predictors of area burned. Trend analysis from 1985 to 2016 concluded that several landscape metrics exhibited significant monotonic trends. The number of patches, landscape shape index, and Shannon’s diversity index were found to be increasing while mean patch area, and proportion of like adjacency were found to be decreasing. Additionally, no significant change in the area burned was observed. These results suggest that another agent, likely timber harvesting and climate change, are driving trends in landscape metrics. Overall, landscapes are becoming more heterogeneous. This does not necessarily translate to decreasing wildfire severity or size.
DOI: 10.5683/sp2/n45050
title: A Random Forest Algorithm for Learning and Updating Fuel Types for Fire Research

description: Wildfire drives a tremendous amount of forest and land cover change in the central interior of British Columbia, Canada. Fuel type maps have been acknowledged as critical references to conduct landscape-level fire simulations as well as fire behavior predictions. Nonetheless, the current thematic maps are not updated on an annual basis and cannot be easily produced at a certain scale and speed. The objective of this research was to test the hypothesis – that machine learning algorithm can augment the current manual wildfire fuel types identification system and can help to update fuel types on an annual basis, in the meantime the accuracy of the algorithm can meet the standards of The Ministry of Forests, Lands, Natural Resource Operations and Rural Development (BC FLNRO). The random forest algorithm was applied over a 40 000-km² landscape in central interior British Columbia that burned from a megafire in 2017. Fuel maps were obtained from the years 2013-2017, with the cross-validation overall accuracy reached 98.57% and the overall accuracy of confusion matrix tested on the validation set reached 92.35%. Various recommendations are given for future research using machine learning algorithms for fuel mapping such as assuring pre-processing procedure follows delicate standards, modifying the machine learning algorithm, and adopting other sources of remotely sensed data.
DOI: 10.5683/sp2/1akn18
title: Pollinators Habitat Capability Analysis of the University of British Columbia (UBC) Vancouver Campus

description: Pollination is an essential ecosystem service for crop production, where species such as insects and birds help to transfer pollen for plants to reproduce. Many of the pollinators are fully dependent on the plants and trees as their food sources and habitat. From a perspective of an urban planner, we must be able to recognize the values brought by pollinators to our ecosystems. Thus, we are attempting to discover the relationship between species and their habitats as much as possible. Nevertheless, assessing and quantifying habitat for species is a particular difficult task when we are dealing with a complex structured urban landscape. This study has two main objectives: the first one is to delineate and assess the biodiversity hotspots as pollinators habitat to determine which to prioritize for planting design management, and the second one is to determine the difference in tree crown delineation using Light Detection and Ranging (LiDAR) point cloud and Orthophoto interpretation. As a result, we found that each delineation method has different limitations and advantages. With the implementation of habitat overlay analysis, the mean patch size of biodiversity hotspots is around 2100 m^2, indicating that most green spaces in urban landscapes tend to be more fragmented rather than shrinking in size.
DOI: 10.5683/sp2/x5wnsb
title: Urban Green Space Analysis in University of British Columbia Vancouver Campus

description: Due to the rapid growth of urbanization, species biodiversity is threatened and the innate relationship between humans and nature begins to fade gradually. Urban green spaces play a vital role in reconnecting human and urbanized landscape with its unique characteristics. Meanwhile, virtual gaming technology with applied geographic information has made a spectacular process to promote interactions between humans and their surroundings. Five types of green space were identified in the University of British Columbia Vancouver campus: lawn, planting bed, planting bed on structure, athletic field, and urban forest. A novel approach of combining Light Detection and Ranging (LiDAR) data, ground-based inventory data, geographic information system (GIS) data, and geocoordinates derived from reality game Pokémon GO was applied to explore geospatial gaming technology’s application in mapping cultural use and biodiversity hotspots at a university campus. LiDAR-derived individual tree crown polygons contributed to estimate canopy cover. Manually delineated tree crown from the study area's orthophoto was used to compare the crown area accuracy with LiDAR technology. The point density heat map illustrated the study area's cultural interests, which were generated by Pokestops' geospatial coordinates. A dataset containing two green space assessments was conducted with various factors: native species ratio, species richness, canopy cover, and cultural interest. Both assessments highlighted the importance of urban forest. This green space type achieved 0.396 in the first assessment and 0.501 for the second assessment of cultural and biodiversity values.
DOI: 10.5683/sp2/tyn44t
title: The Impacts of Burn Severity on Forest Resiliency in British Columbia

description: Understanding how resilient forests are after wildfire events is important to forest management practices. The objective of this study was to use Landsat-8 data to understand how the burn severity of the Little Bobtail Lake wildfire has impacted forest regrowth several years later. This was done by deriving different vegetation indices to see how the changes in vegetation health were impacted by burn severity. Additionally, landscape pattern metrics were used to understand the changes in the spatial patterns of the burn severity and vegetation health over time. The results showed that the higher the burn severity, the greater the impact on vegetation health immediately after the wildfire, as well as a slower return to pre-fire conditions. When compared to the pre-fire values, the post-fire Normalized Difference Vegetation Index and the Tasseled Cap Greenness values showed an initial drop in the R2 value and then a rise in value four years after the wildfire. This differed from the Tasseled Cap Wetness values as the R2 showed a continual drop in value. When looking at the changes in the spatial patterns for vegetation health, the Sparse Vegetation class showed the greatest change for Core Area with a 3.72 hectare increase every year. When looking at burn severity, the High Severity class showed the greatest decrease in core area with a loss of 2.63 hectares every year. These results show that monitoring vegetation regrowth can be done using Landsat-8 derived vegetation health indices as well as with spatial pattern analysis.
DOI: 10.5683/sp2/ujnwdf
title: Predictive Ecosystem Mapping of the Chinchaga Watershed Management Basin, North Eastern British Columbia

description: The purpose of this project was to identify and map the location of wetlands in the Chinchaga Watershed Management Basin within the Fort St. John Timber Supply Area. The Chinchaga study area covers 75,000 hectares (ha), and serves as an ecologically significant unit within the Fort St. John Timber Supply Area, representing the gentle, low relief terrain characteristic to Boreal Plains. The products created in this project are intended to provide input for the amendment of the Fort St. John Land & Resources Management Plan. Input data for this project consisted of Sentinel 2 spectral imagery, the provincial Digital Elevation Model, ClimateBC data, and Predictive Ecosystem Mapping provincial products. Both spectral and topographic indices were created to assist in model performance. A geographically stratified sampling approach was taken in order to generate 1012 points. These training points were attributed to allow for the prediction of three simplified land cover realms, as well as seven identified provincial Eco-Groups across the Chinchaga. Together, the explanatory variables and training points were fed into a Random Forest model, and the classes were predicted across the study area with 84.9% (Three Class) and 78.2% (Seven Class) overall accuracy, respectively. Included in this report are a brief discussion on explanatory variable contribution to model performance, as well as an analysis of sample design and its implications on class accuracies in the 7 Class prediction.
DOI: 10.5683/sp2/dwoxbb
title: Mapping 'Blue Carbon' in Cambodian Mangroves: A Case Study Analysis for the Koh Rong Archipelago

description: Mangrove forests, and other coastal ecosystems, are recognized for their role in climate change mitigation due to their significant potential for carbon storage and carbon sequestration. These ecosystems are some of the most threatened ecosystems on earth and are at risk of loss and degradation because of anthropogenic activities. Carbon financing mechanisms offer a way to help implement, finance and support the conservation and restoration of coastal ecosystems. This study focuses on conducting a Tier 1 blue carbon assessment for the Koh Rong Archipelago in Cambodia. Satellite images from Landsat TM and Landsat 8 OLI were used to create land cover classification maps for the archipelago for 2009 and 2020, respectively. Mangrove extent was derived from the classified maps in order to analyze the decadal change in mangrove forests and subsequent blue carbon stock for the archipelago. The results of this study represent an initial blue carbon assessment for the archipelago that can be expanded upon in future studies.
DOI: 10.5683/sp2/cvi1eb
title: Estimating Productivity on the University of British Columbia (UBC, Vancouver Campus) Farm in 2019 using Normalized Difference Vegetation Index (NDVI) Time Series

description: Remote sensing is an emerging tool in the agricultural sector with proven diverse applications, including monitoring indicators of soil health and crop productivity. These applications are currently predominantly limited to monocultural large-scale farms, and effectively monitoring small, diverse farms still presents many challenges. However, the high global prevalence of smallholder farms under diversified production warrants further research to expand the potential of remote sensing to small-scale farms. Using the 24-hectare University of British Columbia (UBC) Farm in Vancouver, British Columbia as a case study, this research calculated a Normalized Difference Vegetation Index (NDVI) time series from twelve monthly 3m resolution Planet satellite images from 2019 for the Farm’s annual production plots. NDVI was used as a proxy for soil coverage by vegetation and therein for productivity, and significance was tested for temporal and spatial variations in NDVI values. October was found to be the most productive month, while June was observed to be the least productive. There were no statistically significant differences in NDVI values across the farm plots. The seasonal trend detected from the time series may represent the stronger influences of climate on NDVI over different forms of vegetation. However, unexpected NDVI results may also more accurately reflect variations in activity across the farm throughout the year. This study lays the groundwork for future research with higher spectral, spatial, and temporal resolution imagery (perhaps collected via drones or ground-based robots) to enable finer-scaled analyses of spatial variations in vegetation coverage. These advancements will be necessary to provide more suitable data for informing small-scale farm soil and farm management protocols.
DOI: 10.5683/sp2/hvogcw
title: Accessibility Analysis and Mapping at the University of British Columbia

description: The purpose of this dataset is to allow for an accessibility analysis and mapping project to be conducted on the University of British Columbia (UBC), Vancouver Campus. In a university setting, campus navigation is a foundational part of seizing opportunities, networking with other scholars, and having an all-around positive student experience. Yet, inaccessible features of an urban landscape (like stairs, rough terrain, or steep slopes) often leave mobility-limited individuals at a great disadvantage or cut off from certain opportunities. The accessibility analysis and mapping project (AAMP) is geared to try and answer what and where barriers to wheelchair accessibility exist on the UBC campus. To do this, (1) two cost paths for accessible and inaccessible terrain were calculated and compared to identify barriers to accessibility, (2) a least-cost path analysis is conducted to test if wheelchair routes are statistically longer than walking routes, and (3) a wayfinding map geared toward wheelchair users is created with the intention of increasing campus navigation equity and as a visualization for urban planners to see where campus accessibility improvements need to be made. It was discovered that 10% of the total walkable path area was some sort of accessibility barrier to wheelchair users. Through a visual investigation and comparison with the previous literature, three main types of barriers were identified on the campus. Next, an online map was created of the study site which highlighted accessibility barriers and difficult terrain. Finally, the paper ends with a discussion around why certain types of accessibility barriers exist on the campus and what urban planners can do to fix these and create more equitable wayfinding experiences across urban landscapes.
DOI: 10.5683/sp2/pjjfcr
title: How are vulnerable populations exposed to restorative nature in the City of Vancouver, Canada?

description: Living in cities can be stressful. While studies have shown that exposure to nature can help reduce stress and promote mental wellbeing, some research has shown that some characteristics of nature provide more psychological support than others depending on one’s state of wellbeing. City planning and urban forestry strategy need to consider the qualitative attributes of urban nature in the landscape in order to better support the different needs of the residents. This study focused on restorative nature: nature elements that help relieve stress and alleviate mental fatigue. A novel measurement, Local Restorative Nature (LRN) index was developed to allow for a quantitative comparison of restorative nature across a landscape using data from remote sensing imagery and municipality’s geographical information system (GIS). Using the City of Vancouver as a case study, we assessed the distribution of restorative nature and investigated how various populations in the city, particularly vulnerable groups, are exposed to restorative nature. A geographically weighed regression analysis was performed using the computed LRN index and the Canadian Index of Multiple Deprivation data to examine localized relationships between vulnerability groups and their exposure to restorative nature, with a resulted r-squared value of 0.71. Our study found that vulnerable groups in the City of Vancouver have different exposure to restorative nature. This exposure to restorative nature can be different for populations with similar vulnerability depending on their local area of residence. The proposed LRN index demonstrated an easy and effective way to quantify urban restorative nature. When combining the LRN index with demographic data, city planners and policymakers could identify and prioritize future planning efforts in neighborhoods that are more in need to promote equity in nature-based care services across the city and improve residents’ wellbeing.
DOI: 10.5683/sp2/rdmgda
title: Distribution of suitable habitat in winter and summer for plains bison (Bison bison bison) in the Pink Mountain Region of British Columbia

description: The plains bison (Bison bison bison) is threatened in Canada, with the biggest threat to their recovery being a lack of habitat in which they are considered compatible. Although the Pink Mountain, British Columbia, bison herd currently has about 1,000 individuals, there has never been a formal study of the suitability of the area to sustain the herd. Therefore, this study was performed to determine if the Pink Mountain region contains plains bison suitable habitat based on guiding principles for developing a Habitat Suitability Index. Data used for analysis was aggregated from Landsat8, Canadian Digital Elevation Model, Daymet, and the Freshwater Data Atlas of British Columbia. The final habitat suitability index combined a value for slope, distance to water, landcover, aspect, and snow depth. Separate calculations were performed for summer and winter. Based on the habitat suitability model conducted, there appears to be significant habitat of sufficient quality to support a population of plains bison year-round in the Halfway River drainage. Areas with acceptable habitat were predominately in the river bottoms or valleys and the far eastern section of the study area. In the summer, approximately 60% of the area was categorized as a habitat suitability index of at least six. In winters with maximum snow coverage, only 17% was similarly categorized. Despite this, it is my impression that the area acceptable as habitat is sufficient for a considerable population of bison. Extending the area under study and creating estimates of forage production could lead to a more detailed assessment of carrying capacity for bison in the region, leading to improved management of this iconic species.
DOI: 10.5683/sp2/kaq9xd
title: Examining the relationship between Landsat-derived spectral reflectance and multispectral Light Detection and Ranging (LiDAR)-derived intensity in Petawawa Research Forest, Ontario, Canada

description: Traditionally, Light Detection and Ranging (LiDAR) was used for forest structure analysis rather than capturing spectral information. A variety of studies were conducted to investigate the spectral characteristics of multispectral LiDAR but relatively few ones have integrated it with Landsat imageries. To compare their recorded reflectance and explore the prediction potential, we evaluated two series of regression modules measuring forest reflected energy with Landsat-derived reflectance and multispectral LiDAR-derived intensity, collected over 85 plots with the same area of 1000 m2 in Petawawa Research Forest, Ontario. The raw datasets were filtered, classified and the invalid plots were removed in the pre-processing step with three spectral bands, Green, NIR, and SWIR. Due to the value range differences of two sensors, limited and whole-area scaling algorithms were applied for subsequent comparisons. Following this, we used a dummy variable and further filtrations considering the reflectance differentiation by softwood and hardwood-dominated plots, and the effect of spatial autocorrelation. The histogram results showed no excessive change from the original datasets for all three spectral bands after scaling, and the limited scaling performed better with slightly higher root mean square errors (RMSE = 0.599 – 0.6815) and lower p-values (p = 0.005649 – 0.04193). A clear separation was demonstrated between softwood and hardwood reflectance capabilities in all three bands, suggesting influences of chlorophyll, spongy mesophyll structure, and water content in three spectral ranges respectively. Compared with hardwood-dominated plots, softwoods showed significantly higher correlation coefficients, especially in the SWIR band (r = 0.82). These results demonstrated a connection between Landsat- and multispectral LiDAR-derived outputs and offered the potential of using one sensor instead of two, suggesting the prospect of time and investment saving for further forest investigation and management.
DOI: 10.5683/sp2/plmjcn
title: Remote Sensing Seagrasses in Cambodia: Comparing Sentinel-2 and Planetscope for detecting and monitoring seagrasses in Cambodia’s Koh Rong and Koh Sdach Archipelagos

description: Tropical seagrass is an integral part of the world’s fisheries as it acts as a nursery for benthic reproduction, leading to it being the center of many conservation efforts. Monitoring seagrass by high resolution satellite is commonplace, but such data quality is often unavailable for many conservation groups, leading to a question of whether open-source satellite data is sufficient to meet the rigorous needs of seagrass patch detection. 10m images from the European Space Agency’s (ESA) Sentinel-2 and 3m images from Planet Labs’ Planetscope covering the Koh Sdach and Koh Rong Archipelagos in Cambodia were collected, processed, and classified, with outputs being compared for seagrass detection ability and suitability. Image scenes were merged and processed to produce depth invariant indexes using the Lyzenga algorithm. Random forest classification with ground truth data yielded accuracies of 79.5% for Sentinel and 79.7% for Planetscope using unprocessed rasters, while depth invariant rasters resulted in accuracies of 80.8% and 70.0% respectively. Sentinel-2 and Planetscope are qualitatively compared based on technical specifications and factors such as band wavelength, radiometric quality, and revisit time. Sentinel-2 shows that it is able to match the seagrass detection ability of the higher resolution Planetscope due to its additional blue band and deeper radiometric depth. Results of this analysis are placed in the context of wider research on detecting seagrass patches through satellite image classification.