Browsing by Author "Hall-Beyer, Mryka"

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    Advanced overview of multitemporal and hypertemporal remote sensing techniques
    (2017-05-25) Hall-Beyer, Mryka
    This item contains three parts: a narrated module providing an overview of practical and theoretical aspects of using remote sensing images of multiple dates; a case study of applying Principal Components Analysis (PCA) to a regional vegetation dataset to extract temporal patterns over 6 years; and a case study of applying harmonic analysis adapted for use with temporal spatial datasets to the extraction of data informing about spatial detail of ecoregional definition. The module and case studies are intended for the use of advanced remote sensing students and researchers. Material may be used and adapted/updated, with attribution.
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    Exploring the Relationship between Monthly Precipitation and EVI Vegetation Productivity Index of Serengeti Nation Park
    (2012-09-13) Ding, Junyan; Hall-Beyer, Mryka
    Precipitation has significant impact on the vegetation productivity in the Serengeti area of eastern Africa, an arid and semi-arid region. Previous studies indicate that the response of vegetation productivity to precipitation varies as the consequence of difference in total precipitation, soil properties, and vegetation type. In order to explore and explain the non-stationary relation between vegetation productivity and each of the mean and the variation of precipitation, I examine the correlation between vegetation and precipitation of Serengeti National Park using monthly precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) derived Enhanced Vegetation Index (EVI) images from 2001 to 2009. First, I compute the mean EVI and mean precipitation based on monthly images. To separate the mean and variation of precipitation, the mean precipitation is subtracted from each of the original precipitation images; the new series images represent the variation of precipitation. Then the new series of variation of precipitation is subjected to Fourier PCA (principal component analysis) analysis to generate a few (usually five) most representative PCA components. Among the PCA components, PC1 and PC3 are found not correlated with mean precipitation and thus are used together to represent the variation of precipitation of the entire period. To explore the non-stationary relation between EVI and precipitation, geographically weighted regression (GWR) models are used with mean precipitation and PC1 and PC3 as independent variables and mean EVI as dependent variable. Three GWR models are created with 1) mean precipitation alone, 2) PCA components, and 3) both mean precipitation and PCA components together, as independent variables. Finally, global linear least square models are used to detect how the correlations between mean EVI and precipitation are affected by soil WHC (water holding capacity) and N content, and vegetation type (independent variables). It is found that the highest correlation between mean EVI and mean precipitation occurs at north SNP (Serengeti National Park), and decreases towards both the west and east; the highest correlation between mean EVI and the variation of precipitation occurs in the north SNP and the south-east regions, and decreases towards the north and west. The correlation between mean EVI and both mean and variation of precipitation together are maximized at the west side of north SNP and decrease towards the south-east. The amount of precipitation and soil water holding capacity have a significant impact on the correlations between mean EVI and both mean precipitation and the variation of precipitation; soil nitrogen content has significant impact on the correlation between mean EVI and the variation of precipitation; only forest, woody savanna, and savanna are found to have significant impact on the correlation between mean EVI and the variation of precipitation.
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    GLCM Texture: A Tutorial v. 3.0 March 2017
    (Previously available at http://www.ucalgary.ca/UofC/nasdev/mhallbey/research.htm, 2017-03) Hall-Beyer, Mryka
    This tutorial describes both the theory and practice of the use of Grey Level Co-occurrence Matrix (GLCM) textures as originally described by Haralick and others in 1973. It leads users through the practical construction and use of a small sample image, with the aim of deep understanding of the purpose, capabilities and limitations of this set of descriptive statistics. Explanations and examples are concentrated on use in a landscape scale and perspective for enhancing classification accuracy, particularly in the cases where spatial arrangement of tonal (spectral) variability provides independent data relevant to the class identification. Background information is provided to answer the questions arising from 15 years of use of the tutorial, and increased practical experience of the author in teaching and research. Some information is provided to make the material accessible to specialists in fields other than remote sensing, for example medical imaging and industrial quality control. However the author is not an expert in these fields and texture's use there is not covered in detail. A basic bibliography is provided for research that has promoted the field of remote sensing GLCM texture; research projects that simply make use of it are not systematically covered.
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    Lithologic and Mineral Mapping in the Chocolate Mountains, California, Using ASTER Data and Image Processing Techniques
    (2013-05-27) Zou, Huihui; Hall-Beyer, Mryka
    The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a multispectral sensor, which has broad band coverage and is suitable for mineral and lithologic mapping. This research evaluated the use of ASTER imagery for both lithologic and mineral mapping in an arid area, Chocolate Mountains, southeast California. Various remote sensing techniques were applied to map alteration minerals including false colour composite, band ratios, Principal Components Analysis (PCA). Three classification techniques were used to map the lithologies, including Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF). Finally, two prospectivity maps were produced by using weighted overlay and logistic regression models. The results indicate ASTER is very effective in mapping various alteration minerals. In lithologic mapping, MLC has the highest overall accuracy of 70.56%. SAM and MTMF have very low overall accuracy (37.15% and 16.78% in respect). The efficiency score for logistic regression is 90.5%, weighted overlay has an efficiency score of 84.7%. However, the results need further aseessment using field data.
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    Personalizing Tsunami Risk on Vancouver Island with GIS: A Mixed-Methods Case Study Exploring the Use of GIS in Public Tsunami Risk Assessment and Personal Preparedness in Port Alberni, B.C.
    (2017) Reynolds, Ryan; Draper, Dianne; Einsiedel, Edna; Hall-Beyer, Mryka; Cloutier, Denise; Levy, Richard
    This work explores how vulnerable populations learn about, perceive, and incorporate natural hazards risks into their lives, and explores whether access to assistive online tools would result in an increased likelihood that at-risk residents would develop and maintain a set of household emergency preparedness and disaster response plans. I focus on a case study conducted on tsunami hazards risk in the community of Port Alberni, British Columbia, Canada. Located on the western coast of Vancouver Island, Port Alberni is the site of the most destructive tsunami disaster to have occurred in Canada during historic times, and remains a community vulnerable to tsunami hazards from sources located throughout the Pacific Ocean, including along the Cascadia Subjection Zone located just west of Vancouver Island. I first explore how various spatial data may be used to establish a community-wide tsunami evacuation map using a set of Geographic Information System (GIS) models, establishing the ease or difficulty associated with evacuating each household located within the official tsunami inundation zone. Next, I examine how household and personal factors can influence a household’s social vulnerability, potentially affecting their ability to recover following a tsunami disaster. Finally, I explore how these risk elements can be combined with household demographic information using a web-based application to assist study participants to develop a comprehensive set of household tsunami preparedness and response plans customized to the unique needs of participants’ household members and pets. Participant responses to a set of questionnaires and a short interview was used to assess the effectiveness of the online tool in encouraging the development of household emergency plans, and to provide guidance on how future versions of such a tool could be modified to better meet the needs of vulnerable households in other Canadian communities.
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    Plant functional groups of an ultramafic environment in North-Western Greece
    (2005) McAndrews, Sandra MacDonald; Hall-Beyer, Mryka
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    Remote Sensing: Geology and Planetary applications
    (2017-04-10) Hall-Beyer, Mryka
    This is an overview of remote sensing as it applies to geological applications. It is intended for those with some basic background in remote sensing image processing and analysis, and preferably introductory mineralogy or petrology. Topics covered at : miner and rock spectra and why they are as they are; spatial data of use to geology; planetary remote sensing.The presentation is in audio format, however clicking on "notes" in the pdf screen will bring up an approximate transcription of the lecture for quick referral or for use by those unable to use the audio. The total run time is slightly under 1 hr 20 minutes; I do not recommend viewing in one sitting1
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    Sea Ice Melt Onset Dynamics in the Northern Canadian Arctic Archipelago from RADARSAT, 1997-2014
    (2016-01-08) Mahmud, Mallik Sezan; Yackel, John; Beauchamp, Benoit; Howell, Stephen; Hall-Beyer, Mryka
    An algorithm was developed to detect melt onset over Arctic sea ice using high-resolution SAR images from RADARSAT. The algorithm is based on the temporal evolution of the SAR backscatter coefficient (σ⁰), using an ice type specific threshold approach that also corrects for backscatter incidence angle variation. Using 4457 RADARSAT images, the algorithm was applied over sea ice in the northern CAA, thus generating a new time series of melt onset from 1997-2014. The mean annual melt onset date was on YD 164±4 (mid–June). No significant trend was found over the 18-year period, however, variability increased in post-2007 years. An earlier (later) melt onset was associated with increased (decreased) solar energy absorption and subsequently associated in lighter (heavier) September sea ice coverage in the northern CAA. RADARSAT estimates of melt onset were found to be in good agreement but more robust compared to passive microwave and scatterometer estimates.
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    Spatiotemporal Clusters of GPS Locations and Prediction of Grizzly Bear Behaviour
    (2017) Kermish-Wells, Joseph; Musiani, Marco; Hall-Beyer, Mryka; Massolo, Alessandro; Stenhouse, Gordon
    Changes to grizzly bears foraging patterns caused by natural or anthropogenic alterations in their environment could have substantial consequences for both ungulate populations of prey and for bears in Alberta, where the species is considered as “Threatened”. I developed a method for identifying foraging sites of bears fitted with Geographic Positioning System collars, which allowed downloads of locations through satellites. Using spatiotemporal collar data and landscape data I created logistic regression models to predict occurrence of bedding, predation, and other foraging behaviours. I was therefore able to precisely identify sites where grizzly bears concentrated their activity and also increased the predictability of predation event locations by 2.75 times, compared to visits of random GPS-collar locations. My study also determined the natural and human factors influencing bear behavioural patterns, prominently forestry operations and human infrastructure; i.e. factors to be considered in conservation planning for grizzly bears.
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    UAV-LiDAR and Structure from Motion Photogrammetry: Spatial Accuracy in Vegetated Terrain
    (2017) Kucharczyk, Maja; Hugenholtz, Chris; Hall-Beyer, Mryka; Lichti, Derek
    Multiview stereo images acquired by uninhabited / unmanned aerial vehicles (UAVs) in combination with structure from motion (SfM) photogrammetry have created new capacity to develop high-resolution geospatial data, but vertical error is typically higher in vegetated areas because the ground surface is not visible in stereo. Miniaturized LiDAR systems for UAVs have potential to overcome this limitation, but their vertical accuracy in different vegetation types is not well documented. This thesis evaluated the accuracy of UAV-LiDAR and UAV-SfM in six vegetation types: grasses (short and tall), shrubs (short and tall), and trees (deciduous and coniferous). Results indicate UAV-LiDAR was more accurate in estimating ground elevation in all types, while vegetation height accuracy was higher for some types with UAV-SfM. UAV-LiDAR consistently sampled sub-canopy tree structure, while UAV-SfM only captured tree tops. Several factors are proposed to explain these differences and direct future research.
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