The debris-flow initiation variable of clay mineralogy is examined for Holocene age debris-flow deposits across the Colorado Plateau. A Kolmogorov-Smirnov two-sample test between 25 debris-flow producing shale units and 23 shale units rated as not producing debris-flows found a highly significant difference between shale unit kaolinite-illite and montmorillonite clay content. Debris-flow producers tend to have abundant kaolinite and illite (61.5% of clays) and small amounts of montmorillonite (10.4%). Clay sample soluble cation (Na, Ca, K, and Mg) content could not be used to accurately divide the data set into debris-flow producers and debris-flow non-producers by either cluster analysis or a Kolmogorov-Smirnov two-sample test.AVIRIS hyperspectral data reveal that debris-flow deposits, colluvium, and some shale units in Cataract Canyon, Utah display the double-absorption feature characteristic of kaolinite at 2.2 µm. Lab-based reflection spectra and semi-quantitative x-ray diffraction results show that Cataract Canyon debris-flow matrix clays are dominated by kaolinite and illite and lacking in montmorillonite. A surface material map showing the spectral stratigraphy of the study area was created from AVIRIS data classified using an artificial neural network and compares favorably to existing geologic data for Cataract Canyon. A debris-flow initiation potential map created from a GIS-based analysis of surface materials, slope steepness, slope aspect, and fault maps shows the greatest debris-flow initiation potential in the study area to coincide with outcrops of the Moenkopi Formation on steep (>20%), southwest-facing slopes. Small areas of extreme debris-flow initiation potential are located where kaolinite and illite clay-rich colluvial wedges are located on southwest-facing walls of Colorado River tributary canyons. The surface materials map shows formations clearly when they remain relatively consistent in composition and exposure throughout the study area, such as the White Rim Sandstone and most clay-rich members of the Moenkopi Formation. The debris-flow producing Organ Rock Shale and Halgaito Formation were shown inconsistently on the surface materials map, likely as a result of compositional variations in the study area. The results of this study provides evidence that hyperspectral imagery classified using an ANN can be successfully used to map the spectral stratigraphy of a sparsely vegetated area such as Cataract Canyon.

No description available.

Wheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the manual inspection process. Insect-damaged wheat samples by the species of rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum); fungal-damaged wheat samples by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger; and artificially sprouted wheat kernels were obtained from the Cereal Research Centre (CRC), Agriculture and Agri-Food Canada, Winnipeg, Canada. Field damaged sprouted (midge-damaged) wheat kernels were procured from five growing locations across western Canada. Healthy and damaged wheat kernels were imaged using a long-wave near-infrared (LWNIR) and a short-wave near-infrared (SWNIR) hypersprctral imaging systems and an area scan color camera. The acquired images were stored for processing, feature extraction, and algorithm development. The LWNIR classified 85-100% healthy and insect-damaged, 95-100% healthy and fungal-infected, and 85-100% healthy and sprouted/midge-damaged kernels. The SWNIR classified 92.7-100%, 96-100% and 93.3-98.7% insect, fungal, and midge-damaged kernels, respectively (up to 28% false positive error). Color imaging correctly classified 93.7-99.3%, 98-100% and 94-99.7% insect, fungal, and midge-damaged kernels, respectively (up to 26% false positive error). Combined the SWNIR features with top color image features correctly classified 91-100%, 99-100% and 95-99.3% insect, fungal, and midge- damaged kernels, respectively with only less than 4% false positive error.

Wheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the manual inspection process. Insect-damaged wheat samples by the species of rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum); fungal-damaged wheat samples by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger; and artificially sprouted wheat kernels were obtained from the Cereal Research Centre (CRC), Agriculture and Agri-Food Canada, Winnipeg, Canada. Field damaged sprouted (midge-damaged) wheat kernels were procured from five growing locations across western Canada. Healthy and damaged wheat kernels were imaged using a long-wave near-infrared (LWNIR) and a short-wave near-infrared (SWNIR) hypersprctral imaging systems and an area scan color camera. The acquired images were stored for processing, feature extraction, and algorithm development. The LWNIR classified 85-100% healthy and insect-damaged, 95-100% healthy and fungal-infected, and 85-100% healthy and sprouted/midge-damaged kernels. The SWNIR classified 92.7-100%, 96-100% and 93.3-98.7% insect, fungal, and midge-damaged kernels, respectively (up to 28% false positive error). Color imaging correctly classified 93.7-99.3%, 98-100% and 94-99.7% insect, fungal, and midge-damaged kernels, respectively (up to 26% false positive error). Combined the SWNIR features with top color image features correctly classified 91-100%, 99-100% and 95-99.3% insect, fungal, and midge- damaged kernels, respectively with only less than 4% false positive error.

The agriculture industry experiences severe economic losses each year when wheat crops become infected with Fusarium and the mycotoxin Deoxynivalenol (DON). This research investigated the feasibility of using near infrared hyperspectral imaging to detect Fusarium damage and DON in Canadian Western Red Spring wheat. Four samples were selected from each grain grade resulting in 16 samples and 240 hyperspectral data cubes. The data cubes were calibrated to the system, the consistent spectra was found and then a 1- nearest neighbour classifier was generated. Grade percentages were computed and used to generate two 3- nearest neighbour classifiers, one for identifying Fusarium damage and the other for identifying DON content. The Fusarium damage classifier had an accuracy of 85% and the DON content classifier had an accuracy of 80%. While a single sample image classification will not replace manual testing, the use of multiple samples from one harvest could reduce manual inspections.

There is an increasing need for more detailed knowledge about the spatial distribution and structure of shallow water benthic habitats for marine conservation and planning. This, linked with improvements in hyperspectral image sensors provides an increased opportunity to develop new techniques to better utilise these data in marine mapping projects. The oligotrophic, optically-shallow waters surrounding Rottnest Island, Western Australia, provide a unique opportunity to develop and apply these new mapping techniques. The three flight lines of HyMap hyperspectral data flown for the Rottnest Island Reserve (RIR) in April 2004 were corrected for atmospheric effects, sunglint and the influence of the water column using the Modular Inversion and Processing System. A digital bathymetry model was created for the RIR using existing soundings data and used to create a range of topographic variables (e.g. slope) and other spatially relevant environmental variables (e.g. exposure to waves) that could be used to improve the ecological description of the benthic habitats identified in the hyperspectral imagery. A hierarchical habitat classification scheme was developed for Rottnest Island based on the dominant habitat components, such as Ecklonia radiata or Posidonia sinuosa. A library of 296 spectral signatures at HyMap spectral resolution (~15 nm) was created from >6000 in situ measurements of the dominant habitat components and subjected to spectral separation analysis at all levels of the habitat classification scheme. A separation analysis technique was developed using a multivariate statistical optimisation approach that utilised a genetic algorithm in concert with a range of spectral metrics to determine the optimum set of image bands to achieve maximum separation at each classification level using the entire spectral library. These results determined that many of the dominant habitat components could be separated spectrally as pure spectra, although there were almost always some overlapping samples from most classes at each split in the scheme. This led to the development of a classification algorithm that accounted for these overlaps. This algorithm was tested using mixture analysis, which attempted to identify 10 000 synthetically mixed signatures, with a known dominant component, on each run. The algorithm was applied directly to the water-corrected bottom reflectance data to classify the benthic habitats. At the broadest scale, bio-substrate regions were separated from bare substrates in the image with an overall accuracy of 95% and, at the finest scale, bare substrates, Posidonia, Amphibolis, Ecklonia radiata, Sargassum species, algal turf and coral were separated with an accuracy of 70%. The application of these habitat maps to a number of marine planning and management scenarios, such as marine conservation and the placement of boat moorings at dive sites was demonstrated.

This research investigates the spectral reflectance characteristics of soil and vegetation using multi-angular and single view hyperspectral data. The question of the thesis is ???How much information can be obtained from multi-angular hyperspectral remote sensing in comparison with single view angle hyperspectral remote sensing of soil and vegetation???? This question is addressed by analysing multi-angular and single view angle hyperspectral remote sensing using data from the field, airborne and space borne hyperspectral sensors. Spectral mapping, spectral indices and Functional Data Analysis (FDA) are used to analyse the data. Spectral mapping has been successfully used to distinguish features of soil and cotton with hyperspectral data. Traditionally, spectral mapping is based on collecting endmembers of pure pixels and using these as training areas for supervised classification. There are, however, limitations in the use of these algorithms when applied to multi-angular images, as the reflectance of a single ground unit will differ at each angle. Classifications using six-class endmembers identified using single angle imagery were assessed using multi-angular Compact High Resolution Imaging Spectrometer (CHRIS) imagery, as well as a set of vegetation indices. The results showed no significant difference between the angles. Low nutrient content in the soil produced lower vegetation index values, and more nutrients increased the index values. This research introduces FDA as an image processing tool for multi-angular hyperspectral imagery of soil and cotton, using basis functions for functional principal component analysis (fPCA) and functional linear modelling. FDA has advantages over conventional statistical analysis because it does not assume the errors in the data are independent and uncorrelated. Investigations showed that B-splines with 20-basis functions was the best fit for multi-angular soil spectra collected using the spectroradiometer and the satellite mounted CHRIS. Cotton spectra collected from greenhouse plants using a spectrodiometer needed 30-basis functions to fit the model, while 20-basis functions were sufficient for cotton spectra extracted from CHRIS. Functional principal component analysis (fPCA) of multi-angular soil spectra show the first fPCA explained a minimum of 92.5% of the variance of field soil spectra for different azimuth and zenith angles and 93.2% from CHRIS for the same target. For cotton, more than 93.6% of greenhouse trial and 70.6% from the CHRIS data were explained by the first fPCA. Conventional analysis of multi-angular hyperspectral data showed significant differences exist between soil spectra acquired at different azimuth and zenith angles. Forward scan direction of zenith angle provides higher spectral reflectance than backward direction. However, most multi-angular hyperspectral data analysed as functional data show no significant difference from nadir, except for small parts of the wavelength of cotton spectra using CHRIS. There is also no significant difference for soil spectra analysed as functional data collected from the field, although there was some difference for soil spectra extracted from CHRIS. Overall, the results indicate that multi-angular hyperspectral data provides only a very small amount of additional information when used for conventional analyses.

Dissertation (Ph.D.)--Auburn University, / Abstract. Vita. Includes bibliographic references (p.102-108).

Hyperspectral imaging is the collection, processing and analysis of spectral data in numerous contiguous wavelength bands while also providing spatial context. Some of the commonly used instruments for hyperspectral imaging are pushbroom scanning imaging systems, grating based imaging spectrometers and more recently electronically tunable filters. Electronically tunable filters offer the advantages of compactness and absence of mechanically movable parts. Electronically tunable filters have the ability to rapidly switch between wavelengths and provide spatial and spectral information over a large wavelength range. They involve the use of materials whose response to light can be altered in the presence of an external stimulus. While these filters offer some unique advantages, they also present some equally unique challenges. This research work involves the design and development of a multichannel imaging system using electronically tunable Liquid Crystal Fabry-Perot etalons. This instrument is called the Liquid Crystal Hyperspectral Imager (LiCHI). LiCHI images four spectral regions simultaneously and presents a trade-off between spatial and spectral domains. This simultaneity of measurements in multiple wavelengths can be exploited for dynamic and ephemeral events. LiCHI was initially designed for multispectral imaging of space plasmas but its versatility was demonstrated by testing in the field for multiple applications including landscape analysis and anomaly detection. The results obtained after testing of this instrument and analysis of the images are promising and demonstrate LiCHI as a good candidate for hyperspectral imaging. The challenges posed by LiCHI for each of these applications have also been explored.

Five penguin species breed in Antarctica: emperors, Adélies, chinstraps, gentoos and macaronis. These are important Antarctic mid-trophic level predators and under predicted climate change are believed threatened. Accurate monitoring of populations is therefore of growing importance owing to the changing environment in which they live, particularly on the Western Antarctic Peninsula where rapid warming is occurring. The inaccessibility and size of many colonies makes ground based monitoring difficult with remote sensing providing an alternative, relatively low cost, monitoring method. Advancing current monitoring methods will help improve estimates of population trajectories at a regional scale. Recent and future progress in remote sensing, with new satellite sensors and platforms, offers increased potential for accurate, consistent large-scale data collection. The work in this thesis focuses on difficult to monitor brush-tailed penguins (Adélies, chinstraps and gentoos), aiming to develop new techniques and algorithms to improve their monitoring by satellite imagery. Penguin detection in satellite imagery is based on the red/brown guano stains that colonies create, with these stains evident from space. Fieldwork undertaken in Antarctica (Nov 2014-Jan 2015) using a field spectroradiometer obtained the first in situ hyperspectral reflectance spectra of Adélie and chinstrap guano. These spectra are used to identify the features responsible for varying guano types and suggest new indices for differentiating these in satellite imagery. Satellite imagery coincident with the fieldwork, obtained from WorldView-3 (~40 cm resolution) and Landsat 8 (~15 m resolution), are used to trial the index derived from the field spectra. Analysis of the field data and satellite images includes examination of guano colour for different species and comparison of methods of guano detection, aiming to enhance species detection from satellite imagery. In addition, Landsat 8 imagery from further locations is used to produce time series of this index for colonies, examining how guano colour changes over the breeding season are seen in satellite imagery. This dissertation concludes with recommendations for future developments of satellite-based methods based on the results of these analyses. Such improvements should help improve our current understanding of penguin population and continuing population changes in relation to climate change.