Native vegetation condition provides an indication of the state of vegetation health or function relative to a stated objective or benchmark. Measures of vegetation condition provide an indication of the vegetation's capacity to provide habitat for a range of species and ecosystem functions through the assessment of selected vegetation attributes. Subsets of vegetation attributes are often combined into vegetation condition indices or metrics, which are used to provide information for natural resource management. Despite their value as surrogates of biota and ecosystem function, measures of vegetation condition are rarely used to inform biodiversity assessments at scales beyond individual stands. The extension of vegetation condition information across landscapes, and approaches for achieving this, using remote sensing technologies, is a key focus of the work presented in this thesis. The aim of this research is to assess the utility of multi-spectral remotely sensed data for the recovery of stand-level attributes of native vegetation condition at landscape scales. The use of remotely sensed data for the assessment of vegetation condition attributes in fragmented landscapes is a focus of this study. The influence of a number of practical issues, such as spatial scale and ground data sampling methodology, are also explored. This study sets limitations on the use of this technology for vegetation condition assessment and also demonstrates the practical impact of data quality issues that are frequently encountered in these types of applied integrated approaches. The work presented in this thesis demonstrates that while some measures of vegetation condition, such as vegetation cover and stem density, are readily recoverable from multi-spectral remotely sensed data, others, such as hollow-bearing trees and log length, are not easily derived from this type of data. The types of information derived from remotely sensed data, such as texture measures and vegetation indices, that are useful for vegetation condition assessments of this nature are also highlighted. The utility of multi-spectral remotely sensed data for the assessment of stand-level vegetation condition attributes is highly dependent on a number of factors including the type of attribute being measured, the characteristics of the vegetation, the sensor characteristics (i.e. the spatial, spectral, temporal, and radiometric resolution), and other spatial data quality considerations, such as site homogeneity and spatial scale. A series of case studies are presented in this thesis that explores the effects of these factors. These case studies demonstrate the importance of different aspects of spatial data and how data manipulation can greatly affect the derived relationships between vegetation attributes and remotely sensed data. The work documented in this thesis provides an assessment of what can be achieved from two sources of multi-spectral imagery in terms of recovery of individual vegetation attributes from remotely sensed data. Potential surrogate measures of vegetation condition that can be derived across broad scales are identified. This information could provide a basis for the development of landscape scale multi-spectral remotely sensed based vegetation condition assessment approaches, supplementing information provided by established site-based vegetation condition assessment approaches.

Bibliography: p. 231-232. / xvi, 232 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The aim of this thesis is detecting and tracking objects at large ranges, when no target features are visible, with imaging type sensors. A system which estimates the optical flow of the scene in a parallel architecture is developed. The architecture is similar to that of an artifical neural network. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994

<p>The subject of this thesis is the application of Support Vector Machines on two totally different applications, facial expressions recognition and remote sensing.</p><p>The basic idea of kernel algorithms is to transpose input data in a higher dimensional space, the feature space, in which linear operations on the data can be processed more easily. These operations in the feature space can be expressed in terms of input data thanks to the kernel functions. Support Vector Machines is a classifier using this kernel method by computing, in the feature space and on basis of examples of the different classes, hyperplanes that separate the classes. The hyperplanes in the feature space correspond to non linear surfaces in the input space.</p><p>Concerning facial expressions, the aim is to train and test a classifier able to recognise, on basis of some pictures of faces, which emotion (among these six ones: anger, disgust, fear, joy, sad, and surprise) that is expressed by the person in the picture. In this application, each picture has to be seen has a point in an N-dimensional space where N is the number of pixels in the image.</p><p>The second application is the detection of camouflage nets hidden in vegetation using a hyperspectral image taken by an aircraft. In this case the classification is computed for each pixel, represented by a vector whose elements are the different frequency bands of this pixel.</p>

<p><p>Abstract</p><p>3D visualization of geological structures is a very efficient way to create a good understanding of geological features. It is not only an illustrative way for common people, but also a comprehensive method to interpret results of the work. Geologists, geophysics engineers and GIS experts sometimes need to visualize an area to accomplish their researches. It can show how sample data are distributed over the area and therefore they can be applied as suitable approach to validate the result. Among different 3D modeling methods, some are expensive or complicated. Therefore, such a methodology enabling easy and cheap creation of a 3D construction is highly demanded.</p><p>However, several obstacles have been faced during the process of constructing a 3D model of geology. The main debate over suitable interpolation methods is the fact that 3D modelers may face discrepancies leading to different results even when they are working with the same set of data. Furthermore, most often part of data can be source of errors, themselves. Hence, it is extremely important to decide whether to omit those data or adopt another strategy. However, even after considering all these points, still the work may not be accurate enough to be used for scientific researches if the interpretation of work is not done precisely. This research sought to explain an approach for 3D modeling of Sedini platform in Sardinia, Italy. GIS was used as a flexible software together with Surfer and Voxler. Data manipulation, geodatabase creation and interpolation test all have been done with aid of GIS. A variety of interpolation methods available in Surfer were used to opt suitable method together with Arc view.</p><p>A solid 3D model is created in Voxler environment. In Voxler, in contrary to many other 3D types of software there are four components needed to construct 3D. C value as 4^th component except for XYZ coordinates was used to differentiate special features in platform and do gridding based on chosen value. With the aid of C value, one can mark layer of interest to identify it from other layers.</p><p>The final result shows a 3D solid model of the Sedini platform including both surfaces and subsurfaces. An Isosurface with its unique value (Isovalue) can mark layer of interest and make it easy to interpret the results. However, the errors in some parts of model are also noticeable. Since data acquisition was done for studying geology and mineralogy characteristics of the area, there is less number of data points collected per volume according to the main goals of the initial project. Moreover, in some parts of geological border lines, the density of sample points is not high enough to estimate accurate location of lines.</p><p>The study result can be applicable in a broad range of geological studies. Resource evaluation, geomorphology, structural geology and GIS are only a few examples of its application. The results of the study can be compared to the results of similar works where different softwares have been used so as to comprehend pros and cons of each as well as appropriate application of each software for a special task.</p><p> </p><p> </p><p><em>Keywords: GIS, Image Interpretation, Geodatabase, Geology, Interpolation, 3D Modeling</em></p><p> </p><p> </p></p><p> </p>

<p>Semi-analytical models for remote sensing of water quality parameters need to be parameterized with specific inherent optical properties. In this thesis, data on specific inherent optical properties of Swedish lakes and coastal waters is presented. Also, the problems of measuring in situ spectral backscattering are addressed. It is shown how measured specific inherent optical properties are used to parameterize semi-analytical bio-optical models. The models are then used to produce large synthetic data sets based on the distribution of water quality parameters, and from these data sets, band ratio or single band ratio algorithms for remote estimation of water quality parameters are constructed. A similar model was also used to calculate under water PAR from measured water quality parameters.</p><p>The specific inherent optical properties of Swedish lakes and coastal waters are very similar to earlier reported data from the oceanic environment. However, different relations of the water quality parameters will affect the inherent optical properties absorption and backscattering. The absorption spectra are dominated by yellow substance with terrestrial origin. Phytoplankton absorption is low, and account in general only for about 10 % of the total absorption in regions where phytoplankton pigments are active. The spectral backscattering is dominated by suspended particulate inorganic matter. Phytoplankton backscattering is almost negligible, except in cases where the phytoplankton community is dominated by highly scattering cyanobacteria. Experiences from remote sensing campaigns and modeling shows that remote chlorophyll estimation is most effective at longer wavelengths, where the absorption of yellow substance is low. However, modeling also predicts that large uncertainties have to be expected in the estimation of chlorophyll, both from variation in the specific phytoplankton absorption and from influences of other optically active water quality parameters.</p>

The atmospheric radiative transfer model MOCARAT was developed and is presented in this thesis. MOCARAT employs a Monte Carlo Technique for the accurate modeling of band radiances and reflectances in an atmospheric system with a ruffled ocean surface as a lower boundary. The atmospheric radiative transfer is modeled with consideration of molecular Rayleigh scattering, Mie Scattering and absorption on particulate matter, as well as band absorption by molecules in the wavelength channels of interest. The bidirectional reflection of downwelling light at the ocean surface is computed using the empirical relationship between surface wind field and the slope distribution of wave facets derived by Cox and Munk (1954a). A method is proposed to use the oceanic sun glint for remote sensing applications. The sensitivity of channel correlations to aerosol burden and type as well as other atmospheric and observational parameters is assessed. Comparisons of observed correlations with model results are used to check the consistency of the calibration of the airborne Multichannel Cloud Radiometer (MCR) that was employed during the Indian Ocean Experiment (INDOEX). The MCR calibration exhibited large variability from flight to flight. The method was applied to MODIS observations. Unlike the MCR, MODIS was stable where expected, although numerical values for some of the wavelengths appear to depart from theory. / Graduation date: 2004

As human populations increase along coastal watersheds, the understanding and monitoring of Harmful Algal Blooms (or red tides) is an increasingly important issue. A consistent method for accurately detecting red tides using satellite measurements would bring tremendous societal benefits to resource managers, the scientific community and to the public as well. In the West Florida Shelf, blooms of the toxic dinoflagelate Karenia brevis are responsible for massive red tides causing fish kills, massive die-offs of marine mammals, shellfish poisoning, and acute respiratory irritation in humans. In this work, for the first time a long-term dataset (2002~2006) the MODerate Resolution Imaging Spectroradiometer (MODIS) is compared (i.e., matched-up) to an extensive data set of in situ cell counts of K. brevis; provided by the Florida Fish and Wildlife Conservation Commission's Fish and Wildlife Research Institute. The pairing of remote sensing data with near-coincident field measurements of cell abundance was successfully used to derive the basis for the development of an alternative ocean color based algorithm for detecting the optical signatures associated with blooms of K. brevis in waters of the West coast of Florida. Conclusions are geographically limited to the Central West Florida Shelf during the boreal Summer-Fall (i.e., the K. brevis blooming season). The new simpler Empirical approach is compared with other two more complicated published techniques. Their potential is verified and uncertainties involved in the identification of blooms of K. brevis are presented. The results shown here indicate that the operational NOAA method for detecting red tides in the Gulf of Mexico (Stumpf et al., 2003; Tomlinson et al., 2004) performs less accurately than the other two algorithms at identifying K. brevis blooms. The sensitivity and specificity of the Bio-optical (Cannizzaro, 2004; Cannizzaro et al., 2008) and Empirical algorithms are simultaneously maximized with an optimization procedure. The combined use of these two optimized algorithms in sequence provides another new monitoring tool with improved accuracy at detecting K. brevis of blooms. The ability of this Hybrid scheme ranges about 80% for both sensitivity and specificity; and the capability at predicting a correct red tides is 70%, and ~85% for non-blooms conditions. The spatial and temporal knowledge of K. brevis blooms can improve the direction of field monitoring to areas that should receive special attention, allowing better understanding of the red tide phenomenon by the scientific community. The relevant agencies can also develop more appropriate mitigation action plans, and public health guidance can be improved with the enhancement of sustainable costal management strategies.

Dengue is currently the most important arboviral disease globally and is usually associated with built environments in tropical areas. Control measures are currently focused on community participation in control of the vector Aedes aegypti and larval source reduction. In Costa Rica, dengue fever is a relatively recent re-emerging disease and has become a serious public health problem. Remotely sensed information can facilitate the study of urban mosquito-borne diseases like dengue by providing multiple temporal and spatial resolutions appropriate to investigate urban structure and ecological characteristics associated with infectious disease. Initial studies showed that although dengue is a serious public health problem in Costa Rica, there is a need for interdisciplinary scientific research to guide vector control. Therefore, the dengue situation in Puntarenas, Costa Rica, and applications of remote sensing to study infectious diseases like dengue within urban environments was analyzed. Satellite imagery of high and medium spatial resolution was obtained to evaluate relationships between urban structure and incidence of dengue fever at the locality level. Using the satellite imagery, a geographical sampling method was developed and applied for seasonal entomological field surveys in Puntarenas. Very high resolution imagery from QuickBird was utilized to determine the relationships between Ae. aegypti larval habitat abundance and tree cover or built areas. Results showed that the most relevant Ae. aegypti larval habitats in Puntarenas were outdoor miscellaneous containers, cans and plastic food containers that fill with rain water in the wet season, while washtubs were the most productive habitats in the dry season. Dengue incidence and abundance of larval habitats in the urban environment were directly associated with tree cover and inversely associated with built areas. Environmental conditions and urban structure, as well as human behavior were related in different ways to dengue incidence and Ae. aegypti larval habitats. Overall, remotely sensed information was useful in developing sampling strategies for field surveys and determining factors within the urban environment that may promote persistence of mosquito larval habitats and increased dengue risk. The geographical methods and relationships revealed will be useful in determining target areas for more efficient vector control.

Marine stratocumulus cloud regimes exert a strong climatic influence through their high solar reflectivity. Human-induced changes in stratocumulus clouds, attributed to an increase of the aerosol burden (indirect effects), can be significant given the cloud decks proximity to the continents; nevertheless, the magnitude and the final climatic consequences of these changes are uncertain. This thesis investigates further the interactions between aerosols, cloud microphysics, regional circulation, and radiative response in the Southeast Pacific stratocumulus cloud deck, one of the largest and most persistent cloud regimes in the planet. Specifically, three different aspects are addressed by this thesis: The importance of the synoptic atmospheric variability in controlling cloud microphysical and radiative changes, a validation analysis of satellite retrievals of cloud microphysics from MOderate Resolution Imaging Spectroradiometer (MODIS), and the quantitative assessments of cloud aerosol interactions along with their associated radiative forcing using primarily aircraft remote sensing data. Synoptic and satellite-derived cloud property variations for the Southeast Pacific region associated with changes in coastal satellite-derived cloud droplet number concentration (Nd) are analyzed through a composite technique. MAX and MIN Nd composites are defined by the top and bottom terciles of daily area-mean Nd values over the Arica Bight, the region with the largest mean oceanic Nd, for the five October months of 2001, 2005, 2006, 2007, and 2008. The MAX-Nd composite is characterized by a weaker subtropical anticyclone and weaker winds than the MIN-Nd composite. Additionally, the MAX-Nd composite clouds over the Arica Bight are thinner than the MIN-Nd composite clouds, have lower cloud tops, lower near-coastal cloud albedos, and occur below warmer and drier free tropospheres. At 85˚W, the top-of-atmosphere shortwave fluxes are significantly higher (50%) for the MAX-Nd, with thicker, lower clouds and higher cloud fractions than for the MIN-Nd. The change in Nd at this location is small, suggesting that the MAX-MIN Nd composite differences in radiative properties primarily reflects synoptic changes. The ability of MODIS level 2 retrievals to represent the cloud microphysics is assessed with in-situ measurements of droplet size distributions, collected during the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx). The MODIS cloud optical thickness (t) correlates well with the in-situ values with a positive bias (1.42). In contrast, the standard 2.1 micron-derived MODIS cloud effective radius (r_e) is found to systematically exceed the in-situ cloud-top r_e, with a mean bias of 2.08 um. Three sources of errors that could contribute to the MODIS r_e positive bias are investigated further: the spread of the cloud droplet size distribution, the presence of a separate drizzle mode, and the sensor viewing angles. The sensor zenith viewing angles were found to have little impact, while the algorithm assumption about the cloud droplet spectra and presence of a precipitation mode could affect the retrievals but not by enough to fully explain the positive MODIS r_e bias. The droplet spectra effects account for r_e offsets smaller than 0.6 um, 0.9 um, and 1.6 um for non-drizzling, light-drizzling, and heavy-drizzling clouds respectively. An explanation for the observed MODIS bias is lacking although three-dimensional radiative effects were not considered. This investigation supports earlier studies documenting a similar bias, this time using data from newer probes. MODIS r_e and t were also combined to estimate a liquid water path (LWP) and Nd. A positive bias was also apparent in LWP, and attributed to r_e. However, when selected appropriate parameters a priori, the MODIS Nd estimate was found to agree the best with the insitu aircraft observations of the four MODIS variables. Lastly, the first aerosol indirect effect (Twomey effect) is explicitly investigated with VOCALS-REx observations, collected during three daytime research flights (Nov 9, 11, and 13), utilizing an aerosol-cloud interactions metric, and defined as ACI=dln(t)/dln(Na), with Na corresponding to the accumulation mode aerosol concentration, t derived from a broadband pyranometer, and ACI binned by cloud LWP derived from a millimeter-wavelength radiometer. Aircraft remote sensing estimates of the ACI, during sub-cloud transects, show that the cloud aerosol-interactions are strong and close to the maximum theoretical value for thin clouds, with a decrease of ACI with LWP. Although an explanation for the dependence of ACI on LWP is lacking, we found that a decrease in ACI with LWP is associated with decreases in both surface meridional winds and Nd. Similar to ACI, albedo fractional changes due to Nd fractional changes also tended to be smaller for higher LWPs, but with an overall radiative forcing larger than conservative global estimates obtained in global circulation models. The findings of this thesis emphasize the strong stratocumulus albedo response to an aerosol perturbation and its dependence on the regional scale atmospheric configuration. The results presented here can be used as a benchmark for testing regional and climate models, as well as helping to improve the current parameterizations of the first aerosol indirect effect.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.