Thesis (MSc)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: The availability of multispectral data from the satellite-borne ASTER (Advanced Space borne Thermal Emission Reflection Radiometer) sensor with 14 spectral bands, launched on 18 December 1999, ushers in a new dimension in large-scale mineral exploration. The ASTER bands are strategically positioned to map distinctive absorptive features for mapping alteration mineralogy, which has increased the potential to map hydrothermal alteration zones as compared to the conventional Landsat TM satellite and aerial photographs. This research tests the ability of ASTER to map hydrothermal alteration zones by applying various image enhancement techniques and comparing them. The study area is the Haib copper prospect in Namibia. The Crosta technique, standard colour composites, spectral band ratioing, the software defoliant technique, log residuals and spectral linear unmixing were applied and compared against results from the previous detailed geophysical and geochemical exploration. The results from all the techniques corresponded with published geological maps from previous work and indicated ASTER's ability to detect alteration zonations. Comparison of the methods applied showed that choice of technique is usually dependent of the level of detail which one seeks to achieve. Standard colour composite and log residuals are more useful for a generalized overview of the alteration mineralogy, whilst uniquely defining mineral end members is achieved by application of the Crosta technique, ratioing and spectral linear unmixing. Application of the software defoliant techniques involved ratioing results, which are affected by spectral interferences from other minerals. The presence of a highly fractured system has been established by application of Sobel filtering. A spatial association of the extracted fracture system with alteration areas suggest mineralization at the Haib is fracture controlled. The results support the presence of argillicphyllic and prophylitic alteration zones on a regional scale, a scenario which can be equated to the Lowell-Guilbert model. The potassic-phyllic zone boundary could not be spectrally detected which also supports previous studies which suggest the potassic zone is nondefinitive and is over-printed by the phyllic zone. The results demonstrate that ASTER is an effective tool to map hydrothermal alteration systems in arid areas. / AFRIKAANSE OPSOMMING: Multispektrale data van die ASTER (' Advanced Space Borne Thermal Emmission Reflection Radiometer') sensors bestaande uit 14 spektrale bande, afkomstig van 'n satelliet gelanseer op 18 Desember 1999 lui 'n nuwe era in vir grootskaalse minerale eksplorasie. Die ASTER bande is strategies sodanig op die spektrum geposisioneer om onderskeidende absorberende verskynsels van veranderde mineralisasie te identifiseer. Hierdie data het die potensiaal verhoog om hidrotermale veranderingstelsels meer suksesvol te karteer as met konvensionele Landsat TM beelde en lugfotos. Hierdie navorsing toets die vermoeëns van ASTER om hidrotermale veranderingstelsels te karteer deur 'n wye reeks beeldverrykings tegnieke toe te pas en te vergelyk. Die studiegebied IS die Haib koperafsetting in Namibië. Die Crosta tegniek, standaard kleursamestellings ('colour composites'), ratio-tegnieke, plantegroeistroping ('software defoiliant'), log residuele ('log residual '), en spektrale lineêre ontmenging ('spectral linear unmixing') is toegepas en die resultate met vorige gedetailleerde geofisiese en geo-chemiese veldopnames te vergelyk. Die verkreë resultate van al die verskillende tegnieke het grootliks met gepubliseerde geologiese kaarte van die gebied ooreengestem en bevestig dat ASTER data geskik is om sulke mineralogies veranderde gebiede te karteer. Vergelykings tussen die tegnieke het getoon dat die keuse van tegniek bepaal word deur die vlak en tipe detail wat verlang word. Standaard kleursamestellings en die log residuele tegnieke lewer goeie resultate om veralgemeende oorsigte van mineralogies veranderde sones te verskaf, terwyl die Crosta-, ratio- en spektrale lineêre ontmengingstegnieke meer suksesvol is om spesifieke minerale te identifiseer. Die plantegroei stropingtegniek is nodig in gevalle waar spektrale ratios deur plantegroeiresponse geaffekeer word. Die aanwesigheid van 'n intensief gefraktuurde sisteem is bepaal deur 'n Sobel filter toe te pas. Ruimtelike assosiasies tussen die voorkoms van die gefraktuurde sone met en die minealogies gewysigde sones dui aan dat mineralogiese wysiging in die Haib gebied deur frakturering beheer is. Die resultate steun die aanwesigheid van argillities-fillitiese enpropolities gewysigde sones op 'n streekskaal, 'n scenario wat deur die Lowell-Guilbertmodel voorgehou word. Die grense van die kalium-fillitiese sone kon nie spektraal waargeneem word nie. Dit steun ook vorige studies wat suggereer dat die kaliumsone nieafbakenbaar is en waarskynlik deur die fillitiese sone oorlê word. Die resultate bevestig onomwonde dat ASTER data benut kan word om hidrotermaal veranderde sones in semiariede gebiede effektief te karteer.

Ma Fung-yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 137-141). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Tables --- p.ix / List of Figures --- p.x / Chapter CHAPTER 1 --- INTRODUCTION / Chapter 1.1 --- Introduction and background --- p.1 / Chapter 1.2 --- Objectives --- p.4 / Chapter 1.3 --- Significance --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.5 / Chapter CHAPTER 2 --- LITERATURE REVIEW / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Hyperspectral remote sensing --- p.7 / Chapter 2.2.1 --- Current imaging spectrometers available --- p.8 / Chapter 2.2.2 --- Applications of hyperspectral remote sensing --- p.9 / Chapter 2.2.2.1 --- Biochemistry of vegetation --- p.10 / Chapter 2.2.2.2 --- Spatial and temporal patterns of vegetation --- p.12 / Chapter 2.3 --- Tree species recognition --- p.12 / Chapter 2.3.1 --- Factors affecting spectral reflectance of vegetation --- p.14 / Chapter 2.3.1.1 --- Optical properties of leaf --- p.14 / Chapter 2.3.1.2 --- Canopy structure --- p.15 / Chapter 2.3.1.3 --- Canopy cover --- p.16 / Chapter 2.3.1.4 --- Illumination and viewing geometry --- p.16 / Chapter 2.3.1.5 --- Spatial and temporal dynamics of plants --- p.17 / Chapter 2.3.2 --- Classification algorithms for hyperspectral analysis --- p.17 / Chapter 2.3.2.1 --- Use of derivative spectra for tree species recognition --- p.17 / Chapter 2.3.2.2 --- Linear discriminant analysis --- p.18 / Chapter 2.3.2.3 --- Artificial neural network --- p.19 / Chapter 2.3.3 --- Tree species recognition using hyperspectral data --- p.21 / Chapter 2.4 --- Data compression and feature extraction --- p.22 / Chapter 2.4.1 --- Analytical techniques of data compression --- p.23 / Chapter 2.4.2 --- Analytical techniques of feature extraction --- p.25 / Chapter 2.4.2.1 --- Feature selection by correlation with biochemical and biophysical data --- p.25 / Chapter 2.4.2.2 --- Spatial autocorrelation-based feature selection --- p.27 / Chapter 2.4.2.3 --- Spectral autocorrelation-based feature selection --- p.29 / Chapter 2.4.2.3.1 --- Optimization with distance metrics --- p.29 / Chapter 2.4.2.3.2 --- Stepwise linear discriminant analysis --- p.30 / Chapter 2.5 --- Summary --- p.31 / Chapter CHAPTER 3 --- METHODOLOGY / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Study site --- p.33 / Chapter 3.3 --- Instrumentation --- p.34 / Chapter 3.4 --- Data collection --- p.35 / Chapter 3.4.1 --- Laboratory measurement --- p.36 / Chapter 3.4.2 --- In situ measurement --- p.39 / Chapter 3.5 --- Methods of data analysis --- p.40 / Chapter 3.5.1 --- Preprocessing of data --- p.40 / Chapter 3.5.2 --- Compilation of hyperspectral database --- p.42 / Chapter 3.5.3 --- Tree species recognition --- p.42 / Chapter 3.5.3.1 --- Linear discriminant analysis --- p.44 / Chapter 3.5.3.2 --- Artificial neural network --- p.44 / Chapter 3.5.3.3 --- Accuracy assessment --- p.45 / Chapter 3.5.3.4 --- Comparison of different data processing strategies and classifiers --- p.45 / Chapter 3.5.3.5 --- Comparison of data among different seasons --- p.46 / Chapter 3.5.3.6 --- Comparison of laboratory and in situ data --- p.46 / Chapter 3.5.4 --- Data compression --- p.47 / Chapter 3.5.5 --- Band selection --- p.47 / Chapter 3.6 --- Summary --- p.48 / Chapter CHAPTER 4 --- RESULTS AND DISCUSSIONS OF TREE SPECIES RECOGNITION / Chapter 4.1 --- Introduction --- p.50 / Chapter 4.2 --- Characteristics of hyperspectral data --- p.50 / Chapter 4.3 --- Tree species recognition --- p.79 / Chapter 4.3.1 --- Comparison of different classifiers --- p.82 / Chapter 4.3.1.1 --- Efficiency of the classifiers --- p.83 / Chapter 4.3.1.2 --- Discussions --- p.83 / Chapter 4.3.2 --- Comparison of different data processing strategies --- p.84 / Chapter 4.3.3 --- Comparison of data among different seasons --- p.86 / Chapter 4.3.4 --- Comparison of laboratory and in situ data --- p.88 / Chapter 4.4 --- Summary --- p.92 / Chapter CHAPTER 5 --- RESULTS AND DISCUSSIONS OF DATA COMPRESSION AND BAND SELECTION / Chapter 5.1 --- Introduction --- p.93 / Chapter 5.2 --- Data compression --- p.93 / Chapter 5.2.1 --- PCA using in situ spectral data --- p.93 / Chapter 5.2.1.1 --- Characteristics of PC loadings --- p.95 / Chapter 5.2.1.2 --- Scatter plots of PC scores --- p.96 / Chapter 5.2.2 --- PCA using laboratory spectral data --- p.99 / Chapter 5.2.2.1 --- Characteristics of PC loadings --- p.102 / Chapter 5.2.2.2 --- Scatter plots of PC scores --- p.103 / Chapter 5.2.2.3 --- Results of tree species recognition using PC scores --- p.107 / Chapter 5.2.3 --- Implications --- p.107 / Chapter 5.3 --- Band selection --- p.108 / Chapter 5.3.1 --- Preliminary band selection using stepwise discriminant analysis --- p.108 / Chapter 5.3.1.1 --- Selection of spectral bands --- p.109 / Chapter 5.3.1.2 --- Classification results of the selected bands --- p.109 / Chapter 5.3.1.3 --- Seasonal comparison using stepwise linear discriminant analysis --- p.114 / Chapter 5.3.1.4 --- Implications --- p.116 / Chapter 5.3.2 --- Band selection using hierarchical clustering technique --- p.116 / Chapter 5.3.2.1 --- Hierarchical clustering procedure --- p.116 / Chapter 5.3.2.2 --- Selection of spectral band sets --- p.119 / Chapter 5.3.2.3 --- Classification results of the selected band sets --- p.124 / Chapter 5.4 --- Summary --- p.127 / Chapter CHAPTER 6 --- SUMMARY AND CONCLUSION / Chapter 6.1 --- Introduction --- p.129 / Chapter 6.2 --- Summary --- p.129 / Chapter 6.2.1 --- Tree species recognition --- p.129 / Chapter 6.2.2 --- Data compression --- p.130 / Chapter 6.2.3 --- Band selection --- p.131 / Chapter 6.3 --- Limitations of this study --- p.132 / Chapter 6.4 --- Recommendations for further studies --- p.133 / Chapter 6.5 --- Conclusion --- p.136 / BIBLIOGRAPHY --- p.137 / APPENDICES / Appendix 1 Reflectance of the 25 tree species in four seasons with three levels of leaf density --- p.142-166 / "Appendix 2 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by linear discriminant analysis for each season" --- p.167-178 / "Appendix 3 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by neural networks for each season" --- p.179-190 / Appendix 4 Confusion matrices of tree species recognition using 21 tree species with original spectra classified by linear discriminant analysis for seasonal comparison --- p.191-193 / Appendix 5 Confusion matrices of tree species recognition using the first eight PC scores classified by linear discriminant analysis for each season --- p.194-197 / "Appendix 6 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 2) for each season" --- p.198-209 / "Appendix 7 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 3) for each season" --- p.210-220 / "Appendix 8 Confusion matrices of tree species recognition using 21 tree species with original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis for seasonal comparison" --- p.221-229 / Appendix 9 Confusion matrices of tree species recognition using the spectral bands selected by hierarchical clustering procedures and classified by linear discriminant analysis for each season --- p.230-257

The Sahel and Soudan regions of Africa are hot-spots for vegetation change due to climatic and anthropogenic causes. Recent studies using remote sensing have demonstrated that vegetation recovery has occurred across the region questioning the occurrence of widespread land degradation in the region. These studies have focused on proxy measurements of net primary productivity, but have not addressed seasonal characteristics of vegetation such as phenology. This thesis examines how vegetation phenology has changed from 1982--2005 in the Sahel and Soudan regions and how phenology relates to observed bio-productivity and regional precipitation patterns. This is the first research to assess multi-decadal phenology change for a tropical ecosystem. Results show that while bio-productivity has significantly increased in the Sahel, significant phenology change has primarily been detected in the Soudan region. Furthermore, the relationship between phenology and bio-productivity and precipitation differs between the Sahel and Soudan. This research demonstrates the utility of measuring phenological change of a tropical ecosystem for vegetation monitoring applications.

No description available.

Dry and wet fine earth spectral measurements were made on the Ap soil surface horizons on the Maricopa Agricultural Center by using a Barnes Modular Multiband Radiometer. Three subsets were used in the analyses 552, 101 and 11. There were three soil series, Casa Grande, Shontik and Trix, four soil mapping units, and three texture classes identified on the farm. The wet soil condition reduced the amplitude of the spectral curves over the entire spectrum range (0.45 to 2.35 μm). The spectral curves were statistically related to the soil mapping units to determine if the soil mapping units and texture classes could be separated. The wet soil condition and the smaller sample size increased the correct classification percentages for soil mapping units and texture classes. LSD tests showed there were significant differences between these groups. Simple- and Multiple-linear regression analysis were used to relate some soil physical (sand, silt and clay contents and color components) and chemical (iron oxide, organic carbon and calcium carbonate contents) to soil spectral responses in the seven bands under dry and wet conditions. There were high correlations levels among the spectral bands showing an overlap of spectral information. Generally, the red (MMR3) and near-infrared (MMR4) bands had the highest correlations with the studied soil properties under dry and wet conditions. Usually, the wet soil condition resulted in higher correlations than that for the dry soil condition over the total spectrum range. The predictive equations for sand, silt and clay and iron oxide contents were satisfactory. For organic carbon and color components, the greatest success was achieved when variation in spectral response within individual samples are smaller than that between soil mapping unit group averages. There was a poor relation between calcium carbonate and spectral response. A comparison of multi-level remotely sensed data collected by SPOT, aircraft, and ground instruments showed a strong agreement among the data sets, which correlated well to fine earth data, except for the SPOT data. Rough soil surfaces showed a reduction in reflectance altitude compared to laser level, and it appears to be directly proportional to the percent shadow in the viewing area measured by SPOT satellite and aircraft.

This project hypothesizes that the use of multiple ground-based remote sensing methods can collectively characterize the geophysical signatures of four marked human burials at the Confederated Tribes of Siletz Indians historic period cemetery. If the geophysical signatures of the marked burials can be characterized, these signatures may be used to located unmarked burials within the Siletz cemetery. To investigate this hypothesis, several research questions focused on the results from topographic, cesium gradiometer, and ground-penetrating radar surveys. A 15m x 15m region of the cemetery containing four marked burials defines the survey region. The results of each survey were individually and collectively examined to identify the characteristic geophysical signatures of the four marked burials. The topographic and magnetic surveys identified geophysical anomalies spatially associated with the some of the marked burials. The ground-penetrating radar survey was the most productive by identifying geophysical anomalies spatially associated with all four marked burials. Even though signals from the burials appeared with mixed results, it proved difficult to characterize the geophysical signatures of the burials in the individual and collective geophysical data. Without a characterization of the geophysical signature of the marked burials, it is difficult to identify unmarked burials at the Siletz cemetery. Due to the success of the radar at "seeing" all four marked burials it may be possible to identify areas free of unmarked burials. / Graduation date: 2001

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission provides unique measurements of vertical profiles of aerosols and clouds and their properties during day and night-time over all types of surfaces. This information has the potential to significantly improve our understanding of the properties and effects of aerosol and clouds. This dissertation presents the results of a comprehensive analysis of CALIPSO lidar (version 2 and version 3.01) data in conjunction with A-Train satellite and ground-based observations aimed at characterizing mineral aerosol in East Asia and other major dust sources. The specific objectives were to characterize the spatial distribution and properties of atmospheric dust in the dust source regions using new CALIOP (version 3.01) data in conjunction with satellite MODIS, OMI, and CloudSat data and ground-based meteorological and lidar data; investigate changes in the vertical distribution and properties of dust during mid- and long-range transport; perform a modeling of the optical properties of nonspherical dust particles, and assess the radiative forcing and heating/cooling rates of atmospheric dust by performing radiative transfer modeling constrained by satellite data in major dust source regions.

The objective of this research is to map snow cover in the Quebec area using passive microwave and other remote sensing data. The areal snow extent and snow water equivalent are determined and a twelve year snow water equivalent map is produced for the purpose of analyzing interannual snow variability. / The presence of vegetation cover will affect the data obtained with passive systems. For heavily vegetated areas such as Quebec, the vegetation effect should be predetermined and classified to reduce the error on snow water equivalence calculation. / In dry snow conditions, forest coverage and snow density are the two major error parameters in passive microwave snow mapping. The error on snow water equivalence estimation is directly proportional to the error in estimated snow density and forest coverage. For Quebec, ignoring the fraction of the forest cover may cause up to 49% snow depth or water equivalence underestimation. / The ground measured snow depth and snow density data are necessary for calibrating satellite derived snow depth and mean snow density within forest covered regions.

The aim of this study was to investigate the role of spatial and categorical resolution of satellite images in landcover classification. Three images namely, SPOT 5, Landsat TM, and MODIS were used, each of varying spatial resolution. Landcover classes were chosen for each of the classifications, were placed into groups of 11, and then merged to 8. This was to evaluate the effect that the categorical resolution plays on the final classification algorithm. Three traditional classifiers were used to create landcover maps. It was found that the higher resolution imagery produced higher accuracies at the 11 class level and these accuracies were improved by reducing the number of classes to 8. The coarser resolution imagery was able to classify larger features more accurately than the smaller features. This allowed the conclusion to be drawn that, before classifications are to be done, the size of the features to be detected should be considered when deciding which imagery to use. To improve upon the accuracy of the maximum likelihood classifier, an Artificial Neural Network was trained using ancillary data and the SPOT 5 image. Results showed an increase of over 30% in the classification accuracy of the ANN. Specific classes were easily identified, showing the ability of the ANN to classify imagery from a complex savanna environment. Experiments with various parameters of the neural network confirmed that there are no general guidelines that can be applied to a neural network to obtain high classification accuracy. / Thesis (M.Sc.) - University of KwaZulu-Natal, Pietermaritzburg, 2007.

The object of this research program has been to provide a baseline for microwave remote sensing of ammonia and water vapor in the atmosphere of Jupiter through laboratory measurements of their microwave absorption properties. Jupiter is not only the largest planet in our solar system, but one of the most interesting and complex. Despite a handful of spacecraft missions and many astronomical measurements, much of Jupiter s atmospheric dynamics and composition remain a mystery. Although constraints have been formed on the amount of certain gases present, the global abundances and distributions of water vapor (H2O) and ammonia (NH3) are relatively unknown. Measurements of H2O and NH3 in the Jovian atmosphere to hundreds of bars of pressure are best accomplished via passive microwave emission measurements. For these measurements to be accurately interpreted, however, the hydrogen and helium pressure-broadened microwave opacities of H2O and NH3 must be well characterized, a task that is very difficult if based solely on theory and limited laboratory measurements. Therefore, accurate laboratory measurements have been taken under a broad range of conditions that mimic those of the Jovian atmosphere. These measurements, performed using a newly redesigned high-accuracy system, and the corresponding models of microwave opacity that have been developed from them comprise the majority of this work. The models allow more accurate retrievals of H2O and NH3 abundances from previous as well as future missions to Jupiter and the outer planets, such as the NASA New Frontiers class Juno mission scheduled for launch in 2011. This information will enable a greater understanding of the concentration and distribution of H2O and NH3 in the Jovian atmosphere, which will reveal much about how Jupiter and our solar system formed and how similar planets could form in other solar systems, even planets that may be hospitable to life.