The aim of' this investigation is to determine the combination of signal coding and modulation for satellite links, that, for a given degree of equipment complexity needed for the detection of the received signal, achieves the best tolerance to noise. Computer simulation tests and theoretical analyses are used to compare the various proposed signal designs The trellis coded M-ary phase-shift-keyed (MPSK) modulation method is introduced as the scheme for which different codes are to be devised. A class of known binary convolutional codes for 8 and 16 PSK signals is studied, and new correlative-level codes using modulo-M arthimetic are designed for MPSK signals. The soft-decision maximum likelihood Viterbi decoding algorithm is considered for the two proposed signal designs, and a more conventional near-maximum likelihood (reduced-state Viterbi) decoding scheme is also investigated for both types of coded signals. Two novel decoding schemes, derived from a more conventional near-maximum likelihood decoder, are proposed for coded 8PSK signals. In both decoders the amount of computation involved in decoding each data-symbol is adjusted to meet the prevailing noise level in transmission. Results of extensive computer simulation tests for both decoding schemes are presented. These results suggest that the new schemes come very close to achieving the maximum likelihood decoding of the coded signals without, however, requiring nearly as much storage and computation per decoded data symbol as does the Viterbi decoder. The carrier-phase synchronisation prob1em in a coherent trellis coded MPSK system is investigated. Eight new rotationally invariant rate-2/3 and rate-3/U convolutional codes for 8 and 16 PSK signals are designed. The new coded MPSK signals, when combined with a simple phase-error correction system proposed for the receiver, are able to tolerate the likely carrier-phase changes in the reference carriers of the coherent demodulation process and therefore avoid the prolonged error bursts that are otherwise caused in the decoded data symbols by such phase shifts. coding gains of the majority of the new codes The asymptotic here are either the same as, or come close to, those of the best known but not rotationally invariant convolutional codes of the same rates.

No description available.

Landfalling tropical cyclone (TC) induced precipitation poses a great risk to the rising coastal population globally. However, the impacts of tropical cyclone precipitation (TCP) are still difficult to predict due to rapid structural changes during landfall. This study applies a shape metric methodology to quantify the spatiotemporal evolution of TCP in the North Indian (NI), Western Pacific (WP), and North Atlantic (NA) basins. The International Best Track Archive for Climate Stewardship (IBTrACS) data and the Global Precipitation Mission (GPM)'s advanced Integrated Multisatellite Retrievals for GPM (IMERG) dataset is employed to study the 2014-2020 landfalling TCP at three analysis times: pre-landfall, landfall, and post-landfall. We examine three thresholds (2, 5, and 10 mm hr-1) and use six spatial metrics (area, closure, solidity, fragmentation, dispersion, and elongation) to quantify the shape of the precipitation pattern. To identify precipitation changes among the three analysis times and three basins, the Kruskal-Wallis test is applied. The three basins show important differences in size evolution. The greatest structural changes occur during post-landfall when the rainfall extent shrinks. The WP has the largest area of TCP and generates the highest maximum TCP of all basins. NA is the only basin where the precipitation area expands after landfall. NA also has the lowest closure for the three precipitation thresholds. NI precipitation has the lowest dispersion and maximum closure. Shape metrics such as closure and dispersion show a consistent inverse correlation. The maximum precipitation direction within the TCs is also examined in each basin. These results can inform guidelines that contribute to improved TCP forecasting and disaster mitigation strategies for vulnerable coastal populations globally. Future studies can apply shape metrics to the sub-basins in NI and WP to examine regional variability as there has been no such study in these basins. Future work can also investigate if the location of heavy rainfall within the TC structure affects flooding or other water hazards. / Master of Science / Landfalling tropical cyclones (TC) pose a significant threat to coastal populations worldwide, primarily due to the heavy rainfall. Predicting the rainfall during landfall is challenging as they undergo rapid changes. This study uses shape metrics to measure how this rainfall changes over time and space in three ocean basins: North Indian (NI), Western Pacific (WP), and North Atlantic (NA). The study uses a comprehensive collection of global TC best-track data i.e., International Best Track Archive for Climate Stewardship (IBTrACS). The rainfall measurement is derived from the satellite data i.e., the Global Precipitation Mission (GPM)'s advanced Integrated Multisatellite Retrievals for GPM (IMERG) to study landfalling rainfall between 2014 to 2020. Six spatial metrics (area, closure, solidity, fragmentation, dispersion, and elongation) were applied to quantify the shape and size of the precipitation pattern at three landfall times: pre-landfall, landfall, and post-landfall. The values of the shape metrics are compared between the ocean basins and landfall times using a statistical test. The results show that the most significant changes occur after landfall when the rainfall area decreases. WP has the largest area of rainfall and generates the highest maximum rainfall of all basins. NA is the only basin where the rainfall area expands after landfall. Shape metrics such as closure and dispersion share a consistent negative relationship. The maximum precipitation direction within the TCs is also examined in each basin. These results can contribute to improved tropical cyclone rainfall forecasting and disaster mitigation strategies for vulnerable coastal populations globally. Future studies can apply shape metrics to the sub-basins in NI and WP to examine regional variability as there has been no such study in these basins.

Land-cover identification and mapping are an integral part of natural resource planning and management. Satellite imagery provides a way to obtain land cover information, particularly for large tracts of land such as those administered by federal and state agencies. This study assesses the usefulness of the Brightness/Greenness Transformation of Landsat Thematic Mapper data for differentiating conifer forest types in northern Utah. Satellite data for the Logan Ranger District of the Wasatch-Cache National Forest were classified into 27 vegetation classes. Of these, nine were determined to be conifer classes and were used in subsequent analyses. Ten sites of each conifer class type were field checked and vegetation and physical site characteristics recorded. The Brightness/Greenness Transformation was able to distinguish conifer areas from other vegetation types. High-density conifer classes were classified at 94 percent accuracy. Low-density conifer classes were classified correctly 65 percent of the time. The Brightness/Greenness Transformation alone met with limited success in distinguish ing between conifer species. Each class showed great variability with respect to major overstory species. Analysis of variance indicated that none of the site factors measured consistently corresponded with the spectrally designated classes. While several factors differed significantly among classes, no factor was significantly different for all c l ass-pair combinations. Correlation analysis revealed that brightness, greenness, and wetness values related more to environmental values than to conifer species. Brightness was highly correlated with percent of exposed soil on the site. Greenness was highly correlated to the presence of deciduous and herbaceous vegetation. Wetness was highly correlated to total tree and conifer cover values. Adding slope and aspect data to the Brightness/Greenness Transformation classes with the highest percentages of canopy cover did allow separation of lodgepole pine and Douglas fir. High percentagecanopy cover sites on slopes less than 35 percent were classified as lodgepole pine with 89 percent accuracy. On slopes greater than or equal to 35 percent, Douglas fir was found with 79 percent accuracy.

>Magister Scientiae - MSc / This work aimed at assessing the response of wetland vegetation productivity to the 2014-2017 climate-induced drought at the Soetendalsvlei wetland system in the Western Cape province of South Africa. To achieve this objective, firstly a literature review on the progress of remotely sensed data applications in assessing and monitoring wetland vegetation productivity was conducted. The review elaborates on the role of remote sensing in monitoring and assessing wetland vegetation productivity, with a detailed discussion of the climate change and variability impacts on wetland vegetation productivity. Accurate assessment results are produced when suitable processing techniques are selected as well as appropriate spatial and spectral resolution for extracting spectral information of wetland vegetation productivity. Secondly, wetland vegetation changes and productivity status was assessed using multi-temporal resolution Landsat series imagery and Normalized Difference Vegetation Index (NDVI) during the wet and dry seasons for the period between 2014 and 2018.

>Magister Scientiae - MSc / Groundwater dependent vegetation (GDV) communities are increasingly threatened by the transformation of the natural environment to different land use/land cover, over-exploitation of groundwater resources and the proliferation of invasive species within the Cape Floristic Region (CFR). These changes affect the groundwater regime, level, and quality, which supports GDV. Natural resource managers often lack an understanding at appropriate scales of the nature of dependency of GDV to make informed sustainable decisions. This work thus assesses the spatial distribution of GDV and their responses to groundwater variability within the Cape floristic region from June 2017 to July 2018. To achieve this aim, firstly a literature review on the background of GDV, threats and the impact of climate change was assessed.

>Magister Scientiae - MSc / The occurrence of droughts is a threat to global water resources and natural ecosystems, with the impact being more profound in semi-arid environments. The frequency of droughts is likely to increase because of climate change, and this poses a huge threat to the available water resources, to livelihoods and to ecosystems. Routine drought monitoring is fundamental for developing an early warning system and an area-specific drought mitigation and adaptation framework. Surface waterbodies, especially those in arid and semi-arid environments, are vulnerable to the impacts of drought. The development of moderate-resolution sensors, such as the Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI), allow new opportunities to monitor droughts and their impact on surface waterbodies.

No description available.

<p> Satellite data from GOES and LANDSAT where evaluated as a source of information for hydrologic distributed models applied to large watersheds. Three basins within the Llanos area of the Orinoco River basin, Venezuela, were selected as study areas. The specific objectives of the study were; (1) To test the applicability of meteorological satellite data for improving information on the temporal and areal distribution of precipitation,as well as estimates of amount over large areas. (2) To investigate photographic and digital LANDSAT data as a source of land surface information for hydrologic distributed models. The satellite and ground data used in this research were: (1) GOES WEFAX electrostatic facsimiles, (2) LANDSAT photographic and digital data, (3) Reports and maps on soil studies by Desarrollo Industrial Agricola C.A (1958) and Comerma and Luque (1971). </p> <p> The analysis of the data was carried out by visual analysis on the photographic products of GOES and LANDSAT using r·egular photo-interpretation techniques. GOES photographic data allowed the analysis of temporal and areal distribution of precipitation over large areas. Follansbe's (1973) method for estimating precipitation using satellite imagery was found potentially applicable to hydrologic distributed models. Variations to the method are suggested. </p> <p> The visual analysis of a single LANDSAT image allowed the mapping of broad land-cover classes and some soil characteristics in the study area. Analysis of the multidate imagery was found very useful in detecting seasonal and non-seasonal changes. </p> <p> Digital analysis of LANDSAT data was carried out on the Image 100 system at the Canada Centre for Remote Sensing in Ottawa. Contrast stretched images and breakpoint enhancement supervised and unsupervised classifications were produced.The results showed that LANDSAT digital analysis either by unsupervised or supervised classification can be used for the extraction of land-use/land-cover information for application in hydrologic distributed models. </p> / Thesis / Master of Arts (MA)

This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased. / Ph. D.