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Title: RAINDROP SIZE DISTRIBUTION RETRIEVAL AND EVALUATION USING AN S-BAND RADAR PROFILER.
Creator: Fang, Fang, Jones, Linwood, University of Central Florida
Abstract / Description: Vertical pointing Doppler radar profilers are used to explore the vertical structure of precipitation cloud systems and to provide validation information for use in weather research. In this thesis, a theoretical radar rain-backscatter model was developed to simulate profiler Doppler spectra as a function of assumed rain parameters, of which the raindrop size distribution (DSD) is the fundamental quantity used to describe the characteristics of rain. Also, profiler observations during...
Show moreVertical pointing Doppler radar profilers are used to explore the vertical structure of precipitation cloud systems and to provide validation information for use in weather research. In this thesis, a theoretical radar rain-backscatter model was developed to simulate profiler Doppler spectra as a function of assumed rain parameters, of which the raindrop size distribution (DSD) is the fundamental quantity used to describe the characteristics of rain. Also, profiler observations during stratiform rain are analyzed to retrieve the corresponding rain DSD's. In particular, a gamma distribution model is introduced, which uses Rayleigh scattering portion of the Doppler velocity spectrum to estimate the raindrop size distribution. This theoretical scattering model was validated by simulating atmospheric profiles of precipitation Doppler spectra and three moments (reflectivity, mean Doppler velocity and spectral width) and then comparing these with the corresponding measurements from an S-band radar profiler during a NASA conducted Tropical Rainfall Measuring Mission (TRMM) field experiment in Central Florida in 1998. Also, the results of my analysis yielding precipitation retrievals are validated with an independent, simultaneous Joss-Waldvogel Disdrometer rain DSD observations that were collocated with the radar profiler.
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Date Issued: 2004
Identifier: CFE0000127, ucf:46190
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000127

Title: EVALUATION OF A MICROWAVE RADIATIVE TRANSFER MODEL FOR CALCULATING SATELLITE BRIGHTNESS TEMPERATURE.
Creator: Thompson, Simonetta, Jones, Linwood, University of Central Florida
Abstract / Description: Remote sensing is the process of gathering and analyzing information about the earth's ocean, land and atmosphere using electromagnetic "wireless" techniques. Mathematical models, known as Radiative Transfer Models (RTM), are developed to calculate the observed radiance (brightness temperature) seen by the remote sensor. The RTM calculated brightness temperature is a function of fourteen environmental parameters, including atmospheric profiles of temperature, pressure and moisture, sea...
Show moreRemote sensing is the process of gathering and analyzing information about the earth's ocean, land and atmosphere using electromagnetic "wireless" techniques. Mathematical models, known as Radiative Transfer Models (RTM), are developed to calculate the observed radiance (brightness temperature) seen by the remote sensor. The RTM calculated brightness temperature is a function of fourteen environmental parameters, including atmospheric profiles of temperature, pressure and moisture, sea surface temperature, and cloud liquid water. Input parameters to the RTM model include data from NOAA Centers for Environmental Prediction (NCEP), Reynolds weekly Sea Surface Temperature and National Ocean Data Center (NODC) WOA98 Ocean Salinity and special sensor microwave/imager (SSM/I) cloud liquid water. The calculated brightness temperatures are compared to collocated measurements from the WindSat satellite. The objective of this thesis is to fine tune the RadTb model, using simultaneous environmental parameters and measured brightness temperature from the well-calibrated WindSat radiometer. The model will be evaluated at four microwave frequencies (6.8 GHz, 10.7 GHz, 18.7 GHz, and 37.0 GHz) looking off- nadir for global radiance measurement.
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Date Issued: 2004
Identifier: CFE0000318, ucf:46280
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000318

Title: Sinkhole detection and quantification using LiDAR data.
Creator: Rajabi, Amirarsalan, Nam, Boo Hyun, Wang, Dingbao, Singh, Arvind, University of Central Florida
Abstract / Description: The state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock which is susceptible to dissolution and groundwater recharge that causes internal soil erosions. Numerous sinkholes, particularly in Central Florida, have occurred. Florida Subsidence Incident Report (FSIR) database contains verified sinkholes with Global Positioning System (GPS) information. In addition to existing detection methods such as subsurface exploration and...
Show moreThe state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock which is susceptible to dissolution and groundwater recharge that causes internal soil erosions. Numerous sinkholes, particularly in Central Florida, have occurred. Florida Subsidence Incident Report (FSIR) database contains verified sinkholes with Global Positioning System (GPS) information. In addition to existing detection methods such as subsurface exploration and geophysical methods, a remote sensing method can be an alternative and efficient means to detect and characterize sinkholes with a wide coverage. the first part of this study is aimed at developing a method to detect sinkholes in Missouri by using Light Detection and Ranging (LiDAR) data. Morphometrical parameters such as TPI (Topographic Position Index), CI (Convergence Index), SI (Slope Index), and DEM (Digital Elevation Model) have a high potential to help detect sinkholes, based on local ground conditions and study area. The GLM (General Linear Model) built in R software is used to obtain morphometrical indices of the study terrain to be trained and build a logistic regression model to detect sinkholes. In the second part of the study, a semi-automated model in ArcMap is then developed to detect sinkholes and also to estimate geometric characteristics of sinkholes (e.g. depth, length, circularity, area, and volume). This remote sensing technique has a potential to detect unreported sinkholes in rural and/or inaccessible areas.
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Date Issued: 2018
Identifier: CFE0007084, ucf:51992
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007084

Title: A Microwave Radiometer Roughness Correction Algorithm For Sea Surface Salinity Retrieval.
Creator: Hejazin, Yazan, Jones, W, Mikhael, Wasfy, Wei, Lei, University of Central Florida
Abstract / Description: The Aquarius/SAC-D is an Earth Science remote sensing satellite mission to measure global Sea Surface Salinity (SSS) that is sponsored by the NASA and the Argentine Space Agency (CONAE). The prime remote sensor is the Aquarius (AQ) L-band radiometer/scatterometer, which measures the L-band emitted blackbody radiation (brightness temperature) from the ocean. The brightness temperature at L-band is proportional to the ocean salinity as well as a number of physical parameters including ocean...
Show moreThe Aquarius/SAC-D is an Earth Science remote sensing satellite mission to measure global Sea Surface Salinity (SSS) that is sponsored by the NASA and the Argentine Space Agency (CONAE). The prime remote sensor is the Aquarius (AQ) L-band radiometer/scatterometer, which measures the L-band emitted blackbody radiation (brightness temperature) from the ocean. The brightness temperature at L-band is proportional to the ocean salinity as well as a number of physical parameters including ocean surface wind speed. The salinity retrieval algorithm make corrections for all other parameters before retrieving salinity, and the greatest of these is the increased brightness temperature due to roughness caused by surface wind speed. This thesis presents an independent approach for the AQ roughness correction, which is derived using simultaneous measurements from the CONAE Microwave Radiometer (MWR). When the wind blows over the ocean's surface, the brightness temperature is increased because of the ocean wave surface roughness. The MWR provides a semi-empirical approach by measuring the excess ocean emissivity at 36.5 GHz and then applying radiative transfer theory (improved ocean surface emissivity model) to translate this to the AQ 1.4 GHz frequency (L-band). The theoretical basis of the MWR algorithm is described and empirical results are presented that demonstrate the effectiveness in reducing the salinity measurement error due to surface roughness.
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Date Issued: 2012
Identifier: CFE0004212, ucf:49007
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004212

Title: Engineering Evaluation of Multi-beam Satellite Antenna Boresight Pointing using Land/Water Crossings.
Creator: May, Catherine, Jones, W, Mikhael, Wasfy, Wahid, Parveen, University of Central Florida
Abstract / Description: The Microwave Radiometer (MWR) on the Aquarius/SAC-D mission measures microwave radiation from earth and intervening atmosphere in terms of brightness temperature (Tb). It takes measurements in a push-broom fashion at K (23.8GHz) and Ka (36.5 GHz) band frequencies using two separate antenna systems, each producing eight antenna beams. Pre-launch knowledge of the alignment of these beams with respect to the space-craft is used to geolocate the antenna footprints on ground. As a part of MWR's...
Show moreThe Microwave Radiometer (MWR) on the Aquarius/SAC-D mission measures microwave radiation from earth and intervening atmosphere in terms of brightness temperature (Tb). It takes measurements in a push-broom fashion at K (23.8GHz) and Ka (36.5 GHz) band frequencies using two separate antenna systems, each producing eight antenna beams. Pre-launch knowledge of the alignment of these beams with respect to the space-craft is used to geolocate the antenna footprints on ground. As a part of MWR's on-orbit engineering check-out, the verification of MWR's pointing accuracy is discussed here. The technique used to assess MWR's pointing involved comparing the radiometer image of land with high-resolution maps. When the beam's instantaneous field of view (IFOV) passes over a land water boundary, the brightness temperature changes from a radiometrically hot land scene to a radiometrically cold ocean scene. This (")step-function(") change in brightness temperature provides a very sensitive way to characterize the mispointing error of the MWR sensor antenna footprints. This thesis describes the algorithm used for the MWR geolocation calibration. MWR sensor observed boundaries are determined by the absolute maximum Tb slope location. A system of linear equations is produced for each sensor observed land/water crossing to determine the true intersection of the MWR track with the coastline. The observed and expected boundary locations are compared by means of an error distance. Results, presented for all eight beams of the three MWR channels, show that the mispointing error (standard deviations) are overall less than 15 km from the true coastline.
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Date Issued: 2012
Identifier: CFE0004245, ucf:49523
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004245

Title: Flying under the LiDAR: relating forest structure to bat community diversity.
Creator: Butterfield, Anna, Weishampel, John, Noss, Reed, King, Joshua, University of Central Florida
Abstract / Description: Bats are important to many ecological processes such as pollination, insect (and by proxy, disease) control, and seed dispersal and can be used to monitor ecosystem health. However, they are facing unprecedented extinction risks from habitat degradation as well as pressures from pathogens (e.g., white-nose syndrome) and wind turbines. LiDAR allows ecologists to measure structural variables of forested landscapes with increased precision and accuracy at broader spatial scales than previously...
Show moreBats are important to many ecological processes such as pollination, insect (and by proxy, disease) control, and seed dispersal and can be used to monitor ecosystem health. However, they are facing unprecedented extinction risks from habitat degradation as well as pressures from pathogens (e.g., white-nose syndrome) and wind turbines. LiDAR allows ecologists to measure structural variables of forested landscapes with increased precision and accuracy at broader spatial scales than previously possible. This study used airborne LiDAR to classify forest habitat/canopy structure at the Ordway-Swisher Biological Station (OSBS) in north central Florida. LiDAR data were acquired by the National Ecological Observatory Network (NEON) airborne observation platform in summer 2014. OSBS consists of open-canopy pine savannas, closed-canopy hardwood hammocks, and seasonally inundated basin marshes. Multiple forest structural parameters (e.g., mean, maximum, and standard deviation of canopy height) were derived from LiDAR point clouds using the USDA software program FUSION. K-means clustering was used to segregate each 5x5 m raster across the ~3765 ha OSBS area into six different clusters based on the derived canopy metrics. Cluster averages for maximum, mean, and standard deviation of return heights ranged from 0 to 19.4 m, 0 to 15.3 m, and 0 to 3.0 m, respectively. To determine the relationships among these landscape-canopy features and bat species diversity and abundances, AnaBat II bat detectors were deployed from May to September in 2015 stratified by these distinct clusters. A statistical regression model selection approach was performed in order to evaluate how forest structural attributes such as understory clutter, vertical canopy structure, open and closed canopy, etc. and landscape metrics influence bat communities. The most informative models showed that a combination of site-specific (e.g., midstory clutter and entropy) and landscape level attributes (e.g., area of water and service road length) contributed to bat community patterns. This knowledge provides a deeper understanding of habitat-species interactions to better manage survival of these species and provides insight into new tools for landscape management as they apply to specific species.
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Date Issued: 2016
Identifier: CFE0006177, ucf:51151
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006177

Title: Evaluation of the Hurricane Imaging Radiometer (HIRAD) Brightness Temperatures.
Creator: Sahawneh, Saleem, Jones, W Linwood, Mikhael, Wasfy, Wahid, Parveen, Zec, Josko, University of Central Florida
Abstract / Description: The Hurricane Imaging Radiometer (HIRAD) is an experimental, airborne, microwave remote sensor that was developed to measure hurricane surface wind speed and rain rate, and thereby, provide data for scientific research and for the next generation operational hurricane surveillance. The object of this dissertation is to develop objective procedures and techniques that can be used to evaluate and characterize the HIRAD brightness temperature (Tb) image product provided by NASA MSFC.First, the...
Show moreThe Hurricane Imaging Radiometer (HIRAD) is an experimental, airborne, microwave remote sensor that was developed to measure hurricane surface wind speed and rain rate, and thereby, provide data for scientific research and for the next generation operational hurricane surveillance. The object of this dissertation is to develop objective procedures and techniques that can be used to evaluate and characterize the HIRAD brightness temperature (Tb) image product provided by NASA MSFC.First, the approach that was developed for geolocation (latitude and longitude) accuracy determination of HIRAD image pixels is presented. Using statistical estimation theory, high-contrast HIRAD imagery are compared with high resolution maps at land/water boundaries, and an error model and measurement results are presented for a variety of pixel locations. Also, a procedure is presented for estimating the HIRAD feature resolution, i.e., the effective spatial resolution (instantaneous field of view, IFOV) in the HIRAD Tb images. Next, the objective technique developed to evaluate HIRAD reconstructed ocean brightness temperature (Tb) images is described and presented. Examples are presented for several ocean scenes, which covers a wide range of ocean wind speed conditions that include Hurricanes. For these cases, surface truth in the form of independent ocean brightness temperatures measurements are obtained by airborne microwave radiometers for comparison.
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Date Issued: 2017
Identifier: CFE0006653, ucf:51221
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006653

Title: Remote Sensing of Coastal Wetlands: Long term vegetation stress assessment and data enhancement technique.
Creator: Tahsin, Subrina, Medeiros, Stephen, Singh, Arvind, Mayo, Talea, University of Central Florida
Abstract / Description: Apalachicola Bay in the Florida panhandle is home to a rich variety of salt water and freshwater wetlands but unfortunately is also subject to a wide range of hydrologic extreme events. Extreme hydrologic events such as hurricanes and droughts continuously threaten the area. The impact of hurricane and drought on both fresh and salt water wetlands was investigated over the time period from 2000 to 2015 in Apalachicola Bay using spatio-temporal changes in the Landsat based NDVI. Results...
Show moreApalachicola Bay in the Florida panhandle is home to a rich variety of salt water and freshwater wetlands but unfortunately is also subject to a wide range of hydrologic extreme events. Extreme hydrologic events such as hurricanes and droughts continuously threaten the area. The impact of hurricane and drought on both fresh and salt water wetlands was investigated over the time period from 2000 to 2015 in Apalachicola Bay using spatio-temporal changes in the Landsat based NDVI. Results indicate that salt water wetlands were more resilient than fresh water wetlands. Results also suggest that in response to hurricanes, the coastal wetlands took almost a year to recover while recovery following a drought period was observed after only a month. This analysis was successful and provided excellent insights into coastal wetland health. Such long term study is heavily dependent on optical sensor that is subject to data loss due to cloud coverage. Therefore, a novel method is proposed and demonstrated to recover the information contaminated by cloud. Cloud contamination is a hindrance to long-term environmental assessment using information derived from satellite imagery that retrieve data from visible and infrared spectral ranges. Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Landsat has an ongoing high resolution NDVI record starting from 1984. Unfortunately, the time series NDVI data suffers from the cloud contamination issue. Though simple to complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques are subject to many limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum with the aid of a machine learning tool, namely random forest (RF) trained and tested utilizing multi-parameter hydrologic data. The RF based OCPR model was compared with a simple linear regression (LR) based OCPR model to understand the potential of the model. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance for two specific dates. The RF based OCPR method achieves a root mean squared error of 0.0475 sr?1 between predicted and observed NDVI reflectance values. The LR based OCPR method achieves a root mean squared error of 0.1257 sr?1. Findings suggested that the RF based OCPR method is effective to repair cloudy values and provide continuous and quantitatively reliable imagery for further analysis in environmental applications.
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Date Issued: 2016
Identifier: CFE0006546, ucf:51331
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006546

Title: HURRICANE WIND RETRIEVAL ALGORITHM DEVELOPMENT FOR AN AIRBORNE CONICAL SCANNING SCATTEROMETER.
Creator: Vasudevan, Santhosh, Jones, Linwood, University of Central Florida
Abstract / Description: Reliable ocean wind vector measurements can be obtained using active microwave remote sensing (scatterometry) techniques. With the increase in the number of severe hurricanes making landfall in the United States, there is increased emphasis on operational monitoring of hurricane winds from aircraft. This thesis presents a data processing algorithm to provide real-time hurricane wind vector retrievals (wind speed and direction) from conically scanning airborne microwave scatterometer...
Show moreReliable ocean wind vector measurements can be obtained using active microwave remote sensing (scatterometry) techniques. With the increase in the number of severe hurricanes making landfall in the United States, there is increased emphasis on operational monitoring of hurricane winds from aircraft. This thesis presents a data processing algorithm to provide real-time hurricane wind vector retrievals (wind speed and direction) from conically scanning airborne microwave scatterometer measurements of ocean surface backscatter. The algorithm is developed to best suit the specifications for the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division's airborne scatterometer – Integrated Wind and Rain Airborne Profiler (IWRAP). Based on previous scatterometer wind retrieval methodologies, the main focus of the work is to achieve rapid data processing to provide real-time measurements to the NOAA Hurricane Center. A detailed description is presented of special techniques used. Because IWRAP flight data were not available at the time of this development, the wind retrieval performance was evaluated using a Monte Carlo simulation, whereby radar backscatter measurements were simulated with instrument and geophysical noise and then used to infer the surface wind conditions in a simulated (numerical weather model) hurricane wind field
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Date Issued: 2006
Identifier: CFE0001477, ucf:47093
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001477

Title: HURRICANE WIND SPEED AND RAIN RATE RETRIEVAL ALGORITHM FOR THE STEPPED FREQUENCY MICROWAVE RADIOMETER.
Creator: Amarin, Ruba, Jones, Linwood, University of Central Florida
Abstract / Description: This thesis presents the development and validation of the Hurricane Imaging Retrieval Algorithm (HIRA) for the measurement of oceanic surface wind speed and rain rate in hurricanes. The HIRA is designed to process airborne microwave brightness temperatures from the NOAA, Stepped Frequency Microwave Radiometer (SFMR), which routinely collects data during NOAA hurricane hunter aircraft flights. SFMR measures wind speeds and rain rates at nadir only, but HIRA will soon be integrated with an...
Show moreThis thesis presents the development and validation of the Hurricane Imaging Retrieval Algorithm (HIRA) for the measurement of oceanic surface wind speed and rain rate in hurricanes. The HIRA is designed to process airborne microwave brightness temperatures from the NOAA, Stepped Frequency Microwave Radiometer (SFMR), which routinely collects data during NOAA hurricane hunter aircraft flights. SFMR measures wind speeds and rain rates at nadir only, but HIRA will soon be integrated with an improved surface wind speed model for expanded utilization with next generation microwave hurricane imagers, such as the Hurricane Imaging Radiometer (HIRad). HIRad will expand the nadir only measurements of SFMR to allow the measurement of hurricane surface winds and rain over a wide swath Results for the validation of HIRA retrievals are presented using SFMR brightness temperature data for 22 aircraft flights in 5 hurricanes during 2003-2005. Direct comparisons with the standard NOAA SFMR empirical algorithm provided excellent results for wind speeds up to 70 m/s. and rain rates up to 50 mm/hr.
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Date Issued: 2006
Identifier: CFE0001313, ucf:47024
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001313

Title: EVALUATION OF A MICROWAVE RADIATIVE TRANSFER MODEL FOR CALCULATING SATELLITE BRIGHTNESS TEMPERATURE.
Creator: Thompson, Simonetta, Jones, Linwood, University of Central Florida
Abstract / Description: Remote sensing is the process of gathering and analyzing information about the earth's ocean, land and atmosphere using electromagnetic "wireless" techniques. Mathematical models, known as Radiative Transfer Models (RTM), are developed to calculate the observed radiance (brightness temperature) seen by the remote sensor. The RTM calculated brightness temperature is a function of fourteen environmental parameters, including atmospheric profiles of temperature, pressure and moisture, sea...
Show moreRemote sensing is the process of gathering and analyzing information about the earth's ocean, land and atmosphere using electromagnetic "wireless" techniques. Mathematical models, known as Radiative Transfer Models (RTM), are developed to calculate the observed radiance (brightness temperature) seen by the remote sensor. The RTM calculated brightness temperature is a function of fourteen environmental parameters, including atmospheric profiles of temperature, pressure and moisture, sea surface temperature, and cloud liquid water. Input parameters to the RTM model include data from NOAA Centers for Environmental Prediction (NCEP), Reynolds weekly Sea Surface Temperature and National Ocean Data Center (NODC) WOA98 Ocean Salinity and special sensor microwave/imager (SSM/I) cloud liquid water. The calculated brightness temperatures are compared to collocated measurements from the WindSat satellite. The objective of this thesis is to fine tune the RadTb model, using simultaneous environmental parameters and measured brightness temperature from the well-calibrated WindSat radiometer. The model will be evaluated at four microwave frequencies (6.8 GHz, 10.7 GHz, 18.7 GHz, and 37.0 GHz) looking off- nadir for global radiance measurement.
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Date Issued: 2004
Identifier: CFE0000325, ucf:46303
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000325

Title: MULTISENSOR FUSION REMOTE SENSING TECHNOLOGY FOR ASSESSING MULTITEMPORAL RESPONSES IN ECOHYDROLOGICAL SYSTEMS.
Creator: Makkeasorn, Ammarin, Chang, Ni-Bin, University of Central Florida
Abstract / Description: Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements...
Show moreEarth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing–based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.
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Date Issued: 2007
Identifier: CFE0001767, ucf:47267
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001767

Title: An Emissive Antenna Correction for The Tropical Rainfall Measuring Mission Microwave Imager (TMI).
Creator: Alquaied, Faisal, Jones, W Linwood, Mikhael, Wasfy, Wei, Lei, Zec, Josko, Wilheit, Thomas, University of Central Florida
Abstract / Description: This dissertation deals with the radiometric calibration of a satellite microwave radiometer known as the TRMM Microwave Imager (TMI), which operated on NASA's Tropical Rainfall Measuring Mission (TRMM). This multi-frequency, conical-scanning, passive microwave, remote sensor measures the earth's blackbody emissions (brightness temperature, Tb) from a low earth orbit and covers the tropics ((&)#177;35(&)deg; latitude). The original scientific objective for TRMM's 3-year mission was to measure...
Show moreThis dissertation deals with the radiometric calibration of a satellite microwave radiometer known as the TRMM Microwave Imager (TMI), which operated on NASA's Tropical Rainfall Measuring Mission (TRMM). This multi-frequency, conical-scanning, passive microwave, remote sensor measures the earth's blackbody emissions (brightness temperature, Tb) from a low earth orbit and covers the tropics ((&)#177;35(&)deg; latitude). The original scientific objective for TRMM's 3-year mission was to measure the statistics of rainfall in the tropics. However, the mission was quite successful, and TRMM was extended for greater than 17 years to provide a long-term satellite rain measurements, which has contributed significantly to the study of global climate change.A significant part of the extended TRMM mission was the establishment of a constellation of satellite radiometer that provide frequent global rainfall measurements that enable severe storm warnings for operational hazard forecast by the international weather community. TRMM played a key role by serving as the radiometric calibration standard for the TRMM constellation microwave radiometers.The objective of this dissertation is to improve the radiometric calibration of TMI and to provide to NASA a new robust, physics-based algorithm for the legacy data processing of the TRMM brightness temperature data product, which will be called TMI 1B11 V8. Moreover, the results of this new procedure have been validated using the double difference techniques with the Global Precipitation Mission Microwave Imager (GMI), which is the replacement satellite mission to TRMM.
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Date Issued: 2017
Identifier: CFE0006711, ucf:51900
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006711

Title: INTER-SATELLITE MICROWAVE RADIOMETER CALIBRATION.
Creator: Hong, Liang, Jones, W. Linwood, University of Central Florida
Abstract / Description: The removal of systematic brightness temperature (Tb) biases is necessary when producing decadal passive microwave data sets for weather and climate research. It is crucial to achieve Tb measurement consistency among all satellites in a constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at...
Show moreThe removal of systematic brightness temperature (Tb) biases is necessary when producing decadal passive microwave data sets for weather and climate research. It is crucial to achieve Tb measurement consistency among all satellites in a constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at different frequencies and viewing angles, Tb normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this dissertation, a new approach is investigated to perform these normalizations from one satellite's measurements to another. It uses Taylor's series expansion around a source frequency to predict Tb of a desired frequency. The relationship between Tb's and frequencies are derived from simulations using an oceanic Radiative Transfer Model (RTM) over a wide variety of environmental conditions. The original RTM is built on oceanic radiative transfer theory. Refinements are made to the model by modifying and tuning algorithms for calculating sea surface emission, atmospheric emission and attenuations. Validations were performed with collocated WindSat measurements. This radiometric calibration approach is applied to establish an absolute brightness temperature reference using near-simultaneous pair-wise comparisons between a non-sun synchronous radiometer and two sun-synchronous polar-orbiting radiometers: the Tropical Rain Measurement Mission (TRMM) Microwave Imager (TMI), WindSat (on Coriolis) and Advanced Microwave Scanning Radiometer (AMSR) on Advanced Earth Observing System –II (ADEOSII), respectively. Collocated measurements between WindSat and TMI as well as between AMSR and TMI, within selected 10 weeks in 2003 for each pair, are collected, filtered and applied in the cross calibration. AMSR is calibrated to WindSat using TMI as a transfer standard. Accuracy prediction and error source analysis are discussed along with calibration results. This inter-satellite radiometric calibration approach provides technical support for NASA's Global Precipitation Mission which relies on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements.
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Date Issued: 2008
Identifier: CFE0002003, ucf:47626
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002003

Title: Forecasting Volcanic Activity Using An Event Tree Analysis System and Logistic Regression.
Creator: Junek, William, Jones, W, Simaan, Marwan, Foroosh, Hassan, Woods, Mark, University of Central Florida
Abstract / Description: Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into...
Show moreForecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation process produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78 - 0.81, which indicate the algorithm has good predictive capabilities. In addition, ROC curves also allowed for the determination of a false positive rate and optimum detection threshold for each stage of the algorithm. The results demonstrate the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. The incorporation of source modeling results into the event tree's decision making process has begun the transition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based forecasting system.
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Date Issued: 2012
Identifier: CFE0004253, ucf:49517
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004253

Title: DEVELOPMENT OF AN IMPROVED MICROWAVE OCEAN SURFACE EMISSIVITY RADIATIVE TRANSFER MODEL.
Creator: El-Nimri, Salem, Jones, W. Linwood, University of Central Florida
Abstract / Description: An electromagnetic model is developed for predicting the microwave blackbody emission from the ocean surface over a wide range of frequencies, incidence angles, and wind vector (speed and direction) for both horizontal and vertical polarizations. This ocean surface emissivity model is intended to be incorporated into an oceanic radiative transfer model to be used for microwave radiometric applications including geophysical retrievals over oceans. The model development is based on a collection...
Show moreAn electromagnetic model is developed for predicting the microwave blackbody emission from the ocean surface over a wide range of frequencies, incidence angles, and wind vector (speed and direction) for both horizontal and vertical polarizations. This ocean surface emissivity model is intended to be incorporated into an oceanic radiative transfer model to be used for microwave radiometric applications including geophysical retrievals over oceans. The model development is based on a collection of published ocean emissivity measurements obtained from satellites, aircraft, field experiments, and laboratory measurements. This dissertation presents the details of methods used in the ocean surface emissivity model development and comparisons with current emissivity models and aircraft radiometric measurements in hurricanes. Especially, this empirically derived ocean emissivity model relates changes in vertical and horizontal polarized ocean microwave brightness temperature measurements over a wide range of observation frequencies and incidence angles to physical roughness changes in the ocean surface, which are the result of the air/sea interaction with surface winds. Of primary importance are the Stepped Frequency Microwave Radiometer (SFMR) brightness temperature measurements from hurricane flights and independent measurements of surface wind speed that are used to define empirical relationships between C-band (4 Ã‚Â– 7 GHz) microwave brightness temperature and surface wind speed. By employing statistical regression techniques, we develop a physical-based ocean emissivity model with empirical coefficients that depends on geophysical parameters, such as wind speed, wind direction, sea surface temperature, and observational parameters, such as electromagnetic frequency, electromagnetic polarization, and incidence angle.
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Date Issued: 2010
Identifier: CFE0003085, ucf:48323
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003085

Title: HURRICANE WIND SPEED AND RAIN RATE MEASUREMENTS USING THE AIRBORNE HURRICANE IMAGING RADIOMETER (HIRAD).
Creator: Amarin, Ruba, Jones, W. Linwood, University of Central Florida
Abstract / Description: This dissertation presents results for an end-to-end computer simulation of a new airborne microwave remote sensor, the Hurricane Imaging Radiometer, HIRAD, which will provide improved hurricane surveillance. The emphasis of this research is the retrieval of hurricane-force wind speeds in the presence of intense rain and over long atmospheric slant path lengths that are encountered across its wide swath. Brightness temperature (Tb) simulations are performed using a forward microwave radiative...
Show moreThis dissertation presents results for an end-to-end computer simulation of a new airborne microwave remote sensor, the Hurricane Imaging Radiometer, HIRAD, which will provide improved hurricane surveillance. The emphasis of this research is the retrieval of hurricane-force wind speeds in the presence of intense rain and over long atmospheric slant path lengths that are encountered across its wide swath. Brightness temperature (Tb) simulations are performed using a forward microwave radiative transfer model (RTM) that includes an ocean surface emissivity model at high wind speeds developed especially for HIRAD high incidence angle measurements and a rain model for the hurricane environment. Also included are realistic sources of errors (e.g., instrument NEDT, antenna pattern convolution of scene Tb, etc.), which are expected in airborne hurricane observations. Case studies are performed using 3D environmental parameters produced by numerical hurricane models for actual hurricanes. These provide realistic Ã‚Â"nature runsÃ‚Â" of rain, water vapor, clouds and surface winds from which simulated HIRAD TbÃ‚Â's are derived for various flight tracks from a high altitude aircraft. Using these simulated HIRAD measurements, Monte Carlo retrievals of wind speed and rain rate are performed using available databases of sea surface temperatures and climatological hurricane atmospheric parameters (excluding rain) as a priori information. Examples of retrieved hurricane wind speed and rain rate images are presented, and comparisons of the retrieved parameters with the numerical model data are made. Statistical results are presented over a broad range of wind and rain conditions and as a function of path length over the full swath.
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Date Issued: 2010
Identifier: CFE0003082, ucf:48330
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003082

Title: Assessing Interactions between Estuary Water Quality and Terrestrial Land Cover in Hurricane Events with Multi-sensor Remote Sensing.
Creator: Mostafiz, Chandan, Chang, Ni-bin, Wanielista, Martin, Kibler, Kelly, University of Central Florida
Abstract / Description: Estuaries are environmentally, ecologically and environmentally important places as they act as a meeting place for land, freshwater and marine ecosystems. They are also called nurseries of the sea as they often provide nesting and feeding habitats for many aquatic plants and animals. These estuaries also withstand the worst of some natural disasters, especially hurricanes. The estuaries as well as the harbored ecosystems undergo significant changes in terms of water quality, vegetation cover...
Show moreEstuaries are environmentally, ecologically and environmentally important places as they act as a meeting place for land, freshwater and marine ecosystems. They are also called nurseries of the sea as they often provide nesting and feeding habitats for many aquatic plants and animals. These estuaries also withstand the worst of some natural disasters, especially hurricanes. The estuaries as well as the harbored ecosystems undergo significant changes in terms of water quality, vegetation cover etc. and these components are interrelated. When hurricane makes landfall it is necessary to assess the damages as quickly as possible as restoration and recovery processes are time-sensitive. However, assessment of physical damages through inspection and survey and assessment of chemical and nutrient component changes by laboratory testing are time-consuming processes. This is where remote sensing comes into play. With the help of remote sensing images and regression analysis, it is possible to reconstruct water quality maps of the estuary affected. The damage sustained by the vegetation cover of the adjacent coastal watershed can be assessed using Normalized Difference Vegetation Index (NDVI) The water quality maps together with NDVI maps help observe a dynamic sea-land interaction due to hurricane landfall. The observation of hurricane impacts on a coastal watershed can be further enhanced by use of tasseled cap transformation (TCT). TCT plots provide information on a host of land cover conditions with respect to soil moisture, canopy and vegetation cover. The before and after TCT plots help assess the damage sustained in a hurricane event and also see the progress of recovery. Finally, the use of synthetic images obtained by use of data fusion will help close the gap of low temporal resolution of Landsat satellite and this will create a more robust monitoring system.
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Date Issued: 2017
Identifier: CFE0006900, ucf:51729
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006900

Title: On-orbit Inter-satellite Radiometric Calibration of Cross-track Scanning Microwave Radiometers.
Creator: Ebrahimi, Hamideh, Jones, W Linwood, Mikhael, Wasfy, Wahid, Parveen, Wang, James, Wilheit, Thomas, University of Central Florida
Abstract / Description: This dissertation concerns the development of an improved algorithm for the inter-satellite radiometric calibration (XCAL) for cross track scanning microwave radiometers in support of NASA's Global Precipitation Mission (GPM). This research extends previous XCAL work to assess the robustness of the CFRSL (")double difference(") technique for sounder X-CAL. In this work, using a two-year of observations, we present a statistical analysis of radiometric biases performed over time and viewing...
Show moreThis dissertation concerns the development of an improved algorithm for the inter-satellite radiometric calibration (XCAL) for cross track scanning microwave radiometers in support of NASA's Global Precipitation Mission (GPM). This research extends previous XCAL work to assess the robustness of the CFRSL (")double difference(") technique for sounder X-CAL. In this work, using a two-year of observations, we present a statistical analysis of radiometric biases performed over time and viewing geometry. In theory, it is possible to apply the same X-CAL procedure developed for conical-scanning radiometers to cross-track scanners; however the implementation is generally more tedious. For example, with the cross-track scan angle, there is a strong response in the observed Tb due to changes in the atmosphere slant path and surface emissivity with the Earth incidence angle. For ocean scenes this is trivial; however for land scenes there is imperfect knowledge of polarized emissivity. However, for the sounder channels the surface emissivity is not the dominant component of top-of-the-atmosphere Tb, which is a mitigating factor. Also, cross-track scanners introduce changes in the radiometer antenna observed polarization with scan angle. The resulting observation is a mixture of un-polarized atmospheric emissions and vertical and horizontal polarized surface emissions. The degree of polarization mixing is known from geometry; however, reasonable estimates of the surface emissivity are required, which complicate over land comparisons. Finally, the IFOV size monotonically increases over the cross-track scan. Thus, when inter-comparing cross-track scanning radiometers, it will be necessary to carefully consider these effects when performing the double difference procedure.
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Date Issued: 2016
Identifier: CFE0006453, ucf:51411
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006453

Title: Microwave Radiometer (MWR) Evaluation of Multi-Beam Satellite Antenna Boresight Pointing Using Land-Water Crossings, for the Aquarius/SAC-D Mission.
Creator: Clymer, Bradley, Jones, W Linwood, Mikhael, Wasfy, Flitsiyan, Elena, University of Central Florida
Abstract / Description: This research concerns the CONAE Microwave Radiometer (MWR), on board the Aquarius/SAC-D platform. MWR's main purpose is to provide measurements that are simultaneous and spatially collocated with those of NASA's Aquarius radiometer/scatterometer. For this reason, knowledge of the MWR antenna beam footprint geolocation is crucial to mission success.In particular, this thesis addresses an on-orbit validation of the MWR antenna beam pointing, using calculated MWR instantaneous field of view ...
Show moreThis research concerns the CONAE Microwave Radiometer (MWR), on board the Aquarius/SAC-D platform. MWR's main purpose is to provide measurements that are simultaneous and spatially collocated with those of NASA's Aquarius radiometer/scatterometer. For this reason, knowledge of the MWR antenna beam footprint geolocation is crucial to mission success.In particular, this thesis addresses an on-orbit validation of the MWR antenna beam pointing, using calculated MWR instantaneous field of view (IFOV) centers, provided in the CONAE L-1B science data product. This procedure compares L-1B MWR IFOV centers at land/water crossings against high-resolution coastline maps. MWR IFOV locations versus time are computed from knowledge of the satellite's instantaneous location relative to an earth-centric coordinate system (provided by on-board GPS receivers), and a priori measurements of antenna gain patterns and mounting geometry.Previous conical scanning microwave radiometer missions (e.g., SSM/I) have utilized observation of rapid change in brightness temperatures (T_B) to estimate the location of land/water boundaries, and subsequently to determine the antenna beam-pointing accuracy. In this thesis, results of an algorithm to quantify the geolocation error of MWR beam center are presented, based upon two-dimensional convolution between each beam's gain pattern and land-water transition. The analysis procedures have been applied to on-orbit datasets that represent land-water boundaries bearing specific desirable criteria, which are also detailed herein. The goal of this research is to gain a better understanding of satellite radiometer beam-pointing error and thereby to improve the geolocation accuracy for MWR science data products.
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Date Issued: 2015
Identifier: CFE0005591, ucf:50269
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005591

Remote Sensing (16)	+ -
remote sensing (10)	+ -
SFMR (5)	+ -
Microwave Remote Sensing (4)	+ -
Calibration (3)	+ -

HIRAD (3)	+ -
Microwave Radiometer (3)	+ -
QuikSCAT (3)	+ -
Remote sensing (3)	+ -
WindSat (3)	+ -
AMSR (2)	+ -
Data Fusion (2)	+ -
Florida (2)	+ -
HIRad (2)	+ -
Hurricane Observation (2)	+ -
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Satellite Remote Sensing (2)	+ -
SeaWinds (2)	+ -
TMI (2)	+ -
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artificial neural network (2)	+ -
brightness temperature (2)	+ -
genetic programming (2)	+ -
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lidar (2)	+ -
machine learning (2)	+ -
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