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Title: NATIVE FIRE REGIME AS A REFERENCE FOR ESTABLISHING MANAGEMENT PRACTICES.
Creator: Duncan, Brean, Weishampel, John, University of Central Florida
Abstract / Description: Our understanding of natural fire regimes in human-dominated landscapes is limited. Fire regimes operating in the pyrogenic ecosystems of Florida have been altered by fire suppression and fuel fragmentation. This is especially true of North Merritt Island, Florida, where human impacts have led to an incomplete knowledge of current fire regimes. We know that growing season fires frequently occurred within general return intervals and that many native terrestrial species require fire to remain...
Show moreOur understanding of natural fire regimes in human-dominated landscapes is limited. Fire regimes operating in the pyrogenic ecosystems of Florida have been altered by fire suppression and fuel fragmentation. This is especially true of North Merritt Island, Florida, where human impacts have led to an incomplete knowledge of current fire regimes. We know that growing season fires frequently occurred within general return intervals and that many native terrestrial species require fire to remain viable. A 20-year plus period of fire suppression caused structural and compositional changes to vegetation/fuels that led to catastrophic fires and the decline of native species populations such as the Florida Scrub-Jay. Fire has been reintroduced as a means to reduce fuels and maintain habitat requirements for native species. Scientific studies have documented the effects and benefits of prescribed burning on KSC/MINWR habitat/fuels structure. The necessity for fire to maintain vegetation/fuels structure and composition on the landscape is clear so fire is being applied to the landscape despite our imperfect knowledge of the native fire regime. It is imperative for the survival of many native species that fire managers be able to mimic the results of the native fire regime. Fire regime research is difficult in shrublands, and using dendrochronologic techniques are often not possible in flatwoods communities. I therefore used a process of remote sensing, GIS mapping, and spatial modeling to quantify lightning fire ignition properties, the current managed fire regime, and the natural fire regime. Chapter one develops a new remote sensing technique to accurately map burned areas in Florida scrub and pine flatwoods dominated communities on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. At the center of this technique is a newly developed separation index (SI) that was used to evaluate each individual satellite image band for its power to discriminate unburned and burned areas. Burned areas were classified and found to be highly accurate in relation to empirical fire records. This chapter addressed a number of issues relevant to the classification of burned areas including: the effect of geographic extent of remote sensing data on classification, determining the best bands for classification, and cleaning classification results by using GIS masking. It also serves as the first published effort to map fire scars in the Florida scrub and flatwoods vegetative communities of the southeastern U.S. using image processing techniques. Chapter two quantified a managed fire regime on John F. Kennedy Space Center, Florida and surrounding federal properties by mapping all fires between 1983 to 2005 using the image processing technique developed in chapter one, time series satellite imagery, and GIS techniques. The goals were to: (1) determine if an image processing technique designed for individual fire scar mapping could be applied to an image time series for mapping a managed fire regime in a rapid re-growth pyrogenic system; (2) develop a method for labeling mapped fire scar confidence knowing that a formal accuracy analysis was not possible; and (3) compare results of the managed fire regime with regional information on natural fire regimes to look for similarities/differences that might help optimize management for persistence of native fire-dependent species. The area burned by managed fire peaked when the drought index was low and was reduced when the drought index was high. This contrasts with the expectations regarding the natural fire regime of this region. Chapter three quantified the natural lightning ignition regime on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. Lightning is the natural ignition source in Florida, substantiating the need for understanding lightning fire incidence. Sixteen years of lightning data (1986-2003, excluding 1987 and 2002 due to missing data) from the NASA Cloud to Ground Lightning Surveillance System and fire ignition records were used to quantify the relationship between lightning incidence and fire ignition. Precipitation influenced the efficiency of lightning ignitions, particularly July precipitation. Negative polarity strikes caused the majority of ignitions. Pine flatwoods was ignited more frequently than expected given equal chance of ignition among landcover types. About half (51%) of detected fires were instantaneous ignitions and the other 49% were delayed an average of two days. Summer lightning ignitions were dominant, especially during July, with only one winter lightning ignition. Chapter four used an existing fire regime model (HFire) to simulate the natural fire regime (prior to European settlement) on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. A sensitivity analysis was performed to establish which parameters were most important and the range of variation surrounding empirically derived model information from the same model. A mosaic pattern of small fires dominated this fire regime with extremely large fires occurring during dry La Nina periods. Dead fuel moisture and wind speed had the largest influence on model outcome. The majority of variability was found to be in the largest fires. The research presenter here provides a comprehensive perspective on current and historic fire regimes that may be useful for optimizing land management on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida and throughout the southeastern United States. Native fire dependent species are suffering from many changes imposed from human alteration. The success of conservation efforts protecting native fire dependent species hinge on my factors. Two of the largest factors are first protecting native habitat and then secondly managing that protected habitat to mimic natural maintenance processes for suitable structure and composition which may favor their demography. This study focuses on developing techniques necessary for producing information that can aid the optimization of fire management on these properties and within the southeastern United States, but may be useful in other fire maintained ecosystems globally.
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Date Issued: 2009
Identifier: CFE0002862, ucf:48051
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002862

Title: AN IMPROVED MICROWAVE RADIATIVE TRANSFER MODEL FOR OCEAN EMISSIVITY AT HURRICANE FORCE SURFACE WIND SPEED.
Creator: EL-Nimri, Salem, Jones, Linwood, University of Central Florida
Abstract / Description: An electromagnetic model for predicting the microwave blackbody emission from the ocean surface under the forcing of strong surface winds in hurricanes is being developed. This ocean emissivity model will be incorporated into a larger radiative transfer model used to infer ocean surface wind speed and rain rate in hurricanes from remotely sensed radiometric brightness temperature. The model development is based on measurements obtained with the Stepped Frequency Microwave Radiometer (SFMR),...
Show moreAn electromagnetic model for predicting the microwave blackbody emission from the ocean surface under the forcing of strong surface winds in hurricanes is being developed. This ocean emissivity model will be incorporated into a larger radiative transfer model used to infer ocean surface wind speed and rain rate in hurricanes from remotely sensed radiometric brightness temperature. The model development is based on measurements obtained with the Stepped Frequency Microwave Radiometer (SFMR), which routinely flys on the National Oceanic and Atmospheric Administration's hurricane hunter aircraft. This thesis presents the methods used in the wind speed model development and validation results for wind speeds up to 70 m/sec. The ocean emissivity model relates changes in measured C-band radiometric brightness temperatures to physical changes in the ocean surface. These surface modifications are the result of the drag of surface winds that roughen the sea surface, produce waves, and create white caps and foam from the breaking waves. SFMR brightness temperature measurements from hurricane flights and independent measurements of surface wind speed are used to define empirical relationships between microwave brightness temperature and surface wind speed. The wind speed model employs statistical regression techniques to develop a physics-based ocean emissivity model dependent on geophysical parameters, such as wind speed and sea surface temperature, and observational parameters, such as electromagnetic frequency, electromagnetic polarization, and incidence angle.
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Date Issued: 2006
Identifier: CFE0001312, ucf:47019
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001312

Title: VALIDATION OF QUICKSCAT RADIOMETER (QRAD) MICROWAVE BRIGHTNESS TEMPERTURE MEASURMENTS.
Creator: Hanna, Rafik, Jones, W.Linwood, University of Central Florida
Abstract / Description: After the launch of NASA's SeaWinds scatterometer in 1999, a radiometer function was implemented in the Science Ground Data Processing Systems to allow the measurement of the earth's microwave brightness temperature. This dissertation presents results of a comprehensive validation to assess the quality of QRad brightness temperature measurements using near-simultaneous ocean Tb comparisons between the SeaWinds on QuikSCAT (QRad) and WindSat polarimetric radiometer on Coriolis. WindSat...
Show moreAfter the launch of NASA's SeaWinds scatterometer in 1999, a radiometer function was implemented in the Science Ground Data Processing Systems to allow the measurement of the earth's microwave brightness temperature. This dissertation presents results of a comprehensive validation to assess the quality of QRad brightness temperature measurements using near-simultaneous ocean Tb comparisons between the SeaWinds on QuikSCAT (QRad) and WindSat polarimetric radiometer on Coriolis. WindSat was selected because it is a well calibrated radiometer that has many suitable collocations with QuikSCAT; and it has a 10.7 GHz channel, which is close to QRad frequency of 13.4 GHz. Brightness temperature normalizations were made for WindSat before comparison to account for expected differences in Tb with QRad because of incidence angle and channel frequency differences. Brightness temperatures for nine months during 2005 and 2006 were spatially collocated for rain-free homogeneous ocean scenes (match-ups) within 1° latitude x longitude boxes and within a ± 60 minute window. To ensure high quality comparison, these collocations were quality controlled and edited to remove non-homogenous ocean scenes and/or transient environmental conditions, including rain contamination. WindSat and QRad Tb's were averaged within 1° boxes and these were used for the radiometric inter-calibration analysis on a monthly basis. Results show that QRad calibrations are stable in the mean within ± 2K over the yearly seasonal cycle.
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Date Issued: 2009
Identifier: CFE0002820, ucf:48068
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002820

Title: SIMULATION OF BRIGHTNESS TEMPERATURES FOR THE MICROWAVE RADIOMETER ON THE AQUARIUS/SAC-D MISSION.
Creator: Khan, Salman, Jones, W. Linwood, University of Central Florida
Abstract / Description: Microwave radiometers are highly sensitive receivers capable of measuring low levels of natural blackbody microwave emissions. Remote sensing by satellite microwave radiometers flying on low-earth, polar orbiting, satellites can infer a variety of terrestrial and atmospheric geophysical parameters for scientific and operational applications, such as weather and climate prediction. The objective of this thesis is to provide realistic simulated ocean brightness temperatures for the 3-channel...
Show moreMicrowave radiometers are highly sensitive receivers capable of measuring low levels of natural blackbody microwave emissions. Remote sensing by satellite microwave radiometers flying on low-earth, polar orbiting, satellites can infer a variety of terrestrial and atmospheric geophysical parameters for scientific and operational applications, such as weather and climate prediction. The objective of this thesis is to provide realistic simulated ocean brightness temperatures for the 3-channel Microwave Radiometer (MWR), which will be launched in May 2010 on the joint NASA/CONAE Aquarius/SAC-D Mission. These data will be used for pre-launch geophysical retrieval algorithms development and validation testing. Analyses are performed to evaluate the proposed MWR measurement geometry and verify the requirements for spatial/temporal sampling. Finally, a preliminary study is performed for the post-launch inter-satellite radiometric calibration using the WindSat polarimetric radiometer on the Coriolis satellite.
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Date Issued: 2009
Identifier: CFE0002821, ucf:48074
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002821

Title: REMOTE SENSING WITH COMPUTATIONAL INTELLIGENCE MODELLING FOR MONITORING THE ECOSYSTEM STATE AND HYDRAULIC PATTERN IN A CONSTRUCTED WETLAND.
Creator: Mohiuddin, Golam, Chang, Ni-bin, Lee, Woo Hyoung, Wanielista, Martin, University of Central Florida
Abstract / Description: Monitoring the heterogeneous aquatic environment such as the Stormwater Treatment Areas (STAs) located at the northeast of the Everglades is extremely important in understanding the land processes of the constructed wetland in its capacity to remove nutrient. Direct monitoring and measurements of ecosystem evolution and changing velocities at every single part of the STA are not always feasible. Integrated remote sensing, monitoring, and modeling technique can be a state-of-the-art tool to...
Show moreMonitoring the heterogeneous aquatic environment such as the Stormwater Treatment Areas (STAs) located at the northeast of the Everglades is extremely important in understanding the land processes of the constructed wetland in its capacity to remove nutrient. Direct monitoring and measurements of ecosystem evolution and changing velocities at every single part of the STA are not always feasible. Integrated remote sensing, monitoring, and modeling technique can be a state-of-the-art tool to estimate the spatial and temporal distributions of flow velocity regimes and ecological functioning in such dynamic aquatic environments. In this presentation, comparison between four computational intelligence models including Extreme Learning Machine (ELM), Genetic Programming (GP) and Artificial Neural Network (ANN) models were organized to holistically assess the flow velocity and direction as well as ecosystem states within a vegetative wetland area. First the local sensor network was established using Acoustic Doppler Velocimeter (ADV). Utilizing the local sensor data along with the help of external driving forces parameters, trained models of ELM, GP and ANN were developed, calibrated, validated, and compared to select the best computational capacity of velocity prediction over time. Besides, seasonal images collected by French satellite Pleiades have been analyzed to address the seasonality effect of plant species evolution and biomass changes in the constructed wetland. The key finding of this research is to characterize the interactions between geophysical and geochemical processes in this wetland system based on ground-based monitoring sensors and satellite images to discover insight of hydraulic residence time, plant species variation, and water quality and improve the overall understanding of possible nutrient removal in this constructed wetland.
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Date Issued: 2014
Identifier: CFE0005533, ucf:52864
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005533

Title: Biomass density based adjustment of LiDAR-derived digital elevation models: a machine learning approach.
Creator: Abdelwahab, Khalid, Medeiros, Stephen, Mayo, Talea, Wahl, Thomas, University of Central Florida
Abstract / Description: Salt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in...
Show moreSalt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in the salt marshes near Saint Marks Lighthouse in Crawfordville, Florida. Between summer of 2017 and spring of 2018, two field trips were conducted to acquire true elevation and biomass density measures. Lidar point cloud data were combined with vegetation monitoring imagery acquired from Sentinel-2 and Landsat Thematic Mapper (LTM) satellites, and 64 field biomass density samples were used as target variables for developing the models. Biomass density classes were assigned to each biomass sample using a quartile approach. Moreover, 346 in-situ elevation measures were used to calculate the lidar DEM errors. The best model was then used to estimate biomass densities at all 346 locations. Finally, an adjusted DEM was produced by deducting the quartile-based adjustment values from the original lidar DEM. A random forest regression model achieved the highest pseudo R2 value of 0.94 for predicting biomass density in g/m2. The adjusted DEM based on the estimated biomass densities reduced the root mean squared error of the original DEM from 0.38 m to 0.18 m while decreasing the raw mean error from 0.33 m to 0.14 m, improving both measures by 54% and 58%, respectively.
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Date Issued: 2019
Identifier: CFE0007594, ucf:52535
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007594

Title: AN IMPROVED HURRICANE WIND VECTOR RETRIEVAL ALGORITHM USING SEAWINDS SCATTEROMETER.
Creator: Laupattarakasem, Peth, Jones, Linwood, University of Central Florida
Abstract / Description: Over the last three decades, microwave remote sensing has played a significant role in ocean surface wind measurement, and several scatterometer missions have flown in space since early 1990's. Although they have been extremely successful for measuring ocean surface winds with high accuracy for the vast majority of marine weather conditions, unfortunately, the conventional scatterometer cannot measure extreme winds condition such as hurricane. The SeaWinds scatterometer, onboard the QuikSCAT...
Show moreOver the last three decades, microwave remote sensing has played a significant role in ocean surface wind measurement, and several scatterometer missions have flown in space since early 1990's. Although they have been extremely successful for measuring ocean surface winds with high accuracy for the vast majority of marine weather conditions, unfortunately, the conventional scatterometer cannot measure extreme winds condition such as hurricane. The SeaWinds scatterometer, onboard the QuikSCAT satellite is NASA's only operating scatterometer at present. Like its predecessors, it measures global ocean vector winds; however, for a number of reasons, the quality of the measurements in hurricanes are significantly degraded. The most pressing issues are associated with the presence of precipitation and Ku-band saturation effects, especially in extreme wind speed regime such as tropical cyclones (hurricanes and typhoons). Under this dissertation, an improved hurricane ocean vector wind retrieval approach, named as Q-Winds, was developed using existing SeaWinds scatterometer data. This unique data processing algorithm uses combined SeaWinds active and passive measurements to extend the use of SeaWinds for tropical cyclones up to approximately 50 m/s (Hurricane Category-3). Results show that Q-Winds wind speeds are consistently superior to the standard SeaWinds Project Level 2B wind speeds for hurricane wind speed measurement, and also Q-Winds provides more reliable rain flagging algorithm for quality assurance purposes. By comparing to H*Wind, Q-Winds achieves ~9% of error, while L2B-12.5km exhibits wind speed saturation at ~30 m/s with error of ~31% for high wind speed (> 40 m/s).
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Date Issued: 2009
Identifier: CFE0002654, ucf:48242
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002654

Title: Investigation of the effect of rain on sea surface salinity.
Creator: Santos Garcia, Andrea, Jones, W Linwood, Mikhael, Wasfy, Wahid, Parveen, Junek, William, Asher, William, Wilheit, Thomas, University of Central Florida
Abstract / Description: The Aquarius/SAC-D mission provided Sea Surface Salinity (SSS), globally over the ocean, for almost 4 years. As a member of the AQ/SAC-D Cal/Val team, the Central Florida Remote Sensing Laboratory has analyzed these salinity measurements in the presence of precipitation and has noted the high correlation between the spatial patterns of reduced SSS and the spatial distribution of rain. It was determined that this is the result of a cause and effect relation, and not SSS measurement errors....
Show moreThe Aquarius/SAC-D mission provided Sea Surface Salinity (SSS), globally over the ocean, for almost 4 years. As a member of the AQ/SAC-D Cal/Val team, the Central Florida Remote Sensing Laboratory has analyzed these salinity measurements in the presence of precipitation and has noted the high correlation between the spatial patterns of reduced SSS and the spatial distribution of rain. It was determined that this is the result of a cause and effect relation, and not SSS measurement errors. Thus, it is important to understand these salinity changes due to seawater dilution by rain and the associated near-surface salinity strati?cation. This research addresses the effects of rainfall on the Aquarius (AQ) SSS retrieval using a macro-scale Rain Impact Model (RIM). This model, based on the superposition of a one-dimension eddy diffusion (turbulent diffusion) model, relates SSS to depth, rainfall accumulation and time since rain. To identify instantaneous and prior rainfall accumulations, a Rain Accumulation product was developed. This product, based on the NOAA CMORPH precipitation data set, provides the rainfall history for 24 hours prior to the satellite observation time, which is integrated over each AQ IFOV. In this research results of the RIM validation are presented by comparing AQ and SMOS measured and RIM simulated SSS. The results show the high cross correlation for these comparisons and also with the corresponding SSS anomalies relative to HYCOM.
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Date Issued: 2016
Identifier: CFE0006175, ucf:51133
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006175

Title: Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining.
Creator: Imen, Sanaz, Chang, Ni-bin, Wang, Dingbao, Wanielista, Martin, Bohlen, Patrick, University of Central Florida
Abstract / Description: Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las...
Show moreAdjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection byproducts in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of nonlinear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, teleconnection signals were included on a seasonal basis in the Upper Colorado River basin which provides 97% of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are utilized to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply.
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Date Issued: 2015
Identifier: CFE0005632, ucf:50215
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005632

Title: A Roughness Correction for Aquarius Ocean Brightness Temperature Using the CONAE MicroWave Radiometer.
Creator: Hejazin, Yazan, Jones, W Linwood, Wahid, Parveen, Mikhael, Wasfy, Junek, William, Piepmeier, Jeffrey, University of Central Florida
Abstract / Description: Aquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that (")warms(") the ocean Tb. The Aquarius (AQ)...
Show moreAquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that (")warms(") the ocean Tb. The Aquarius (AQ) instrument (L-band radiometer/scatterometer) baseline approach uses the radar scatterometer to provide this ocean roughness correction, through the correlation of radar backscatter with the excess ocean emissivity.In contrast, this dissertation develops an ocean roughness correction for AQ measurements using the MicroWave Radiometer (MWR) instrument Tb measurements at Ka-band to remove the errors that are caused by ocean wind speed and direction. The new ocean emissivity radiative transfer model was tuned using one year (2012) of on-orbit combined data from the MWR and the AQ instruments that are collocated in space and time. The roughness correction in this paper is a theoretical Radiative Transfer Model (RTM) driven by numerical weather forecast model surface winds, combined with ancillary satellite data from WindSat and SSMIS, and environmental parameters from NCEP. This RTM provides an alternative approach for estimating the scatterometer-derived roughness correction, which is independent. The theoretical basis of the algorithm is described and results are compared with the AQ baseline scatterometer method. Also results are presented for a comparison of AQ SSS retrievals using both roughness corrections.
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Date Issued: 2015
Identifier: CFE0005625, ucf:50218
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005625

Title: Integrated Remote Sensing and Forecasting of Regional Terrestrial Precipitation with Global Nonlinear and Nonstationary Teleconnection Signals Using Wavelet Analysis.
Creator: Mullon, Lee, Chang, Ni-bin, Wang, Dingbao, Wanielista, Martin, University of Central Florida
Abstract / Description: Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in...
Show moreGlobal sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and terrestrial responses of greenness and precipitation along multiple pristine sites in the northeastern U.S., including (1) White Mountain National Forest (-) Pemigewasset Wilderness, (2) Green Mountain National Forest (-) Lye Brook Wilderness and (3) Adirondack State Park (-) Siamese Ponds Wilderness. Each site was selected to avoid anthropogenic influences that may otherwise mask climate teleconnection signals. Lagged pixel-wise linear teleconnection patterns across anomalous datasets found significant correlation regions between SST and the terrestrial sites. Non-stationary signals also exhibit salient co-variations at biennial and triennial frequencies between terrestrial responses and SST anomalies across oceanic regions in agreement with the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) signals. Multiple regression analysis of the combined ocean indices explained up to 50% of the greenness and 42% of the precipitation in the study sites. The identified short-term teleconnection signals improve the understanding and projection of climate change impacts at local scales, as well as harness the interannual periodicity information for future climate projections. Part 2 of this research paper builds upon the earlier short-term study by exploring a long-term (30-year) teleconnection signal investigation between SST at the North Atlantic and Pacific oceans and the precipitation within Adirondack State Park in upstate New York. Non-traditional teleconnection signals are identified using wavelet decomposition and teleconnection mapping specific to the Adirondack region. Unique SST indices are extracted and used as input variables in an artificial neural network (ANN) prediction model. The results show the importance of considering non-leading teleconnection patterns as well as the known teleconnection patterns. Additionally, the effects of the Pacific Ocean SST or the Atlantic Ocean SST on terrestrial precipitation in the study region were compared with each other to deepen the insight of sea-land interactions. Results demonstrate reasonable prediction skill at forecasting precipitation trends with a lead time of one month, with r values of 0.6. The results are compared against a statistical downscaling approach using the HadCM3 global circulation model output data and the SDSM statistical downscaling software, which demonstrate less predictive skill at forecasting precipitation within the Adirondacks.
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Date Issued: 2014
Identifier: CFE0005535, ucf:50319
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005535

Title: Evaluation of the three-dimensional patterns and ecological impacts of the invasive Old World climbing fern (Lygodium microphyllum).
Creator: Maldonado, Alexis, Weishampel, John, VonHolle, Mary, Hinkle, Charles, University of Central Florida
Abstract / Description: Invasion by non-native species has had significant ecological and economic impacts on a global scale. In the state of Florida, Old World climbing fern (Lygodium microphyllum) is an invasive plant listed by FLEPPC as a category one invader with significant ecological impacts that threaten native plant diversity. This species relies on existing vegetative structures for support to climb into the forest canopy and forms dense mats that cover tree crowns. This subsequently affects the resources...
Show moreInvasion by non-native species has had significant ecological and economic impacts on a global scale. In the state of Florida, Old World climbing fern (Lygodium microphyllum) is an invasive plant listed by FLEPPC as a category one invader with significant ecological impacts that threaten native plant diversity. This species relies on existing vegetative structures for support to climb into the forest canopy and forms dense mats that cover tree crowns. This subsequently affects the resources available to other species present. Quantifying the structural changes due to the presence of this species has proved logistically difficult, especially on a large spatial scale.Airborne LiDAR (Light Detection And Ranging) technology is a form of remote sensing that measures the elevation of surfaces over a site. In this study I utilized LiDAR to calculate various forest structure metrics at Jonathan Dickinson State Park (JDSP) in Hobe Sound, Florida across various management frequencies and densities of Old World climbing fern. These data were used to quantify the degree to which this invasive species alters forest structure across these two gradients. I also recorded species composition in the field to relate how Old World climbing fern impacts native plant diversity. Structural measurements including average canopy height, height of median energy (HOME), rugosity, canopy openness, and vertical structural diversity (LHDI) were calculated for a total of three hundred 0.25ha sites stratified by invasion density and management frequency.Using a combination of univariate and multivariate statistical analyses I found that the presence of Old World Climbing fern altered the physical structure of the forest communities itivinvades. Higher percent cover of Old World climbing fern decreased structural diversity while increased management effort was found to mitigate those impacts. The management for Old World Climbing fern was also found to impact both species richness and diversity at JDSP. I also demonstrated that there were several species that were not found and others that were more common in the presence of Old World climbing fern and that there was a relationship between management and what species were present. The results show that both Old World climbing fern and the management practices used to control it have had significant ecological impacts on the natural communities in South Florida.
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Date Issued: 2014
Identifier: CFE0005206, ucf:50651
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005206

Title: AN IMPROVED OCEAN VECTOR WINDS RETRIEVAL APPROACH USING C- AND KU-BAND SCATTEROMETER AND MULTI-FREQUENCY MICROWAVE RADIOMETER MEASUREMENTS.
Creator: Alsweiss, Suleiman, Jones, W. Linwood, University of Central Florida
Abstract / Description: This dissertation will specifically address the issue of improving the quality of satellite scatterometer retrieved ocean surface vector winds (OVW), especially in the presence of strong rain associated with tropical cyclones. A novel active/passive OVW retrieval algorithm is developed that corrects Ku-band scatterometer measurements for rain effects and then uses them to retrieve accurate OVW. The rain correction procedure makes use of independent information available from collocated multi...
Show moreThis dissertation will specifically address the issue of improving the quality of satellite scatterometer retrieved ocean surface vector winds (OVW), especially in the presence of strong rain associated with tropical cyclones. A novel active/passive OVW retrieval algorithm is developed that corrects Ku-band scatterometer measurements for rain effects and then uses them to retrieve accurate OVW. The rain correction procedure makes use of independent information available from collocated multi-frequency passive microwave observations provided by a companion sensor and also from simultaneous C-band scatterometer measurements. The synergy of these active and passive measurements enables improved correction for rain effects, which enhances the utility of Ku-band scatterometer measurements in extreme wind events. The OVW retrieval algorithm is based on the next generation instrument conceptual design for future US scatterometers, i.e. the Dual Frequency Scatterometer (DFS) developed by NASA's Jet Propulsion Laboratory. Under this dissertation research, an end-to-end computer simulation was developed to evaluate the performance of this active/passive technique for retrieving hurricane force winds in the presence of intense rain. High-resolution hurricane wind and precipitation fields were simulated for several scenes of Hurricane Isabel in 2003 using the Weather Research and Forecasting (WRF) Model. Using these numerical weather model environmental fields, active/passive measurements were simulated for instruments proposed for the Global Change Observation Mission- Water Cycle (GCOM-W2) satellite series planned by the Japanese Aerospace Exploration Agency. Further, the quality of the simulation was evaluated using actual hurricane measurements from the Advanced Microwave Scanning Radiometer and SeaWinds scatterometer onboard the Advanced Earth Observing Satellite-II (ADEOS-II). The analysis of these satellite data provided confidence in the capability of the simulation to generate realistic active/passive measurements at the top of the atmosphere. Results are very encouraging, and they show that the new algorithm can retrieve accurate ocean surface wind speeds in realistic hurricane conditions using the rain corrected Ku-band scatterometer measurements. They demonstrate the potential to improve wind measurements in extreme wind events for future wind scatterometry missions such as the proposed GCOM-W2.
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Date Issued: 2011
Identifier: CFE0003757, ucf:48774
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003757

Title: ESTIMATION OF OCEANIC RAINFALL USING PASSIVE AND ACTIVE MEASUREMENTS FROM SEAWINDS SPACEBORNE MICROWAVE SENSOR.
Creator: Ahmad, Khalil, Jones, Linwood, University of Central Florida
Abstract / Description: The Ku band microwave remote sensor, SeaWinds, was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Two identical SeaWinds instruments were launched into space. The first was flown onboard NASA QuikSCAT satellite which has been orbiting the Earth since June 1999, and the second instrument flew onboard the Japanese Advanced Earth Observing Satellite II (ADEOS-II) from December 2002 till October 2003 when an irrecoverable solar panel failure...
Show moreThe Ku band microwave remote sensor, SeaWinds, was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Two identical SeaWinds instruments were launched into space. The first was flown onboard NASA QuikSCAT satellite which has been orbiting the Earth since June 1999, and the second instrument flew onboard the Japanese Advanced Earth Observing Satellite II (ADEOS-II) from December 2002 till October 2003 when an irrecoverable solar panel failure caused a premature end to the ADEOS-II satellite mission. SeaWinds operates at a frequency of 13.4 GHz, and was originally designed to measure the speed and direction of the ocean surface wind vector by relating the normalized radar backscatter measurements to the near surface wind vector through a geophysical model function (GMF). In addition to the backscatter measurement capability, SeaWinds simultaneously measures the polarized radiometric emission from the surface and atmosphere, utilizing a ground signal processing algorithm known as the QuikSCAT / SeaWinds Radiometer (QRad / SRad). This dissertation presents the development and validation of a mathematical inversion algorithm that combines the simultaneous active radar backscatter and the passive microwave brightness temperatures observed by the SeaWinds sensor to retrieve the oceanic rainfall. The retrieval algorithm is statistically based, and has been developed using collocated measurements from SeaWinds, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, and Numerical Weather Prediction (NWP) wind fields from the National Centers for Environmental Prediction (NCEP). The oceanic rain is retrieved on a spacecraft wind vector cell (WVC) measurement grid that has a spatial resolution of 25 km. To evaluate the accuracy of the retrievals, examples of the passive-only, as well as the combined active / passive rain estimates from SeaWinds are presented, and comparisons are made with the standard TRMM rain data products. Results demonstrate that SeaWinds rain measurements are in good agreement with the independent microwave rain observations obtained from TMI. Further, by applying a threshold on the retrieved rain rates, SeaWinds rain estimates can be utilized as a rain flag. In order to evaluate the performance of the SeaWinds flag, comparisons are made with the Impact based Multidimensional Histogram (IMUDH) rain flag developed by JPL. Results emphasize the powerful rain detection capabilities of the SeaWinds retrieval algorithm. Due to its broad swath coverage, SeaWinds affords additional independent sampling of the oceanic rainfall, which may contribute to the future NASA's Precipitation Measurement Mission (PMM) objectives of improving the global sampling of oceanic rain within 3 hour windows. Also, since SeaWinds is the only sensor onboard QuikSCAT, the SeaWinds rain estimates can be used to improve the flagging of rain-contaminated oceanic wind vector retrievals. The passive-only rainfall retrieval algorithm (QRad / SRad) has been implemented by JPL as part of the level 2B (L2B) science data product, and can be obtained from the Physical Oceanography Distributed Data Archive (PO.DAAC).
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Date Issued: 2007
Identifier: CFE0001969, ucf:47441
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001969

Title: INTERNATIONAL SPACE STATION REMOTE SENSING POINTING ANALYSIS.
Creator: Jacobson, Craig, Leonessa, Alexander, University of Central Florida
Abstract / Description: This paper analyzes the geometric and disturbance aspects of utilizing the International Space Station for remote sensing of earth targets. The proposed instrument is SHORE (Station High-Sensitivity Ocean Research Experiment), a multi-band optical spectrometer with 15 m pixel resolution. The analysis investigates the contribution of the error effects to the quality of data collected by the instrument. The analysis begins with the discussion of the coordinate systems involved and then...
Show moreThis paper analyzes the geometric and disturbance aspects of utilizing the International Space Station for remote sensing of earth targets. The proposed instrument is SHORE (Station High-Sensitivity Ocean Research Experiment), a multi-band optical spectrometer with 15 m pixel resolution. The analysis investigates the contribution of the error effects to the quality of data collected by the instrument. The analysis begins with the discussion of the coordinate systems involved and then conversion from the target coordinate system to the instrument coordinate system. Next the geometry of remote observations from the Space Station is investigated including the effects of the instrument location in Space Station and the effects of the line of sight to the target. The disturbance and error environment on Space Station is discussed covering factors contributing to drift and jitter, accuracy of pointing data and target and instrument accuracies. Finally, there is a brief discussion of image processing to address any post error correction options.
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Date Issued: 2005
Identifier: CFE0000855, ucf:46661
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000855

Title: IDENTIFICATION OF SPATIOTEMPORAL NUTRIENT PATTERNS AND ASSOCIATED ECOHYDROLOGICAL TRENDS IN THE TAMPA BAY COASTAL REGION.
Creator: Wimberly, Brent, Chang, Ni-Bin, University of Central Florida
Abstract / Description: The comprehensive assessment techniques for monitoring of water quality of a coastal bay can be diversified via an extensive investigation of the spatiotemporal nutrient patterns and the associated eco-hydrological trends in a coastal urban region. With this work, it is intended to thoroughly investigate the spatiotemporal nutrient patterns and associated eco-hydrological trends via a two part inquiry of the watershed and its adjacent coastal bay. The findings show that the onset of drought...
Show moreThe comprehensive assessment techniques for monitoring of water quality of a coastal bay can be diversified via an extensive investigation of the spatiotemporal nutrient patterns and the associated eco-hydrological trends in a coastal urban region. With this work, it is intended to thoroughly investigate the spatiotemporal nutrient patterns and associated eco-hydrological trends via a two part inquiry of the watershed and its adjacent coastal bay. The findings show that the onset of drought lags the crest of the evapotranspiration and precipitation curve during each year of drought. During the transition year, ET and precipitation appears to start to shift back into the analogous temporal pattern as the 2005 wet year. NDVI shows a flat receding tail for the September crest in 2005 due to the hurricane impact signifying that the hurricane event in October dampening the severity of the winter dry season in which alludes to relative system memory. The k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa Bay had the minimum values of total nitrogen (TN) concentrations, chlorophyll a (Chl-a) concentrations, and ocean color values in every season as well as the minimum concentration of total phosphorus (TP) in three consecutive seasons in 2008. Cluster 5, located in Middle Tampa Bay, displayed elevated TN concentrations, ocean color values, and Chl-a concentrations, suggesting that high colored dissolved organic matter values are linked with some nutrient sources. The data presented by the gravity modeling analysis indicate that the Alafia River Basin is the major contributor of nutrients in terms of both TP and TN values in all seasons. Such ecohydrological evaluation can be applied for supporting the LULC management of climatic vulnerable regions as well as further enrich the comprehensive assessment techniques for estimating and examining the multi-temporal impacts and dynamic influence of urban land use and land cover. Improvements for environmental monitoring and assessment were achieved to advance our understanding of sea-land interactions and nutrient cycling in a coastal bay.
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Date Issued: 2012
Identifier: CFH0004132, ucf:44878
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0004132

Title: Incorporating Remotely Sensed Data into Coastal Hydrodynamic Models: Parameterization of Surface Roughness and Spatio-Temporal Validation of Inundation Area.
Creator: Medeiros, Stephen, Hagen, Scott, Weishampel, John, Wang, Dingbao, Yeh, Gour-Tsyh, University of Central Florida
Abstract / Description: This dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness...
Show moreThis dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness parameterization schemes, an application of a cutting edge remotely sensed inundation detection method to assess the performance of a tidal model, and a preliminary investigation into using 3-dimensional airborne laser scanning data to parameterize surface roughness.A thorough academic review of wetting and drying algorithms employed by contemporary numerical tidal models was conducted. Since nearly all population centers and valuable property are located in the overland regions of the model domain, the coastal models must adequately describe the inundation physics here. This is accomplished by techniques that generally fall into four categories: Thin film, Element removal, Depth extrapolation, and Negative depth. While nearly all wetting and drying algorithms can be classified as one of the four types, each model is distinct and unique in its actual implementation.The use of spatial elevation data is essential to accurate coastal modeling. Remotely sensed LiDAR is the standard data source for constructing topographic digital terrain models (DTM). Hydrographic soundings provide bathymetric elevation information. These data are combined to form a seamless topobathy surface that is the foundation for distributed coastal models. A three-point inverse distance weighting method was developed in order to account for the spatial variability of bathymetry data referenced to tidal datums. This method was applied to the Tampa Bay region of Florida in order to produce a seamless topobathy DTM.Remotely sensed data also contribute to the parameterization of surface roughness. It is used to develop land use / land cover (LULC) data that is in turn used to specify spatially distributed bottom friction and aerodynamic roughness parameters across the model domain. However, these parameters are continuous variables that are a function of the size, shape and density of the terrain and above-ground obstacles. By using LULC data, much of the variation specific to local areas is generalized due to the categorical nature of the data. This was tested by comparing surface roughness parameters computed based on field measurements to those assigned by LULC data at 24 sites across Florida. Using a t-test to quantify the comparison, it was proven that the parameterizations are significantly different. Taking the field measured parameters as ground truth, it is evident that parameterizing surface roughness based on LULC data is deficient.In addition to providing input parameters, remotely sensed data can also be used to assess the performance of coastal models. Traditional methods of model performance testing include harmonic resynthesis of tidal constituents, water level time series analysis, and comparison to measured high water marks. A new performance assessment that measures a model's ability to predict the extent of inundation was applied to a northern Gulf of Mexico tidal model. The new method, termed the synergetic method, is based on detecting inundation area at specific points in time using satellite imagery. This detected inundation area is compared to that predicted by a time-synchronized tidal model to assess the performance of model in this respect. It was shown that the synergetic method produces performance metrics that corroborate the results of traditional methods and is useful in assessing the performance of tidal and storm surge models. It was also shown that the subject tidal model is capable of correctly classifying pixels as wet or dry on over 85% of the sample areas.Lastly, since it has been shown that parameterizing surface roughness using LULC data is deficient, progress toward a new parameterization scheme based on 3-dimensional LiDAR point cloud data is presented. By computing statistics for the entire point cloud along with the implementation of moving window and polynomial fit approaches, empirical relationships were determined that allow the point cloud to estimate surface roughness parameters. A multi-variate regression approach was chosen to investigate the relationship(s) between the predictor variables (LiDAR statistics) and the response variables (surface roughness parameters). It was shown that the empirical fit is weak when comparing the surface roughness parameters to the LiDAR data. The fit was improved by comparing the LiDAR to the more directly measured source terms of the equations used to compute the surface roughness parameters. Future work will involve using these empirical relationships to parameterize a model in the northern Gulf of Mexico and comparing the hydrodynamic results to those of the same model parameterized using contemporary methods. In conclusion, through the work presented herein, it was demonstrated that incorporating remotely sensed data into coastal models provides many benefits including more accurate topobathy descriptions, the potential to provide more accurate surface roughness parameterizations, and more insightful performance assessments. All of these conclusions were achieved using data that is readily available to the scientific community and, with the exception of the Synthetic Aperture Radar (SAR) from the Radarsat-1 project used in the inundation detection method, are available free of charge. Airborne LiDAR data are extremely rich sources of information about the terrain that can be exploited in the context of coastal modeling. The data can be used to construct digital terrain models (DTMs), assist in the analysis of satellite remote sensing data, and describe the roughness of the landscape thereby maximizing the cost effectiveness of the data acquisition.
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Date Issued: 2012
Identifier: CFE0004271, ucf:49506
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004271

Title: Integrated Data Fusion and Mining (IDFM) Technique for Monitoring Water Quality in Large and Small Lakes.
Creator: Vannah, Benjamin, Chang, Ni-bin, Wanielista, Martin, Wang, Dingbao, University of Central Florida
Abstract / Description: Monitoring water quality on a near-real-time basis to address water resources management and public health concerns in coupled natural systems and the built environment is by no means an easy task. Furthermore, this emerging societal challenge will continue to grow, due to the ever-increasing anthropogenic impacts upon surface waters. For example, urban growth and agricultural operations have led to an influx of nutrients into surface waters stimulating harmful algal bloom formation, and...
Show moreMonitoring water quality on a near-real-time basis to address water resources management and public health concerns in coupled natural systems and the built environment is by no means an easy task. Furthermore, this emerging societal challenge will continue to grow, due to the ever-increasing anthropogenic impacts upon surface waters. For example, urban growth and agricultural operations have led to an influx of nutrients into surface waters stimulating harmful algal bloom formation, and stormwater runoff from urban areas contributes to the accumulation of total organic carbon (TOC) in surface waters. TOC in surface waters is a known precursor of disinfection byproducts in drinking water treatment, and microcystin is a potent hepatotoxin produced by the bacteria Microcystis, which can form expansive algal blooms in eutrophied lakes. Due to the ecological impacts and human health hazards posed by TOC and microcystin, it is imperative that municipal decision makers and water treatment plant operators are equipped with a rapid and economical means to track and measure these substances.Remote sensing is an emergent solution for monitoring and measuring changes to the earth's environment. This technology allows for large regions anywhere on the globe to be observed on a frequent basis. This study demonstrates the prototype of a near-real-time early warning system using Integrated Data Fusion and Mining (IDFM) techniques with the aid of both multispectral (Landsat and MODIS) and hyperspectral (MERIS) satellite sensors to determine spatiotemporal distributions of TOC and microcystin. Landsat satellite imageries have high spatial resolution, but such application suffers from a long overpass interval of 16 days. On the other hand, free coarse resolution sensors with daily revisit times, such as MODIS, are incapable of providing detailed water quality information because of low spatial resolution. This issue can be resolved by using data or sensor fusion techniques, an instrumental part of IDFM, in which the high spatial resolution of Landsat and the high temporal resolution of MODIS imageries are fused and analyzed by a suite of regression models to optimally produce synthetic images with both high spatial and temporal resolutions. The same techniques are applied to the hyperspectral sensor MERIS with the aid of the MODIS ocean color bands to generate fused images with enhanced spatial, temporal, and spectral properties. The performance of the data mining models derived using fused hyperspectral and fused multispectral data are quantified using four statistical indices. The second task compared traditional two-band models against more powerful data mining models for TOC and microcystin prediction. The use of IDFM is illustrated for monitoring microcystin concentrations in Lake Erie (large lake), and it is applied for TOC monitoring in Harsha Lake (small lake). Analysis confirmed that data mining methods excelled beyond two-band models at accurately estimating TOC and microcystin concentrations in lakes, and the more detailed spectral reflectance data offered by hyperspectral sensors produced a noticeable increase in accuracy for the retrieval of water quality parameters.
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Date Issued: 2013
Identifier: CFE0005066, ucf:49979
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005066

Remote Sensing (16)	+ -
remote sensing (10)	+ -
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Microwave Remote Sensing (4)	+ -
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Satellite Remote Sensing (2)	+ -
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