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Barnhouse, Willard DMethane Plume Detection Using Passive Hyper-Spectral Remote Sensing
Master of Science (MS), Bowling Green State University, 2005, Geology
The work in this thesis used passive hyperspectral remote sensing analysis with data collected form the high altitude MODIS Airborne Simulator (MAS). This study examined multiple remote sensing band ratios designed to capitalize on methane’s 3.314µm absorption feature. Other ratios were also developed to detect atmospheric gas changes associated with possible methane plumes. Much of the analysis utilized datasets covering two California regions known to contain active oil/gas seeps and production. It was determined that no single MAS ratio algorithm could be used to confidently detect a methane gas plume. The presence of other atmospheric gases has the potential to affect the algorithms and produce complications for interpretation. However, by using a concurrence of ratio algorithm results, a suspected plume was thought to be detected in one of the off-shore datasets. The analysis for the land datasets found the high degree of surface material and temperature variations dramatically interfered with the ability to interpret the algorithm results with any significant confidence.

Committee:

Robert Vincent (Advisor)

Keywords:

geology; remote sensing; geologic remote sensing; methane; hyperspectral; hyper-spectral; atmospheric methane; methane plume; atmospheric remote sensing

Chae, Chun SikStudies of the Interferometric Phase and Doppler Spectra of Sea Surface Backscattering Using Numerically Simulated Low Grazing Angle Backscatter Data
Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

Range-resolved interferometric phase and Doppler spectra are two subjects of interest with regard to the retrieval of sea surface height profiles from coherent marine radar measurements. The studies of this dissertation attempt to improve understanding of the properties and associated measurement errors of these quantities through the use of numerically simulated low-grazing-angle backscatter data.

In the first part of the dissertation, studies of the interferometric phase are described. Backscattered fields computed using the method of moments for one dimensional ocean-like surface profiles are used to examine statistical properties of the single-look interferometric phase estimator, in order to investigate the applicability of standard expectations for height retrieval accuracy in this problem. The results show that shadowing and multipath propagation effects cause errors in interferometric phase estimation beyond those caused by speckle effects alone. In addition, the decorrelation between the fields received at two antennas is found to be impacted by shadowing and multipath propagation effects, making standard models for this quantity less applicable as well. These results show that modeling the expected performance of interferometric sea surface height retrieval approaches at low grazing angles is difficult.

The second part of the dissertation involves studies of the range-resolved Doppler spectra at low-grazing-angles. Backscattered fields are computed for a single realization of a one-dimensional ocean-like surface profile as the realization evolves in time. Transformation into the range-Doppler domain enables examination of properties of the resulting Doppler spectra (for both HH and VV polarizations) and their relationship to properties of the surface profile. In general, a strong correspondence between the long wave orbital velocity of the surface and the Doppler centroid frequency is observed for visible portions of the surface, as well as some evidence of relationships between the width of the Doppler spectrum and variations of the orbital velocity in time at a given range point. Evidence of similar relationships even in some shadowed portions of the surface is also provided. Doppler spectra from HH and VV polarizations are qualitatively similar in most respects, although the portion of shadowed surface points from which Doppler information is available is somewhat larger in VV polarization. A further examination is conducted using backscattered fields computed with a "single scattering" method that neglects shadowing and any multiple scattering effects. The remarkable similarities observed in Doppler spectra for the complete and single scattering models even in some shadowed portions of the surface suggests that non-line-of-sight propagation effects do not significantly in fluence Doppler properties in such regions.

The studies in this dissertation provide improved understandings of range-resolved interferometric phase and Doppler spectra at low grazing angles. These results provide new information for the design of coherent marine radars for the retrieval of sea surface profiles.

Committee:

Joel Johnson (Advisor); Robert Burkholder (Committee Member); Fernando Teixeira (Committee Member)

Subjects:

Electrical Engineering

Keywords:

electromagnetic scattering; surface scattering; interferometric phase; sea doppler spectra; retrieval of sea surface height; microwave remote sensing; method of moments; interferometry; doppler radar; low grazing angle; ocean remote sensing

Hong, Chang-KiEfficient differential code bias and ionosphere modeling and their impact on the network-based GPS positioning
Doctor of Philosophy, The Ohio State University, 2007, Geodetic Science and Surveying
One of the major error sources in using Global Positioning System (GPS) measurements for modeling the ionosphere is the receiver differential code bias (DCB). Therefore, the determination of the receiver DCB is important, and to date, it has been done mostly using the single-layer ionospheric model assumption. In this dissertation, a new and efficient algorithm using the geometry conditions between the satellite and the tracking receivers is proposed to determine the receiver DCB using permanent reference stations. In this method, an assumption that ionosphere is represented by a single-layer model is not required, which makes DCB computation independent on the pre-selected ionosphere model. In addition, this method is simple, accurate and computationally efficient. The principal idea is that the magnitude of the signal delay caused by the ionosphere is, under normal conditions, highly dependent on the geometric range between the satellite and the receiver. The proposed algorithm was tested with the Ohio Continuously Operating Reference Stations (CORS) and the Transantarctic Mountains Deformation (TAMDEF) sub-network data. The results show that quality comparable to the traditional DCB estimation method is obtainable with greater computational efficiency and simple algorithmic implementation.

Committee:

Dorota Grejner-Brzezinska (Advisor)

Subjects:

Geodesy

Keywords:

GPS; GPS remote sensing; GPS positioning; ionosphere modeling

Saraswat, DharmendraGeospatial technology applications to strawberry, grape and citrus production systems
Doctor of Philosophy, The Ohio State University, 2007, Food, Agricultural, and Biological Engineering
The objective of this study was to investigate the spatial and temporal variability in strawberry and grape production systems using aerial digital multispectral imagery, along with reference ground-truth data. Another objective was to identify and extract citrus trees from QuickBird satellite image for obtaining tree count. Two images were acquired for strawberries in 2002 to study differences in crop growth. Two images for grapes were acquired, one year apart in 2004 and 2005 around the same crop growth stage (veraison). The ground-truth for both the crops included: 1) soil and plant information, 2) apparent soil electrical conductivity data, and 3) yield data. A QuickBird satellite image was acquired in 2004 to identify and extract citrus trees and to determine tree count. The reference tree count was acquired through a 30 cm spatial resolution digital ortho quadrangle quad (DOQQ). The aerial and satellite images were geometrically corrected, reprojected and subsetted to obtain images of the study site. The images used for studying grape production were converted to a reflectance image using empirical line approach. The digital count value contained in the QuickBird image was converted to spectral radiance values before extracting citrus trees. The relationships between image data and reference ground-truth were determined for strawberries and grapes. Different pan sharpening methods were tried to enhance the spatial details on multispectral QuickBird satellite image. A methodology was developed and tested for extracting citrus trees from pan sharpened QuickBird image using commercially available image object analysis software. The relationships between soil-plant variables and image data were found to be dependent on the growth stage in strawberries. The best linear and non-linear model estimated 42% and 56% of strawberry yields, respectively. Vegetation indices (NDVI, RVI and SAVI) derived from digital multispectral aerial image data provided a better estimate of leaf area index (r2 = 0.81, 0.78, and 0.58, respectively) than yield of grapes (r2 = 0.14, 0.15, and 0.11, respectively). The best net and gross accuracy of tree count was found to be 78.1.6% and 62.1% on QuickBird satellite image pan sharpened by Gram-Schmidt method.

Committee:

Larry Brown (Advisor)

Subjects:

Engineering, Agricultural

Keywords:

geospatial; remote sensing; gis; gps; ec; yield variability

Wuite, JanSpatial and temporal dynamics of three East Antarctic outlet glaciers and their floating ice tongues
Doctor of Philosophy, The Ohio State University, 2006, Geological Sciences
Observations show that some glaciers in Greenland and Antarctica undergo rapid changes in flow velocity and thickness. There is concern about the implications for global sea levels and ocean circulation. Part of the changes has been ascribed to changes in glacier dynamics. Measuring velocity and velocity gradients are first steps in studying their dynamics and possible response to climatic changes. With the RADARSAT-1 Antarctic Mapping Project (RAMP) a great opportunity arose to derive flow velocity of Antarctica’s glaciers remotely. This study uses RAMP imagery to derive ice flow velocity and, in combination with other datasets, to study spatial and temporal fluctuations in velocity and stress fields of selected Antarctic glaciers. The derived high-resolution surface velocity maps form an important benchmark for gauging possible changes in velocity and dynamics. The maps are derived using pre-established feature tracking techniques that we improved and streamlined in order to extract as much velocity data as possible. To determine important flow governing forces we use force-budget theory. We include a detailed error analysis and investigate the implications of a recently established flow law on derived stresses. The investigations of our study areas suggest that flow has been rather constant over decadal timescales. Based on this we infer that stress fields have not changed significantly either, permitting combinations of various data sets to optimize the velocity field in order to study dynamics in greater detail then previously possible. We find that the relative contribution of side drag declines along the fjords, but demonstrate that, once they leave the valley walls, the glaciers are not immediately free floating ice shelves. Measurements show that ice tongues spread faster in the across flow direction than the along flow direction for a considerable length. In addition there appears to be some lateral drag, once a glacier leaves the coast, which could be associated with sub-surface valley walls or an adjacent ice shelf. This could lead to an increase in along flow creep if the ice tongue were to break off. Finally we conclude that ice tongues are important, because they can provide clues to past ice sheet behavior and fluctuations.

Committee:

Kenneth Jezek (Advisor)

Keywords:

Glaciology; Remote Sensing; Antarctica; Glacier dynamics

Bonini, NickAssessing the Variability of Phytoplankton Assemblages in Old Woman Creek, Ohio
PHD, Kent State University, 2016, College of Arts and Sciences / Department of Geology
Various techniques for assessing, monitoring, and predicting algal blooms in an estuarine ecosystem are analyzed. In one section, routine water samples are collected at previously established monitoring sites in Old Woman Creek, filtered onto a 47 mm, 0.7 µm glass-fiber filter (GF/F), and then measured using a visible/near-infrared spectrophotometer. Varimax-rotated principal component analysis (VPCA) is applied to reflectance data and then used to quantify and identify pigments, phytoplankton taxa, and sediments by comparing the measured spectral signatures to known standards. Common assemblages that are reported throughout the three-year study include: bacillariophyceae (diatoms), chlorophyta (green algae), cyanobacteria (blue-green algae), and illite. A similar approach is taken in the next section by applying multivariate statistics to Landsat 8 satellite imagery in order to determine the distribution of in-water constituents at a high spatial resolution. Only four bands in the visible range are available for this analysis, but it is possible to identify several of the same groups of algae and sediments, providing a useful complement to the hyperspectral work. Finally, a bloom prediction model based on springtime discharge is created by applying VPCA to in-water sonde data from one of the monitoring sites at Old Woman Creek during a recent 11-year time period. In this model, a proxy for net community production (NCP) is determined using oxygen and pH dynamics and then compared to daily rates of streamflow. Possible monthly sequences between January and June are considered in order to determine which timeframe is the best indicator of the average annual NCP. Time of day (daytime versus nighttime) and mouth bar conditions (barrier beach present versus absent) are important factors in determining production in the estuary. Based on the results, the best predictor for NCP is stream discharge from March through May, which produces correlations that are significant at even the 1% level. A positive relationship is found between NCP and discharge when flow from Old Woman Creek into Lake Erie is permitted. When flow is blocked by the barrier beach, however, the relationship is reversed.

Committee:

Joseph Ortiz (Advisor); Anne Jefferson (Committee Member); Alison Smith (Committee Member); Darren Bade (Committee Member)

Subjects:

Aquatic Sciences; Biological Oceanography; Environmental Geology; Environmental Science; Geology; Limnology; Water Resource Management

Keywords:

Old Woman Creek; Lake Erie; algal blooms; phytoplankton; water quality; estuary; barrier beach; VNIR derivative spectroscopy; VPCA; principal component analysis; remote sensing; prediction model; net community production; streamflow

Morman, Christopher JosephHyperspectral Target Detection Performance Modeling
Master of Science (M.S.), University of Dayton, 2015, Electrical Engineering
Hyperspectral remote sensing has become a popular topic of research due to the numerous applications stemming from the high dimensionality of the recorded spectral data. From the design perspective, hyperspectral sensors are generally more complex than standard color or infrared imaging systems because there are more optical components in the system. The quality of each of these components directly affects the target detection performance of the system. In addition to the integrity of optical components, target detection performance is also affected by signal variations due to sensor noise. This research addresses the design of an end-to-end hyperspectral imaging system performance model that incorporates the optical design of the system as well as the stochastic nature of data collected by electronic remote sensing. A system transmission model is presented that calculates the camera signal as a function of input radiance and accounts for each individual optical element in the imaging system. This model can be used to analyze the performance sensitivities of a specific component for a variety of target detection scenarios. The accuracy of the system transmission model is assessed using calibrated hyperspectral data. In addition to the system transmission model, a realistic statistical data model is proposed. Many data models currently account for sensor noise with an additive, stationary variance. This research expands upon this by implementing an additive, signal-dependent sensor noise model that more accurately represents the true phenomena driving the sensor noise. The same data set is used to test target detection performance using the signal-dependent noise model. The results are analyzed to investigate the possible benefits of using the proposed noise model. The data used for this research was collected at Wright Patterson Air Force Base 25-26 June 2014. The scene consists of a grassy background with eight painted wooden panel targets. Data collections took place at different times of day in order to capture varying solar angles and illumination levels. Additionally, data was collected with varying exposure times in an effort to observe performance effects due to varying signal-to-noise ratios. Conclusions about the performance of the system transmission and data modeling techniques are framed within the context of collection time and exposure time.

Committee:

Russell Hardie, Ph.D. (Advisor); Joseph Meola, Ph.D. (Committee Member); Eric Balster, Ph.D. (Committee Member)

Subjects:

Electrical Engineering; Remote Sensing; Statistics

Keywords:

Hyperspectral; Remote Sensing; Target Detection; Performance Modeling;

Westbrook, Matthew RLocal scale forest encroachment into alpine habitat: past patterns and future predictions
MS, University of Cincinnati, 2014, Arts and Sciences: Biological Sciences
Temperature is a major factor predicting global patterns of forest establishment in alpine habitats. However, factors predicting patterns of forest distribution at the local scale are more discrete. Variation in soil moisture caused by geomorphology, soil characteristics, and facilitation are likely to be more accurate predictors of forest encroachment at the local scale than broad-scale climate pattern. The goals of this research are 1) to describe how forest has changed over time with respect to encroachment into alpine areas at a local scale, 2) to determine non-climatic factors that are important for this forest encroachment, probably by way of regulation of soil moisture and 3) to use these factors to predict where forest is likely to occur. This study was conducted in the front ranges of the Canadian Rocky Mountains along Jumpingpound Ridge (elevation ˜ 2100 m, lat. 647039.43, long. 5646351.15, UTM zone 11N). I used a GIS analysis of orthorectified aerial photos taken in 1952, 1962, 1982, 1988, 1993, 1999, and 2008 to examine past patterns of forest encroachment and change in forested areas over time. This change included substantial loss of unforested area to forest encroachment along Jumpingpound Ridge from 503 ha in 1952 to 138 ha in 2008, the majority of which occurred from 1952 to 1962 (a loss of 237 ha). Forest encroachment also resulted in increased fragmentation of unforested areas, a decrease in both their size and isolation. I used variables corresponding to soil moisture including geomorphology, facilitation, and soil characteristics in generalized linear models (GLMs) to forecast where trees will occur. Results from generalized linear models indicate that there are very specific geomorphic and facilitative conditions that must be met for further encroachment. Therefore, areas in which all conditions for encroachment are met are significantly limited. Between the three tree species located on Jumpingpound Ridge, encroachment is expected to occur over an additional 13.47 ha (9.7% of total unforested area in 2008).

Committee:

Stephen Matter, Ph.D. (Committee Chair); Edward Arnold Johnson, Ph.D. (Committee Member); David Lentz, Ph.D. (Committee Member); Hongxing Liu, Ph.D. (Committee Member); Steven Rogstad, Ph.D. (Committee Member)

Subjects:

Ecology

Keywords:

tree-line;treeline;Canadian Rockies;geomorphology;remote sensing;soil moisture

Garris, Heath WilliamRestructuring of Wetland Communities in Response to a Changing Climate at Multiple Spatial and Taxonomic Scales
Doctor of Philosophy, University of Akron, 2013, Integrated Bioscience
Climate change threatens to alter the current distribution, productivity, and community composition of wetlands in the Midwestern United States. Increasing rainfall variability and rising temperatures will yield unique stresses for wetland vegetation, including an increase in flooding severity and a higher frequency of potentially harmful heat events. This dissertation explores the interactions and impacts of climate warming and hydrologic variability on productivity, morphological plasticity, reproduction, and functional composition within wetland communities, followed by an evaluation of the connection between wetland distribution and climate on a regional scale. Climate warming led to depressions in productivity during the warmest months while hydrologic variation consistent with climate projections yielded decreases in spring production and peak biomass. Reproductive allocation and other functional trait differences suggested that the future climate will limit productivity in many wetland ecosystems in the Midwest. A distribution model based on Artificial Neural Networks projected significant increases in flooding leading to wetland expansion concentrated in the Midwestern Corn Belt and potential declines in wetland area in Minnesota and northern Michigan. These results suggest that, though wetland area is projected to increase for the Midwest, without hydrologic management, many wetland systems are at risk of community turnover and degradation resulting from a shifting climate.

Committee:

Randall Mitchell, Dr. (Advisor); Linda Barrett, Dr. (Committee Member); Lauchlan Fraser, Dr. (Committee Member); Stephen Weeks, Dr. (Committee Member); Gregory Smith, Dr. (Committee Member); John Senko, Dr. (Committee Member)

Subjects:

Ecology; Geographic Information Science; Geography

Keywords:

functional traits; vegetation; climate change; heat stress; wetland community; artificial neural networks; GIS; midwest wetland distribution; hydrology; mesocosms; open top chambers;OTC;Ratio vegetation index; plot-level remote sensing;

Thompson, JamesIdentifying Subsurface Tile Drainage Systems Utilizing Remote Sensing Techniques
Master of Arts, University of Toledo, 2010, Geography

The purpose of this research is to develop a method that will identify subsurface tile drainage systems in agricultural areas. Subsurface tile drainage systems allow ground water to drain out of a field in order to control the water level but they also allow nutrients such as nitrogen and phosphorous to be drained into surrounding waterways, affecting the water quality in negative ways. These subsurface drainage systems are common in the Midwest because they were used as the primary land drainage strategy when developing the land for agricultural uses. Many of the tile drain locations are not known because of the age of the systems, change in land ownership, or the lack of documentation during installation. Due to research that indicates their potential impact on surface water and new developments in sustainable agriculture practices, it is important to locate and document the existence of subsurface tile drainage systems.

This research project focused on Wood County which is a predominantly agricultural area located in Northwest Ohio and covers a large portion of the Maumee River Watershed. Aerial photographs of Wood County with one meter spatial resolution collected through the United States Department of Agriculture’s National Agricultural Imagery Program were used for this research. Moisture retained in soil that is not drained shows up as dark in the imagery while drier soil, such as that above the subsurface tile drainage lines, has a lighter reflectance. Remote sensing software was used to extract the edges between light and dark soils that indicate the presence of subsurface tile drainage systems. The results of the detection process showed the most discernable tile patterns in the 2005 imagery, with similar results in the 2006 imagery. The tile lines were detected evenly across all eleven areas of interest in Wood County which was expected. The process was only able to validate 13.5 percent of detected tile drains, leaving room for additional research to increase the accuracy. Crop cover, tillage practice, and soil classification were analyzed in relation to the presence of subsurface tile drainage systems to create a holistic perception of when and where tile drains can be detected. Soybean fields yielded the highest amount of tile drain lines with corn fields in a close second. Tile detection in relation to tillage practices was overwhelmingly biased towards fields that were not tilled. Tile line detection on soil classifications was consistent throughout the three years of imagery, matching the soil type predictions based on drainage characteristics.

Committee:

Kevin Czajkowski, PhD (Committee Chair); Patrick Lawrence, PhD (Committee Member); Alison Spongberg, PhD (Committee Member)

Subjects:

Agriculture; Environmental Science; Geographic Information Science; Geography; Remote Sensing

Keywords:

Remote sensing; subsurface tile drains; aerial photographs; NAIP; GIS; subsurface tile drain detection

Wijekoon, NishanthiSPATIAL AND TEMPORAL VARIABILITY OF SURFACE COVER IN AN ESTUARINE ECOSYSTEM FROM SATELLITE IMAGERY AND FIELD OBSERVATIONS
PHD, Kent State University, 2007, College of Arts and Sciences / Department of Geology
This study determined the capability of moderate resolution satellite imagery of 30 meter pixel dimension to investigate the spatial and temporal changes of Old Woman Creek National Estuarine Research Reserve, which is a dynamic coastal wetland of Lake Erie. Water quality and land cover reflectance data is interpreted with respect to in-situ sample measurements collected every 16 days in coincidence with the Landsat-5 TM over passing days mainly in summer 2005 and 2006. The study involved a variety of qualitative and quantitative, physical and remote sensing measurements generated from surface water and its constituents, aquatic emergent and terrestrial macrophytes, exposed mudflats, and radiometrically corrected Landsat-5 TM imagery. The prevailing environmental and climatic conditions of the area regulated the spatial and temporal variability of those land cover types.The study developed a suspended sediment concentration calibration method and two land cover variability mapping methods using Landsat-5 TM data. The two wetland mapping methods are based on principal component analysis (PCA) and scattergram segmentation of selected normalized difference remote sensing indices. In addition, the mineralogy and morphology of suspended particulates were investigated using an X-Ray diffraction (XRD) technique and environmental scanning electron microscopy (ESEM) which revealed the dominance of silica and calcite in surface water.The surface water samples provided total suspended particulate concentration (TSP) measurements which reported 0.7 correlation against normalized difference water index (NDWI) of bands 1 and 5, establishing a model to quantify TSP concentration in surface water. The principal component analysis (PCA) extracted endmember land covers reporting 87 % of total variance and their spatial and temporal distribution was mapped in order to identify the seasonal variability of macrophytes, open-water, and exposed ground. One dimensional spaces of normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference ground index (NDGI) segmented their respective scattergrams to identify the land cover interfaces in order to re-map the same land cover variability for better evaluation of the two wetland mapping techniques.

Committee:

Joseph Ortiz (Advisor)

Keywords:

Land Cover; Remote Sensing; Old Woman Creek; Wetland; Suspended Sediment; Principal Component Analysis;

Makaudze, Ephias MDo seasonal climate forecasts and crop insurance really matter for smallholder farmers in Zimbabwe? Using contingent valuation method and remote sensing applications
Doctor of Philosophy, The Ohio State University, 2005, Agricultural, Environmental and Development Economics
As smallholder farmers in Zimbabwe face inevitable drought, the need to develop drought-mitigation strategies and risk transfer mechanisms becomes an important and challenging task for policy makers. Rather than treating drought as a natural disaster that warrants emergency declarations whenever it strikes, countries in Southern Africa could alter their policy to embrace drought as an integral part of their national policy framework. Although drought cannot be eliminated, its impact can be reduced through implementation of pro-active and pro-poor risk management policy programs. This study explored two potential policy programs. One program proposes wide-scale adoption of improved seasonal forecasts by smallholder farmers as a drought mitigation strategy, and the second program proposes implementation of area-yield drought-indexed insurance as a risk-transfer and risk-protection mechanism for the smallholder farmers. To investigate whether adoption of seasonal forecasts and drought insurance is possible in Zimbabwe this dissertation explored three hypotheses: First, do seasonal forecasts really matter to smallholder farmers in Zimbabwe? Second, given the prevalence of food-aid in Zimbabwe, does drought insurance really matter for smallholder farmers? Third, given drought is a catastrophic risk, will a drought-index insurance scheme intended for smallholder farmers be viable and/or feasible? The first two questions were empirically investigated via surveys based on the contingency valuation method (CVM). More than 1,000 smallholder farmers were surveyed throughout Zimbabwe’s agro-ecological regions II-V where willingness-to-pay (WTP) for the proposed programs was elicited. With respect to the first hypothesis, results showed that for the improved seasonal forecasts program, estimated WTP (Z$) based on a single-bound model ranged from Z$2,427 to Z$4,676. For a double-bound model, WTP ranged from Z$2,532 to Z$4,225. A distinct differential pattern in WTP was observed across districts and natural regions, where households in wet districts revealed WTP that was consistently lower than those in drier districts. In fact WTP for households in natural region II was 36% and 30% lower than in regions IV and V, respectively. A similar pattern was observed for households in natural region III whose WTP was 17% and 9.3% lower than in regions IV and V, respectively. Because the perceived drought risk is more ominous in drier regions (IV and V) than in wet regions (II and III), households in the former are willing to sacrifice more for the provision of improved seasonal forecasts. With respect to the second hypothesis, results showed that in the presence of food-aid, WTP or rather potential demand for drought insurance decreases by more than 35% for households in regions IV and V, while for regions II and III it decreases by 10.6%. The results imply that disincentive to purchase insurance in the presence of food-aid is greatest in drier regions IV and V and least in wet regions II and III. Across all regions/districts the demand for insurance is likely to decrease by more than 20% in the presence of food-aid. Thus, food-aid will discourage farmers from seeking more efficient drought risk protection mechanisms such as formal drought insurance. With respect to the third hypothesis, results indicate that VCI showed appreciably high correlation with crop yields sufficient to consistently track yield losses and these results were fairly comparable with rainfall index. In addition, reasonable premium rates were recovered that are actuarially sound and inexpensive enough to attract participation of the rural poor. In as far as hedging against extreme drought events is concerned, a VCI-based contract could be sufficient. Basis risk becomes an issue, if the index is used to protect drought events of moderate intensity.

Committee:

Brent Sohngen (Advisor)

Subjects:

Economics, Agricultural

Keywords:

Agricultural risk, food insecurity and drought index insurance;; Remote sensing and vegetation condition index (VCI);; Contingent Valuation Method and Willingness-to-pay (WTP)

Ramalingam, NagarajanNon-contact multispectral and thermal sensing techniques for detecting leaf surface wetness
Doctor of Philosophy, The Ohio State University, 2005, Food, Agricultural, and Biological Engineering
Leaf surface wetness detection is important in plant production for pesticide application evaluation, disease management, and misting control. Efficient application of pesticides may be possible using the feedback from the leaf wetness detection system reducing both the overall input cost and environmental contamination. The goal of this study was to develop non-contact sensing techniques for leaf surface wetness detection. Several non-contacting techniques using spectral, thermal, and imaging sensors were evaluated for the development of an automated feedback controlled spraying system. The study was divided into several sub studies focusing on leaf level and canopy level experiments inside a laboratory under artificial illumination, and canopy level experiments in a greenhouse under natural solar illumination. Multispectral reflectance of the leaves and canopies was measured using a spectroradiometer and a custom built low cost multispectral imaging system. The changes of surface temperature and spectral reflectance in visible (400-700 nm), very-near-infrared (700-1300 nm) and near-infrared ranges (1300-2500 nm) caused by leaf surface moisture were investigated. The spectral information collected using the non-contact sensors was a mixture of reflectance of the objects of interest and also the background in the sensor’s field of view. For accurate leaf surface water analysis, background compensation techniques were evaluated to obtain compensated reflectance spectra. Two approaches were investigated for background reflectance compensation, a spectral approach, which aimed at compensating the measured reflectance for background-contamination using a linear unmixing technique, and a spatial approach, which aimed at extracting only the vegetation pixels from the multispectral images using a vegetation index. Visible and near-infrared regions were found less affected by background whereas very-near-infrared regions had large background effects. Background-reflectance compensation significantly improved the accuracy of leaf surface water assessment. Leaf wetness was assessed using the relative differences in the spectral and thermal measurements recorded before and after spraying. Leaf surface wetness was quantified as the difference between the average equivalent water thickness (EWT) values of sprayed and non-sprayed canopy. The EWT values were calculated using model inversion techniques from the measured multispectral reflectance. Leaf and canopy temperatures were measured using infrared thermometry. The studies on both the leaf and canopy levels indicated that the multispectral reflectance and infrared thermometry techniques were able to differentiate plants with and without surface wetness. It was found that the multispectral sensors could be used to detect leaf surface wetness resulting from a high volume pesticide application. The temperatures of the canopies without surface water were found to be 4.4-5.5 0C higher than that of the canopies with surface water. In the canopy level studies under solar illumination, the feasibility of using the developed sensing methodology to detect leaf surface wetness was evaluated in a greenhouse. A non-contact sensor array consisting of spectral, temperature, and imaging sensors was constructed and mounted on a commercial irrigation boom in the greenhouse. Algorithms were developed to compensate for outdoor lighting variation and background interference on the reflectance measurements of the vegetation. Spectral ratioing techniques were used to differentiate canopies with different surface moisture conditions. The irrigation boom had capabilities to be controlled locally using a handheld controller and also remotely from a personal computer. An onboard computer was used to collect data from the sensor array, process the information, and make spraying decisions. This dissertation explains the results of the experiments that were conducted to validate the concept of non-contact leaf wetness sensing techniques. The multispectral technique had sufficient sensitivity to detect leaf surface water thickness of 0.006 cm or more. A quantitative spectral index has been established to quantify surface water thickness. The infrared temperature sensing technique was able to differentiate wet and non-wet canopies rapidly.

Committee:

Peter Ling (Advisor)

Subjects:

Engineering, Agricultural

Keywords:

Remote sensing; plant monitoring; machine vision; image processing; feedback control; instrumentation; data acquisition; equvalent water thickness; spray coverage assessment; dynamic thresholding; segmentation; background compensation; sensors

DeWalt, Heather A.Evaluating 25 Years of Environmental Change Using a Combined Remote Sensing Earth Trends Modeling Approach: A Northern California Case Study
Master of Arts (MA), Ohio University, 2011, Geography (Arts and Sciences)
Mountain glaciers are an important resource for monitoring how regions are being affected by global environmental changes because their advance and retreat are influenced by fluctuations in precipitation and temperature. Using Mt. Shasta in northern California as the study area, this thesis employed a time-series approach to remote sensing image analysis coupled with a Markov-based procedure to demonstrate how remote sensing can be used to define the environmental trajectories active in the region and project those trends into the future. This experimental approach was applied to a series of yearly images from 1985 to 2010 to examine the long-term implications of environmental change and then the trends were projected forward in varying increments to 2110. The long-term change signal showed that El Nino cycles strongly influenced regional land cover patterns and controlled glacial advance and retreat. When this pattern was projected into the future, two scenarios were observed: 1) growth if El Nino cycles strengthen or 2) recession if El Nino cycles weaken.

Committee:

James Lein (Advisor); Dr. Dorothy Sack (Committee Member); Dr. Gaurav Sinha (Committee Member)

Subjects:

Environmental Science; Environmental Studies; Geography; Physical Geography; Remote Sensing

Keywords:

long sequence time series; remote sensing; principal components analysis; Markov; Mt. Shasta; environmental change

Paska, Eva PetraState-of-the-art remote sensing geospatial technologies in support of transportation monitoring and management
Doctor of Philosophy, The Ohio State University, 2009, Geodetic Science and Surveying

The widespread use of digital technologies, combined with rapid sensor advancements resulted in a paradigm shift in geospatial technologies the end of the last millennium. The improved performance provided by the state-of-the-art airborne remote sensing technology created opportunities for new applications that require high spatial and temporal resolution data. Transportation activities represent a major segment of the economy in industrialized nations. As such both the transportation infrastructure and traffic must be carefully monitored and planned. Engineering scale topographic mapping has been a long-time geospatial data user, but the high resolution geospatial data could also be considered for vehicle extraction and velocity estimation to support traffic flow analysis.

The objective of this dissertation is to provide an assessment on what state-of-the-art remote sensing technologies can offer in both areas: first, to further improve the accuracy and reliability of topographic, in particular, roadway corridor mapping systems, and second, to assess the feasibility of extracting primary data to support traffic flow computation. The discussion is concerned with airborne LiDAR (Light Detection And Ranging) and digital camera systems, supported by direct georeferencing.

The review of the state-of-the-art remote sensing technologies is dedicated to address the special requirements of the two transportation applications of airborne remotely sensed data. The performance characteristics of the geospatial sensors and the overall error budget are discussed. The error analysis part is focused on the overall achievable point positioning accuracy performance of directly georeferenced remote sensing systems.

The QA/QC (Quality Assurance/Quality Control) process is a challenge for any airborne direct georeferencing-based remote sensing system. A new method to support QA/QC is introduced that uses the road pavement markings to improve both sensor data accuracy as well as the position of road features. The identification of the pavement markings is based on LiDAR intensity data and is guided by the ground control information available. The centerline of the markings in LiDAR data is modeled and matched to the reference data, providing the observation to the QA/QC process.

The discussion on the innovative use of remote sensing technologies investigates the feasibility of providing remotely sensed traffic data for monitoring and management. An advantage of air-based platforms, including manned and unmanned fixed-wing aircraft and helicopters, is that they can be rapidly deployed to observe traffic incidents that occur in areas where there are no ground-based sensors. To support vehicle extraction from airborne imagery, a method was introduced that provides a true object scale data representation that can facilitate the vehicle extraction. The vehicle extraction from LiDAR data was followed by coarse classification of the extracted vehicles to support coarse velocity estimation; basically, grouping the vehicles into three major categories based on their size. Finally, a novel method was introduced for simultaneously acquired LiDAR and image data, which can combine the advantages of the two sensors for obtaining better velocity estimates of LiDAR-extracted vehicles.

Committee:

Dorota Grejner-Brzezinska, PhD (Advisor); Mark McCord, PhD (Committee Member); Alper Yilmaz, PhD (Committee Member); Charles Toth, PhD (Advisor)

Subjects:

Engineering

Keywords:

LiDAR; optical imagery; remote sensing; transportation

CHANG, DYI-HUEYANALYSIS AND MODELING OF SPACE-TIME ORGANIZATION OF REMOTELY SENSED SOIL MOISTURE
PhD, University of Cincinnati, 2002, Engineering : Environmental Engineering
The characterization and modeling of the spatial variability of soil moisture is an important problem for various hydrological, ecological, and atmospheric processes. This dissertation proposes a compact representation of interdependencies among soil moisture distribution and environmental factors using two complimentary approaches. In the first approach, a stochastic framework is developed for characterizing the soil moisture distribution. The resulting model provides closed form analytical solutions for (a) the variance of soil moisture distribution; (b) the covariance between soil moisture distribution and soil properties; and (c) the covariance between soil moisture distribution and topography as a function of soil heterogeneity, topography and soil moisture. Series of simulations are performed using various combinations of parameters. Comparisons between simulated results and a number of field observations show qualitative agreement. Application of the proposed stochastic framework requires statistical information of soil characteristics. In the second approach, possibility of inferring soil physical properties from remotely sensed brightness temperature maps is explored. Remotely sensed brightness temperature data from a single drying cycle from Washita '92 Experiment and two different ANN architectures (Feed-Forward Neural Network (FFNN), Self Organizing Map (SOM)) are used to classify soil types into three categories. Results show that FFNN yield better classification accuracy (about 80% accuracy) than SOM (about 70% accuracy). The SOM, however, has an advantage because it requires very little information regarding soil properties. To classify soil into more than three categories, this study suggests the use of multiple-drying-cycle brightness temperature data. Use of multiple-drying-cycle brightness temperature data from the Southern Great Plains suggests that it is possible to classify soil into more than three groups. It appears that the requirement of rapidly changing decision boundary, in the case of space-time evolution of brightness temperature data, will restrict the FFNN model to yield better accuracy. Motivated by these observations, a simple prototype-based classifier, known as 1-NN model, is used which yield 86% classification accuracy for six textural groups. A comparison of classification error regions for both models suggests that, for the given input representation, further improvement in classification accuracy is feasible with different ANN structure.

Committee:

Dr. Shafiqul Islam (Advisor)

Keywords:

soil moisture; stochastic; artificial neural network; remote sensing; soil properties

Palem, Srikanth VenkataDesign and implementation of an Internet based Spatial Decision Support System(SDSS) for Freight Management
Master of Arts, University of Toledo, 2004, Geography and Planning
Growing freight has been a major concern for the transportation planning community. Increasing freight movements by all modes of transportation across the nations has lead to congestion and inadequate infrastructure. There is a rising need for Internet based freight management spatial decision support systems (SDSS) exploiting the latest Geographical Information Systems (GIS) and Remote Sensing (RS) technologies that can assist the transportation planning community in making informed decisions about freight related issues consisting of congestion, demand and capacity. The system being an online or web based system has the advantage of being accessed from anywhere thus making it an easy tool for sharing information across different regions. This can also be utilized for asset management, data dissemination and to model alternative freight management plans and “what if?” scenarios. There is no established framework to date for the development of such systems. A pragmatic approach is taken in this study to design and develop a conceptual framework for an Internet or web based freight management spatial decision support system (SDSS). Different components, features and technology that are required to create such systems were discussed in detail along with a variety of development and implementation strategies. The developed framework was utilized in creating a freight management SDSS for the Upper Midwest Freight Corridor Study currently underway at The University of Toledo encompassing the states in the Midwest. This has given an opportunity to look at the feasibility of implementing such systems and the difficulties faced. The freight management SDSS is currently online and is anticipated to be used by Department of Transportation officials, urban and transportation planners and homeland security officials in making informed decisions. Thus, the conceptual framework developed in this study can be used as the rudimentary framework for creating a robust freight management SDSS in the future.

Committee:

Peter Lindquist (Advisor)

Keywords:

GIS; Remote Sensing; Internet GIS; SDSS; Freight Management SDSS; Online SDSS

Su, HaibinDerivation of Coastal Bathymetry and Stream Habitat Attributes Using Remote Sensing Images and Airborne LiDAR
PhD, University of Cincinnati, 2011, Arts and Sciences: Geography

Bathymetric information pertaining to oceans, inland lakes, and rivers is crucial to the safety of nautical navigation, coastal management, and various scientific studies of aquatic environments. Optical remote sensing imagery offers a cost-effective alternative to echo sounding and bathymetric LiDAR surveys for deriving high density bottom depth estimates for coastal and inland water bodies. Most previous studies utilized a global log-linear regression model to invert multi-spectral images into bathymetric data for an entire image scene. The performance of conventional global models is limited when the bottom type and water quality vary spatially within the scene, or when bottom albedo is low. To address the inadequacy of conventional log-linear global inversion models, I proposed two methods to improve the accuracy of depth estimates.

The first method, which is based on the Levenberg-Marquardt optimization algorithm, can automated calibrate the parameters for a non-linear inversion model. This method has been successfully applied to an IKONOS multispectral image. Bathymetric data derived from the non-linear inversion model are slightly more accurate and stable, particularly for deeper benthic habitats, than those derived from a conventional log-linear model although their overall performances are very similar. The second method is geographically adaptive inversion model. Although the general mathematical form of the geographically adaptive model is the same, model parameters are optimally determined within a geographical region or a local area, in contrast to the entire scene in the global inversion model. By using high-resolution IKONOS and moderate-resolution Landsat satellite images, I demonstrated that regionally- and locally-calibrated inversion models can effectively address spatial heterogeneity problem of water quality and bottom type, and provide significantly improved bathymetric estimates for more complex coastal waters.

Besides bathymetry information, management of aquatic habitat in streams requires knowledge of conditions and processes both inside the stream channel and in the adjacent riparian zones. To build up our monitoring and modeling capability for the stream ecosystems, I developed an automated approach to the extraction of quantitative attributes about channel geomorphology and riparian vegetation for stream habitats assessment, by integrating airborne LiDAR and aerial photography. A bottom-up segmentation method is proposed to identify the critical morphological points on a channel cross-section and partition the cross-section into meaningful segments for geomorphologic attribute calculation. Numerical algorithms have been designed to derive the channel cross-section attributes, channel longitudinal attributes, and planform attributes by integrating LiDAR and aerial photographs. Attributes about the type, vertical structure and complexity of vegetation in riparian zone have been derived by synergistically combining LiDAR data and aerial photographs. The derived cross-section geomorphologic and vegetation attributes are associated with the corresponding segments of river reach along the channel for longitudinal analysis of habitat variation. A case study is presented to illustrate the computational procedure and utility of our method. I demonstrated that my automated method can generate dense measurements on various geomorphology and vegetation attributes at user-defined intervals along the stream for better quantifying the longitudinal variability of reach conditions.

Committee:

Richard Beck, PhD (Committee Chair); Hongxing Liu, PhD (Committee Chair); Ishi Buffam, PhD (Committee Member); Lin Liu, PhD (Committee Member); Tak Yung Tong, PhD (Committee Member)

Keywords:

water depth;remote sensing;optimization;geographically adaptive inversion;stream habitats assessment;river channel geomorphology

Xi, ZhouxinEstimating and Mapping the LAI and Mean Crown Radius of Forest from Airborne Images: A Case Study in the Zaleski State Forest
Master of Science, The Ohio State University, 2013, Geography
Remote sensing development stimulates a plethora of approaches to retrieving forest biophysical information. Various models have emerged to build a link from the forest information to the remotely sensed data, among which models based on radiation physics attract great research interest for high accuracy. However, complexity arises from three aspects: model parameters estimation, field measurement and robust model inversion method. Currently, a practical solution to these three problems is still at pilot stage. Using high resolution aerial photographs and Lidar data from an experimental region in Zaleski State Forest, this study proposed an innovative procedure for retrieving forest Leaf Area Index (LAI) and crown radius simultaneously at plot level based on the 4-scale physical model. Specifically, key model parameters, e.g. tree height, tree count and sunlit fraction are estimated through image processing; field labor such as measuring spectra and LAI can be minimized, or only for validation use, for the convenience of delineating crown area as reference; robust model inversion can be achieved based on a cost function integrating a priori information. A low root mean square and high correlation has been observed between the retrieved crown radius and reference crown radius, indicating the potential for reliably mapping plot-level crown radius and LAI for further application use.

Committee:

Desheng Liu (Advisor); Darla Munroe (Committee Member); Jialin Lin (Committee Member)

Subjects:

Geography; Remote Sensing

Keywords:

Remote Sensing, 4-scale, canopy model, model inversion, LAI, crown radius

Weghorst, Pamela L.MODIS algorithm assessment and principal component analysis of chlorophyll concentration in Lake Erie
MS, Kent State University, 2008, College of Arts and Sciences / Department of Geology

The purpose of this study was to use Moderate Resolution Imaging Spectroradiometer (MODIS) chlorophyll data to identify the predominant spatial and temporal patterns in chlorophyll variability in Lake Erie. Three algorithms were tested against in situ chlorophyll measurements: O’Reilly’s OC2 and OC3 algorithms (1998) and Cannizzaro’s shallow water algorithm (2005). These algorithms can be calculated from atmospherically corrected reflectance data distributed by NASA; algorithms that required data without atmospheric corrections were not considered. The initial regression results showed no correlation for any of the algorithms tested. However, outliers for all three algorithm regressions were consistently missing reflectance values at one or more wavelengths. Removing pixels that had missing reflectances at any wavelength, even those not required to compute the algorithm, greatly improved algorithm performance. This result supports the hypothesis that a more advanced correction procedure for atmospheric scattering that produced more reliable reflectance values for turbid inland waters would improve the performance of chlorophyll retrieval algorithms for Lake Erie.

While none of the algorithms were valid in Lake Erie’s turbid western basin, OC3 performed best in the central and eastern basins, with an R2 of 0.56 and RMSE of 0.73. OC3 chlorophyll concentrations were calculated for all the available 2002-2007 non-winter MODIS images of Lake Erie. Principal component analysis (PCA) was applied to the resulting time series, which extracted patterns of seasonal variability from the data set. The central basin showed more seasonal variability than the eastern basin, with elevated chlorophyll concentrations in spring and fall.

Committee:

Donna Witter, PhD (Advisor)

Subjects:

Freshwater Ecology; Geology; Hydrology; Remote Sensing

Keywords:

chlorophyll; Lake Erie; remote sensing; algorithm; atmospheric correction

Ek, EdgarMonitoring Land Use and Land Cover Changes in Belize, 1993-2003: A Digital Change Detection Approach
Master of Science (MS), Ohio University, 2004, Environmental Studies (Arts and Sciences)

In Belize, the use of remotely sensed information for monitoring landscape dynamics is a relatively new area. This study takes advantage of contemporary technologies, such as remote sensing, for monitoring land use and land cover changes in Belize. The study area covers approximately 6,190 square miles. Two Landsat images of 1993 and 2003 were used to identify, quantify, assess and map changes in land use and land cover. The Landsat images were classified using an unsupervised K-means algorithm. Comparison of ground truth points and the 2003 classification result shows a classification accuracy of 92%. The digital change detection methodology involved a pixel-by-pixel comparison of the classified images using ENVI software. The results show that urban expansion (12%/year) is occurring at a faster rate than population growth (3.5%/year). In addition, agricultural land expansion is occurring at a rate of 32 square miles per annum. Urban development, agricultural land expansion and extensive pine forest cover loss are contributing to an estimated deforestation rate of 35 square miles per annum. In general, this study provides urgent and needed information that will guide the Government of Belize to achieve the desired goals of sustainable development.

Committee:

James Lein (Advisor)

Subjects:

Environmental Sciences

Keywords:

Monitoring Land use and Land cover; Monitoring Landscape Dynamics; Landscape change; Digital Change Detection; Environmental Monitoring Using Remote Sensing

Cummins, Shannon E.Remote Sensing Technology for Environmental Plan Monitoring: A Case Study of the Comprehensive Monday Creek Watershed Plan
Master of Arts (MA), Ohio University, 2002, Geography (Arts and Sciences)

Remote sensing and GIS offer useful tools for monitoring vegetation health and riparian coverage in the Monday Creek Watershed. Monitoring programs evaluate environmental plan success and measure indicators to identify change. This research demonstrates how remote sensing can be integrated into a local watershed management process and applies the technology as a tool to effectively monitor selected indicators that direct plan implementation. Data includes 2 anniversary date Landsat satellite images and digitized GIS watershed layers. A 3-phase methodology involves land use/cover mapping, normalized difference vegetation index (NDVI) analysis and change detection through cross tabulation. Forty meter stream buffers assist riparian habitat analysis. Image Classification utilized an unsupervised K-means algorithm. Analysis of regional and subwatershed conditions shows that vegetation overall remains healthy although a slight decline in regional vegetation health is visible, especially along roads and settlements. Forest cover increased, forest fragmentation declined and trouble spots in subwatersheds are identified.

Committee:

James Lein (Advisor)

Subjects:

Geography

Keywords:

Remote Sensing; Environmental Plan; Monitoring; Normalized Difference Vegetation Index(NDVI)

Bourne, Michael G.The Effects of Nonpoint Source Pollution on Cyanobacterial Blooms in Lake Erie From Agriculturally Applied Fertilizers in Northwestern Ohio, USA, for the Years (1999-2003)
Master of Science (MS), Bowling Green State University, 2006, Geology
Since the mid 1990’s, Lake Erie has experienced seasonal eutrophication. This investigation was designed to look at potential causes for eutrophication in Lake Erie, particularly the effects of agriculturally applied fertilizers in Northwestern, Ohio. This study was designed to see if any correlations exist between agriculturally applied fertilizers (including sewage sludge) and cyanobacterial blooms in the Western Basin of Lake Erie that occurred during the months of July, August, and September for the years 1999-2003. An algorithm created by Vincent et al., (2004) was used on available LANDSAT frames to monitor phycocyanin growth caused by cyanobacteria. These images were analyzed in conjunction with Maumee River water quality data, planted winter wheat, local weather data, and records of agriculturally applied sewage sludge nutrient data from the local wastewater treatment plant. The year 2003 provided the largest algal bloom in this study, which extended beyond the upper threshold of the phycocyanin algorithm of 15 micrograms per liter. The largest total acreage of high phycocyanin content occurred on September 20, 2003 which had 285,451 phycocyanin-rich acres present in the Western Basin of Lake Erie. The average acres of high phycocyanin content for the month of September in the Western Basin of Lake Erie, display strong correlations with increased Maumee River flow rate, increased Maumee River nutrients (including both nitrogen and phosphorus), as well as planted winter wheat acreage for Northwestern, Ohio. Agriculturally applied sewage sludge provides circumstantial evidence that it contributes to cyanobacterial blooms in the Western Basin of Lake Erie, but there is not enough evidence to implicate or exonerate whether sewage sludge is the main driving force promoting cyanobacterial blooms.

Committee:

Robert Vincent (Advisor)

Keywords:

cyanobacterial blooms; phycocyanin; sewage sludge; Maumee River; fertilizers; nonpoint source pollution; Lake Erie; Remote Sensing; LANDSAT

Demir, Metin AytekinPerturbation theory of electromagnetic scattering from layered media with rough interfaces
Doctor of Philosophy, The Ohio State University, 2007, Electrical Engineering
The Small Perturbation Method (SPM) is a low frequency approximation to the electromagnetic scattering from rough surfaces. The theory involves a small height expansion in conjunction with a perturbation series expansion of the unknown scattering coefficients. Recently, an arbitrary order, iterative solution procedure has been derived for SPM: kernels at any order are expressed as a summation over lower order kernels in an iterative fashion. Such a form is very useful, because it allows evaluation of the field statistical moments in a direct manner, when considering stochastic surfaces. In this dissertation, this procedure is extended to the two layer (two rough surfaces on top of each other) problem and the complete solution is given. Utilizing this formulation, the second and fourth order bi-static scattering coefficients for two rough surfaces characterized by two uncorrelated Gaussian Random Processes (GRP) are obtained. The effects of upper and lower roughnessesand the interaction effect in the total fourth order cross section can be identified in the theory. Studies on the ratio of the interaction effect to the total cross section are presented for example cases, investigating the relative importance of interactions among surfaces. Results show the interaction term contributes most to the cross-pol cross sections when surfaces are close to each other at near grazing incidence. In addition, the previously developed arbitrary order SPM solution for the single layer problem is utilized to derive the fourth order term in the small slope approximation (SSA) of thermal emission from the sea surface. It is shown that this term has the form of a four-fold integration over a product of two sea spectra for a Gaussian random process sea, thereby describing emission “interaction” effects among pairs of sea waves. Interaction effects between “long” and “short” waves are considered, both through numerical and approximate evaluations of the fourth order theory. The approximation developed is a theoretical alternative to the “two-scale” model, and enables comparisons of short wave “tilting” effects between the two models in terms of spectrum independent “weighting” functions. The weighting functions obtained are found to be similar, but not identical.

Committee:

Joel Johnson (Advisor)

Keywords:

Electromagnetic Scattering; Rough Surface Scattering; Microwave Remote Sensing

Romanko, MatthewRemote Sensing in Precision Agriculture: Monitoring Plant Chlorophyll, and Soil Ammonia, Nitrate, and Phosphate in Corn and Soybean Fields
Master of Science (MS), Bowling Green State University, 2017, Geology
Precision agricultural practices attempt to increase the efficiency of agricultural chemical usage in order to reduce pollution by implementing a variety of technology driven strategies. This study evaluated the effectiveness of remote sensing (RS) technology to model several important agricultural chemistry parameters. In situ hyperspectral and satellite multispectral measurements were used to examine the capability of a large range of soil and vegetation spectral indices (established indices from literature and spectral ratios) to model soil and vegetation chemistry, in particular plant chlorophyll and soil ammonia, nitrate, and phosphate contents. Data were collected from two farm fields (site 1: corn; and site 2: soybeans) in Wyandot County, Ohio at multiple times in 2015 (T1; early-May, through T6; early-November) including data collected prior to planting and fertilization and data collected post-harvest. The method used in the current study included the scaling up process from the in situ hyperspectral data to satellite observations (Landsat 8 OLI and Pleiades 1B) using aggregation of a) individual features reflectance measurements (soil or plants), and b) mixed reflectance data (soil and plants) to offer insight into the spatial, spectral, and temporal aspects of RS analyses. Laboratory testing for soil ammonium, phosphate and nitrate, and field measurements for plant chlorophyll were used in the assessment of the spectral indices. This research found that hyperspectral ratios were most effective for modeling the soil and plant parameters. The highest r2 values were reached for soil ammonia (r2 = 0.68); soil nitrate (r2 = 0.59) and soil phosphate (r2 = 0.75) using soil spectral ratios R1357/2024.3, R1379.5/2024.3, and R1854.7/1892.6, respectively (e.g., corn, site 1) during the peak of the growing season (T4). In addition to soil hyperspectral ratios, soil chemistry parameters were also statistically significantly correlated with several plant hyperspectral reflectance ratios, typically during early season (T2). Multispectral satellite data from Landsat 8 and Pleiades 1B showed that the relatively finer spectral resolution of the Landsat 8 sensor offered a distinct advantage in modeling the soil chemistry parameters when compared with Pleiades images during mid-season (T4). Landsat 8 was able to model each soil chemistry parameter more effectively producing the highest r2 values for site 1 soil ammonia (r2 = 0.31), site 2 soil nitrate (r2 = 0.33), site 1 soil phosphate (r2 = 0.41), and site 2 plant chlorophyll (r2 = 0.40) using satellite multispectral ratios; LS8 Rb1/b7, LS8 Rb2/b5, LS8 Rb3/b1, and; LS8 Rb4/b2 respectively. Analysis of spectrally aggregated hyperspectral data and satellite data indicated that Landsat 8 spectral bands in the Ultra-blue and SWIR regions were useful in RS estimation of soil chemistry from soil/plant/and mixed reflectance data. A variety of parameters known to influence soil and plant chemistry were also examined in this study. Soil moisture, pH, and potential wetness via Compound Topographic Index (CTI) index were statistically evaluated for influence on soil and plant chemistry measurements. This study showed that wetness significantly impacted the distribution of some soil chemistry parameters at times throughout the crop production season. The variability in canopy cover (referred to as `gap fraction’ in this study) was also estimated at each sample location during the field campaign using Digital Hemispherical Photography (DHP) to combine hyperspectral soil and plant reflectance measurements in the appropriate proportions for comparison with satellite data. It was found that gap fraction explained 89% of the variation in Pleiades Rb2/b4 at site 1 and 51% of the variation in Pleiades Rb3/b1 at site 2.

Committee:

Anita Simic (Advisor); Sheila Roberts (Committee Member); John Farver (Committee Member)

Subjects:

Geology; Remote Sensing

Keywords:

remote sensing; precision agriculture; soil chemistry; hyperspectral; multispectral

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