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MULTISENSOR FUSION REMOTE SENSING TECHNOLOGY FOR ASSESSING MULTITEMPORAL RESPONSES IN ECOHYDROLOGICAL SYSTEMS
Title
MULTISENSOR FUSION REMOTE SENSING TECHNOLOGY FOR ASSESSING MULTITEMPORAL RESPONSES IN ECOHYDROLOGICAL SYSTEMS.
Creator
Makkeasorn, Ammarin, Chang, Ni-Bin, University of Central Florida
Abstract / Description
Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements...
Show moreEarth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing–based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.
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Date Issued
2007
Identifier
CFE0001767, ucf:47267
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001767
Biogeochemical Cycling and Nutrient Control Strategies for Groundwater at Stormwater Infiltration Basins
Title
Biogeochemical Cycling and Nutrient Control Strategies for Groundwater at Stormwater Infiltration Basins.
Creator
O'Reilly, Andrew, Chang, Ni-bin, Wanielista, Martin, Chopra, Manoj, Wang, Dingbao, Katz, Brian, University of Central Florida
Abstract / Description
Elevated concentrations of nutrients, particularly nitrate, in groundwater and springs in Florida are a growing resource management concern. Stormwater infiltration basins, which are a common stormwater management practice in the well-drained karst terrain areas of Florida, are a potentially important source of nutrients to the groundwater system because stormwater exits the basin by only evaporation or infiltration. To better understand the biogeochemical processes integrating stormwater...
Show moreElevated concentrations of nutrients, particularly nitrate, in groundwater and springs in Florida are a growing resource management concern. Stormwater infiltration basins, which are a common stormwater management practice in the well-drained karst terrain areas of Florida, are a potentially important source of nutrients to the groundwater system because stormwater exits the basin by only evaporation or infiltration. To better understand the biogeochemical processes integrating stormwater infiltration impacts on groundwater resources in a field-scale setting, a combination of hydrologic, soil chemistry, water chemistry, dissolved and soil gas, isotope, and microbiological data was collected from 2007 through 2010 at two stormwater infiltration basins receiving runoff from predominantly residential watersheds in north-central Florida. Substantially different biogeochemical processes affecting nitrogen fate and transport were observed beneath the two stormwater infiltration basins. Differences are related to soil textural properties that deeply link hydroclimatic conditions with soil moisture variations in a humid, subtropical climate. During 2008, shallow groundwater beneath the basin with predominantly clayey soils (median 41% silt+clay content) exhibited decreases in dissolved oxygen from 3.8 to 0.1 mg/L and decreases in nitrate-nitrogen from 2.7 mg/L to less than 0.016 mg/L, followed by manganese and iron reduction, sulfate reduction, and methanogenesis. In contrast, beneath the basin with predominantly sandy soils (median 2% silt+clay content), aerobic conditions persisted from 2007 through 2009 (dissolved oxygen of 5.0(-)7.8 mg/L), resulting in nitrate-nitrogen of 1.3(-)3.3 mg/L in shallow groundwater. Soil extractable nitrate-nitrogen was significantly lower and the copper-containing nitrite reductase gene density was significantly higher beneath the clayey basin. Differences in moisture retention capacity between fine- and coarse-textured soils resulted in median volumetric gas-phase contents of 0.04 beneath the clayey basin and 0.19 beneath the sandy basin, inhibiting surface/subsurface oxygen exchange beneath the clayey basin. Subsurface biogeochemical processes at the clayey stormwater infiltration basin were further analyzed to better understand the effects of the highly variable hydrologic conditions common in humid, subtropical climates. Cyclic variations in biogeochemical processes generally coincided with wet and dry hydroclimatic conditions. Oxidizing conditions in the subsurface persisted for about one month or less at the beginning of wet periods with dissolved oxygen and nitrate showing similar temporal patterns. Reducing conditions in the subsurface evolved during prolonged flooding of the basin. At about the same time oxygen and nitrate reduction concluded, manganese, iron, and sulfate reduction began, with the onset of methanogenesis one month later. Reducing conditions persisted up to six months, continuing into subsequent dry periods until the next major oxidizing infiltration event. Evidence of denitrification in shallow groundwater at the site is supported by median nitrate-nitrogen less than 0.016 mg/L, excess nitrogen gas up to 3 mg/L progressively enriched in delta-15N during prolonged basin flooding, and isotopically heavy delta-15N and delta-18O of nitrate (up to 25 and 15 per mil, respectively). Isotopic enrichment of newly infiltrated stormwater suggests denitrification was partially completed within two days. Soil and water chemistry data suggest a biogeochemically active zone exists in the upper 1.4 m of soil, where organic carbon was the likely electron donor supplied by organic matter in soil solids or dissolved in infiltrating stormwater. The cyclic nature of reducing conditions effectively controlled the nitrogen cycle, switching nitrogen fate beneath the basin from nitrate leaching to reduction in the shallow saturated zone. Soil beneath the sandy stormwater infiltration basin was amended using biosorption activated media (BAM) to study the effectiveness of this technology in reducing inputs of nitrogen and phosphorus to groundwater. The functionalized soil amendment BAM consists of a 1.0:1.9:4.1 mixture (by volume) of tire crumb (to increase sorption capacity), silt and clay (to increase soil moisture retention), and sand (to promote sufficient infiltration), which was applied to develop an innovative best management practice (BMP) utilizing nutrient reduction and flood control sub-basins. Construction and materials costs, excluding profit and permit fees, for the innovative BMP were about $US 65 per square meter of basin bottom. Comparison of nitrate/chloride ratios for the shallow groundwater indicate that prior to using BAM, nitrate concentrations were substantially influenced by nitrification or variations in nitrate input. In contrast, for the new basin utilizing BAM, nitrate/chloride ratios indicate minor nitrification and nitrate losses with the exception of one summer sample that indicated a 45% loss. Biogeochemical indicators (denitrifier activity derived from real-time polymerase chain reaction and variations in major ions, nutrients, dissolved and soil gases, and stable isotopes) suggest nitrate losses are primarily attributable to denitrification, whereas dissimilatory nitrate reduction to ammonium and plant uptake are minor processes. Denitrification was likely occurring intermittently in anoxic microsites in the unsaturated zone, which was enhanced by increased soil moisture within the BAM layer and resultant reductions in surface/subsurface oxygen exchange that produced conditions conducive to increased denitrifier activity. Concentrations of total dissolved phosphorus and orthophosphate were reduced by more than 70% in unsaturated zone soil water, with the largest decreases in the BAM layer where sorption was the most likely mechanism for removal. Post-BAM orthophosphate/chloride ratios for shallow groundwater indicate predominantly minor increases and decreases in orthophosphate with the exception of one summer sample that indicated a 50% loss. Differences in nutrient variations between the unsaturated zone and shallow groundwater may be the result of the intensity and duration of nutrient removal processes and mixing ratios with water that had undergone little biogeochemical transformation. In order to quantify potential processes leading to observed nitrogen losses beneath the innovative BMP, an integrated infiltration basin(-)nitrogen reduction (IBNR) system dynamics model was developed. Based on two simulation periods, the IBNR model indicated denitrification accounted for a loss of about one-third of the total dissolved nitrogen mass inflow and was occurring predominantly in the BAM layer. The IBNR model results in combination with the field-based biogeochemical assessment demonstrated that the innovative BMP using the functionalized soil amendment BAM is a promising passive, economical, stormwater nutrient-treatment technology. Further field- and laboratory-scale research on the long-term sustainability of nutrient losses and further elucidation of causative physicochemical and biogeochemical mechanisms would contribute to improved BAM performance and green infrastructure development in the future.
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Date Issued
2012
Identifier
CFE0004419, ucf:49391
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004419