Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

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 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

On Site Wastewater Treatment Using a Functionalized Green Filtration Media Sorption Field (DIVB)

Methods, systems and compositions for a green sorption media for bioretention soil amendments in drainfields for on-site waste water systems filled with the green sorption media to foster an anaerobic or anoxic environment saturated. The green sorption media includes one or more recycled materials, including tire crumb, sawdust, orange peel, coconut husks, leaf compost, oyster shell, soy bean hulls and one or more naturally occuring materials including peat, sands, zeolites, and clay. The wastewater filtration system for a passive drainfield includes the green sorption material mixture, a cell including baffled compartments and a riser, the cell filled with green sorption material mixture to provide an alternating cycle of aerobic and anoxic environments, an influent distribution system to distribute the influent over the cell, and a piping system arranged for dosing the cell to sustain the functionality of the green sorption material mixture to remove nutrient content in wastewater

Passive Underground Drainfield for Septic Tank Nutrient Removal Using Functionalized Green Filtration Media

Methods, systems and compositions for a green sorption media for bioretention soil amendments in drainfields for on-site waste water systems filled with the green sorption media to foster an anaerobic or anoxic environment saturated. The green sorption media includes one or more recycled materials, including tire crumb, sawdust, orange peel, coconut husks, leaf compost, oyster shell, soy bean hulls and one or more naturally occuring materials including peat, sands, zeolites, and clay. The wastewater filtration system for a passive drainfield includes the green sorption material mixture, a cell including baffled compartments and a riser, the cell filled with green sorption material mixture to provide an alternating cycle of aerobic and anoxic environments, an influent distribution system to distribute the influent over the cell, and a piping system arranged for dosing the cell to sustain the functionality of the green sorption material mixture to remove nutrient content in wastewater

Green Sorption Material Mixes for Water Treatment CIP

Media compositions for use in wastewater treatment, stormwater treatment, CSO treatment and greenroof stormwater management systems as filtration media, plant growth media or pollutant retention media. Media compositions includes at least one of the recycled material selected from a group consisting of tire crumb, wood sawdust, and paper and naturally occuring material selected from a group consisting of sand, limestone, sandy clay, expanded clay, organics used for processing geothermal water, organics used for agricultural drainage basins and filtration, and organics for aquaculture drainage and organics used for silviculture and forest drainage, and organics used as growing media

Functionalized Green Filtration Media for Passive Underground Drainfield for Septic Tank Nutrient Removal

Methods, systems and compositions for a green sorption media for bioretention soil amendments in drainfields for on-site waste water systems filled with the green sorption media to foster an anaerobic or anoxic environment saturated. The green sorption media includes one or more recycled materials, including tire crumb, sawdust, orange peel, coconut husks, leaf compost, oyster shell, soy bean hulls and one or more naturally occuring materials including peat, sands, zeolites, and clay. The wastewater filtration system for a passive drainfield includes the green sorption material mixture, a cell including baffled compartments and a riser, the cell filled with green sorption material mixture to provide an alternating cycle of aerobic and anoxic environments, an influent distribution system to distribute the influent over the cell, and a piping system arranged for dosing the cell to sustain the functionality of the green sorption material mixture to remove nutrient content in wastewater

Retention/Detention Pond and Green Roof Passive Nutrient Removal Material Mixes (Green Roof)

Materials, compositions, substances, and methods and systems for stormwater treatment in wet ponds, dry ponds and a green roof system. A first embodiment provides in-situ treatment unit within the retention pond by withdrawing the stored stormwater tocirculate the stored stormewater into the in-situ treatment unit to sorb nitrogen from the stored stormwater. A second embodiment provided uses a riprap apron, a preforated riser loacted at the bottom of the riprap apron and a goetextile media encased in a sorption media jacket around the perforated riser. A third embodiment provides a greenroof stormwater treatment system that includes protections for waterproofing and insulating the roof, a pollution control media layer for filtration and sorption of solids and dissolved materials found in stormwater, a growing media for growing vegetation, and a cistern to store the runoff stormwater between irrigation events. The green roof system includes recycling runoff stormwater by irrigating th

Functionalized Green Filtration Media for Passive Underground Drainfield for Septic Tank Nutrient Removal DIV

Methods, systems and compositions for a green sorption media for bioretention soil amendments in drainfields for on-site waste water systems filled with the green sorption media to foster an anaerobic or anoxic environment saturated. The green sorption media includes one or more recycled materials, including tire crumb, sawdust, orange peel, coconut husks, leaf compost, oyster shell, soy bean hulls and one or more naturally occuring materials including peat, sands, zeolites, and clay. The wastewater filtration system for a passive drainfield includes the green sorption material mixture, a cell including baffled compartments and a riser, the cell filled with green sorption material mixture to provide an alternating cycle of aerobic and anoxic environments, an influent distribution system to distribute the influent over the cell, and a piping system arranged for dosing the cell to sustain the functionality of the green sorption material mixture to remove nutrient content in wastewater

Optimal Site Selection Of Watershed Hydrological Monitoring Stations Using Remote Sensing And Grey Integer Programming

Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are all intimately related with each other to form water balance dynamics on the surface of the Earth. To monitor change in hydrological systems with minimum effort, however, hydrological monitoring networks at the watershed scale should be deployed at critical locations to advance the monitoring and sensing capability. One of the science questions is how to develop an optimum arrangement/distribution strategy of those monitoring platforms with respect to hydrological components subject to technical and resources constraints. While the complexities arise from the integration of highly heterogeneous data streams in the hydrological cycle under uncertainty, there is an acute need to develop a site screening and sequencing procedure permitting a cost-effective search for final site selection. This paper purports to develop such an approach to address the optimal site selection strategy by integrating satellite remote sensing images with a grey integer programming (GIP) model. The approach uses spatial information on the range of likely values temporally encountered for a number of biophysical descriptors in support of the optimization analysis under uncertainty. Practical implementation was assessed by a case study in a semi-arid watershed-the Choke Canyon Reservoir watershed, south Texas. GIS-based GIP modeling technique successfully supports the screening and sequencing mechanism based on the composite satellite images, which smoothly prioritizes the relative importance and provides the rank order scores across all candidate sites. With the aid of such a synergistic approach, seven locations out of 563 candidate sites were eventually selected and confirmed by a field investigation. © 2009 Springer Science+Business Media B.V

Short-Term Stream Flow Forecasting With The Aid Of Global Climate Change Indices

To more efficiently use the limited amount of water under the impact of global climate change or to resourcefully provide adequate time for flood and drought warning, there is an acute need to seek an advanced modeling technique for improving streamflow forecasting on a short-term basis. This study aims to expand forecasting capacity by incorporating sea surface temperature (SST), spatiotemporal rainfall distribution from the Next Generation Radar (NEXRAD), meteorological data, and historical stream flow data to forecast discharges in a semi-arid watershed in South Texas, U.S.A. Comparative study was conducted by comparing the performance of traditional time-series model against the outputs of neural network (NN) and genetic programming (GP) models. The case study elicits microclimatological factors and the resultant stream flow rate in a river system given the influence of dynamic basin features, such as soil moisture, soil temperature, ambient relative humidity, air temperature, and precipitation. SST data were acquired from three locations including the Atlantic Ocean, the Gulf of Mexico, and the Pacific Ocean. Five numeric evaluators were defined and applied to evaluate all models involved. Research findings show that GP-based models have relatively better performance in most cases compared to neural network (NN) time-series models based on 16-week historical data. SST and meteorological data may significantly improve the GP-derived stream forecasting model that is highly recommended. The developed GP models can do pretty well forecasting for next 30-day discharges with r-square value of 0.84 and percentage error (PE) of 7%

Water Demand Analysis In Urban Region By Neural Network Models

Statistical water demand models are usually developed as time series coefficients using historically available water demand data, together with any other relevant variables. But structure identification turns out difficult for most of the applications. This study would count on the artificial neural networks (ANN) to forecast the water demand patterns. The ANN model may exhibit a nonlinear feature learned from historical data, in the same way as humans learn from experience. The nonlinearity, high complexity, and uncertainty associated with water demands may favor the potential use of ANNs to compete with or outperform the conventional time series methods for forecasting the similar topics. If the ANNs model is learned correctly, as verified by the accuracy of the predictions using input data not used during the training, the ANN algorithm can be robust with low computation time requirements, even if there are some errors or noise in the input data. Two types of cities, including Oviedo (fast growth) and Winter Springs (slow growth) in the Great Orlando Metropolitan Area in Florida were investigated with respect to monthly data. Such pattern recognition practices would help water utilities identify the expansion and operation strategies in water distribution systems in the long run. Copyright ASCE 2006