Greg Easson

Satellite-based Flood Modeling Using TRMM-based Rainfall Products

Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. However, most real-time and quasi-global satellite rainfall products are currently available at spatial scales ranging from 0.25° to 0.50° and hence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scale flood events. This study assesses the question: what are the hydrologic implications of uncertainty of satellite rainfall data at the coarse scale? We investigated this question on the 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall product assessed was NASAs Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real time with a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data can improve application in flood prediction to some extent with the trade-off of more false alarms in peak flow. However, a more rational and regime-based adjustment procedure needs to be identified before the use of satellite data can be institutionalized among flood modelers.

The Effects of Riparian Vegetation on Bank Stability

In northern Mississippi, roots in riparian zones were studied in an attempt to quantify the effects of root reinforcement of the soil matrix. The roots of trees can be treated as elastic-reinforced elements, and a function of the tensile strength of the roots can be added directly to the Mohr-Coulomb equation for failure criteria. Estimating root reinforcement and root-soil matrix interactions allows for the determination of whether bank vegetation is beneficial or detrimental. The research was conducted at the Goodwin Creek Experimental Watershed, located near Batesville, MS. This investigation quantifies root tensile strength of the sweet gum ( Liquidamar syraciflua ) in a cohesive, fine-grained, primarily loess-derived fluvial material. During the field research, trenches were excavated to gain access to the roots being studied. These trenches allowed mapping of the roots, as well as direct tensile testing of the roots. Increased tensile strength due to root reinforcement was found to be between 0.0 and 245 kPa, depending on depth. For a given depth of 40 cm, the increased tension due to root reinforcement averaged 148 kPa, depending on lateral distance from tree. A modified root reinforcement model was developed to explain the root-soil interaction observed at the research site. Itascas Fast Lagrangian Analysis of Continua model was employed in determining the role of root reinforcement. The modeling results showed a contrast between root-reinforced and unreinforced soil. When no root reinforcement existed, the slope failed marginally. When simulated root reinforcement of 20 kPa was applied, the slope was shown to be completely stable.

Using at-sensor radiance and reflectance tasseled cap transforms applied to change detection for the ASTER sensor

The Tasseled Cap Transform (TCT) was originally created for agricultural land investigations. It is a vegetative index commonly used as an indicator of vegetation health and assessing vegetation and land cover change. The nature of the TCT requires linear combinations specific to each sensor. Additionally, the varying units of the reported digital number (DN) require supplementary eigenvectors. TCTs were derived for the at-sensor radiance and at-sensor reflectance and compared using differing change detection application in Mississippi. The Tasseled Cap Soil Brightness Index (SBI) and the Greenness Vegetative Index (GVI) were conducted and evaluated. It was found that the at-sensor radiance based TCT was most useful in a change detection analysis. The desired spectral characteristics were well contrasted while the at-sensor reflectance based TCT tended to be less effective.

Investigating Spatial Downscaling of Satellite Rainfall Data for Streamflow Simulation in a Medium-Sized Basin

Abstract The objective of this study was to investigate spatial downscaling of satellite rainfall data for streamflow prediction in a medium-sized (970 km2) river basin prone to flooding. The spatial downscaling scheme used in the study was based on the principle of scale invariance. It reproduced the rainfall variability at finer scales while being conditioned on the large-scale rainfall. Two Tropical Rainfall Measuring Mission (TRMM)-based real-time global satellite rainfall products were analyzed: 1) the infrared (IR)-based 3B41RT product available at 1 hourly and 0.25° scales and 2) the combined passive microwave (PMW) and IR-based 3B42RT product available at 3 hourly and 0.25° scales. The conceptual Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) was used for the simulation of streamflow. It was found that propagation of spatially downscaled satellite rainfall in the hydrologic model increased simulation uncertainty in streamflow as rainfall grid scales became smaller than 0.25°. T...

Evaluating the Potential of VI-LST Triangle Model for Quantitative Estimation of Soil Moisture using Optical Imagery

This research evaluates the potential of the vegetation index (VI) - land surface (LST) triangle model for quantitative soil moisture study using Moderate Resolution Imaging Spectroradiometer (MODIS) data in a semi-arid environment. The preliminary results indicate that the VI-LST triangle model has potential to be used for quantitative soil moisture estimation by optical imagery if combined with a reference soil moisture data. The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) provides quantitative soil moisture estimation at 25 km spatial resolution. AMSR-E soil moisture information can be used in the VI-LST triangle model in conjunction with MODIS reflectance and thermal data to estimate soil moisture at the spatial resolution of MODIS (1 km).

DEVELOPING A WETLAND CONDITION PREDICTION MODEL USING LANDSCAPE STRUCTURE VARIABILITY

Recent studies found substantial variability in plant community integrity of wetlands in the Prairie Pothole Region (PPR) of central North Dakota, USA. We speculated that this variability might be connected to the nature of the surrounding landscapes and that a link might exist between landscape spatial metrics and wetland condition. We explored this potential link, using a case study in the PPR. A combination of remote sensing, geographic information systems (GIS), and landscape spatial metrics was used to: 1) examine the condition-landscape pattern relationship of temporary and seasonal wetlands, and 2) develop a landscape-level decision support tool for rapid assessment of wetland condition. We sampled 73 wetlands in the study area. We used the Index of Plant Community Integrity (IPCI) as our measure of wetland condition. A wetland landscape was defined by a 300 m radius circular area (0.283 km2) around each habitat. Quantitative characterization of landscape pattern was conducted using metrics computed from land cover categorization maps processed from multi-temporal Landsat satellite data. Ordination of wetland samples in a multivariate space of landscape metrics using non-metric multidimensional scaling revealed strong associations between wetland condition and 10 landscape metrics, primarily among seasonal wetlands. The Landscape Wetland Condition Analysis Model (LWCAM) was developed and validated for rapid quantitative assessment of wetland condition. The model was based on three landscape metrics considered most important for use in the PPR: 1) grassland percent core area of landscape, 2) grassland largest patch index, and 3) the number of patches per unit area. We concluded that surrounding natural grasslands and landscape fragmentation were the most important influences on the structure and plant community condition of wetland ecosystems.

Evaluation of the use of spectral and textural information by an evolutionary algorithm for multi-spectral imagery classification

Considerably research has been conducted on automated and semi-automated techniques that incorporate image textural information into the decision process as an alternative to improve the information extraction from images while reducing time and cost. The challenge is the selection of the appropriate texture operators and the parameters to address a specific problem given the large set of available texture operators. In this study we evaluate the optimization characteristic of an evolutionary framework to evolve solutions combining spectral and textural information in non-linear mathematical equations to improve multi-spectral image classification. Twelve convolution-type texture operators were selected and divided into three groups. The application of these texture operators to a multi-spectral satellite image resulted into three new images (one for each of the texture operator groups considered). These images were used to evaluate the classification of features with similar spectral characteristics but with distinct textural pattern. Classification of these images using a standard image classification algorithm with and without the aid of the evolutionary framework have shown that the process aided by the evolutionary framework yield higher accuracy values in two out of three cases. The optimization characteristic of the evolutionary framework indicates its potential use as a data mining engine to reduce image dimensionality as the system improved accuracy values with reduced number of channels. In addition, the evolutionary framework reduces the time needed to develop custom solutions incorporating textural information, especially when the relation between the features being investigated and the image textural information is not fully understood.

Estimating Speed and Direction of Small Dynamic Targets through Optical Satellite Imaging

Moving Target Indicators (MTI) are systems used to distinguish movement from stationary scenes and sometimes to derive the spatial attributes of these objects. These systems are currently used in many sectors such as traffic studies, border surveillance, and military applications. The proposed MTI reveals vehicles and their velocities using commercial imagery from a passive optical satellite-mounted sensor. With simple process of image differencing, the MTI can automatically recognize conveyances in motion (speed and direction) represented by polygons formed by a group of pixels from successive images. Micro-change detection with an existing commercial satellite requires special considerations of differences in spatial and spectral resolution between images. Complications involving the movement detection system such as vehicle overlap, vehicle clusters, and zones of low confidence are refined by adding error-reducing modules. This process is tested on a variety of vehicles, their concentrations, and environments, confirming the feasibility of utilizing an MTI with commercial optical satellite imagery for movement recognition and velocity estimation.

Predicting shallow surficial failures in the Mississippi River levee system using airborne hyperspectral imagery

Shallow surficial failures or levee slides in the Mississippi River levee system are very common. There is currently no system to identify or predict the location of these slides before they occur. Studies of slide occurrence mechanisms suggest that probable slide-affected areas are characterized by anomalous vegetation. Compact Airborne Spectrographic Imager II (CASI II) imagery was analysed for selected levee sites in association with slide inventory data and field observations. Normalized Difference Vegetation Index (NDVI), Red edge Vegetation Stress Index (RVSI) and Red Edge Position Index (REP) were calculated from the acquired CASI II imagery. The vegetation indices were used to locate the stressed or anomalous vegetation and predict levee slides. The statistical significance of the predictors was determined by logistic regression. All predictors were found statistically significant for developing a slide prediction model. The slide prediction model was developed by combining the single predictors, categorized vegetation indices, into a model based on all three predictors. Percentage of Search Area Reduction (PSAR) and Failure Index (FI) were used to evaluate the performance of the slide prediction model. Evaluation of the model performance shows that it achieves a maximum FI of 0.43 and PSAR of 99.5.

Improved feature extraction from high-resolution remotely sensed imagery using object geometry

Information extraction from high spatial resolution imagery is sometimes hampered by the limited number of spectral channels available from these systems. Standard supervised classification algorithms found in commercial software packages may misclassify different features with similar spectral characteristics; leading to a high occurrence of false positives. An additional step in the information extraction process was developed incorporating the concept of object geometry. Objects are defined as a contiguous group of pixels identified as belonging to a single class in the spectral classification. Using results from the spectral classification, a supervised approach was developed using genetic programming to select and mathematically combine feature-specific shape descriptors from a larger set of shape descriptors, to form a new classifier. This investigation focused on extraction of residential housing from QuickBird and IKONOS imagery of the Mississippi Gulf Coast before and immediately after hurricane Katrina. Use of genetic programming significantly reduced false positives caused by asphalt pavement and isolated roofing material scattered throughout the image.