Land-Use/Land-Cover Characterization Using an Object-Based Classifier for the Buffalo River Sub-Basin in North-Central Arkansas

Sensors for remote sensing have improved enormously over the past few years and now deliver high resolution multispectral data on an operational basis. Most Land-use/Land-cover (LULC) classifications of high spatial resolution imagery, however, still rely on basic image processing concepts (i.e., image classification using single pixel-based classifiers) developed in the 1970s. This study developed the methodology using an object-based classifier to characterize the LULC for the Buffalo River sub-basin and surrounding areas with a 0.81- hectare (2-acre) minimum mapping unit (MMU). Base imagery for the 11-county classification was orthorectified color-infrared aerial photographs taken from 2000 to 2002 with a one-meter spatial resolution. The object-based classification was conducted using Feature Analyst® , Imagine® , and ArcGIS® software. Feature Analyst® employs hierarchical machine learning techniques to extract the feature class information from the imagery using both spectral and inherent spatial relationships of objects. The methodology developed for the 7-class classification involved both automated and manual interpretation of objects. The overall accuracy of this LULC classification method, which identified more than 146,000 features, was 87.8% for the Buffalo River sub basin and surrounding areas

Comparison of Pixel-based versus Object-based Land Use/Land Cover Classification Methodologies

Land Use/Land Cover (LULC) classification data have proven to be valuable assets for various governmental agencies, park managers, and natural resource managers. Traditional pixel-based classification methods have difficulty with high resolution imagery, resulting in a “salt and pepper” appearance. Newer object-based methods may prove to be more accurate. This study compared an object based classification procedure utilizing Feature Analyst© software with a traditional pixel-based methodology (supervised classification) when applied to medium-spatial resolution satellite imagery merged with high-spatial resolution aerial imagery. This study utilized two multi-spectral SPOT-5 satellite images, leaf-on and leaf-off, merged with a color infrared aerial image. Because of correlation between some of the bands of the merged image, Principal Component Analysis (PCA) was used to reduce redundancy in the data. Field data was collected in the study area to serve as a reference for the accuracy assessment. A training set was produced by selecting and identifying specific LULC class-types using 1-foot high-spatial resolution aerial imagery. This training set was used by both of the classification methods (supervised and object-based) to identify the various cover types within the study area. An accuracy assessment was performed on each image utilizing error matrices, the Kappa coefficient, and a two-tailed Z-test. Results indicate that the overall accuracy of the object-based classification was 82.0%, while the pixel-based classification was 66.9%. A Kappa analysis and a two tailed Z test were calculated. These values indicated a significant difference in the overall accuracies of the classifications

Impervious Surface Area Change in Arkansas from 2001 to 2006

Impervious Surface Area (ISA) is a measurement used to determine stream quality as well as urban sprawl. ISA was calculated as part of the National Land Cover Dataset (NLCD) using Landsat imagery by the Multi-Resolution Land Characteristics Consortium (MRLC) in both 2001 and 2006. ISA for each of the 75 counties in Arkansas was taken from the NLCD for both 2001 and 2006. Using the ISA data, percent imperviousness was determined for each county in each time period as well as the difference between the two periods. These data were also compared to census projections for the two time periods as well as the difference between them. The differences between percent ISA change and census change were compared to determine consistency

Online motion correction for diffusion-weighted segmented-EPI and FLASH imaging

This paper explores the application of online motion correction using navigator echoes to the segmented-EPI and FLASH techniques. In segmented EPI this has the advantage over post-acquisition correction that the position in k-space of each segment is no longer subject to arbitrary shifts caused by rotation. In diffusion-weighted FLASH it has the advantage that the full magnetisation can be utilised in comparison to other methods of eliminating the sensitivity to bulk motion, in which the sensitivity is halved. Healthy subjects were investigated on a 3 T whole-body system in which the hardware has been modified so that navigator echoes can be recorded on a personal computer which generates the necessary magnetic field gradient correction pulses and shifts in the Larmor frequency within 800 μs. ECG triggering was used to avoid the period of non-rigid-body brain motion. Two orthogonal navigator echoes were employed. For segmented EPI it was found essential to minimise the T2* weighting of the navigator echoes to about 10 ms to obtain reliable results. High quality images were obtained for both methods examined. Online motion correction brings direct benefits to both the diffusion-weighted segmented-EPI and FLASH techniques

Impaired hemodynamics and neural activation? A fMRI study of major cerebral artery stenosis

Functional MRI motor mapping was performed in two women with unilateral high-grade stenosis of the middle cerebral artery (MCA) to determine the influence of impaired hemodynamics on the blood oxygenation level dependent (BOLD) response. In both patients no structural lesions were present in primary motor pathways. A redistribution of the motor network to the healthy hemisphere was the main indicator of chronic hemodynamic compromise

Accuracy Assessment of Recreational and Mapping Grade GPS Receivers

Since its development in the early 1970s, Global Positioning System (GPS) technology has become more accessible and affordable for consumers. GPS applications have become ubiquitous in society. With the increased use of GPS, the question of accuracy is of concern. This study assessed the accuracy of four Garmin recreational GPS receivers, eTrex® , eTrex Legend® , eTrex Vista® , GPSMAP® 76CS, and three Trimble® mapping GPS receivers JunoTM , GeoExplorer3TM and GeoXHTM. Thirty-three ground control points (GCPs) were established in three different landscapes using survey grade GPS (Trimble’s 4700) that were corrected using National Geodetic Survey’s Online Positioning User Service (OPUS). Eleven GCPs were established in a forest landscape, eleven near buildings to simulate an urban landscape, and eleven with a clear unobstructed sky. The GPS receivers were tested with the Wide Angle Augmentation System (WAAS) on and off. In addition, results from averaging 30 GPS positions were evaluated. This study showed the GeoXH was the most accurate receiver and that the accuracy of the recreational (Garmin) receivers was from 2.52 to 18.42 meters depending on the landscape. The accuracies of the Garmin GPS receivers were similar

Quantifying Forest Ground Flora Biomass Using Proximal Sensing

Current focus on forest conservation and forest sustainability has increased the level of attention given to measures of ground flora in forest ecosystems. Traditionally, such data are collected via time- and resource-intensive methods of field identification, clipping, and weighing. With increased focus on community composition and structure measures of forest ground flora, the manner in which these data are collected must change. This project uses color and color infrared digital cameras to proximally sense forest ground flora and to develop regression models to predict green and dry biomass (g/m^) from the proximally sensed data. Traditional vegetative indices such as the Normalized Difference Vegetative Index (NDVI) and the Average Visible Reflectance Index (AVR) explained 35-45% of the variation in forest ground flora biomass. Adding individual color band variables, especially the red and near infrared bands, to the regression model allowed the model to explain 66% and 58% of the variation in green and dry biomass, respectively, present

Beyond moments: relativistic Lattice-Boltzmann methods for radiative transport in computational astrophysics

We present a new method for the numerical solution of the radiative-transfer equation (RTE) in multidimensional scenarios commonly encountered in computational astrophysics. The method is based on the direct solution of the Boltzmann equation via an extension of the Lattice Boltzmann (LB) equation and allows to model the evolution of the radiation field as it interacts with a background fluid, via absorption, emission, and scattering. As a first application of this method, we restrict our attention to a frequency independent ("grey") formulation within a special-relativistic framework, which can be employed also for classical computational astrophysics. For a number of standard tests that consider the performance of the method in optically thin, optically thick and intermediate regimes with a static fluid, we show the ability of the LB method to produce accurate and convergent results matching the analytic solutions. We also contrast the LB method with commonly employed moment-based schemes for the solution of the RTE, such as the M1 scheme. In this way, we are able to highlight that the LB method provides the correct solution for both non-trivial free-streaming scenarios and the intermediate optical-depth regime, for which the M1 method either fails or provides inaccurate solutions. When coupling to a dynamical fluid, on the other hand, we present the first self-consistent solution of the RTE with LB methods within a relativistic-hydrodynamic scenario. Finally, we show that besides providing more accurate results in all regimes, the LB method features smaller or comparable computational costs compared to the M1 scheme.Comment: 22 pages, 16 figures, matches version accepted in MNRA

Central immune tolerance depends on crosstalk between the classical and alternative NF-κB pathways in medullary thymic epithelial cells

Medullary thymic epithelial cells (mTECs) contribute to self-tolerance by expressing and presenting peripheral tissue antigens for negative selection of autoreactive T cells and differentiation of natural regulatory T cells. The molecular control of mTEC development remains incompletely understood. We here demonstrate by TEC-specific gene manipulation in mice that the NF-κB transcription factor subunit RelB, which is activated by the alternative NF-κB pathway, regulates development of mature mTECs in a dose-dependent manner. Mice with conditional deletion of Relb lacked mature mTECs and developed spontaneous autoimmunity. In addition, the NF-κB subunits RelA and c-Rel, which are both activated by classical NF-κB signaling, were jointly required for mTEC differentiation by directly regulating the transcription of Relb. Our data reveal a crosstalk mechanism between classical and alternative NF-κB pathways that tightly controls the development of mature mTECs to ensure self-tolerance