The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally requires more time in scan preparation and field data acquisition. In contrast, mobile laser scanning (MLS) is being increasingly utilized in mapping due to its mobility. However, the geometrical peculiarity of forests introduces challenges. In this article, a test backpack-based MLS system, i.e., backpack laser scanning (BLS), is designed for forest plot mapping without a global navigation satellite system/inertial measurement unit (GNSS-IMU) system. To achieve accurate matching, this article proposes to combine the line and point features for calculating transformation, in which the line feature is derived from trunk skeletons. Then, a scan-to-map matching strategy is proposed for correcting positional drift. Finally, this article evaluates the effectiveness and the mapping accuracy of the proposed method in forest sample plots. The experimental results indicate that the proposed method achieves accurate forest plot mapping using the BLS; meanwhile, compared to the existing methods, the proposed method utilizes the geometric attributes of the trees and reaches a lower mapping error, in which the mean errors and the root square mean errors for the horizontal/vertical direction in plots are less than 3 cm.

This paper presents a 2D flow visualization user study that we conducted using new methodologies to increase the objectiveness. We evaluated grid-based variable-size arrows, evenly spaced streamlines, and line integral convolution (LIC) variants (basic, oriented, and enhanced versions) coupled with a colorwheel and/or rainbow color map, which are representative of many geometry-based and texture-based techniques. To reduce data-related bias, template-based explicit flow synthesis was used to create a wide variety of symmetric flows with similar topological complexity. To suppress task-related bias, pattern-based implicit task design was employed, addressing critical point recognition, critical point classification, and symmetric pattern categorization. In addition, variable-duration and fixed-duration measurement schemes were utilized for lightweight precision-critical and heavyweight judgment intensive flow analysis tasks, respectively, to record visualization effectiveness. We eliminated outliers and used the Ryan REGWQ post-hoc homogeneous subset tests in statistical analysis to obtain reliable findings. Our study shows that a texture-based dense representation with accentuated flow streaks, such as enhanced LIC, enables intuitive perception of the flow, while a geometry-based integral representation with uniform density control, such as evenly spaced streamlines, may exploit visual interpolation to facilitate mental reconstruction of the flow. It is also shown that inappropriate color mapping (e.g., colorwheel) may add distractions to a flow representation.

Lane Selection algorithm (LSA) was introduced by MIT under FHWA's New Generation of Simulation program. Recently, it has been integrated into some commercial software, such as VISSIM and PARAMICS. In this study, we first compared the difference of LSA and lane changing algorithms in CORridor SIMulation theoretically, and then calculated three LSA scenarios based on the simulated data from CORSIM. It is found the difference between these two models is obvious, therefore analysis on the advantages and shortcomings of these two models are performed. Moreover, the case studies attested that all the information that NGSIM LSA requires can be pulled out from CORSIM. It is possible to incorporate NGSIM LSA into CORSIM. Finally, improvements to exciting LSA are proposed.

Displaying the abundant information contained in a remotely sensed hyperspectral image is a challenging problem. Currently, no approach can satisfactorily render the desired information at arbitrary levels of detail. In this paper, we present a feature-driven multilayer visualization technique that automatically chooses data visualization techniques based on the spatial distribution and importance of the endmembers. It can simultaneously visualize the overall material distribution, subpixel level details, and target pixels and materials. By incorporating interactive tools, different levels of detail can be presented per users' request. This scheme employs five layers from the bottom to the top: the background layer, data-driven spot layer, pie-chart layer, oriented sliver layer, and anomaly layer. The background layer provides the basic tone of the display; the data-driven spot layer manifests the overall material distribution in an image scene; the pie-chart layer presents the precise abundances of endmember materials in each pixel; the oriented sliver layer emphasizes the distribution of important anomalous materials; and the anomaly layer highlights anomaly pixels (i.e., potential targets). Displays of the airborne AVIRIS data and spaceborne Hyperion data demonstrate that the proposed multilayer visualization scheme can efficiently display more information globally and locally.

Abstract We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to determine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter‐clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter‐clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.

This paper investigates RGB color composition schemes for hyperspectral imagery display. A three-channel composite inevitably loses a significant amount of information contained in the original high-dimensional data. The objective here is to display the useful information as distinctively as possible for high-class separability. To achieve this objective, it is important to find an effective data processing step prior to color display. A series of supervised and unsupervised data transformation and classification algorithms are reviewed, implemented, and compared for this purpose. The resulting color displays are evaluated in terms of class separability using a statistical detector and perceptual color distance. We demonstrate that the use of the data processing step can significantly improve the quality of color display, whereas data classification generally outperforms data transformation, although the implementation is more complicated. Several instructive suggestions for practitioners are provided.

Displaying the abundant information contained in a hyperspectral image is a challenging problem. Almost any visualization approach reduces the information content. However, we want to maximize the amount of object or material information presented. A visualization approach that uses classification as an intermediate step may maximize the information transfer. In our research, we are particularly interested in the display of mixed-pixel classification results, since most pixels in a remotely sensed hyperspectral image are mixed pixels. In this paper, we propose a visualization technique that employs two layers to integrate the mixture information (i.e., endmembers and their abundances) in each pixel. Images can be displayed with any desired level of details.

Remote sensing imagery has relatively low spatial resolution. Since different materials/objects may be present in the area covered by a pixel, the pixel reflectance can be considered as the linear mixture of reflectance of these materials/objects (i.e., endmembers). Thus in remote sensing image processing we deal with mixed pixels instead of pure pixels as in traditional digital image processing. A multi-spectral or hyper-spectral image has tens to hundreds of spectral bands. How to display the plentiful information contained in such a 3D image cube is a challenging problem. In this paper, we will propose a visualization technique that employs two layers to integrate the mixture information (i.e., endmembers and their functional abundances) in each pixel With user-friendly interactive tools, images can be displayed at any desired level of details.

A hyperspectral image contains information in hundreds of spectral channels. The information is sparsely distributed in such a huge 3D image cube. In practical applications, it may be helpful if such a high dimensional data can be displayed into an informative color image. This 3-band color image provides a vivid and quick overview of existing objects/materials and their distribution in the image scene. Condensing the data from the original high dimensional space into such a low dimensional space will lose information. Keeping this in mind, our objective is to display the useful information contained in an original data as much as possible and as distinctively as possible. Our display approaches are based on the techniques in the class of principal components analysis (PCA). The generated color image is evaluated using a detection algorithm, and the result is compared with the one using the original data.