Web-Based Mapping Puts the World at Your Fingertips

NASA's award-winning Earth Resources Laboratory Applications Software (ELAS) package was developed at Stennis Space Center. Since 1978, ELAS has been used worldwide for processing satellite and airborne sensor imagery data of the Earth's surface into readable and usable information. DATASTAR Inc., of Picayune, Mississippi, has used ELAS software in the DATASTAR Image Processing Exploitation (DIPEx) desktop and Internet image processing, analysis, and manipulation software. The new DIPEx Version III includes significant upgrades and improvements compared to its esteemed predecessor. A true World Wide Web application, this product evolved with worldwide geospatial dimensionality and numerous other improvements that seamlessly support the World Wide Web version

The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data

The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below

The Infrared Imaging Spectrograph (IRIS) for TMT: Data Reduction System

IRIS (InfraRed Imaging Spectrograph) is the diffraction-limited first light instrument for the Thirty Meter Telescope (TMT) that consists of a near-infrared (0.84 to 2.4 $\mu$m) imager and integral field spectrograph (IFS). The IFS makes use of a lenslet array and slicer for spatial sampling, which will be able to operate in 100's of different modes, including a combination of four plate scales from 4 milliarcseconds (mas) to 50 mas with a large range of filters and gratings. The imager will have a field of view of 34$\times$34 arcsec$^{2}$ with a plate scale of 4 mas with many selectable filters. We present the preliminary design of the data reduction system (DRS) for IRIS that need to address all of these observing modes. Reduction of IRIS data will have unique challenges since it will provide real-time reduction and analysis of the imaging and spectroscopic data during observational sequences, as well as advanced post-processing algorithms. The DRS will support three basic modes of operation of IRIS; reducing data from the imager, the lenslet IFS, and slicer IFS. The DRS will be written in Python, making use of open-source astronomical packages available. In addition to real-time data reduction, the DRS will utilize real-time visualization tools, providing astronomers with up-to-date evaluation of the target acquisition and data quality. The quicklook suite will include visualization tools for 1D, 2D, and 3D raw and reduced images. We discuss the overall requirements of the DRS and visualization tools, as well as necessary calibration data to achieve optimal data quality in order to exploit science cases across all cosmic distance scales.Comment: 13 pages, 2 figures, 6 tables, Proceeding 9913-165 of the SPIE Astronomical Telescopes + Instrumentation 201

Direct measurement of tree height provides different results on the assessment of LiDAR accuracy

open8noopenSibona, Emanuele; Vitali, Alessandro; Meloni, Fabio; Caffo, Lucia; Dotta, Alberto; Lingua, Emanuele; Motta, Renzo; Garbarino, MatteoSibona, Emanuele; Vitali, Alessandro; Meloni, Fabio; Caffo, Lucia; Dotta, Alberto; Lingua, Emanuele; Motta, Renzo; Garbarino, Matte

Optimal mapping of terrestrial gamma dose rates using geological parent material and aerogeophysical survey data

Regulatory authorities need ways to estimate natural terrestrial gamma radiation dose rates (nGy h−1) across the landscape accurately, to assess its potential deleterious health effects. The primary method for estimating outdoor dose rate is to use an in situ detector supported 1 m above the ground, but such measurements are costly and cannot capture the landscape-scale variation in dose rates which are associated with changes in soil and parent material mineralogy. We investigate the potential for improving estimates of terrestrial gamma dose rates across Northern Ireland (13542 km2) using measurements from 168 sites and two sources of ancillary data: (i) a map based on a simplified classification of soil parent material, and (ii) dose estimates from a national-scale, airborne radiometric survey. We used the linear mixed modelling framework in which the two ancillary variables were included in separate models as fixed effects, plus a correlation structure which captures the spatially correlated variance component. We used a cross-validation procedure to determine the magnitude of the prediction errors for the different models. We removed a random subset of 10 terrestrial measurements and formed the model from the remainder (n = 158), and then used the model to predict values at the other 10 sites. We repeated this procedure 50 times. The measurements of terrestrial dose vary between 1 and 103 (nGy h−1). The median absolute model prediction errors (nGy h−1) for the three models declined in the following order: no ancillary data (10.8) > simple geological classification (8.3) > airborne radiometric dose (5.4) as a single fixed effect. Estimates of airborne radiometric gamma dose rate can significantly improve the spatial prediction of terrestrial dose rate

Species-specific forest variable estimation using non-parametric modeling of multi-spectral photogrammetric point cloud data

The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing nation-wide availability of Digital Elevation Model (DEM) data, are about to revolutionize data capture for forest management planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58° N, long. 13° E). Digital aerial images from the Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were calculated for 696 field plots (10 m radius) from point-cloud data and used in k-MSN to estimate forest variables. For these stands, the tree height ranged from 1.4 to 33.0 m (18.1 m mean), stem volume from 0 to 829 m3 ha-1 (249 m3 ha-1 mean) and basal area from 0 to 62.2 m2 ha-1 (26.1 m2 ha-1 mean), with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet) showed RMSEs (in percent of the surveyed stand mean) of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry

Real-time computer data system for the 40- by 80-foot wind tunnel facility at Ames Research Center

The background material and operational concepts of a computer-based system for an operating wind tunnel are described. An on-line real-time computer system was installed in a wind tunnel facility to gather static and dynamic data. The computer system monitored aerodynamic forces and moments of periodic and quasi-periodic functions, and displayed and plotted computed results in real time. The total system is comprised of several off-the-shelf, interconnected subsystems that are linked to a large data processing center. The system includes a central processor unit with 32,000 24-bit words of core memory, a number of standard peripherals, and several special processors; namely, a dynamic analysis subsystem, a 256-channel PCM-data subsystem and ground station, a 60-channel high-speed data acquisition subsystem, a communication link, and static force and pressure subsystems. The role of the test engineer as a vital link in the system is also described

Investigating Full-Waveform Lidar Data for Detection and Recognition of Vertical Objects

A recent innovation in commercially-available topographic lidar systems is the ability to record return waveforms at high sampling frequencies. These “full-waveform” systems provide up to two orders of magnitude more data than “discrete-return” systems. However, due to the relatively limited capabilities of current processing and analysis software, more data does not always translate into more or better information for object extraction applications. In this paper, we describe a new approach for exploiting full waveform data to improve detection and recognition of vertical objects, such as trees, poles, buildings, towers, and antennas. Each waveform is first deconvolved using an expectation-maximization (EM) algorithm to obtain a train of spikes in time, where each spike corresponds to an individual laser reflection. The output is then georeferenced to create extremely dense, detailed X,Y,Z,I point clouds, where I denotes intensity. A tunable parameter is used to control the number of spikes in the deconvolved waveform, and, hence, the point density of the output point cloud. Preliminary results indicate that the average number of points on vertical objects using this method is several times higher than using discrete-return lidar data. The next steps in this ongoing research will involve voxelizing the lidar point cloud to obtain a high-resolution volume of intensity values and computing a 3D wavelet representation. The final step will entail performing vertical object detection/recognition in the wavelet domain using a multiresolution template matching approach