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Fusion

http://forsys.cfr.washington.edu/fusion/fusion_overview.html#FUSION_overview

The visualization system consists of two main programs, FUSION and LDV (LIDAR data viewer), implemented as Microsoft Windows applications coded in C++ using the Microsoft Foundation Classes. The primary interface, provided by FUSION, consists of a graphics display window and a control window. The FUSION display presents all project data using a 2D display typical of geographic information systems. It supports a variety of data types and formats but requires that all data be geo-referenced using the same projection system and units of measurement. LDV provides the 3D visualization environment, based on OpenGL, for the examination of spatially-explicit data subsets.

In FUSION, data layers are classified into six categories: images, raw data, points of interest, hotspots, trees, and surface models. Images can be any geo-referenced image but they are typically orthophotos, images developed using intensity or elevation values from LIDAR return data, or other images that depict spatially explicit analysis results. Raw data include LIDAR return data and simple XYZ point files. Points of interest (POI) can be any point, line, or polygon layer that provides useful visual information or sample point locations. Hotspots are spatially explicit markers linked to external references such as images, web sites, or pre-sampled data subsets. Tree files contain data, usually measured in the field, representing individual trees. Surface models must be in a gridded format that represents either a ground surface or other surfaces of interest such as the top of a forest canopy. FUSION uses the PLANS format for its surface models and provides utilities to convert a variety of formats into the PLANS format. The current FUSION implementation limits the user to a single image and a single surface model, however, multiple raw data, POI, and hotspot layers can be specified.

The FUSION interface provides users with an easily understood display of all project data. Users can specify display attributes for all data and can toggle the display of the five data types. The entire raw data layer can be rendered, however, it generally requires excessive time and is often left as a hidden layer.

FUSION allows users to quickly and easily select and display subsets of large LIDAR data. Users specify the subset size, shape, and the rules used to assign colors to individual raw data points and then select sample locations in the graphical display. LDV presents the subset for the user to examine. Subsets include not only raw data but also the related portion of the image and surface model for the area sampled. LDV provides the following data subset types:

· Fixed-size square,

· Fixed-size circle,

· Variable-size square,

· Variable-size circle,

· Variable-width corridor.

Subset locations can be “snapped” to a specific sample location, defined by POI points. to generate subsets centered on or defined by specific locations. Elevation values for LIDAR returns can be normalized using a local surface model prior to display in LDV. This feature is especially useful when viewing data of forested regions in steep terrain as it is much easier to examine returns from vegetation after subtracting the ground elevation.

LDV strives to make effective use of color, lighting, glyph shape, motion, and stereoscopic rendering to help users understand and evaluate LIDAR data. Color is used to convey one or more attributes of the LIDAR data or attributes derived from other data layers. For example, individual returns can be colored using values sampled from an orthophoto of the project area to produce semi-photorealistic visual simulations. LDV uses a variety of shading and lighting methods to enhance its renderings. LDV provides point glyphs that range from single pixels, to simple geometric objects, to complex superquadric objects. LDV operates in monoscopic, stereoscopic, and anaglyph display modes. To enhance the 3D effect on monoscopic display systems, LDV provides a simple rotation feature that moves the data subset continuously through a simple pattern (usually circular). We have dubbed this technique “wiggle vision”, and feel it provides a much better sense of the 3D spatial arrangement of points than provided by a static, fixed display. To further help with understanding LIDAR data, LDV can also map orthographic images onto a horizontal plane that can be positioned vertically within the cloud of raw data. Surface models including “bare-earth” and canopy models are rendered as a shaded 3D surface and can be textured-mapped using an orthographic image.

FUSION and LDV have several features that facilitate direct measurement of LIDAR data. FUSION provides a “plot mode” that defines a buffer around the sample area and includes data from the buffer in a data subset. This option, available only with fixed-size plots, makes it easy to create LIDAR data subsets that correspond to field plots. In the context of this paper, “plot mode” lets us measure tree attributes for trees whose stem is within the plot area using all returns for the tree including those outside the plot area but within the plot buffer. The size of the plot buffer is usually set to include the crown of the largest trees expected for a site. When in “plot mode”, FUSION includes a description of the fixed-area portion of the subset so LDV can display the plot boundary as a wire frame cylinder or cube.

LDV provides several functions to help users place the measurement marker and make measurements within the data cloud. The following “snap functions” are available to help position the measurement marker:

· Set marker to the elevation of the lowest point in the current measurement area (don’t move XY position of marker)

· Set marker to the elevation of the highest point in the current measurement area (don’t move XY position of marker)

· Set marker to the elevation of the point closest to the marker (don’t move XY position of marker)

· Move marker to the lowest point in the current measurement area

· Move marker to the highest point in the current measurement area

· Move marker to point closest to the marker

· Set marker to the elevation of the surface model (usually the ground surface)

The measurement marker in LDV can be elliptical or circular to compensate for tree crowns that are not perfectly round. The measurement area can be rotated to better align with an individual tree crown. Once an individual tree has been isolated and measured, the points within the measurement area can be “turned off” to indicate that they have been considered during the measurement process. This ability makes it much easier to isolate individual trees in stands with dense canopies.

Application Details:

Version: 3.2.1
License:
URL: http://forsys.cfr.washington.e...
Votes: 0
Latest Rating: Platinum
Latest Wine Version Tested: 1.5.20

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Test Results

Old test results
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Selected Test Results

What works

  • Using example data
  • Selecting data to view
  • Rendering data points in LIDAR Data Viewer
  • Rendering data in PDQ Data Viewer
  • Change sample options to
    • Include trees
    • Color by height, return number, intensity, etc
    • Change color


What does not

- Not sure

Workarounds

What was not tested

  • Loading and saving options
  • Plot mode

Hardware tested

Graphics:

  • GPU:
  • Driver:

Additional Comments

selected in Test Results table below
Operating systemTest dateWine versionInstalls?Runs?Used
Workaround?
RatingSubmitter
ShowUbuntu 10.04 "Lucid" amd64 (+ variants like Kubuntu)Dec 28 20121.5.20Yes Yes PlatinumRB 
CurrentUbuntu 10.04 "Lucid" amd64 (+ variants like Kubuntu)Dec 20 20121.4Yes Yes PlatinumRB 

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