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FUSION/LDV LIDAR analysis and visualization software |
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USDA Forest Service, Pacific Northwest Research Station |
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FUSION/LDV LIDAR analysis and visualization software |
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| Home |
USDA Forest Service, Pacific Northwest Research Station |
Download FUSION Send comments to the developer, Bob McGaughey  |
Overview of the FUSION/LDV Analysis and Visualization SystemThe FUSION/LDV software was originally developed to help researchers understand, explore, and analyze LIDAR data. The large data sets commonly produced by LIDAR missions could not be used in commercial GIS or image processing environments without extensive preprocessing. Simple tasks such as extracting a sample of LIDAR returns that corresponded to a field plot were complicated by the sheer size of the data and the variety of ASCII text formats provided by various vendors. The original versions of the software allowed users to clip data samples and view them interactively. As a new data set was delivered, the software was modified to read the data format and features were added depending on the needs of a particular research project. After a year or so of activity, scientists at the Pacific Northwest Research Station and the University of Washington decided to design a more comprehensive system to support their research efforts.The analysis and visualization system consists of two main programs, FUSION and LDV (LIDAR data viewer), and a collection of task-specific command line programs. The primary interface, provided by FUSION, consists of a graphical 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 including shapefiles, images, digital terrain models, canopy surface models, and LIDAR return data. LDV provides the 3D visualization environment for the examination and measurement of spatially-explicit data subsets. Command line programs provide specific analysis and data processing capabilities designed to make FUSION suitable for processing large LIDAR acquisitions. 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, representing the bare ground or canopy surface, must be in a gridded format. 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, surface model, and canopy model, however, multiple raw data, POI, tree, and hotspot layers are allowed. 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 different data types. 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 or by manually entering coordinates for the points defining the sample. LDV presents the subset for the user to examine. Subsets include not only raw data but also the portion of the image and surface models for the area sampled. FUSION 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 the ground surface model prior to display in LDV. This feature is especially useful when viewing data representing forested regions in steep terrain as it is much easier to examine returns from vegetation and compare trees 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 users understand LIDAR data, LDV can also map orthographic images onto a horizontal plane that can be positioned vertically within the cloud of raw data points. Bare-earth surface models are rendered as a shaded 3D surface and can be textured-mapped using the sampled image. Canopy surface or canopy height models are rendered as a mesh so the viewer can see the data points under the surface. 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. “Plot mode” lets the user 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 and inform the user when measurement locations are within or outside the plot boundary.. 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) • Change the shape and alignment of the measurement marker to better fit the data points that represent a tree crown 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. Example dataThe example project contains 4.8
million data points covering about 295 acres (120 ha).
The study area is part of a project designed to study
the effect of several forest management alternatives.
The site is located in western A small footprint, discrete return LIDAR system was used to map the study area in the spring of 1999. The contractor used a Saab TopEye LIDAR system mounted on a helicopter to collect data over the study site. Table 1 presents the flight parameters and instrument settings for the data acquisition. Each return includes the pulse number, return number for the pulse (up to four returns were recorded per pulse), X, Y, elevation, off-nadir angle and return intensity. Table 1. Flight parameters and scanning system settings.
Several “point-of-interest” and
“hotspot” files have been included in the project. The
green lines are treatment area boundaries, the yellow
dots are survey points used to assess the accuracy of
the LIDAR-derived terrain model, and the blue
information markers are hotspots linked to photos of the
forest conditions for treatment areas. The example data are available in two
coordinate systems:
The terrain model was created using ground return points identified by the LIDAR provider. The state plane model is a 5- by 5-foot grid (1.524- by 1.524-meter). The UTM model is a 1- by 1- meter grid (3.2808- by 3.2808-feet). The terrain model has been compared to a network of ground survey points and found to have an RMSE of 22cm. |
This page was last updated on June 17, 2020 by Bob McGaughey
