Comparing Point Cloud, Contour, and Grid

Producer Field Guide

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Producer Field Guide
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Producer

Airborne LiDAR data is most often used as a representation for a terrain surface. However, it may not be useful in the form of discrete points, as many of the existing processing applications expect to be given a terrain as either a set of contours or as a raster grid, that is, a digital elevation model (DEM).

LiDAR Discrete Point Cloud Example

lidar_point_cloud_example

TIN Terrain

Using discrete points, one can vary the density with the complexity of the terrain, providing for smaller data sets. However, to work with such data, an application must be designed to create and interpolate triangulated irregular networks (TINs). If all the data is not read into memory, the TINs must be created in segments, introducing further complexity.

Raster Terrain

If the data are converted into grids, they can be stored in common raster formats. Some benefits of introducing the data as a raster file are:

  • The data are more readily accessible by many applications (DEM data from USGS) are a very common raster surface model).
  • Grids are easier to break into workable segments and can be readily served through several existing protocols.
  • Various image processing tools can be used to extract information from the data. For example, a spatial model can be created using Graphical Modeling to detect and quantify change within an area contained in two data sets.

The images below show two different LAS datasets of Mount St. Helens viewed as shaded reliefs. The image on the left is prior to the eruption that took place during October 2004 and the image on the right is after the eruption, making the displacement from the lava dome visible.

LiDAR Data as Shaded Relief Images

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Change Detection Model using LiDAR Data

lidar_mt_st_helens_change_detect_model

Managing LiDAR Datasets

Managing and serving LiDAR data is a challenge as many organizations are accumulating large collections of LiDAR data, but they have a problem keeping track of what they have and providing it to their end users in an effective manner.

ERDAS APOLLO manages, processes, and delivers massive amounts of imagery and gridded datasets to end users. It can automatically discover imagery, harvesting its metadata and then deliver the data through widely used protocols such as Web Map Service (WMS), Web Coverage Service (WCS) or ECWP (Enhanced Wavelet Compression Protocol).

Because ERDAS APOLLO uses an aggregate data model, it serves up its datasets as individual granules or as composite datasets. An aggregate data model organizes related datasets into a hierarchy like folders, with the higher level aggregates acting like a virtual mosaic of the contained datasets. This, coupled with the "clip-zip-and-ship" capability makes data available to a very large number of existing applications.

ERDAS APOLLO capabilities are also available for LAS data by using a plug-in. During the process of autonomous data discovery called crawling, the LAS files are examined to extract metadata (called harvesting) and a multi-resolution gridded version of the dataset is created and stored transparently to the user. Information such as a spatial reference system is read directly from the LAS file. Information such as the ground sample distance is automatically estimated by examining the data. All of this metadata is then stored in the APOLLO catalog. Once this is done, the LAS files can then be searched using metadata queries, and served using any of the raster protocols. This allows the LAS data to be used in any existing workflow already prepared for dealing with gridded terrains. For example, the LAS data can be viewed as shaded reliefs by using the Web Map Service (WMS), and then included in any WMS aware Web service.