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The LAS Dataset To Raster function is used to render lidar data managed using the ArcGIS LAS dataset. The function will be used when you add lidar data to a mosaic dataset using the LAS dataset raster type. With this function, you need to specify both input and output properties. Also, due to the resolution of the data and the time it can take to convert the point data to raster data, this function will write preprocessed raster data files to an output location (cache).
Input—The path and name of the LAS dataset. You can modify this value if the input is moved.
Return types—A single pulse from the lidar sensor can be returned more than once as it reflects off objects at different heights on or above the ground, resulting in pulses returning to the sensor at different times. Therefore, the return type can be used to differentiate ground returns from other returns, such as tree canopy. You can select one or more return values.
Class types—Classifications are defined for the points by the provider of the LAS files managed within the LAS dataset. You can select Any to add all the points regardless of their classification; you can also select more than one. The classification types (according to LAS specification 1.3) are Any, (0) Never Classified, (1) Unclassified, (2) Ground, (3) Low Vegetation, (4) Medium Vegetation, (5) High Vegetation, (6) Building, (7) Noisy Low Point, (8) Model Key Point, and (9) Water.
Data types—Defines the value to represent when generating the surface.
- Las Data Z—A height (elevation) value will be used.
- Las Data Intensity—Intensity is a measure, collected for every point, of the return strength of the laser pulse that generated the point. It is based, in part, on the reflectivity of the object struck by the laser pulse. Other descriptions for intensity include return pulse amplitude and backscattered intensity of reflection. Keep in mind, reflectivity is a function of the wavelength used, which is most commonly in the near infrared. Intensity is used as an aid in feature detection and extraction, in lidar point classification, and as a substitute for aerial imagery when none is available. If your lidar data includes intensity values, you can make images from them that look something like black-and-white aerial photos.
Output properties affect how the LAS dataset is converted from points to raster and is displayed.
Pixel size—The minimum pixel size that will be generated to create the raster. Generally, if the pixel size is three times greater than the point spacing, the voids in the data should be filled (unless, for example, the voids are due to water).
It's important to understand that the point spacing estimates are for either all points or only the points per return type or class type. For example, with first or last return types, the point density is high, whereas, if you select fifth return types only, the point density will be much less and the average point spacing will be much higher. Typically, the Ground class type has many points, but there will be many voids due to buildings or trees that are removed. If you select buildings only or large trees only, there are even more voids and, therefore, a smaller point density and larger average point spacing.
It is better to go with a pixel size that is several times larger than the average point spacing but small enough to identify gaps or voids. A reasonable size is four times the point spacing. For example, if your data is sampled at 1 meter and your pixel size is 4, you can expect, on average, to get 16 points in a pixel.
The point spacing can be obtained from the properties of the LAS dataset.
Binning—This is the process of determining the value of a pixel by examining the points that fall within the pixel to determine the final value. It uses the following inputs:
Cell aggregation type—Determines which z-value to use when generating the raster surface when there is more than one point to consider.
- Maximum—Uses the largest z-value
- Minimum—Uses the smallest z-value
- Mean—Uses a mean (average) of all the z-values
Void filling—Voids occur when there are no points collected within the area represented by a pixel in the resultant raster. Voids are often caused by water bodies or by class type selection or exclusion. Void filling is most commonly used when generating a ground surface.
- None—No voids will be filled.
- Simple—Computes the average using up to eight neighboring cells (with values). Only small voids will be filled.
- Plane Fitting/IDW—A Simple method is applied first and a plane fitting method is used; however, if the fitting error is too large, an inverse distance weighted algorithm is applied. If the width or height of the bounding box around the void is larger than the Maximum width value, the void is not filled.
- Linear (Triangulation)—Estimates z from the plane defined by the terrain triangle that contains the x,y location of a query point.
- Natural Neighbor (Triangulation)—Estimates z by applying area-based weights to the terrain's natural neighbors of a query point.
- Maximum width—The width value used for void filling when using the Plane Fitting/IDW void filling method. This is defined in the units of the LAS dataset's spatial reference system. No maximum width will be used if this is blank or a value of 0 is entered.
Triangulation—Uses Delaunay triangulation to create a surface from a network of triangular facets defined by nodes and edges that cover the surface, which is then rasterized. This is recommended for low density lidar data, when binning can't be used to create an appealing surface, or when zooming in to an area that will cause a low density lidar surface to be displayed. It uses the following inputs:
- Interpolation method—The estimation of surface values at unsampled points based on known
surface values of surrounding points.
- Linear—Estimates z from the plane defined by the terrain triangle that contains the x,y location of a query point.
- Natural Neighbor—Estimates z by applying area-based weights to the terrain's natural neighbors of a query point.
- Use constraints—By default the constraints set up in the LAS dataset are not used. Check this option to create surfaces from the LAS dataset using any constraints set up in the dataset.
Z factor—The scaling factor used to convert the z-values. The scaling factor is used for two purposes: (1) to convert the elevation units (such as meters or feet) to the horizontal coordinate units of the dataset, which may be feet, meters, or degrees, and (2) to add vertical exaggeration for visual effect.
To convert from feet to meters or vice versa, see the table below. For example, if your z-units are feet and your mosaic dataset's units are meters, you would use a value of 0.3048 to convert your z-units from feet to meters (1 foot = 0.3048 meters).
This is also useful when you have geographic data (such as GCS_WGS84 using latitude and longitude coordinates) where the z-units are in meters. In this case, you need to convert from meters to degrees (0.00001; see below). The values for degree conversions are approximations.
Conversion factor between feet and meters
To apply vertical exaggeration, you must multiply the conversion factor by the exaggeration factor. For example, if both z-values and dataset coordinates are meters and you want to exaggerate by a multiple of 10, the scaling factor would be unit conversion factor (1 from the table) multiplied by the vertical exaggeration factor (10), or 10. As another example, if the z-values are meters and the dataset is geographic (degrees), you would multiply the units conversion factor (0.00001) by 10 to get 0.0001.
When you specify a Z factor value the Arithmetic function is added to the function chain for the item in the mosaic dataset.
Cache folder—The location where the cached LAS dataset surfaces will be stored. By default, the cache is generated and stored in a folder next to where the mosaic dataset resides. This folder has the same name as the geodatabase, with a .cache extension. However, if the mosaic dataset is created in an ArcSDE geodatabase, the cache will be created within that geodatabase.
Number of cached surfaces—The maximum number of caches that can be created using different properties (in this dialog box) for this surface. For example, you might add the LAS dataset to create a surface displaying all the points but you also want to visualize only the points that are classified as ground from the same dataset; therefore, you can create two caches to visualize this data in two ways. Entering a value of 0 will disable caching or clear an existing cache.
Rendering the LAS dataset can be computationally intensive. Without the cache, you may have to wait several minutes for some surfaces to display. The cache is generated when the following occur:
- You view the mosaic dataset where the LAS dataset is used to generate the mosaicked image.
- The overviews are built.
- The Synchronize Mosaic Dataset tool is run with Build Item Cache checked.
The cache will be updated in the following scenarios:
- The input has been updated.
- The cache has been deleted or is missing.
- The function parameters are set to define a different surface than the one that matches the cache (for example, use a different Return type).