lidar solutions in arcgis 10.0

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Assessing lidar coverage and sample densityResource CenterProfessional LibraryExtensions3D AnalystGuide booksLidar solutions in ArcGIS

One basic QA/QC process when you receive lidar data is to ensure the lidar points delivered by your data provider have the

coverage and density expected. Checking any coverage and density problems and resolving them early in your lidar

processing is important. Two geoprocessing tools are useful in this regard:Point File Information, found in the 3D Analyst

toolbox, andPoint To Raster,located in the core Conversion toolbox.

Point File Information

The Point File Information geoprocessing tool reports basic statistics about one or more point data files on disk. The

primary purpose of the Point File Information geoprocessing tool is to help you review and summarize the data before

loading it into your geodatabase. LAS (the industry-standard format for lidar data) and ASCII format files are supported as

input. Since lidar projects often utilize collections of data files, sometimes in the hundreds or even thousands, the tool lets

you specify folder names in addition to individual files. When given a folder, it reads all files inside it that have the suffix

you specify.
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For each input point file,the tool outputs one polygon with accompanying attribution to a target feature class. The polygon

graphically depicts the x,y extent, or bounding box, of the data in the file. Attributes include file name, point count, z-min,

z-max, and point spacing.


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The point spacing reported by Point File Information is not exact and deserves some discussion. For the sake of

performance, it uses a rough estimate that simply compares the area of the file's bounding box with the point count. It's

most accurate when the rectangular extent of the file being examined is filled with data. So files with significant numbers of

points excluded over large water bodies or on the perimeter of a study area, only partially occupied with data, will not

produce accurate estimates. Therefore, the reported point spacing is more meaningful as a summary when looking at

trends for collections of files.

Something useful to do with the output feature class is to display it in ArcMap, open its attribute table, and sort the point

spacing field in ascending order. You can also symbolize on the point spacing field using a graduated color ramp. In the


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image below, the LAS extent polygons are symbolized using a color ramp based on the point spacing estimate. The

polygons symbolized in red indicate a tighter point spacing.

The Point File Information geoprocessing tool works quickly on LAS files because it only needs to scan their headers to

obtain the information it's looking for. It takes significantly longer with ASCII files because, with them, the tool actually hasto read all the data.

You can also examine the classification conducted on the source points in the LAS files. Lidar data can be classified by the

lidar provider into different class codes depending on the surface feature returned by the lidar system. These class codes

can represent ground and nonground lidar returns. When you select the Summarize by class code option in the Point File

Information dialog box, the output attribute table will contain summary statistics for each class code encountered in each

LAS file. Use this option to examine the class codes in each LAS file and the corresponding point information associated to

each class code.

Assuming the Point File Information review process determines everything to be acceptable, the next step is to load yourlidar points into a multipoint feature class with theLAS To Multipointor theASCII 3D To Feature Classtool. Put this feature

class in a feature dataset if you intend to build a terrain dataset from the points. Though you have the choice between

using LAS or ASCII format files, LAS is generally a better way to go. LAS files contain more information and, being binary,

can be read by the importer more efficiently.

Once the points are loaded into a multipoint feature class, you can use the Point To Raster geoprocessing tool to get a

more in-depth view of the point distribution.
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Point To Raster

The Point To Raster geoprocessing tool creates rasters from points and also supports multipoints. It's a generic tool with

many options and uses. For evaluating lidar point density, the tool's COUNT option should be employed. This uses the

number of points falling in a raster cell as the cell value. Being able to look at this graphically over the extent of the project

area is revealing.

There are a few parameters for the Point To Raster tool whose values for this exercise are not obvious. First is the ValueFieldparameter. It does not matter what this is set to, because the value field is ignored when Cell Assignment type is set

to COUNT. Second is the Cellsize parameter. You might think the average point spacing is a good cell size for the output

raster, but this typically results in too many empty, or NoData, cells because lidar points are not evenly spaced. Also the

output raster could end up being unnecessarily large. Instead, it is better to go with a cellsize that is several times larger

than the average point spacing but small enough to identify gaps or voids that warrant further investigation. A reasonable

size is four times the point spacing. As an example, if your data is sampled at 1 meter and you set cellsize to 4, you can

expect, on average, to get 16 points in a cell.


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You can also evaluate the density for different types of points. While most of the time you'll probably just check the density

for all returns, it can be useful to look at those that fall in a certain class, like ground. For example, this can give you anidea of how good your ground penetration is in vegetated areas. The Point To Raster tool doesn not make a distinction

between point types. So you control what points are used by how you create the multipoint feature class with the LAS To

Multipoint tool. It provides options for loading points by class code and return number.

Once your raster has been created, look at it in ArcMap. Use a color ramp renderer to display it so it is easy to distinguish

between cells with high counts and those with low. You can also set the NoData color to something that stands out. Look

for variance in density and data voids. Have your vendor explain anything that doesn't look right.


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In the image below, the black areas represent areas where no data exists, while the red areas represent areas where the

lidar sample density is higher.

Copyright 1995-2012 Esri. All rights reserved.

Creating raster DEMs and DSMs from large lidar point collections

Resource CenterProfessional LibraryExtensions3D AnalystGuide booksLidar solutions in ArcGIS

Summary

Raster, or gridded, elevation models are one of the most common GIS data types. They can be used in many ways for

analysis and are easily shared. Lidar provides you with the opportunity to make high-quality elevation models of two

distinct flavors: first return and ground. A first return surface includes tree canopy and buildings and is often referred to as

a digital surface model (DSM). The ground, or bare earth, contains only the topography and is frequently called a digital

elevation model (DEM).
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These graphics show hillshaded representations of a first return surface, or DSM, on the left and a bare earth model, or

DEM, on the right.

Coming up with a plan

Before beginning to create a raster from lidar, some basic factors need to be evaluated:

Extent of lidar coverage Number of lidar points and point density Desired output raster resolution Extent of output raster(s) Format of output raster(s)Consideration of these factors will help determine whether you will be producing one raster or a collection of rasters. Part of

this process requires that you figure out how many rows and columns you're willing to have in one raster. This depends on

what you intend to do with the raster in terms of analysis, display, and potential sharing or distribution of the data. The

desire to work off one dataset for analysis can be in conflict with practical constraints associated with dataset size. Another

thing to consider is the amount of lidar you have. Trying to process 10 billion lidar points as one dataset, while possible, is


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likely to prove unwieldy. In this situation you would want to make multiple rasters from this volume of lidar data, so

consider splitting up the lidar processing as well. Not only does this keep individual datasets at reasonable sizes, it also

keeps process duration on those datasets shorter. The longer a process takes to execute, the more likely something's going

to go wrong (for example, power outage).

If you have determined you need to split up your data, the next question is how can this be accomplished. Will it be based

on a regular grid system, political boundaries, or an anticipated application? Since lidar collections tend to have multiple

uses, splitting them up based on a regular grid system or political divisions like county boundaries makes the most sense.

An engineer can mosaic the different pieces he or she needs for an individual project. If the intended use is weighted

heavily for one type of application, such as hydrology, then use divisions logical for the application. For example, in the

case of hydrology, watershed boundaries are a good candidate.

ArcGIS supports many raster formats, so you have a choice of what format to write to. This decision is best based on the

intended use of the product. If it's to be shared with the general public you might think about distributing in either TIFF or

JPEG format. For analysis using the ArcGIS platform, consider using the file-based geodatabase format.

The first step in getting from lidar points to raster is loading the points into a geodatabase. To load your lidar points into a

multipoint feature class, use either theLAS To Multipointor theASCII 3D To Feature Classgeoprocessing tool, dependingon the source data format of the lidar data. Put the multipoint feature class in a feature dataset if you intend to build a

terrain dataset from the lidar points. Although you have the choice between using LAS or ASCII format files, LAS is a more

acceptable binary file format. LAS files contain more information and, being binary, can be read by the importer more

efficiently. For more information on importing lidar source measurements into the geodatabase, seeData import and load

tools.

Using the Point To Raster geoprocessing tool

If your only source of data is the lidar, you can use the Point To Raster geoprocessing tool to produce raster elevationmodels. ThePoint To Raster tool does not produce the highest quality result possible. Lidar tends to be so dense that for

many applications the accuracy produced with the Point To Raster geoprocessing tool is good enough and the convenience

and speed of this tool make it worthwhile.

If producing a bare earth raster surface, or DEM, use just the ground lidar points to produce the raster. Set the Value

field parameter on the tool to Shape to use the z-values from the multipoint vertices. Also, set Cell assignment type to

either MIN or MEAN. MIN will bias output heights to local lows, while MEAN is more of a general purpose option. To
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produce a first return surface, or DSM, use the first return lidar points with the MAX option of the tool since you want to

bias the output to local highs.

The Point To Raster geoprocessing tool produces gridded elevation models from lidar point sets.

While Point To Raster offers the easiest and fastest way to produce a raster from lidar, it has a significant drawback. You

can end up with many NoData cells since it only returns values for cells that have one or more points in them. The problem

is exacerbated when only using ground points to make a DEM because gaps in the data occur where there's vegetation and

buildings obscuring the ground. To reduce the effect of NoData versus data cells, you can increase the output cellsize

relative to the average point spacing. You can also reduce the number of NoData cells after execution of Point To Raster by

using the sample below in the Python window if:

The Spatial Analyst extension is on The output name of the last focal stats expression executed is 'outfocalmNN' (outfocalm03, outfocalm04, outfocalm05

etc.)

The output (in this example, the output from Point To Raster is called point2ras) is a layer in the table of contents The valid path is: C:\data\


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from arcpy.sa import *

outfocalm01 = FocalStatistics("point2ras", NbrRectangle(3, 3, "CELL")

outfocalm02 = FocalStatistics(outfocalm01, NbrRectangle(3, 3, "CELL")

#Repeat using output (temporary raster object) in the next processes until

all nodata is gone

out = Con(IsNull("point2ras"), outfocalmNN, "point2ras")

#Save the result to disk

out.save("C:\data\myfinalDEM")

You can run the Python sample multiple times to fill in larger NoData areas, but it is not recommended to run it more than

a couple times. It is better to just accept larger void areas as a consequence of using this approach.

Original points on left, output from Point To Raster in the middle (NoData cells colored white) in the middle, post-processed

raster with NoData filled in on the right.

Using the terrain dataset to create a raster DEM

If you have photogrammetric breaklines to go along with your lidar, or need higher-quality results than can be produced

with the Point To Raster tool, use the terrain dataset. For an overview of the terrain dataset, seeWhat is a terrain dataset?
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On the left is a surface made without breaklines along the river banks. The right-hand side has breakline enforcement.

Breaklines are important for maintaining the definition of water-related features in the elevation model.

Breaklines are used to capture linear discontinuities in the surface. The most common types are edge of pavement, lakeshorelines, single-line drains for small rivers, and double-line drains for large rivers. Sometimes breaklines are also

collected to help define and sculpt the surface without necessarily representing discontinuities. Examples of these

applications include contourlike form lines and the crests of rounded ridges.

Breaklines, while frequently used in bare earth models, tend to be detrimental when used with first return surfaces because

they can be in conflict with the aboveground points. For example, breaklines capturing road edge of pavement can be

coincident in x,y but different in z to points in the tree canopy overhanging the road. Because of this, consider excluding

breaklines from your first return surface or at least those where you know there's potential conflict.

The most efficient means of organizing breaklines for use in a terrain dataset (see table below) is to separate them intodifferent feature classes based on surface feature type (SFType). Surface feature types control how the features are

enforced in the model and how the natural neighbor interpolator, used during rasterization, interprets the surface as it

crosses over these features. A distinct break in slope will occur across "hard" features but not across "soft" features.

SFType for terrain datasets

Measurement type Feature class type SFType


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Lidar points 3D multipoint feature class Mass

Edge of pavement, single- and double-line drains for rivers, sharp ridgelines 3D line feature class Hardline

Lakes, reservoirs 2D polygon feature class with z-value stored as attribute Hardline or hardreplace

Eroded/Rounded ridgelines, contourlike form lines 3D line feature class Softline

Study area boundary 2D polygon feature class;no z-value Softclip

It is best for the sake of terrain performance to place all hardlines together in one feature class. It is understood this might

not be possible, for example, if you need to keep road and water features separate. Keep in mind, the fewer feature classes

used to define a terrain, the better.

Replace SFType is used to set everything inside a polygon at a constant height. It is used mostly for lakes when there's

inadvertently other data inside them, such as lidar points, whose heights are not exactly the same as the shoreline and

therefore prevent the water bodies from being flat. Replace SFType does incur more processing cost than normal hard- or

softlines, so it is best to avoid using it in a terrain dataset. Ideally, there should not be lidar samples in water bodies(consider adding this as a stipulation in the contract with your data provider), but if there are, you can use theDelete

Terrain Pointsgeoprocessing tool to handle them after the terrain dataset is built. Otherwise, you can eliminate any

offending points before building your terrain using theErase Pointgeoprocessing tool.

If you will be producing both bare earth and first return surfaces using terrain datasets, load the lidar points into two

different multipoint feature classes, a feature class for the ground points and a feature class for the aboveground points.

The bare earth terrain is defined with a reference to only the ground points. The first return terrain dataset references the

same ground point feature class as the bare earth terrain with the additional reference to the aboveground points. This

means two different terrain datasets can reference the same feature class.

Starting with ArcGIS 9.3, terrain datasets can be pyramided using one of two point thinning filters: z-tolerance and window

size. For DEM production, you can use either of these pyramid types. If you intend to rasterize from the full-resolution

point set, use the window size filter for terrain construction because it is significantly faster. If you are willing to use

thinned data for analysis, which is reasonable if the lidar data is oversampled for your needs, use the z-tolerance filter.

While more time consuming, it is most appropriate because it provides an estimate of vertical accuracy of the thinned

representation. For DSM production, use the window size filter with the MAX option.
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Use the Terrain To Raster tool to produce your rasterized elevation model. This provides options for interpolation, output

cellsize, and which pyramid level to use from the terrain dataset.

The Terrain To Raster tool produces gridded elevation models from terrain datasets.

For interpolation, the natural neighbors options is the best option. While not as fast as linear interpolation, it generally

produces better results both in terms of aesthetics and accuracy. Set the output cellsize relative to the lidar point sample

density. You will not gain any accuracy by using a cellsize that is substantially smaller than the average point spacing. Also,

make sure to set the analysis extent, as set through the Environments, for the extraction of subsets where appropriate.

The use of a snap raster can also be of use for the sake of alignment of raster outputs.

Overview steps to generate a raster DEM surface from lidar point data in ArcGIS are described below.

First, you will create a terrain dataset in ArcCatalog or the Catalog window inside your appliation, then use geoprocessing

tools to convert this terrain dataset to a raster DEM.

1. Create a terrain dataset.

Steps:


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1. Determine the source data and how that data will contribute to the terrain dataset.For more information on representing terrain source data, seeRepresenting terrain source data in feature

classesandTypes of source data supported in terrain datasets.

Note:

All source data must have the same spatial reference to build a terrain dataset.

2. Create a file geodatabase in ArcCatalog or in the Catalog window. Right-click the folder where the terrain is to bebuilt, point to New, then click File Geodatabase in the context menu.

3. Create a feature dataset. Right-click the file geodatabase, point to New, then click Feature Dataset in the contextmenu.

For more information on correctly generating a feature dataset, seeCreating a feature dataset.

4. Import the source measurements into feature classes. These feature classes must be produced inside the featuredataset created in step 3. For more information on how to import source data for a terrain, seeImporting terrain

dataset source measurements.

5. Build a terrain dataset in ArcCatalog or the Catalog window using the New Terrain wizard.To access the New Terrain wizard, right-click the feature dataset to display the menu, point to New, then

click Terrain.

For more information about using the New Terrain wizard, seeBuilding a terrain dataset with the Terrain wizard.

2. Use the Terrain To Raster geoprocessing tool.

Steps:

1. From the 3D Analyst Tools, double-click the Terrain To Raster geoprocessing tool to open it.2. Click the Input Terrain browse button to add the terrain dataset.3. Click the Output Raster browse button to specify the location where the raster dataset is to be created.
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4. Set the optional Output data type parameter to either 32-bit floating point or 32-bit integer. Floating point is thedefault value.

5. Set the interpolation method to either Linear orNatural Neighbors.These methods are TIN-based interpolation methods applied through the triangulated terrain surface. The Linear

option finds the triangle encompassing each cell center and applies a weighted average of the triangle's nodes to

interpolate a value. TheNatural Neighborsoption uses the Voronoi neighbors of cell centers.

Consider the natural neighbors method for interpolating a terrain surface. Natural neighbor interpolation exhibits a

longer processing time; however, the generated surface is much smoother than that produced with a linear

interpolation. It is also less susceptible to small changes in the triangulation.

6. Set Sampling Distance to either Observations or Cellsize, which controls the horizontal resolution of the raster.Indicate the value next to the option once you have selected the desired method.

The Observations method calculates the cell size for you based on the set value this number represents and the

number of cells you want on the longest edge of the raster surface. You can set the cell size explicitly using

the Cellsize option.

7. Set the resolution to use. The resolution parameter indicates which pyramid level of the terrain dataset to use forconversion. To output a raster dataset at full resolution, set this parameter to 0. Pyramid levels are defined using z-

tolerance or window size, which represents the approximate resolution of the terrain dataset relative to the full

resolution data.

8. Consider using the environment settings to explicitly control the extents of the DEM you want to generate. Toextract a subset of the terrain, click the Environments button on the bottom of the geoprocessing tool. Click

the General Settings tab and define the extent of the output DEM.

Using either Point To Raster or Terrain To Raster geoprocessing tool, you can process hundreds of millions, even billions, of

lidar points into high-resolution gridded DEMs and DSMs. These surface models can then be used with the large collection of

raster tools available in ArcGIS for analysis.

They are also great for making maps (see graphic below) and, due to their simple data structure, are easy to share.
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Data area delineation from lidar pointsResource CenterProfessional LibraryExtensions3D AnalystGuide booksLidar solutions in ArcGIS

It is common for lidar or photogrammetric data for a survey to be delivered without a detailed data area boundary. Often, the

x and y extents of the survey area are defined by a tile system that covers an area of interest, and the data fills these tiles.

The image below depicts lidar data tiles for a project. The extent of these tiles is a gross approximation of the actual study

area boundary.


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As is often the case, the actual lidar data does not fully cover the extent of tiles that are on the perimeter of the project area.

The data is only guaranteed to cover some minimal extent, and there is no explicit or absolute boundary other than what can

be inferred, as shown in the image below. This graphic is centered on one such tile.

Either way, the area of coverage is usually not a cleanly filled rectangle.

The problem

If a surface is made without declaring the data area up front (in other words, by including a clip polygon when defining a

terrain dataset or TIN), some of what are actually voids around the perimeter are treated as data areas. Analytic results inthese areas are unreliable because height estimates are based on samples that can be far away.

The graphic on the left below depicts a dense collection of lidar points shown in green. The gaps in the interior are water

bodies (where lidar is typically omitted). The irregularly shaped data boundary is easy to see, but unless an explicit extent

is provided in the form of a clip polygon, TIN and terrain dataset-related tools will fill in voids, greatly oversimplifying the

actual data extent.


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You know areas outside the data collection extent should be excluded from the surface. The problem is coming up with the

polygon that provides an accurate representation of this extent.

The solution

The solution is to synthesize a data boundary from the points that can be used to enforce a proper interpolation zone in thesurface. Below, the left image depicts the lidar points. The middle image displays a polygon synthesized boundary from the

points. The right image is a surface made from the lidar points and clip polygon.

The point spacing is the primary variable to use when going after the data area. Surveys usually have explicit minimums on

point spacing to provide control for interpolators. Areas that do not meet density requirements are exceptions. They usually

fall in one of the following categories: water bodies, obscured areas, and holidays (send the latter back to the data provider

for repair). The vast majority of the data will meet sample density specifications. Point spacing is usually reported in

metadata. If you do not know the point spacing of the lidar data, use the 3D AnalystPoint File Informationgeoprocessing
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tool. Alternatively, approximate the point spacing using the Measure tool in ArcMap. To learn more about point spacing,

seeAverage point spacing.

Data area delineation from lidar points

Once you know the point spacing of the lidar data, follow these steps to delineate the data area:

Steps:

1. Rasterize the lidar points using the Point To Raster geoprocessing tool.Rasterization of the lidar points helps aggregate the area covered by the lidar points. It provides a good data

structure to work with for subsequent steps. You just need to tell the geoprocessing tool what cell assignment type

to use and the output cell size. Use COUNT as the Cell assignment type value. Specify a value for cellsize that is

several times larger than the average point spacing of the lidar data. Otherwise, you will get a lot of noise because

the points are not evenly spaced. From the standpoint of processing efficiency and noise reduction, the larger cell

size you use, the better, but there will be a trade-off with the tightness of fit in the result. A good place to start is

four times the average point spacing.

2. Assign one value to all data cells using the Con geoprocessing tool.
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Using the Con geoprocessing tool in this workflow is to simply turn any and all data cells of the raster into cells with

one value. This value defines a raster zone that will be expanded in step three. All that is needed is to take the

output from the Point To Raster tool and provide a constant value for a positive expression. All nonzero value cells

will be considered positive and be assigned the constant value. Since COUNT was used as the cell assignment

method during rasterization, any cell with a point in it must have a value greater than zero.

3. Fill small NoData areas using the Expand geoprocessing tool.Unless you used a very coarse cell size relative to your average point spacing, there is a likelihood of many NoData

cells remaining. Most of these can be eliminated using the Expand geoprocessing tool. You want to remove most of

these so the polygon produced during vectorization in a later step is not full of holes. That would be unnecessarily

expensive.


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The Expand tool pushes the zone of interest outward. In this case, the zone is all the data cells coded with a value

of 1. This effectively eliminates small gaps found in the interior.

The left image shows many individual cells and some small clusters of NoData cells (in white). The right image

shows the results from using the Expand geoprocessing tool where the NoData cells (in white) are removed, for the


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most part. It is okay if some isolated NoData areas remain in the output. The remaining NoData cells will be

handled in the last step.

4. Reduce the overall extent of data cells using the Shrink geoprocessing tool.While Expand eliminates isolated NoData cells, it also extends the data area outward. It needs to actually be

brought in a little. Clip polygons need to be smaller than the actual point extent, so when terrain datasets or TINs

try to estimate z-values along the polygon boundary, points can be found on both sides. This is needed to get goodz estimates. To reduce the raster's data boundary, use the Shrink geoprocessing tool as shown below.

Reduce the extent somewhat so the polygon produced in step 5 is smaller than the actual data extent of the points.

This enables the software to estimate better z-values along the polygon boundary.

At this point, you have a relatively clean raster with the extent of its data cells slightly within the lidar point extent.

5. Vectorize the raster with the Raster To Polygon geoprocessing tool.


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The Raster To Polygon geoprocessing tool converts the raster to a polygon feature class. Make sure the Simplify

polygonsoption is checked. If it is not, the output will be stairstepped rather than smooth and contain more vertices

than necessary.

The Raster To Polygon tool outputs a polygon feature class. The result is representative of the data extent of the

points used at the beginning of the process.

At this point, the process is almost complete. You need to review the output for correctness. Chances are there is

one more step remaining, which is the removal of remaining holes inside your clip polygon.

6. Remove any remaining small holes using the Eliminate Polygon Part geoprocessing tool.The Eliminate Polygon Part geoprocessing tool eliminates any internal rings, leaving just the exterior boundaries.

A clip polygon now exists that can be used when defining a terrain dataset or TIN. It should conform to the data extent of the

lidar points but fall slightly inside them. The left image below shows the resulting clip polygon. The right image is a zoomed-in

view showing the extent of the polygon relative to the source points. Note how it lies slightly inside the source point boundary.


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Estimating forest canopy density and height


Forest canopy density and height are used as variables in a number of environmental applications. These applications include

the estimation of biomass, forest extent and condition, and biodiversity. Canopy density, or canopy cover, is the ratio of

vegetation to ground as seen from the air. Canopy height measures how far above the ground the top of the canopy is. Lidar

can be used to determine both of these variables.

The following are steps to calculate canopy density and height from lidar points. First, you need lidar that has been classified

into ground returns (bare earth) versus nonground returns. This type of point classification is usually performed by your data

provider. Secondly, you need to consider when the lidar was collected and the type of vegetation in the study area. If there

are a lot of deciduous trees and the collection was performed during autumn (leaf off),the density calculation is not going to

work.

Loading points into the geodatabase

To calculate canopy density, load the ground, or bare earth, lidar points into one multipoint feature class and aboveground

points into another multipoint feature class. Assuming your data is in LAS format, you can load the lidar points into the

geodatabase using theLAS To Multipointgeoprocessing tool. Make sure to specify the proper class codes to filter the two

multipoint feature classes with. The following table contains the LAS class codes as defined in the LAS 1.1 standard:
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Class code Classification type

0 Created, never classified

1 Unclassified

2 Ground

3 Low vegetation

4 Medium vegetation

5 High vegetation

6 Building

7 Low points (noise)

8 Model key

9 Water

Note:

Any LAS file made in the last few years should use these codes if the points have been classified. Unfortunately, there's still

some ambiguity. For example, class 2 is ground, but class 8 is ground as well. Class 8, or model key, points are a special set

of ground points used for contouring or other applications requiring a thinned set of ground points. Whether you have them

depends on how the data was processed. If you don't know, specify both classes. If it turns out there are not any model key

points, it won't hurt. Vegetation has a similar issue. Sometimes vendors place everything that's above ground into class 1

because they haven't performed a more detailed classification on them. So if you are unsure of the specifics of your data'sclassification, load nonground points using classes 1, 3, 4, and 5. That's a reasonable catch-all to get your vegetation points.

Note, if buildings or other man-made nonground features are in class 1, you will get them too, and they'll skew the results

somewhat.

Calculating the density


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The most effective way to determine the canopy density is to divide the study area into many small equal-sized units through

rasterization. In each raster cell, you compare the number of aboveground returns to the total number of lidar returns.

The important technique to remember here is to determine an appropriate cellsize for this analysis. It needs to be at least

four times the average point spacing. You can go larger but not smaller with the cell size.

Steps:

1. Use thePoint To Rastergeoprocessing tool on the aboveground points with the COUNT option.

2. Convert any resulting NoData cells to 0 so that subsequent operations treat a cell with no points as 0. This isaccomplished using theIsNullgeoprocessing tool followed by theCongeoprocessing tool.
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3. Repeat steps 1 and 2 with the lidar ground multipoints.4. Add the aboveground and bare earth rasters together to get a total count per cell using the Plus geoprocessing tool.


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5. All the rasters you've made so far are long data types. You need one raster to be floating point to get floating pointoutput from theDividegeoprocessing tool that you will use in step 6. To generate a float raster, use the output

raster from thePlusgeoprocessing tool as input to theFloatgeoprocessing tool.

6. Now use the Divide geoprocessing tool to compare the aboveground count raster and the floating point total countraster. This gives you the ratio from 0.0 to 1.0, where 0.0 represents no canopy and 1.0 very dense canopy.
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The following image represents canopy density. The lightest areas have little to no vegetation. These are areas where a large

percentage of lidar shots could "see" the ground. The dark green areas, where lidar could not penetrate to ground as well,

indicate denser vegetation canopy.

Calculating the height


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To determine canopy height, you will need to subtract the bare earth surface (DEM) from the first return surface (DSM).

Follow the steps in theCreating raster DEMs and DSMs from large lidar point collectionstopic to generate these two surfaces.

Steps:

1. Once you have generated the first return and bare earth rasters, use theMinusgeoprocessing tool to determine thedifference between these two raster datasets. The difference results represent, over forest, the canopy height.

The image below represents height above ground. It ranges from blue (little to no height) to orange, which is the tallest.
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Lidar can be used to calculate the density and height of vegetation. This is useful for a variety of purposes, including biomass

and carbon estimates, as well as forest management.


Creating intensity images from lidar in ArcGIS


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 back scattered 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. ArcGIS provides the ability to creating intensity

imagery from lidar data.


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1. Loading points with intensity

The first step in creating an intensity image in ArcGIS is to import your source points into the geodatabase as multipoints.

The source lidar dataset must be in either LAS or ASCII XYZI file format.

If your data is in LAS format, use theLAS To Multipointgeoprocessing tool with intensity selected as an attribute to import,

as shown in the image below. If your data is in ASCII XYZI format, use theASCII 3D To Feature Classgeoprocessing tool.

It is most common to use first return lidar data to create intensity images. If your lidar data is in ASCII format, format the

ASCII files representing first return point data. If your lidar data is in LAS format, specify the class code on theLAS to

Multipointdialog box to represent first return lidar data. The tool will then filter the points and only import the first return

points into the multipoints.
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The data loading process results in a multipoint feature class with an attribute field containing the intensity values (see

below). You cannot read these attributes directly because they are packed into binary large objects (BLOBs) in themultipoint feature class. Every vertex of a multipoint has intensity mapped into the BLOB and is stored for that multipoint

record.


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2. Generating an intensity imageUse thePoint To Rastergeoprocessing tool to generate an intensity image from the multipoint feature class. Populate Input

Featureswith the name of the multipoint feature class and specify Intensity BLOB for Value field. Select MEAN from

the Cell assignment typeoptions for a rasterization method. Set a cell size that is approximately four times larger than the

average point spacing. Once the geoprocessing tool has been run, the resulting raster is the lidar intensity image.

You might find one of the other Cell assignment type options useful for analysis, for example, RANGE of intensity as a

variable used in feature detection.
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Finally, review the presence of NoData areas. Significant numbers of NoData cells will result if the output cell size you

specified is too small relative to the density of the lidar points. You can see NoData cells by assigning a color to them on

the Symbology tab of the raster's Layer Properties dialog box. If there are several areas with no data available, the easiestthing to do is go back and rerun thePoint To Raster geoprocessing tool with a larger cell size. Alternatively, you can fill in

missing values using an expression in the Spatial Analyst calculator. SeeCreating raster DEMs and DSMs from large lidar

point collections.

Image display

The range of values in the image is hard to predict without knowing details about how your data was collected and

processed. For one thing, the original intensity values are sensor dependent. Secondly, the values may have been adjusted

by the vendor (for example, normalized to a range of 0

255). Due to these variables, it is hard to determine what the bestdisplay options are for intensity imagery. Experiment with the intensity raster layer stretch type and contrast. Turning on

bilinear resampling is probably a good idea. If you are looking for more display possibilities, consider combining intensity

with another variable such as hillshade. The intensity image below is displayed with 50 percent transparency on top of a

hillshade of the first return surface.
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The return intensity collected for every lidar point can be used to make images. These images have a variety of uses in GIS

applications including feature detection and extraction. ArcGIS provides tools to make these images.


Updating a portion of a terrain dataset with new measurements


The ability to update a surface is important to provide accurate, current surface information, available for conducting analysis.

Surface updates can come in different forms:

Adding ancillary data (for example, breaklines) Removing or replacing bad data Using newer or more accurate data Increasing extent with additional data Inserting design/modeled data to perform what-if analyses


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These kinds of updates are best performed on the measurements used to construct a surface rather than on derivatives like

raster DEMs. Those can be re-created as needed after the measurement edits have taken place. Terrain datasets support this

editing model because they maintain a direct link to the source measurement data. When you modify the measurements, you

are automatically modifying the terrain in the same process. For more basic information on the terrain dataset, seeWhat is a

terrain dataset?

How terrains are edited

Editing a terrain dataset is really about editing the source measurements. Using standard feature edit tools, you can

manipulate the measurements that reside in feature classes that participate in a terrain.

Terrain datasets are made from one or more feature classes, with simple rules for each feature class that control how they

are used to shape the terrain surface. For example, a multipoint feature class containing lidar points can be added as mass

points, a line feature class containing streams and lakeshores is used as a source of breaklines, and a polygon feature class

can control the data area boundary.

Most feature classes used to define a terrain are what we call referenced. This means that the terrain maintains a pointer,

or handle, to them. The terrain prevents its referenced feature classes from being deleted and pays attention to any editsthat occur on them including the addition, deletion, or modification of feature geometry. You can use the feature editor in

ArcMap as well as geoprocessing tools to modify these feature classes. A terrain will automatically flag itself as dirty in

areas where edits were made. Then the terrain can be rebuilt to bring its pyramid scheme in sync with the updated

features. It does this based on the dirty areas. It is a way to conduct localized processing, and the entire terrain does not

need to be reconstructed.

Multipoint feature classes have the option of being embedded. When multipoints are embedded, the terrain build process

copies the points into pyramid tables held private by the terrain and it becomes the container for the points. The terrain

does not reference the source feature class. This source feature class can be deleted, allowing you to retrieve what is

typically a substantial amount of disk space, approximately 1GB per 150 million points. Terrain-specific tools, Append

Terrain Points (which can both add and replace) andReplace Terrain Points, are used to edit the embedded points based on

an area of interest. These geoprocessing tools also offer the benefit of being BLOB attribute aware, which is necessary if

you have any LAS attributes stored with those multipoints. For more information on BLOBs and lidar attributes,

seeCreating intensity images from lidar.
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These terrain dataset processing tools keep the BLOB-based values in sync with the points relative to the edits. For

example, if a few vertices of a multipoint are deleted from an embedded feature class, the terrain will delete the

corresponding BLOB-based attribute values for those points.

Appending measurements

Measurements can be added to a terrain dataset through theAppendand theAppend Terrain Pointsgeoprocessing tools.

The Appendgeoprocessing tool operates on terrain referenced feature classes. The Append Terrain Points tool is used toadd or replace points in embedded feature classes.

You can also add a feature class to an existing terrain using theAdd Feature Class To Terraingeoprocessing tool. Adding

measurements using the Add Feature Class To Terrain geoprocessing tool creates a schema edit that invalidates the entire

terrain dataset, requiring a full terrain rebuild. If data is to be added incrementally, it is best to append it to a feature class

that already participates in the terrain dataset than add a new feature class to the terrain dataset for each new set of data.

For example, sometimes data is provided in phasesthe bare earth lidar points are made available first and the breaklines

come later in several deliveries. Knowing this schedule, you can create a terrain dataset referencing the lidar multipoint

feature class containing the bare earth points, plus an empty line feature class held in anticipation of the breaklines. Theimage below displays a zoomed-in view of a terrain dataset made solely with bare earth lidar points.
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When portions of the breaklines are made available, they are added to the terrain dataset by adding them to the line

feature class referenced by the terrain dataset. This is done using the Append geoprocessing tool, as shown below.

After running the Append geoprocessing tool, the terrain dataset will become dirty in the areas where the lines were added.

To determine the dirty areas, add a dirty area renderer from the Symbology tab of the terrain layer's Layer

Properties dialog box. The following image displays the Add Rendererdialog box from the terrain dataset Layer

Properties. For steps describing how to add the dirty area renderer for a terrain dataset, seeDisplaying dirty areas of a

terrain.
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The purple lines represent the dirty area tiles of a terrain dataset. The terrain layer draws the dirty area depicting a

rectangular boundary that surrounds the edited area.


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The terrain dataset now must be rebuilt. This is done using either theBuild Terraingeoprocessing tool or the Build

Terrain button on theUpdate tab of the terrain Propertiesdialog box in ArcCatalog or the Catalog window. Once the terrain

dataset is rebuilt, the improvement made by the breaklines is evident. The definition of the water features on the terrain

dataset has been improved with the addition of the breaklines as shown in the image below.
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Replacing features

With lines and polygons in referenced feature classes, the replacement of measurements is a two-step process. First you

delete the old features, then append the new features. If you are only dealing with a handful of features, consider selecting

and deleting them using the Editortools in ArcMap. For larger collections, rely on geoprocessing tools. For example,

use Select By Location followed byDelete Features and Append.

It is easiest to replace lidar points if they are embedded in the terrain dataset. TheReplace Terrain Pointsgeoprocessing

tool has an area of interest or feature layer option to replace points. This will replace all the points within a given area of

interest. So if you discover something was wrong with a few source point files that were used to build a terrain dataset,

you can replace them without needing to rebuild the entire terrain from scratch. The images below show an example of one

area of a bare earth model that was inadvertently loaded with first return lidar data.

The images are hillshades derived from a terrain dataset. The one on the left clearly shows the mistake of either first or all

return data being included for a subarea. On the right is the image of the correct data.

To correct this problem, load the replacement data into a new multipoint feature class, then run the Replace Terrain

Pointsgeoprocessing tool to update the terrain dataset with the correct lidar point data. By default, the replacement area

will come from the extent of the input feature layer, as displayed in the image below.
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Once the points have been replaced, the terrain dataset needs to be rebuilt to update the affected area. Run the Build

Terraingeoprocessing tool or use the Build Terrain button on the Update tab of the Terrain Properties dialog box in

ArcCatalog or the Catalogwindow. Both options will update the terrain area affected by the new lidar points.

There are times when updates to a surface model are needed, whether for quality improvement or what-if scenario analysis. It

is hard to make these types of updates to derivative products like raster DEMs without ending up with some anomalies around

the update area. It is more appropriate to modify the source measurement data from which the surface model is derived. Forlarger datasets, like those coming from lidar, it is also desirable that datasets be reprocessed only where the updates occur

rather than rebuilding everything. Terrain datasets support this by maintaining links to their source measurements in the

geodatabase and their use of dirty areas.



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Minimizing noise from lidar for contouring and slope analysis


Lidar is promoted as an accurate form of elevation data. To a large degree, this comes from its dense sample interval. Some

refer to lidar as "painting the ground with elevation measurements." Indeed, it is common to acquire submeter sampling with

lidar. This facilitates canopy penetration, which improves the accuracy of ground models in forested areas. The high sample

density also improves results for certain applications such as floodplain delineation. However, lidar is not always the most

optimal source for all surface modeling activities. Two areas that tend to be problematic are contour derivation and slope

analysis.

Messy contours and steep slopes

Getting a computer to produce nice-looking contours from traditional data sources is hard enough. It is even more difficult

to produce them from lidar. Contours produced from full-resolution lidar tend to be jagged with many twists and turns and

isolated closed rings.

Slope assessment with lidar data is also problematic. If you examine the average slope from full-resolution lidar, you will

see that it is unusually high. This is true even over relatively flat ground, as shown in the image below depicting the slope

generated from full-resolution lidar. Green is little to no slope, yellow is moderate slope, and red is steepest. Note the high

amounts of yellow and red sprinkled throughout the surface.


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Some people misinterpret these issues with contours and slope as simply a result of lidar being more accurate than other

forms of surface data. At large enough scales, almost any surface is fairly rough. While there is some truth to this

statement, a significant part of the problem really stems from the relationship between horizontal sample density and

vertical accuracy. As with any measurement technology, lidar is not perfectly accurate. Its vertical accuracy generallyranges from 12 to 15centimeters. That provides a random height difference between two adjacent points of 24 to

30centimeters. When the points are only 1 meter or less apart horizontally, that height differential becomes significant.

From a signal processing perspective, this is called high frequency noise.

Techniques to reduce noise

To get less jagged contours and more reasonable slope estimates, you have to remove the noise from the lidar and lose as

little real information as possible while doing so. Although there is no way to prevent all loss, harm can be minimized. The

terrain dataset offers a couple of tools to facilitate this. One is intelligent point thinner; the other is a high-quality

interpolator.

Point thinning occurs during the terrain pyramid building process. Some people think pyramids are strictly a visualization

tool, used solely to speed up drawing. While this is true for raster pyramids, the same does not apply to terrain datasets.

Terrains were designed knowing that lidar is better suited for some applications when it has been generalized.


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There are two terrain dataset point thinning algorithms available in ArcGIS:z-tolerance pyramidingandwindow size

pyramiding. Both algorithms have something specific to offer and are arguably better than random point filters used by

other solutions that, while fast and reasonable for visualization, are not what you would want to use for analysis.

The z-tolerance filter employs a TIN-based algorithm to find a subset of points sufficient to create a surface that is within a

given vertical distance to the full-resolution surface. The use of this filter is recommended when a quantifiable measure of

vertical accuracy is needed.

The window size filter selects points within a given horizontal sample distance. Every so many units in x,y (the samplingwindow size), the points within that area are examined and one or two are selected depending on which option you chose.

The point selection method can be the point closest to the mean of the other points in the sample window, the highest or

lowest of the points in the sample window, or both the highest and lowest. As stated earlier, much of the noise problem

results from a bad ratio of high horizontal sample density to vertical accuracy. The window size filter enables you to

improve that ratio. While it is hard to quantify the accuracy of the thinned data, empirical evidence has shown this

approach works well.

Thinning the data

Lidar points are thinned when a terrain dataset is built. The filter algorithm applied is based on the type of pyramid

selected for the terrain dataset. Fortunately, the same name is used for pyramid type as filter algorithm so there is no

ambiguity. The pyramid type is selected through the Terrain wizard found on the feature dataset context menu in

ArcCatalog or the Catalog window.
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Next, you would need to specify the pyramid levels. For the sake of noise reduction, you are interested in that first pyramid

level, which is one step removed from full resolution. A z-tolerance equal to the vertical accuracy of the data is reasonable

for that level. This will eliminate as much noise as possible while decreasing the accuracy of the result as little as possible.

If you start out with 15 centimeters vertical accuracy, you end up with approximately 30 centimeters accuracy; if you goon to produce contours, set their interval to at least double this, namely 60 centimeters or approximately 2 feet. When

building a window size pyramid, a sample distance equal to twice the nominal point spacing is reasonable.


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After having built a terrain dataset, the thinned points reside in its pyramid. Use theSurface ContourandSurface

Slopegeoprocessing tools to generate contours and slope. The results will be less noisy than if made from the unthinned

points. One drawback is that contours and slope estimates made from these TIN-based surfaces will still be more angular

and discontinuous than necessary. A recommended alternative is to go through a raster where the results will be smoother.

Raster interpolation

Contour and slope outputs can be improved by deriving them from a raster that is made from a terrain dataset. Engineers

have a traditional bias toward working directly on TINs, so they may not like the idea of going through a raster, but when

dealing specifically with contour and slope production from lidar data, this bias is unwarranted. For one thing, lidar points

are essentially collected in a random distribution, and the resulting triangles are not handpicked or guaranteed to fit some

mathematical mode

lidar solutions in arcgis 10.0

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