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Lab 8: Corridor analysis and feature extraction

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Goals and Background: This lab practiced the skill of corridor analysis using LiDAR point cloud data from a terrestrial LiDAR scanner (TLS). Other skills that were practiced included projection of point cloud data and extraction of building footprints. The extraction of building footprints included finding building footprint features with Z characteristics that would make the buildings eligible for a letter of map amendment (LOMA). Details on LOMAs can be found here . Methods: Part 1: Projecting an unprojected point cloud This part made simple use of the LP360 tools included in the license for LP360 for Arcmap. All of the LP360 tools available were added to a new toolbox, and the Define LAS File Projection and then Reproject LAS Files tools were used. The projection specified in the readme file for the Algoma,WI LAS dataset was defined, and then the transverse mercator projection was used in the reprojection. In using both of these tools horizontal and vertical coordinate syste...
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Lab 7: Vegetation Metrics Modeling

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Goals and Introduction:  This lab demonstrated vegetation metrics modeling in the diverse forest of Eau Claire County. The goal of the lab was to extract various vegetation metrics for the different species of trees found in the project area. This was then used to make hypothetical recommendations to the U.S Forest Service about the carbon sink potential of the forest in the project area. This lab made use of LiDAR point cloud data and the Wiscland 2 land use and land use classification raster. Methods: Part 1: Canopy height modeling Section 1: Canopy surface and ground surface creation This section consisted of generating a 3 foot cell size canopy surface as well as a ground (DTM) surface model which were used in the next section for creation of a canopy height raster. The LiDAR was brought into LP360 and the using the Export LiDAR Data function the models were created. For the DTM single returns were used and for the canopy surface model first returns were used. Secti...
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Lab 6: Topo-bathy processing

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Goals and Introduction: The goal of this lab was to work with topo-bathy LiDAR data to gain general skills. The skills practiced were performing basic QA/QC of topo-bathy LiDAR, generating and conflating shoreline breaklines specially tailored to the topo-bathy LiDAR, and configuring the enforcement of the shoreline behavior for creation of derivatives. A DTM and a hillshade raster file were created. The dataset practiced with was located in the Hiawatha National Forest in Delta County, MI. Methods: Part 1: QA/QC of topo-bathy point cloud classification The NAIP imagery and point cloud data supplied for the lab was examined for inaccurate classification using the profile and 3D windows in LP360's standalone Windows application. Using the QA/QC tool, a feature class was created with features that covered areas that needed to be fixed throughout the LiDAR dataset. Theses areas were then visited and corrected. Many points were classified as ground that should have been classif...
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Lab 5: Breakline Creation, Conflation, and Enforcement

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Background and Goals: This lab focused on the process of creating and enforcing hard breaklines for the production of LIDAR derivatives. The lab covered the creation, identification, and fixing of potential topological errors in breaklines. It then worked through the process of conflation of those breaklines and configuring the enforcement of the breaklines for production of the derivative and the production of those derivatives: contours and digital terrain models (DTMs). This lab used both LAS and breakline data from both Eau Claire and Lake County, IL datasets.  Methods:      Part 1: Breakline QA/QC and elevation conflation           Section 1: This section utilized optical imagery to perform QA/QC on soft breaklines. Imagery was brought into ArcMap and breaklines were properly configured so that islands were digitized as holes in polygon shapes. This was performed with the Illinois dataset. Figure 1: Islands incor...
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Lab 4: Quality assurance and quality control (QA/QC)

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Goals and Background: This lab consisted of practicing performing synoptic QC measures including producing a point cloud density image, and producing statistics for local areas in the point cloud data using a stamp tool. Also practiced in this lab were relative accuracy assessment procedures, and absolute accuracy assessment procedures. Relative accuracy assessment consisted of creating a delta Z (dZ) ortho image raster and determining swath-to-swath accuracy in non-vegetated terrain as a root-mean-square difference (RMSD z ). Absolute accuracy consisted of obtaining non-vegetated vertical accuracy and horizontal accuracy. Finally, manual cleanup was performed. The dataset worked on in the past three labs was the dataset assessed in this lab.  Quality assurance (QA) and quality control (QC) are two different processes that ensure that overall accuracy goals of a LiDAR data are met or exceeded. Quality assurance consists of care taken with events taking place up to the end of a...
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Lab 3: Vegetation Classification

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Goals and Background: In this lab vegetation is classified in the LAS dataset from labs 1 and 2. The height filter and the manual cleanup are used to complete this task. Methods: Part 1: Automatic height filtering of vegetation object points Automatic height filtering, a very basic filter for categorization of points into height classes is used for the first, most automated portion of this lab. A filter using the Z values of the points above the classified ground points and thresholds for different levels of classification (low, medium, and high) is used to classify these points. This is why it is important to have correct ground classification properly performed before running this filter. Table 1 shows the different classes of vegetation. Table 1 Figure 1 shows these classifications applied in the filter window. Figure 1 Part 2: Manual cleanup of the vegetation classification This part of the lab involved using the same basic filter tool to clean up omission a...
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Lab 2: LAS Building Classification

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Goals and Background:  This lab practiced the classification of buildings in the same LAS point cloud dataset where ground was previously classified in Lab 1. The planar filter was used to classify the building points in LP360. After this filter was run, manual cleanup was performed on the dataset. Methodology: Part 1: Execution of the planar point filter The planar point filter was used to classify buildings using the macro task type in the point cloud task window. A new macro task was made and then a planar point filter subtask. The unclassified class was used for source points, the ground class for ground points, and the building class for destination class. The maximum height parameter was calculated using the elevation of the tallest building with the lowest elevation found that was still classified as ground subtracted. This made sure that no taller vegetation was classified as buildings. The rest of the parameters were obtained form recommendations in the assi...
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