Lab 9 - Corridor Analysis & Feature Extraction

GOAL AND BACKGROUND

The goal of this lab was to learn to project and navigate corridor LiDAR data. Feature extraction would also be explored.


METHODS

First, data's metadata was used to determine the projection of the data. The LAS tools in ArcMap were then used to define the projection for the data. The projected data was then opened in LP360, and some measurements were taken such as road widths and locations of power lines that were at risk for vegetation encroachment.

Next, building extraction was explored. Lake County data, classified in previous labs, was used for this. First, building footprints were extracted. Two shapefiles were created from this. One was a perfect outline of building points. The other was outlines with straight lines, which removed the noisy appearance of the footprints by making them more square. Lastly, the buildings heights were analyzed to determine qualification for FEMA's LOMA applications.

RESULTS

Shown below are two instances of vegetation encroachment on power lines found in the corridor data.

Shown below is an example of the difference between the non-squared footprints and the squared footprints, respectively. Notice the jagged lines in the non-squared footprints compared to the straight lines in the squared footprints.

Shown below is a map created of Lake County of buildings in the region that qualify for FEMA's LOMA applications. FEMA recently changed the cutoff from 810 ft ASL to 800 ft ASL. The map shows the buildings that the change affects.

SOURCES

Terrestrial LAS for Algoma, WI, project boundary KMZ, and metadata are from Ayres Associates.

Lab 8 - Vegetation Metrics Modeling

GOAL AND BACKGROUND

The goal of this lab was to learn to learn to calculate vegetation statistics using LiDAR data.


METHODS

First, LP360 was used to create a DTM for the canopy height of the study area. This was done by creating DTM images for both the canopy surface, using first return vegetation points, and ground surface, using last return ground points. A raster calculator was then used to subtract the canopy height from the ground to create a vegetation canopy height raster. There were some negative values in the data from error, and were selected and removed for further calculations.

Next, above ground biomass (AGB) was calculated using model builder in ArcMap. This was done by separating vegetation by species in the study area because species have different parameters for calculations.

This data was then used to calculate the breakdown of the AGB for each species based on stem, branch, and foliage mass. This was done by calculating percentages of each of these based on species and multiplying this by the previously found AGB.

RESULTS

A graph of distributions in vegetation height is shown below.

The canopy height raster is shown below. Negative values are indicated by black symbology.

The AGB map for the entire area is shown below. The species are marked with different colors. The black areas were non-vegetated areas.

Maps for the three parameters: stem, branch, and foliage mass are shown below.

The model created to find the AGB is shown below, followed by a section of the model showing the steps for a single species.

SOURCES

Data obtained from Cyril Wilson for use in 358 LiDAR course.