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Calculating the 3D distance of trees derived from LiDAR data to overhead power lines

Blog Post created by xander_bakker on Jul 14, 2016

In a previous post (see: Creating a 3D tree with Arcpy for 3D Analysis  ), we created 3D geometries of trees to be able to determine the 3D distance between the tree crown and the nearest overhead power line.

 

What if you don’t have a forest inventory, but do have LiDAR data? You can still do something similar, but you will have to derive the trees from the LiDAR data. Esri’s 3D GIS Development Team, has created a toolbox with lots of useful tools for working with 3D data. It is available at no cost on ArcGIS Online:

https://www.arcgis.com/home/item.html?id=fe221371b77940749ff96e90f2de3d10 

 

Inside the toolbox you will find the tools you need inside “Vegetation Analysis”:

 

First of all you will need to create a Canopy Height Model (CHM) or normalized Digital Surface Model (nDSM). This is obtained by subtracting the terrain from the surface (CHM = DSM – DTM). Each cell will indicate the height of objects.

 

The Canopy Peaks tool create a set of points that represent the tree locations and will contain information of the height of the tree:

I have had better results using using a TIFF as Canopy Height Model raster. Play around with the Minimum Height and Max Window Size to get the best result.

 

The resulting point featureclass will have information on the height of the tree. The only thing missing variable is the size of the crown. This data can be obtained with the tool “Tree Crown Radius”.

The result of this tool is the creation of an additional field “CrownRadius” with the information we search for.

 

You can visualize the points using the crown radius to evaluate the result and perhaps apply some filtering to filter out false positives.

 

Using the python code to create 3D trees we can proceed with determining the 3D distance to the overhead power lines and classify detect those trees that need to be logged according to the various criteria that apply:

 

Since we have both the partial inventory and the LiDAR data for the same area we can compare both results:

Result of the 3D analysis using the partial inventory and maximum potential size of the trees

 

Same area but now with the trees based on the LiDAR data and current size of the trees

 

Next up: Using Mosaic Datasets and Raster Chain Functions to analyze interference of trees on an overhead power line

 

Managing Data 3D  Imagery and Remote Sensing Lidar and other point clouds The specified item was not found.

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