1. Infrastructure Identification near Island Park Reservoir, Idaho
Neil Falke - Brigham Young University–Idaho
Abstract
Remote sensing is becoming a more viable tool for engineers and other professionals across many fields. One application of this technology is identifying existing infrastructure using aerial imagery. Methods of classification and sequestration of infrastructure from surroundings are beneficial to project planning and execution.
The focus of this project was to create a feature class of rooftops near the Island Park Reservoir with remote sensing methods. Orthophoto imagery and LiDAR data of the reservoir from the Idaho Water Resource Board (IWRB) provided the basis of analysis and method discovery. ENVI LiDAR and ArcGIS software were used to display and analyze data. ENVI's classification and feature extraction tools were used, but these methods were unsuccessful. Classified LiDAR data were used in ENVI, and rooftop identification was successful. Building rooftop shapes were converted to georeferenced shapefile polygons and displayed in ArcGIS.
Method Development
ENVI classification and feature extraction methods were futile in correctly identifying rooftops in this area. Challenges associated with these methods included: tall trees, shadows, varying textures of rooftops and software inexperience and limitations. LiDAR data made extraction based on elevation easier and more accurate. I processed the data at 1 m/pixel resolution and produced a layers for bare earth terrain, buildings, and trees. ENVI complies to specifications for classification by ASPRS, which provides the basis for its classification algorithm.
3D Model in ENVI
The map to the left shows the results of the classification method that uses similar pixel values to group objects. Notice the many small areas that were classified as infrastructure , as well as their irregular shape. This ‘rooftops’ shapefile would not be conducive to engineering standards.
Results
I was able to successfully extrude the rooftops on Bill’s Island and create a georeferenced shapefile from LiDAR elevation data. Some of the rooftop shapes and angles were inaccurate, but modification is possible using quality assurance on ENVI LiDAR. I also was able to identify and remove trees which has immense geological and engineering implications.
Location
Bills Island is located in the Island Park Reservoir of Eastern Idaho.
ENVI is a powerful program that automatically classifies buildings, trees, power lines and other features from multiple return LiDAR data. By processing the data, I was able to export a building rooftop shapefile to display over satellite imagery.
The images to the right and below show the rooftop layer in ENVI and overlying the IWRB imagery respectively. These maps model the northern portion of Bills Island.
Conclusions
Using LiDAR data was the fastest and most accurate method to extrude features and export as shapefiles. This method has wide application in projects involving civil engineering or environmental hazard mitigation. Measuring distances from existing infrastructure is one example. ENVI was most useful in analyzing and classifying imagery, but ArcGIS Pro was more effective in displaying and truthing the produced data.
Study Area - Bills Island
Rooftops Feature Class
Data Sources
Idaho State Boundary: US Census Bureau (census.gov)
Aerial Satellite Imagery of Bills Island: Inside Idaho (inside.uidaho.edu)
Background Photo: Alex S Maclean, Pine Forest
Orthophoto Imagery: IWRB*, IDWR, Forsgren Associates
LiDAR Data: IWRB*, Idaho LiDAR Consortium
*Imagery was funded by the IWRB