GIS595/MEA792:
UAV/lidar Data Analytics

Assignments

Data

• General resources
  • North Carolina dataset (GRASS location)
  • Goggle drive student's resources folder
• UAV data
• boundaries of all COAs obtained by NGAT in North Carolina (google maps, shp)
• Study site (Lake Wheeler): COA boundaries (shp, kml), GCP coordinates (txt, kml), also included in the GRASS location (see processed data)
• Survey data: down-sampled pictures and log from the flight 06/20/2015
• Processed data:
• LakeWheeler_NCspm GRASS location with timeseries of DSMs from 2015 flights
• point clouds from 2015 flights
• point clouds for the gully area (high density, from 2015 flights)
• point clouds used for flow simulation (limited extent)
• Lidar data
• Secref and Mid Pines point clouds
• Secref and Mid Pines interpolated DEM/DSM (use r.unpack)
• for additional point cloud tiles:
• see the overview vector file in GRASS,
• download preselected tiles from here,
• follow the instructions for importing lidar data to GRASS GIS.
• ASPRS LAS specification file with the list of ASPRS Standard LIDAR Point Classes on p.10

Software

• GRASS GIS 7.0.1 (you can use these instructions to install it)
• Trimble Aerial Imaging software and installation instructions here
• Agisoft Photoscan (provided at NCSU) - you can work on the demo version; installation files: Windows, Mac, Linux
• open source software OpenDroneMap (installation will be discussed in class)
• open source software MicMac (documentation can be found here)
• LibLAS and LAStools for managing the .las files in GRASS
• software provided at NCSU is available through Google Drive (accessible with NCSU account)

Assignments

1. Explore different types of UAS
2. Flight plan preparation - use Trimble Aerial Imaging (Assignment 2A) and GRASS GIS (Assignment 2B)
3. Processing the data to create orthophoto and DSM using Agisoft Photoscan and OpenDroneMap
4. GIS-based analysis of UAS derived data geoprocessing outputs
5. Multiple return lidar data analysis and visualization
6. Fusion of lidar and UAV data
7. Create a script to process multiple raster or vector maps
   • In case you are not familiar with Python, go through e.g., Codecademy Python track.
   • Run examples in the introduction to see examples of how GRASS GIS is used in Python.
8. Impact of terrain change on landscape processes: dynamic simulation on fused lidar/UAS DsM time series