Creating Hydro Flow Models from UAS collected imagery by means of advanced point cloud classification methods.

Introduction:

UAS imagery can be used to generate point clouds in a '.las' format, similar to LiDAR data. However, point clouds generated from UAS imagery require a substantially different processing workflow than their LiDAR collected contemporaries. All of the non-ground points should be removed, before flow models can be created from a point cloud. This paper will describe one method for performing this critical step. An orthomosaic and DSM can be useful for specific industries, but by classifying the point cloud and generating a DTM, substantially more value can be added to the data. The DTM surfaces can potentially be used to model runoff, in order to prevent erosion on construction sites or mine operations. If multiple flights of the same area were performed monthly, the classified orthomosaics could be used to perform post-classification land use/land cover (LULC) change detection. Post-classification LULC change detection would allow mine reclamation personnel precise information regarding the progress of the reclamation process, specifically how much land changed from one LULC class to another.

Study Area:

The UAS images were captured of the Litchfield gravel pit on March 13, 2016 between 11:30AM and 2:00PM. The day was overcast, eliminating defined shadows and reducing the overall contrast in the captured images. The imagery used for point cloud generation was flown at 200ft, with 12 megapixel images. The images were captured using a Sony A6000, with a Voightgander 15mm lens at f/5.6 (Figure 1).

Figure 1: The Sony A6000

Methods:

1. The first processing step is to import the imagery, geolocation information, and camera specifications into Pix4D as a new project. The processing should be calibrated to produce a densified point cloud, as well as an orthomosaic.
2. Next, object based classification will be performed on the orthomosaic, using the Trimble eCognition software.
3. The classified output will be converted to a shapefile, using Esri ArcGIS.
4. ArcGIS will be used to reclassify the densified point cloud by using the 'Classify Las by Feature' tool and the shapefiles created in the previous step.
5. The 'Make Las Feature Layer' and 'Las dataset to Raster' tools will be used to create a DTM from only the ground points.
6. (optional) If the output DTM still contained some of the low vegetation, the 'Curvature', 'Reclassify', 'Raster to polygon', and 'Classify Las by Feature' tools will be used to identify and reclassify any remaining low vegetation points.

7. The ArcGIS Spatial Analyst toolbox will be used to perform runoff modeling

Discussion:

The imagery was collected without GCPs, however, previous imagery was collected with GCPs so it was possible to use the image-to-image rectification process to add GCPs to the image - increasing its overall spatial accuracy.

LiDAR sensors also record pulse intensity, which allows the analyst to more easily differentiate between different types of land cover for a given point, however as the UAS collected data using passive remote sensing techniques, this is not possible.

Conclusion:

The classification accuracy could be even further improved by capturing the imagery with a multi-spectral sensor.