Whitemud River Watershed LiDAR

LiDAR acquisition parameters:

The LiDAR systems used for collection of the point cloud were set to record a minimum of three outbound pulses per square meter in non-overlap areas. These systems recorded intensity and detected multiple discrete returns, with a minimum of four potential returns. The followings show more parameters concerning both systems used.

LiDAR acquisition parameters for RIEGL LMS-Q1560:
Altitude above ground level (AGL): 1790 m
Speed of the plane: 142 knots (73m/sec)
Effective Field of View angle: 20° to 26° each side
Effective Corridor swath: 1305 m
Distance between flight lines: 1044 m
Lateral overlap: 20% to 25%
Effective repetition rate: 532 kHz
Width of overlap zone: 261 m
Density in non-overlap zone (pts/m2): 3.62

LiDAR acquisition parameters for ALTM Galaxy
Altitude above ground level (AGL): 1477 m
Speed of the plane: 140 knots (72m/sec)
Effective Field of View angle: 24 ° each side
Effective Corridor swath: 1336 m
Distance between flight lines: 1069 m
Lateral overlap: 20%
Effective repetition rate: 400 kHz
Width of overlap zone: 267 m
Density in non-overlap zone (pts/m2): 3.32 

Many aspects of the point cloud were analysed and validated before the classification step. In addition to the density and the accuracy requirements, GPS time was validated to correspond to adjusted GPS time.

Concerning the scan angle, although it was originally proposed to limit it at ± 20° from the nadir it was agreed with Manitoba Infrastructure to increase this limit to ± 26,25° (F0V +52.5°). It was also agreed to increase lateral overlap up to 25%.



Calibration process:

The vertical and horizontal absolute position (inherent) is validated using GPS static and kinematic precision measured in the calibration zone. During this process, ground controls points are measured and this allows to calibrate the LiDAR system. In order to have high precision on GPS positioning during aerial survey, we used GPS bases. Airborne operations used the existing geodetic network in the reference system NAD83-CSRs as GPS base for aerial surveys. A selection was made in order to ensure GPS bases at a distance necessary to obtain good results (about 30km). When a GPS point was not available, we installed our bases on a temporary point which was measured and linked to the Nad83-CSRs network. We also used Can-NET base station network and PPP correction from NRCan. Also, all flight lines lasted a maximum of 20 minutes. This avoid the drift of the inertial platform.


Point cloud metadata:

The point cloud collected is classified intthese eleven classes following.
Class 0 = Unclassified
Class 1 = Default:
Class 2 = Ground
Class 3 = Low vegetation
Class 4 = Medium vegetation
Class 5 = High vegetation
Class 6 = Buildings, structures
Class 7 = Low / high points
Class 8 = Model key points
Class 9 = Water
Class 10 = Bridge

To carry out cleaning and classification data we used an automatic process coupled with a verification step and manual classification with qualified technicians, based on references control points measured on the ground. Images of laser intensity help to identify many elements such as water, building and bridges.

The level 1 of classification required for the project is normally only an automatic classification process realized with TerraScan macros. Although automatic classification was process, manual verification was done all over the project. For example, concerning bridges and water, it’s impossible to use automatic classification. A manual editing was processed to produce theses classes.

For the water surface, only permanent water connected to hydrographic network are classified. The proposed minimum area for water classification is 1ha for lakes and more than 30m width for rivers. These standards are from Ministère de l’Énergie et des Ressources du Québec.

Concerning classes 2 and 8, an automatic and manual classification were made. A special attention was brought to areas such as cliffs, urban areas, ditches, forest areas, etc.

A final validation was also done on all LiDAR data by generating all the tiles color coded raster images and validating visually each of these images in order to detect all detectable abnormalities (absence of points, small density, inconsistency in Z, etc...).

Groupe Info Consult