Airborne Laser Scanner (ALS) data was acquired from a fixed wing aircraft between 9 September 2016 and 6 February 2017, using AAM Riegl Q1560 LiDAR system.
• Scanner: Riegl Q1560 - 2 laser channel system
• Half Scan Angle: ±20 & 29 degrees
• Laser Pulse Rate: 300kHz
• Laser Pulse Mode: Multipulse
• Laser Return: 1st, 2nd, 3rd... 7th and last
• File Format: ESRI ASCII Grid, LAS 1.2, ESRI Shapefile
• Horizontal Datum: NZGD2000
• Vertical Datum: NZVD2016
• Map Projection: NZTM2000
• Vertical Accuracy Specification: ±0.10m RMS
• Horizontal Accuracy Specification: ±0.30m RMS
The scan angle is < ±42 degrees per the data supplied to us not ±20 & 29 degrees referenced in the survey report specification. 18 LAS files are in LAS 1.2 format, the rest of the data is in LAS 1.3 format.
Airborne Laser Scanner (ALS) data was acquired from a fixed wing aircraft between 1 November 2016 to 29th June 2017 using AAM New Zealand's Riegl Q1560 - 2 laser channel system LiDAR system. This area includes Auckland Region covering southern suburbs and regions.
Classification of the point cloud followed the classification scheme below;
1 - Unclassified
2 - Ground
3 - Low Vegetation
4 - Medium Vegetation
5 - High Vegetation
6 - Buildings, Structures
7 - Low/High Points
9 - Water
10 - Bridge
12 - Overlap
Extra classifications found in three tiles were reclassified as Unclassified (1).
Rail (10) points were reclassified by LINZ as Bridges (17) per survey reference before providing the classified point cloud data to Open Topography.
Reduction of the LiDAR data proceeded without any significant problems. Classification of the point clouds is to Level 3, with reference to ICSM LiDAR Specifications for NZ. Classification accuracy Required: 99% for ground points.
Discussion and clarification on Classification was undertaken between Nathan Sykes as client representative, and the AAM Team during the client review of the data, such as:
The pedestrian footbridges have been classed as structures, the intention was to use the bridge class for more substantial road bridges over water (unless it is a piped culvert)
Train carriages (temporary/moving objects) have been left in class 5, as assigned by classification routines.
The observation was made that the newer sensors, such as the Q1560, are very sensitive. They detect noticeably more atmospheric noise than older sensors (e.g. very light humidity that is not visible, can be detected). It’s not a defect. With these returns included and classified in the dataset, the user gets a feel for the structure of the data, e.g. in open areas where there are no “only returns” because the “first of many returns” are in the noise class.
The Digital Elevation (DEM) and Digital Surface Model (DSM) were derived using a point to TIN and TIN to Raster process, using Linear interpolation. Hydro flattening was undertaken in the DEM over non-tidal water bodies with surface area greater than 625 sq m, to the client specifications.
Tidal areas were flow within 1.5 hours of gazetted low tide (noting local variation in actual lowest water level)
Ground data in this volume has been compared to test points obtained by field survey and assumed to be error-free.
Data classification has been manually checked and edited against any available imagery
Points to Note:
It was noted in tiles BB31_4425 and BB31_4325, there was significant flight line stepping between flights 2 and 10. This was caused by shifting sand dunes, rather than a system error.
The deliverables to LINZ were:
1m gridded bare earth digital elevation model (DEM)
1m gridded digital surface model (DSM)
Classified point cloud