ACT Elevation Acquisition 2015  

Authors / CoAuthors

Abstract

The ACT Elevation Acquisition 2015 is a highly accurate airborne LiDAR dataset, to be used to accurately model the impacts of climate change, disaster management, water security, environmental management, urban planning and infrastructure design. The full dataset covers the entire state of the ACT with a density of 4 pulses per square metre, and the Canberra's City Center at 8 pulses per square metre. LiDAR is classified to ICSM specification Level 3 (for ground) and delivered as LAS v1.4 in both ellipsoidal and othormetric formats. In addition, full waveform datasets have been provided for a small region within the 8 pulses per square metre area of interest. The outputs of the project are compliant with National ICSM LiDAR Product Specifications and the NEDF. The classification scheme is as follows: Unclassified (1), Ground (2), low vegetation (0-0.3m : 3), medium vegetation (0.3-2m : 4), high vegetation (>2m : 5), buildings (6), low noise (7), water (9), bridge (17), and high noise (18). The full waveform LiDAR dataset provides up to 7 returns per pulse depending upon the complexity of the features on the ground. This dataset defines the classified Australian Height Datum (AHD) LiDAR dataset for the full ACT region minus Canberra's City Center at 4 pulses per square metre.

Product Type

dataset

eCat Id

104761

Contact for the resource

Keywords

( Product )

• LiDAR

( Feature type )

• LAND-Topography

• Published_Internal

Publication Date

2017-11-09T01:07:34

Creation Date

2017-03-21T00:00:00

Security Constraints

Australian Government Security ClassificationSystem    

Classification - unclassified

Legal Constraints

Creative Commons Attribution 4.0 International Licence    

Access - license

Use - license

Status

Purpose

Elevation data acquisition

Maintenance Information

notPlanned

Topic Category

elevation

Series Information

Lineage

SOURCE DATA NEDF Metadata Acquisition Start Date: Thursday, 21 May 2015 Acquisition End Date: Tuesday, 5 April 2016 Sensor: LiDAR Device Name: Trimble AX60 Flying Height (AGL): 930 INS/IMU Used: Trimble AP50 GNSS/IMU Number of Runs: 1187 Number of Cross Runs: 20 Swath Width: 1075 Flight Direction: UNK Swath (side) Overlap: 25 Horizontal Datum: GDA94 Vertical Datum: AHD71 ¿ using local Geoid model Map Projection: MGA55 Description of Aerotriangulation Process Used: UNK Description of Rectification Process Used: UNK Spatial Accuracy Horizontal: 0.8 Spatial Accuracy Vertical: 0.3 Average Point Spacing (per/sqm): 4.00 Laser Return Types: Full waveform in excess of 4 returns Data Thinning: none Laser Footprint Size: 0.22 Calibration certification (Manufacturer/Cert. Company): Trimble AX60 calibration certificate supplied Limitations of the Data: The workflow and quality assurance processes were designed to achieve the Level 3 requirement for removal of significant anomalies which remain in the ground class (2) and achieve a ground point misclassification rate of 1% or less. The classification accuracy was not measured. Surface Type: Mixed Product Type: Mass Points Grid Resolution: 1 Distribution Format: LAS Processing/Derivation Lineage: The IMU and post processed airborne GPS logs were used to generate the LiDAR point cloud from the waveform instrument data. Raw LiDAR swaths were levelled to establish internal consistency, merged and 1km x 1km tiles in LAS v1.2 format were created. An automatic classification algorithm was applied in TerraScan software to produce an initial classification of ground (2) and unclassified (1). High and low noise points were automatically classified and allocated to class 18 and 7, respectively. The ground classification was improved manually by visually scanning the ground surface and reassigning points from ground to unclassified to remove spikes and by assigning unclassified points to ground where the ground surface lacked sufficient detail to describe the terrain (i.e. large TIN triangles). The classification of ground points was to the ICSM level 3 standard (99% accuracy with respect to ground points). On completion of the ground classification, automatic algorithms were used to classify unclassified above ground points to low vegetation (Class 3 : 0-0.3m), medium vegetation (Class 4 : 0.3-2m), high vegetation (Class 5 : >2m), buildings (6), water (9), and bridge (17). The classification accuracy was not measured empirically. Upon successful classification, the dataset was converted from ellipsoidal to orthometric using AusGeoid09, and tiles were regenerated to 2km x 2km and LAS v1.4 with Point Data Record Format 6.

Parent Information

Extents

[-35.95874080896988, -35.1049300391416, 148.71002194471654, 149.44610592909154]

Reference System

Spatial Resolution

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