Pixel values are in m. 2m x 2m pixel size.
Maximum water depth which is used to determine flood plain and flood prone areas, and is used an input into the DxV grids used for overland flow path identification. Modelling done by Awa on behalf of TCC. Grids mosaicked to gether by TCC. Data used in Plan Change 27. Dated: 2020.
An aerial thermal infrared (TIR) survey of the Rotorua and Lake Rotokawa-Mokoia Geothermal Fields was undertaken 6-7 March, 2014. Approximately 2700 TIR images were processed and mosaicked to produce a single, 16 bit image with a ground pixel size of approximately 2 m x 2 m (Figure A1). The purpose of this report is to summarise the method of data collection, and present the final TIR image and accompanying brief interpretation.
The TIR data is of good quality and enables numerous geothermal and non-geothermal surface features to be identified and mapped to relatively high spatial accuracies (<5 m) within the limitations of the survey The locations of TIR anomalies interpreted to be caused by geothermal activity are in general agreement With areas of known surface geothermal activity Several areas where thermal seeps may be discharging into Lake Rotorua in the Rotorua City, Mokoia Island and Rotokawa areas are identified.
An aerial thermal infrared (TIR) survey of the Rotorua and Lake Rotokawa-Mokoia Geothermal Fields was undertaken 6-7 March, 2014. Approximately 2700 TIR images were processed and mosaicked to produce a single, 16 bit image with a ground pixel size of approximately 2 m x 2 m (Figure A1). The purpose of this report is to summarise the method of data collection, and present the final TIR image and accompanying brief interpretation.
The TIR data is of good quality and enables numerous geothermal and non-geothermal surface features to be identified and mapped to relatively high spatial accuracies (<5 m) within the limitations of the survey The locations of TIR anomalies interpreted to be caused by geothermal activity are in general agreement With areas of known surface geothermal activity Several areas where thermal seeps may be discharging into Lake Rotorua in the Rotorua City, Mokoia Island and Rotokawa areas are identified.
Please refer to the AAM supplier README document for a full metadata synopsis: PRJ39741_03
Imagery was captured for BOPLASS Limited by AAM NZ Ltd, 6 Ossian St, Napier, New Zealand.
Data comprises: • 4544 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:2000 tile layout
The supplied imagery is in terms of New Zealand Transverse Mercator (NZTM) map projection. Imagery supplied as 20cm pixel resolution (0.20m GSD), 4-band (RGBI) uncompressed GeoTIFF. The final spatial accuracy is ±0.6m at 90% confidence level in clear flat areas.
The complete metadata documentation for this imagery capture project by AAM can be found here: https://objective.envbop.net/id:A4015420/document/versions/published
Index Tiles ONLY, for actual orthophotos see layer [Bay of Plenty 0.1m Urban Aerial Photos (2020)](http://data.linz.govt.nz/layer/106288)
Orthophotography captured over three urban areas in the Bay of Plenty region totaling approximately 414km2. The areas of capture cover Tauranga City, and Ōmokoroa and McLarens Falls in the Western Bay of Plenty District. Imagery was captured for BOPLASS Ltd by AAM NZ Limited, 6 Ossian St, Napier, New Zealand.
Data comprises:
• 4791 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:500 tile layout.
• Tile layout in NZTM projection containing relevant information.
The supplied imagery is in terms of New Zealand Transverse Mercator (NZTM) map projection. Imagery supplied as 10cm pixel resolution (0.1m GSD), 3-band (RGB) uncompressed GeoTIFF. The final spatial accuracy is ±0.3m at 90% confidence level.
The imagery contract summary for this coverage area can be found here: https://objective.envbop.net/id:A4015349/document/versions/published
Please read Project Reports PRJ36500NON01 (Objective Ref: A3550027) and PRJ36500NON02 (Objective Ref: A3550026) for complete metadata.
Acquisition (PRJ36500NON01- Kaituna Wetland, Maketu, Little Waihi):
Acquisition of Imagery occurred on 20th January 2020, airborne LiDAR data was captured concurrently with the Imagery and used as the orthophoto DEM. Trajectories supplied in the ancillary folder of this volume contain date and time information specific to the areas supplied.
Acquisition (PRJ36500NON02 - Frost flats):
Acquisition of Imagery occurred on 23rd, 25th, 27th January and 3rd February 2020. Airborne LiDAR data was captured concurrently with the Imagery and used as the orthophoto DEM. Trajectories supplied in the ancillary folder of this volume contain date and time information specific to the areas supplied.
Ground Support:
RTX processing was used to calculate the GPS trajectory solution. Trimble CenterPointR RTX™ is a proprietary GPS, GLONASS, BeiDou, and QZSS enabled technology that provides high-accuracy GNSS positioning worldwide without the use of traditional local base stations or a VRS network. By combining real-time data from a global reference station infrastructure with innovative positioning and compression algorithms, Trimble RTX technology computes centimeter-level positions based on satellite orbit and clock information. Ground control was surveyed by Sounds Surveying in early 2019 was used to assist in control for the imagery.
Data Processing:
The RGB orthophotos are supplied at 10cm gsd. The imagery was georeferenced with airborne reference data and surveyed ground control, using aero triangulation. Ortho-rectification utilised the LiDAR digital terrain model, and a combination of automated and manual seamline placement. Laser strikes were classified into default, ground using a single algorithm across the project area. No manual editing was performed. The ground points were used for orthorectification of the imagery, and supplied in this volume as DEM.
All documents related to the capture and supply of imagery and LiDAR data can be found in this objective folder: fA1120034
If you have any questions, please contact the GIS Team at BOPRC.
Metadata documents regarding the product specification can be accessed through the following objective links:. 1K coverage metadata 2K coverage metadata
This index feature class allows people to find particular tiles and give the path to the .jpg image on the W drive. It can be used to create a list of images to copy etc.
Polygon Shapefile showing the areas/boundaries of Environment Bay of Plenty's RDAM (Regional Digital Aerial Mosaic) photos.
Polygons outline of boundary coverd by delivered tiles fomr the 2011 Lidar capture season.
Bounding regions of all terrain datasets including the location of the source data
This Feature Service provides access to the geological features for the New Zealand mainland that form part of the 1:250 000 Geological map of the New Zealand. It is a product of the Geological Map of New Zealand project and was produced by GNS Science. The data structure complies with the GeoSciML Portrayal standard, where possible, and uses the relevant CGI Controlled Vocabularies. This service is associated with Heron, D.W. (custodian) 2023: Geological Map of New Zealand 1:250 000 (4th ed.) [digital data]. Lower Hutt (NZ): GNS Science. (GNS Science geological map; 1). For more information on data, distribution options and formats visit https://doi.org/10.21420/5XTJ-5718. To obtain the data visit https://doi.org/10.21420/8BEH-6P24.
This Feature Service provides access to the landslide data for the New Zealand mainland that form part of the 1:250 000 Geological map of the New Zealand. It is a product of the Geological Map of New Zealand project and was produced by GNS Science. The data structure complies with the GeoSciML Portrayal standard, where possible, and uses the relevant CGI Controlled Vocabularies. This service is associated with Heron, D.W. (custodian) 2023: Geological Map of New Zealand 1:250 000 (4th ed.) [digital data]. Lower Hutt (NZ): GNS Science. (GNS Science geological map; 1). For more information on data, distribution options and formats visit https://doi.org/10.21420/5XTJ-5718. To obtain the data visit https://doi.org/10.21420/8BEH-6P24.
The photos were taken from a fixed wing aircraft (Eastbay Aviation Cessna 172 EJD) by Dan Van Nistelrooy of the Geospatial Team using a Nikkon D800 camera. Specs are gpsed full frame 36mp DSLR. All of the photos are geotagged. This Raster was created by firstly post processing the full res raw images in Lightroom, then stitching them together in Photoshop before georeferencing and mosaicking them in Arcmap before being moved to SDE.
The raw images are also available for viewing in brcsvfap05\\Archive\\SharedData\\_Adhoc Aerial Photography
Use: vector.GIS.MonitoringLowFlowCalculationSite
Definition query: MALFSpecificDischargLperSperKm2 IS NOT NULL And LocationName NOT IN ('Lake Rotorua at Mission Bay', 'Wairoa at Above Ruahihi')
Geoprocessing tool: Spatial Analyst Tools > Interpolation > IDW
Input point features: vector.GIS.MonitoringLowFlowCalculationSite
Z value field: MALF Specific Discharge (l/s/km2)
Output raster: LowFlow_MALF_IDW_50m_20220408
Output cell size: 50
All other options: defaults
Environments: Just change Mask to use Q:\\GIS Projects\\33_NPSFM_RNRP\\1_WorkingDraft\\WaterQuantity_20220408_InterpolationSurfaceOfMALF_GSP_662244\\Default.gdb\\Region_Buffer5km
Published as a Map Image Layer from gisimg
Use: vector.GIS.MonitoringLowFlowCalculationSite
Definition query: MALFSpecificDischargLperSperKm2 IS NOT NULL And LocationName NOT IN ('Lake Rotorua at Mission Bay', 'Wairoa at Above Ruahihi')
Geoprocessing tool: Spatial Analyst Tools > Interpolation > IDW
Input point features: vector.GIS.MonitoringLowFlowCalculationSite
Z value field: MALF Specific Discharge (l/s/km2)
Output raster: LowFlow_MALF_IDW_50m_20220408
Output cell size: 50
All other options: defaults
Environments: Just change Mask to use Q:\\GIS Projects\\33_NPSFM_RNRP\\1_WorkingDraft\\WaterQuantity_20220408_InterpolationSurfaceOfMALF_GSP_662244\\Default.gdb\\Region_Buffer5km
Published as a Map Image Layer from gisimg
Please see the extended metadata series for additional details and specs related to this data source and its source LiDAR data:
- BayofPlenty-2019-25-PGFLiDAR_Block1Part1Metadata
- BayofPlenty-2019-25-PGFLiDAR_Block1Part2Metadata
- 20210224_PGF_LiDAR_2020-25_BayOfPlenty_Block1-pt1_Products QA Report
- 20210228_PGF_LiDAR_2020-25_BayOfPlenty_Block1-pt2_Products QA Report
The Classified Point Cloud is also available. This classifies all points captured into feature types such as bare ground, water features, vegetation and buildings and can be viewed in 3D.
The easiest way to access the point cloud at present is through the Open Topography website.
Access for those outside the organisation
If you are working with contractors or consultants who require access to the LiDAR, you can point them to the below locations where they can access the data themselves:
LINZ Data Service - Digital Elevation Model
LINZ Data Service - Digital Surface Model
Any queries about accessing additional related data or if you experience any problems accessing anything, contact the Geospatial team. Capture for the remainder of the region is continuing, with further blocks being released over the next 1-2 years.
Please see the extended metadata series for additional details and specs related to this data source and its source LiDAR data:
- BayofPlenty-2019-25-PGFLiDAR_Block1Part1Metadata
- BayofPlenty-2019-25-PGFLiDAR_Block1Part2Metadata
- 20210224_PGF_LiDAR_2020-25_BayOfPlenty_Block1-pt1_Products QA Report
- 20210228_PGF_LiDAR_2020-25_BayOfPlenty_Block1-pt2_Products QA Report
The Classified Point Cloud is also available. This classifies all points captured into feature types such as bare ground, water features, vegetation and buildings and can be viewed in 3D.
The easiest way to access the point cloud at present is through the Open Topography website.
Access for those outside the organisation
If you are working with contractors or consultants who require access to the LiDAR, you can point them to the below locations where they can access the data themselves:
LINZ Data Service - Digital Elevation Model
LINZ Data Service - Digital Surface Model
Any queries about accessing additional related data or if you experience any problems accessing anything, contact the Geospatial team. Capture for the remainder of the region is continuing, with further blocks being released over the next 1-2 years.
Please read Project Reports PRJ36500NON01 (Objective Ref: A3550027) and PRJ36500NON02 (Objective Ref: A3550026) for complete metadata.
Acquisition (PRJ36500NON01- Kaituna Wetland, Maketu, Little Waihi):
Acquisition of Imagery occurred on 20th January 2020, airborne LiDAR data was captured concurrently with the Imagery and used as the orthophoto DEM. Trajectories supplied in the ancillary folder of this volume contain date and time information specific to the areas supplied.
Acquisition (PRJ36500NON02 - Frost flats):
Acquisition of Imagery occurred on 23rd, 25th, 27th January and 3rd February 2020. Airborne LiDAR data was captured concurrently with the Imagery and used as the orthophoto DEM. Trajectories supplied in the ancillary folder of this volume contain date and time information specific to the areas supplied.
Ground Support:
RTX processing was used to calculate the GPS trajectory solution. Trimble CenterPointR RTX™ is a proprietary GPS, GLONASS, BeiDou, and QZSS enabled technology that provides high-accuracy GNSS positioning worldwide without the use of traditional local base stations or a VRS network. By combining real-time data from a global reference station infrastructure with innovative positioning and compression algorithms, Trimble RTX technology computes centimeter-level positions based on satellite orbit and clock information. Ground control was surveyed by Sounds Surveying in early 2019 was used to assist in control for the imagery.
Data Processing:
The RGB orthophotos are supplied at 10cm gsd. The imagery was georeferenced with airborne reference data and surveyed ground control, using aero triangulation. Ortho-rectification utilised the LiDAR digital terrain model, and a combination of automated and manual seamline placement. Laser strikes were classified into default, ground using a single algorithm across the project area. No manual editing was performed. The ground points were used for orthorectification of the imagery, and supplied in this volume as DEM.
Please read Project Reports PRJ36500NON01 (Objective Ref: A3550027) and PRJ36500NON02 (Objective Ref: A3550026) for complete metadata.
Acquisition (PRJ36500NON01- Kaituna Wetland, Maketu, Little Waihi):
Acquisition of Imagery occurred on 20th January 2020, airborne LiDAR data was captured concurrently with the Imagery and used as the orthophoto DEM. Trajectories supplied in the ancillary folder of this volume contain date and time information specific to the areas supplied.
Acquisition (PRJ36500NON02 - Frost flats):
Acquisition of Imagery occurred on 23rd, 25th, 27th January and 3rd February 2020. Airborne LiDAR data was captured concurrently with the Imagery and used as the orthophoto DEM. Trajectories supplied in the ancillary folder of this volume contain date and time information specific to the areas supplied.
Ground Support:
RTX processing was used to calculate the GPS trajectory solution. Trimble CenterPointR RTX™ is a proprietary GPS, GLONASS, BeiDou, and QZSS enabled technology that provides high-accuracy GNSS positioning worldwide without the use of traditional local base stations or a VRS network. By combining real-time data from a global reference station infrastructure with innovative positioning and compression algorithms, Trimble RTX technology computes centimeter-level positions based on satellite orbit and clock information. Ground control was surveyed by Sounds Surveying in early 2019 was used to assist in control for the imagery.
Data Processing:
The RGB orthophotos are supplied at 10cm gsd. The imagery was georeferenced with airborne reference data and surveyed ground control, using aero triangulation. Ortho-rectification utilised the LiDAR digital terrain model, and a combination of automated and manual seamline placement. Laser strikes were classified into default, ground using a single algorithm across the project area. No manual editing was performed. The ground points were used for orthorectification of the imagery, and supplied in this volume as DEM.
Acquisition of airborne LiDAR data and Imagery over the project occurred between 7th December 2018, 17th January 2019, 13th April 2019 from a fixed wing aircraft.
Please ask the GIS team if you would like to access any of the ancillary files and raw data.
Acquisition of airborne LiDAR data and Imagery over the project occurred between 7th December 2018, 17th January 2019, 13th April 2019 from a fixed wing aircraft.
Please ask the GIS team if you would like to access any of the ancillary files and raw data.
Digital Elevation Model covers Tauranga City Council Area.LIdar captured in 2017
Digital Elevation Model covers Tauranga City Council Area.LIdar captured in 2017
Data comprises:
•DEM: asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile. Planned pulse density is 1.0 pulses/square metre.
The LAS point cloud and vendor project reports are available from
[OpenTopography](http://opentopo.sdsc.edu/datasets).
Data comprises:
•DEM: asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile. Planned pulse density is 1.0 pulses/square metre.
The LAS point cloud and vendor project reports are available from
[OpenTopography](http://opentopo.sdsc.edu/datasets).
An Airborne Laser Scanner survey was conducted over the BOPLASS area of interest totalling approximately 929.38km2. The area is located in the Bay of Plenty Region of the North Island.
A complete metadata document can be viewed at Q:\\SharedData\\AerialPhotography2014-2015\\Delivery 1 July 2015 - LiDAR\\BOPLASS-2015-LiDAR_Metadata.pdf
An Airborne Laser Scanner survey was conducted over the BOPLASS area of interest totalling approximately 929.38km2. The area is located in the Bay of Plenty Region of the North Island.
A complete metadata document can be viewed at Q:\\SharedData\\AerialPhotography2014-2015\\Delivery 1 July 2015 - LiDAR\\BOPLASS-2015-LiDAR_Metadata.pdf
A complete metadata synthesis for each LiDar delivery (1k, 2k, and 5k) can be viewed at Q:\\GeospatialGroupArchive\\Terrains\\RawData\\LiDAR2011\\2011_lidar_metadata_package.zip
A complete metadata synthesis for each LiDar delivery (1k, 2k, and 5k) can be viewed at Q:\\GeospatialGroupArchive\\Terrains\\RawData\\LiDAR2011\\2011_lidar_metadata_package.zip
| Allocation Status | \r\n{AllocationStatus} | \r\n
| MALF Normalised (l/s) | \r\n{MALF_norm_LperSec} | \r\n
| Available Allocation (l/s) | \r\n\t{AvailableAllocation_LperSec} | \r\n
| Allocated Amount (l/s) | \r\n\t{AllocatedSurfaceWater_LperSec} | \r\n
| Allocated Amount (%) | \r\n\t{AllocatedSurfaceWater_Percent}% | \r\n
| Allocation Remaining (l/s) | \r\n\t{AllocationRemaining_LperSec} | \r\n
| Allocation Status | \r\n{AllocationStatus} | \r\n
| Q5 Normalised (l/s) | \r\n{Q5_norm_LperSec} | \r\n
| Available Allocation (l/s) | \r\n\t{AvailableAllocation_LperSec} | \r\n
| Allocated Amount (l/s) | \r\n\t{AllocatedSurfaceWater_LperSec} | \r\n
| Allocated Amount (%) | \r\n\t{AllocatedSurfaceWater_Percent}% | \r\n
| Allocation Remaining (l/s) | \r\n\t{AllocationRemaining_LperSec} | \r\n
| Allocation Status (with permitted) | \r\n{AllocationStatusPermitted} | \r\n
| MALF Normalised (l/s) | \r\n{Q5_norm_LperSec} | \r\n
| Available Allocation (l/s) | \r\n\t{AvailableAllocation_LperSec} | \r\n
| Allocated Amount with permitted (l/s) | \r\n\t{AllocatedSWPermitted_LperSec} | \r\n
| Allocated Amount with permitted (%) | \r\n\t{AllocatedSWPermitted_Percent}% | \r\n
| Allocation Remaining with permitted (l/s) | \r\n\t{AlloRemainPermitted_LperSec} | \r\n
| Allocation Status (with permitted) | \r\n{AllocationStatusPermitted} | \r\n
| MALF Normalised (l/s) | \r\n{MALF_norm_LperSec} | \r\n
| Available Allocation (l/s) | \r\n\t{AvailableAllocation_LperSec} | \r\n
| Allocated Amount with permitted (l/s) | \r\n\t{AllocatedSWPermitted_LperSec} | \r\n
| Allocated Amount with permitted (%) | \r\n\t{AllocatedSWPermitted_Percent}% | \r\n
| Allocation Remaining with permitted (l/s) | \r\n\t{AlloRemainPermitted_LperSec} | \r\n