This dataset provides a seamless cloud-free 10m resolution satellite imagery layer of the New Zealand mainland and offshore islands.

The imagery was captured by the European Space Agency Sentinel-2 satellites between September 2022 - April 2023.

Data comprises: • 450 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:50000 tile layout. • Satellite sensors: ESA Sentinel-2A and Sentinel-2B • Acquisition dates: September 2022 - April 2023 • Spectral resolution: R, G, B • Spatial resolution: 10 meters • Radiometric resolution: 8-bits (downsampled from 12-bits)

This is a visual product only. The data has been downsampled from 12-bits to 8-bits, and the original values of the images have been modified for visualisation purposes.

Also available on: • Basemaps • NZ Imagery - Registry of Open Data on AWS

The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and georeferenced (correctly positioned) to create a unique and crucial record of changes to the New Zealand landscape.
All of the imagery files are Cloud Optimised GeoTIFFs using lossless WEBP compression for the main image and lossy WEBP compression for the overviews. These image files are accompanied by STAC metadata. The imagery is organised by region and survey.

An aerial imagery basemap of New Zealand in Web Mercator (WGS 1984) using the latest quality data from Land Information New Zealand.Add the map service directly to your ArcGIS Online map, or copy the Web Map Tile Service (WMTS) URL below for use in the desktop.This basemap is also available in NZTM from: https://linz.maps.arcgis.com/home/item.html?id=39cf07ebf8a2413696d8fd4d80570b84 The LINZ Aerial Imagery Basemap details New Zealand in high resolution - from a nationwide view all the way down to individual buildings.This basemap combines the latest high-resolution aerial imagery down to 5cm in urban areas and 10m satellite imagery to provide full coverage of mainland New Zealand, Chathams and other offshore islands.LINZ Basemaps are powered by data from the LINZ Data Service and other authoritative open data sources, providing you with a basemap that is free to use under an open licence.A XYZ tile API (Web Mercator only) is also available for use in web and mobile applications.See more information or provide your feedback at https://basemaps.linz.govt.nz/.For attribution requirements and data sources see: https://www.linz.govt.nz/data/linz-data/linz-basemaps/data-attribution.

Planet image metadata associated with collection: Mapping Wetlands with High Resolution Planet SuperDove Satellite Imagery: An Assessment of Machine Learning Models Across the Diverse Waterscapes of New Zealand (10.26021/canterburynz.c.7848596).Each file is a GeoPackage (gpkg) containing two polygon vector layers:"images" contains the footprint of each original Planet SuperDove image within the area of interest, and associated image metadata."coverage" contains polygons identifying which images were used to create a mosaic, then used further processing. Each pixel consisted of the values from only one image (with the highest priority).The file name for each gpkg is xxxxx_meta.gpkg, where xxxxx refers to the UUID of the grid area, which can be found in the file nzgrid_uuid.gpkg.

Context

Maungataketake (Ellett’s Mountain) was a volcanic cone on the shore of the Manukau Harbour, Mangere, New Zealand. In the second half of the twentieth century the mountain was quarried away. Maungataketake was a terraced Māori Pā, and archaeological excavations (only now in the process of being published) were undertaken there between 1972 and 1975, and in 1982, prior to its complete destruction. There is aerial imagery of the mountain available that depicts the mountain prior to quarrying. With these data a 3D model of the site was made using photogrammetry, which was also used to create a digital surface model (DSM) and contour map of the mountain. The resulting data is provided here and is aimed for further geospatial applications. In addition, the resolution of the provided DSM has analogues for the wider region and therefore could be incorporated to represent the landscape pre-destruction. Further to this a representation of the 3D model may be found on SketchFab.

Method

The photogrammetry model was created in Agisoft Metashape version 1.5.4. Ten aerial images were used of Maungataketake and the surrounding area, captured on 19^th August 1960. These images were downloaded from http://retrolens.co.nz and are licensed by LINZ CC-BY 3.0. The model was aligned and the spare point cloud filtered by gradual selection with the following parameters: projection error = 0.2; reconstruction uncertainty = 10; projection accuracy = 2.5. The dense cloud was processed with depth maps of ultra high quality and aggressive filtering. The resulting points cloud was edited to remove outlying points and processed into a 3D model.

The resulting 3D model was manually edited to remove faces representing trees on Maungataketake only, but not the surrounding area. This was done as to obtain representative surface contours of the mountain. The model was georeferenced by the identification of points on the landscape present on the 1960 composite image and contemporary satellite imagery. A 0.5 m DSM and contours at 1 m resolution were calculated of Maungataketake.

Contents of dataset

• A geodatabase with:
  • Control points used for georectification
  • 1 m contours without elevation of Maungataketake
  • 1 m contours with elevation of Maungataketake
  • 0.5 m composite aerial image
  • 0.5 m DSM of area covering control points
  • 0.5 m DSM of Maungataketake
• Aerial photographs Crown_583-1924_22-26, Crown_583_1925_22-26
• Licence for aerial photographs from http://retrolens.co.nz
• Attributes of aerial photographs

The ANZ (Australia and New Zealand) satellite imagery services market, currently valued at approximately $130 million (extrapolated from the global 0.13 Million value and considering the relative economic strength and geographic size of ANZ), is experiencing robust growth, projected at a Compound Annual Growth Rate (CAGR) of 11.61% from 2025 to 2033. This expansion is driven by several key factors. Firstly, increasing government investment in infrastructure projects, particularly in transportation and urban development, fuels demand for high-resolution imagery for planning and monitoring. Secondly, the agricultural sector's adoption of precision farming techniques, leveraging satellite data for crop monitoring and yield optimization, significantly contributes to market growth. Furthermore, the rising need for effective disaster management and environmental monitoring, especially given the region's susceptibility to natural hazards like bushfires and floods, necessitates advanced satellite imagery solutions. Finally, the burgeoning construction and mining industries utilize satellite imagery for site surveying, resource exploration, and project management, further bolstering market demand. The market is segmented by application (geospatial data acquisition and mapping, natural resource management, surveillance and security, conservation and research, disaster management, intelligence) and end-user (government, construction, transportation and logistics, military and defense, forestry and agriculture, other end-users). While the government sector currently holds a dominant market share, the private sector, particularly in construction, agriculture, and mining, is witnessing rapid growth. Key players in the ANZ market include Airbus SE, Geospatial Intelligence Pty Ltd, Geoimage Pty Ltd, Nearmap Australia Pty Ltd, and Aerometrex Limited, among others. Competition is relatively high, driven by technological advancements and the emergence of new entrants offering innovative solutions. The market's future growth hinges on continued technological innovation, including advancements in satellite sensor technology, data processing capabilities, and the development of user-friendly analytical platforms. Government regulations and policies promoting the use of geospatial data also play a crucial role in shaping the market's trajectory. Recent developments include: May 2023: Arlula announced the successful completion of its AUD 2.2 million (USD 1.5 million) initial investment round. This significant investment will enable the firm to increase access to Earth Observation (EO) data and imagery, helping people, small companies, and big corporations entirely use space-based data. This significant support demonstrates an interest in and acceptance of Arlula's aim to transform access to EO data and change businesses utilizing this priceless resource., May 2023: SouthPAN partners Geoscience Australia and Toit Te Whenua Land Information New Zealand have signed a contract with Inmarsat Australia for the new service on one of Inmarsat's three new I-8 satellites, bringing the southern hemisphere one step closer to first-rate satellite positioning. It can assist in preventing fatalities by offering precise safety tracking, increasing farm productivity through automated device tracking, or even supporting upcoming transport management systems.. Key drivers for this market are: Government Initiatives and Investments to Support the Market Growth, Advancements in satellite technology, including High-resolution imagery, multispectral data, SAR, etc.. Potential restraints include: Government Initiatives and Investments to Support the Market Growth, Advancements in satellite technology, including High-resolution imagery, multispectral data, SAR, etc.. Notable trends are: Government Initiatives and Investments to Support the Market Growth.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

Orthophotography of urban areas within Wellington City taken in the flying season (summer period) of 2020-2021.

Imagery was captured for the ‘Wellington City Council’ by AAM NZ Limited, 6 Ossian St, NAPIER, New Zealand.

Data comprises:

• 2020 x 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. Please refer to the supplied tile layout shape file for specific details, naming conventions, etc.

Imagery supplied as 7.5cm pixel resolution (0.075m GSD), 3-band (RGB) uncompressed GeoTIFF.

The final spatial accuracy is ±0.2m @ 90% confidence level.

Index tiles for this dataset are available as layer Wellington City 0.075m Urban Aerial Photos Index Tiles (2021)

The ANZ (Australia and New Zealand) satellite-based Earth observation market is experiencing robust growth, driven by increasing government investments in infrastructure development, precision agriculture, and environmental monitoring. The market's expansion is fueled by the rising adoption of advanced technologies like high-resolution imagery, AI-powered analytics, and improved data processing capabilities. Key segments within this market include earth observation data, value-added services leveraging this data (such as data analytics and consulting), and various satellite orbits like LEO (Low Earth Orbit), MEO (Medium Earth Orbit), and GEO (Geostationary Orbit). End-use sectors such as urban development, agriculture, climate services, and infrastructure are significant contributors to market growth, with agriculture and urban planning likely showing particularly strong expansion. While challenges exist, such as the high initial investment costs associated with satellite technology and data processing, the overall market outlook remains positive, indicating substantial opportunities for both established players and emerging companies in the ANZ region. The presence of several major global players, alongside a burgeoning number of local ANZ firms, contributes to a competitive yet dynamic market landscape. Companies are increasingly focusing on developing innovative solutions and tailored services to cater to the specific needs of various industries. This includes offering data packages and analysis focused on addressing unique challenges faced in the ANZ region, such as climate change mitigation and managing vast and diverse landscapes. The long-term forecast suggests a continuation of this upward trajectory, with significant potential for growth fueled by ongoing technological advancements and increased demand across diverse sectors. The market is expected to witness a high level of consolidation, with larger players acquiring smaller firms to expand their capabilities and market reach. Government regulations aimed at improving data sharing and accessibility will further enhance the market's growth trajectory. Recent developments include: March 2023: Rocket Lab USA, Inc, a global leader in launch services and space systems, announced the establishment of a new wholly-owned subsidiary, Rocket Lab Australia, to explore opportunities to support the expansion of Australia's national space capabilities, which shows the market growth potential for the satellite-based earth observation market in the region., May 2023: Australian space tech startup Arlula announced it had raised USD 1.5 million from Main Sequence, Australia's deep tech investment fund founded by CSIRO, to expand access to Earth Observation (EO) data and imagery, allowing individuals, small businesses, and large enterprises alike to harness its full potential.. Key drivers for this market are: Government Initiatives and Investments, Technological Advancements. Potential restraints include: Government Initiatives and Investments, Technological Advancements. Notable trends are: Government Initiatives and Investments is Driving the Market.

The size of the ANZ Satellite Imagery Services market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 11.61% during the forecast period.ANZ satellite imagery services are the use of space technology to capture images over high resolution of the Earth's surface, mainly for the Australia and New Zealand region. Such imagery offers information in sectors such as agriculture, mining, urban planning, environment monitoring, and disaster management.Crop health monitoring, mining site evaluation, urban development observation, detection of deforestation and pollution, among others, can now be made right by satellite imagery analysis. For instance, farmers would consider satellite data to assess best irrigation and application of fertilizers practices; companies that engage in mining can assess the mine site and look out for possible negative environmental impacts. Urban growth patterns and infrastructure demands may be identified and designed through the use of satellite imagery by urban planners, and these agencies might observe the changes in ecosystems and early signs of climate change. Under disaster response, satellite imagery plays a very important role in making fast appraisals of damage and the allocation of assets and supplies to affected areas.Much satellite imagery use opportunity is offered by the diverse land shapes of the ANZ region. The demand for an ANZ satellite imagery service will advance in a perspective of continually innovating and developing from all sectors through the development of this technology with more resolution and images. Recent developments include: May 2023: Arlula announced the successful completion of its AUD 2.2 million (USD 1.5 million) initial investment round. This significant investment will enable the firm to increase access to Earth Observation (EO) data and imagery, helping people, small companies, and big corporations entirely use space-based data. This significant support demonstrates an interest in and acceptance of Arlula's aim to transform access to EO data and change businesses utilizing this priceless resource., May 2023: SouthPAN partners Geoscience Australia and Toit Te Whenua Land Information New Zealand have signed a contract with Inmarsat Australia for the new service on one of Inmarsat's three new I-8 satellites, bringing the southern hemisphere one step closer to first-rate satellite positioning. It can assist in preventing fatalities by offering precise safety tracking, increasing farm productivity through automated device tracking, or even supporting upcoming transport management systems.. Key drivers for this market are: Government Initiatives and Investments to Support the Market Growth, Advancements in satellite technology, including High-resolution imagery, multispectral data, SAR, etc.. Potential restraints include: Affordability and Accessibility might restrain the Market Growth, Competition from Alternative Technologies such as Aerial Drones, LiDAR, and UAVs. Notable trends are: Government Initiatives and Investments to Support the Market Growth.

New Zealand's Coastal Change Dataset is comprised of two datasets; coastlines (NZCCD Coastlines) and rates (NZCCD Coastal Change Rates) datasets.NZCCD Coastlines: The coastlines dataset comprises all New Zealand’s mapped coastal positions from the 1940’s to 2023, mapped from both historic aerial imagery and high-resolution satellite imagery.NZCCD Rates: The rates of coastal change are provided at 10 m intervals along Aotearoa New Zealand's mapped coast.​​

Methodology

The Canterbury irrigated area dataset combines different data, including:

· farm boundary extents (land ownership and GIS data from Land Information New Zealand)

· high resolution aerial imagery and/or satellite photos

· resource consent data

· analysis of satellite data (using normalised different vegetation index (NDVI) imagery)

· agricultural production statistics (Statistics New Zealand).

A summary of the methodology, and tabulated irrigated area data for the Canterbury region and each of its ten water management zones, are found in the report: Canterbury detailed irrigated area mapping (2016). Prepared for Environment Canterbury by Aqualinc Research Limited.

By “irrigated area” we mean the area actually irrigated for productive gain, not the consented area.

Official Environment Canterbury Tech Report: https://api.ecan.govt.nz/TrimPublicAPI/documents/download/3010557

Orthophotography within the Canterbury Region captured in the flying season of 2022-2023. Coverage encompasses imagery over Christchurch City and the Banks Peninsula region.

Imagery was captured for Environment Canterbury by Landpro Ltd, 13 Pinot Noir Drive, Cromwell 9310, New Zealand.

Data comprises: • 6854 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. Please refer to the tile index layer for specific details, naming conventions, etc.

Imagery supplied with a 7.5cm pixel resolution (0.075m GSD).

Also available on: • LINZ Basemaps • NZ Imagery - Registry of Open Data on AWS

Index tiles for this dataset are available as layer Christchurch 0.075m Urban Aerial Photos Index Tiles (2023)

• STARS data resource is a temporal (time-bound) information that measures the physiognomic and geomorphic characteristics of the Earth’s surface. • STARS imagery used to detect, map and monitor changes in different Earth surface features (such as landslides, volcano eruption, waterbodies, geothermal and alteration mineral sites) in NZ, SW Pacific islands and other regions of interest where GNS Science has geoscience research projects.

• STARS data resource constitutes on Spectral images (visible, multispectral and hyperspectral) and field-based spectral reflectance libraries as well as Thermal and Active (Synthetic Aperture Radar) Remotely Sensed images. • The STARS data resource covers images from 2007 onwards. • The STARS data resource contains raw and processed Spectral images acquired from a range of satellites covering different geographic regions of New Zealand and project sites outside of New Zealand. • Raw, atmospheric and ortho-corrected high-resolution imagery from SPOT series, QuickBird, GeoEye, WorldView series, Pleides, RapidEye and Planet satellites etc. • Mosaics prepared from Sentinel and Landsat series imagery. The raw imagery is available from main data providers’ web portals. • Spectral libraries are captured either in-situ (in the field) or ex-situ (from samples collected from field). • Thermal RS data covers FLIR Thermal images are processed as Land Surface Temperature for research interest areas such as the Crater Lake, the White Island and a few geothermal anomalous areas. • Active RS data resource covers selected SAR images of AirSAR, TopSAR and Sentinel-1A/B images related to flood mapping incidents in NZ.

DOI: https://doi.org/10.21420/ZHF5-VQ33?x=y

Cite as: GNS Science. (2021). Spectral, Thermal and Active Remote Sensing (STARS) data resource [Data set]. GNS Science. https://doi.org/10.21420/ZHF5-VQ33?x=y

Swimming pools are important for property tax assessment because they impact the value of the property. Tax assessors at local government agencies often rely on expensive and infrequent surveys, leading to assessment inaccuracies. Finding pools that are not on the assessment roll (such as those recently constructed) is valuable to assessors and will ultimately mean additional revenue for the community.This deep learning model helps automate the task of finding pools from high resolution satellite imagery. This model can also benefit swimming pool maintenance companies and help redirect their marketing efforts. Public health and mosquito control agencies can also use this model to detect pools and drive field activity and mitigation efforts.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.Input8-bit, 3-band high resolution (5-7.5 centimeters) imageryOutputFeature class containing bounding boxes depicting pool locations with class BuiltinPool | PopupPoolApplicable geographiesThe model is expected to work well in the New Zealand.Model architectureThe model uses the MMDetection model architecture implemented using ArcGIS Pro Arcpy.Accuracy metricsThe model has an average precision score of 0.95.1 BuiltInPool2PopupPoolSample resultsHere are a few results from the model.(Post processing are recommended to filter out False Positive Object. If the confidence are below certain threshold e.g 5%)To learn how to use this model, see this story

We gathered imagery from eight invasion sites across the South Island of New Zealand for multiple time steps (2-4 points in time) using a combination of high resolution aerial imagery gathered from the Land Information New Zealand (LINZ) archives and high resolution satellite imagery downloaded from Google Earth. To detect the pine trees, we used an unsupervised, pixel-based classification method. First, we thresholded the imagery to separate out the dark-coloured trees against the light-coloured background vegetation (Ke and Quackenbush 2011). Then we segmented the pixels identified as trees using a process called watershedding in order to delineate the tree canopies (Komura et al. 2004, Wang et al. 2004, Deng et al. 2016). We extracted the centre point of each polygon identified as a tree, and for each site and time step, we generated a file of the point locations of every tree detected. To prepare the data derived from the image classification and detection m...

The New Zealand Terrestrial Antarctic Biocomplexity Survey (nzTABS) is the largest and most comprehensive interdisciplinary landscape-scale study of terrestrial biology ever undertaken in Antarctica, incorporating fieldwork of 1500+ person days in 6 of the Dry Valleys (total area of 6500 km2), strategic sampling of over 1200 sites designed to encompass the landscape heterogeneities in the ecosystem, and a range of high-resolution remote sensing data. All samples were collected during the month of January in each sampling year.

Initially a 220 km2 study area, consisting of Miers, Marshall, and Garwood Valleys as well as Shangri-La, was divided into more than 600 geographically and geologically distinct ice-free sectors (hereinafter “tiles”) using remote-sensing data and published soil maps. Tile boundaries were delineated where the combination of geographical and geological variables changed, and on-the-ground assessments were carried out in November 2008 to confirm the reliability of delineations. 554 tiles were chosen for sampling to encompass the entire range of geographical and geological heterogeneity. Sampling of soils and biological communities was carried out over two successive austral summers (January 2009 and January 2010). Surveys were conducted for vegetation (i.e., mosses, lichens, algal and cyanobacterial mats), lithic microbial communities, and invertebrates at each sampling site (verified by GPS to be inside its respective tile), followed by collection of bulk soil samples for additional analyses, including molecular analyses of bacteria (total and cyanobacteria-only) and fungi. In addition, a number of key variables were derived from satellite imagery, including surface soil temperature, a topographically derived ‘wetness index’, and distance to the coast. After quality control, data for 490 samples were included in the analysis.

These data represent geochemistry and geomorphology to population genetics and microbial ecology parameters. Further details are provided at https://doi.org/10.1038/s42003-018-0274-5.

Please cite the data with the following citation: Lee, C.K., Laughlin, D.C., Bottos, E.M. et al. Biotic interactions are an unexpected yet critical control on the complexity of an abiotically driven polar ecosystem. Commun Biol 2, 62 (2019). https://doi.org/10.1038/s42003-018-0274-5

Regional Population Connectivity, Oceanic Habitat, and Return Migration Revealed by Satellite Tagging of White Sharks, Carcharodon carcharias, at New Zealand Aggregation Sites. A joint NIWA/Department of Conservation (DOC) tagging programme was launched in 2005. Hi-tech electronic tags are being used to gather information on where the sharks are and when, and to record their depth and the temperature of the surrounding water. Three tag types have been used: popup archival tags, acoustic tags, and dorsal fin tags. Popup tags are implanted in the muscle under the dorsal fin with a tagging pole, and record depth, temperature and location, storing the data for up to a year. They then release themselves from the shark, float to the surface, and transmit summaries of the data to a satellite. If the tags are physically recovered, the high resolution data collected at one minute intervals can be downloaded. Popup tags provide only approximate location data, so they are most useful for tracking long-distance migrations. Acoustic tags send out coded, individually identifiable sound ‘pings’ that can be detected up to a kilometre away by acoustic data loggers. The tag batteries last long enough to monitor the presence of white sharks in the region in the vicinity of a data logger for two years. Acoustic tags provide accurate fine-scale information on sharks at specific locations. Dorsal fin tags bridge the gap between popup and acoustic tags. They provide accurate location information by transmitting to orbiting satellites every time the shark is at the surface and the dorsal fin and the tag’s aerial are exposed to air. Their batteries can last for more than one year, so both fine scale and large scale movement patterns can be recorded. However, dorsal fin tags are more difficult to deploy: the shark has to be caught and restrained while the tag is attached to the dorsal fin. So far, only a few of these tags have been deployed in New Zealand. Since 2005, 44 white sharks have been tagged with popup tags, mainly at the Chatham Islands and Stewart Island. These islands support large colonies of fur seals, which are a major food source for white sharks. Our research has focussed on Stewart Island since 2009. The population there is dominated by males (about 2.2 males for every female). Nearly all of the females are immature, being shorter than the female length at maturity of 4.5 to 5 m. Only about one-third of the males are longer than the male length at maturity of about 3.6 m. This indicates that the white shark aggregations at Stewart Island are not related to mating, and are most likely driven by the abundance of seals for food. Large, mature females are rarely seen anywhere in New Zealand and their distribution, habitat and behaviour are almost completely unknown. Tagging results show that most New Zealand white sharks make annual migrations to tropical waters in winter, travelling as far as 3,300 km away. Sharks have migrated to the Great Barrier Reef in Australia, the Coral Sea, New Caledonia, Vanuatu, Norfolk Island, Fiji and Tonga. They don't cross the equator. Most of the sharks from Stewart Island headed northwest of New Zealand, whereas most Chatham Islands sharks headed north. However, some sharks from the two tagging locations overlap in the tropics. Surprisingly, we have not detected any direct movement between Stewart and Chatham islands. Some popup tags have remained on the sharks for long enough (up to one year) to reveal that the sharks returned to their tagging locations after their tropical holiday. This has been confirmed by photo-identification work at Stewart Island. Each shark has a unique colour pattern, particularly around the gills and on the tail. Some individuals have been seen at Stewart Island each year for multiple years. This indicates that white sharks have a sophisticated navigation mechanism that enables them to make major direct oceanic migrations, and then return to precisely the spot they left from. We do not know how they achieve this.

Orthophotography within the Waikato Region captured in the flying season of 2022-2023. Coverage encompasses Hamilton City District.

Imagery was captured for Hamilton City Council by Woolpert NZ Ltd (formerly AAM NZ Ltd), 6 Ossian St, Napier, New Zealand.

Data comprises: • 1470 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:1000 tile layout. • Tile layout in NZTM projection containing relevant information.

The supplied imagery is in terms of New Zealand Transverse Mercator (NZTM) map projection. Please refer to the tile index layer for specific details, naming conventions, etc.

Imagery supplied as 5cm pixel resolution (0.05m GSD), 3-band (RGB) uncompressed GeoTIFF. The final spatial accuracy is ±0.1 at 68% confidence level in clear flat areas.

Index tiles for this dataset are available as layer Hamilton 0.05m Urban Aerial Photos Index Tiles (2023)