This manual provides guidelines on the use of Airborne LiDAR Bathymetry (ALB) in Norway. The intended audience is organizations procuring ALB and its content is based on the project "Validation and application of Airborne LiDAR Bathymetry (ALB) technology for improved management and monitoring of Norwegian rivers and lakes : a pilot study 2021-2022" carried out by Norwegian Water Resources and Energy Directorate (NVE), The Norwegian Mapping Authority (Kartverket), The Norwegian Environment Agency (Miljødirektoratet), The Norwegian Public Roads Administration (Statens vegvesen), and Hafslund.
- Airborne LiDAR Bathymetry - Best Practice Manual
- Measurement Concept and Sensors
- Strengths and Limitations of ALB
- ALB Use Cases in Norway
- ALB Procurement Process
- ALB Project Workflow
- Example Specifications
- Quality Control
- Glossary
- References
- Appendix
The intention is that the manual has a broad ownership and changes can be proposed via pull requests or by submitting issues that we will follow up.
This manual is licensed under the Norwegian Licence for Open Government Data (NLOD) 2.0.
Light Detection and Ranging (LiDAR) technology has become the primary source for capturing river geometry, enabling rapid and accurate 3D point cloud collection in shallow water where other conventional measurement techniques, such as multibeam echosounders, are impractical or impossible to use due to shallow water or fast flowing white water rivers.
A LiDAR sensor measures the distance to a target by measuring the time between the emission of a laser pulse from a sensor and the detection of the reflected laser from the target. Depending on the wavelength, there are two types of LiDAR sensors: topographic LiDAR (ALS) and bathymetric LiDAR (ALB). Topographic LiDAR is associated with a 1064 nm laser, which cannot penetrate water and is therefore mainly used for topographic and sea surface sensing. Bathymetric LiDAR, on the other hand, uses a 532 nm laser that penetrates the water and is reflected by the seabed.
The ALB is the most widely used type of LiDAR for river studies that require a high degree of mapping accuracy, such as environmental river studies, sediment transport studies and flood modeling. Currently, a variety of bathymetric LiDAR sensors are commercially available, defined by their technical specifications such as laser energy per pulse, laser footprint, and maximum detectable depth.
Sensor types range from heavy, high-energy systems that require fixed-wing aircraft to smaller, lighter, low-energy sensors that can be mounted on a drone. The choice of sensor for bathymetric mapping depends primarily on the size of the project, whether the surrounding topography allows safe operation with fixed-wing aircraft, the required spatial resolution and sensor availability.
Gottfried Mandlburger, a scholar at TU Wien, has over the years been following the advances of ALB sensors and has nominated the following categories as a rudimentary classification:
| Deep Bathymetric Sensors | Shallow Bathymetric Sensors |
|---|---|
| • ~3× Secchi depth (i.e. 50 m @ k=0.1) | • ~1.5× Secchi depth (i.e. 25 m @ k=0.1) |
| • High pulse energy (5–7 mJ) | • Low to medium pulse energy (0.02–0.1 mJ) |
| • Low pulse repetition rate (3–10 kHz) | • High pulse repetition rate (35–550 kHz) |
| • Long pulses (2–6.5 ns = 60–200 cm) | • Short pulses (1.2–2 ns = 36–60 cm) |
| • Large laser footprints (3.5 m @ 500 m AGL) | • Small laser footprints (50 cm @ 500 m AGL) |
| • Low spatial resolution | • High spatial resolution |
| • Focus: maximum penetration | • Focus: high definition in littoral zone, rivers, etc. |
| • Application: charting coastal waters, object detection | • Application: flood simulation, habitat modelling, hydro-morphodynamics, etc. |
The Teledyne Optech CZMIL, the Fugro RAMMS and the Leica Chiroptera series are airborne multi-frequency sensors used for mapping topographic surfaces and coastal areas, and their high laser energy per pulse characteristics make them particularly well suited for mapping deep depths such as those found in coastal applications. However, the higher laser penetration capabilities of the 3xSecci sensors come at the expense of spatioal resolution, as the laser footprint increases with higher pulse energy.
The Riegl VQ880, on the other hand, is an airborne topo-bathy sensor with lower laser energy per pulse and a smaller footprint compared to the high energy systems, which can map with much higher point density but less water penetration. The Riegl VQ840/VQ860 is another topo-bathy sensor that is much lighter than the traditional bathymetric LiDAR sensors and can therefore be operated from an unmanned aerial vehicle or helicopter. There are also dedicated drone-based systems available, such as the Yellowscan bathymetric LiDAR system.
Please note that this deep vs. shallow division does not apply to all sensors, as some can be configured to operate in both or hybrid mode. Therefore, it is important to be clear in the procurement documents what the end use of the dataset will be in order for the contractor to select the appropriate sensor and/or set the appropriate acquisition parameters.
Although they share many of the same sensor components and virtually the same measurement principles, an ALB system differs significantly from a classic ALS setup. Due to the air-water-air propagation, a measurement with an ALB sensor is a function of significant limiting factors and will be more difficult to process than the ALS measurement where the light propagates only in the air. This is illustrated in the following figure by Mandlburger (2022) in his article summarizing the available active and passive methods used in bathymetric surveys.
Fig. 1: Conceptual drawing of the principle of airborne laser bathymetry. (Mandlburger 2020)
In order for the system to reliably detect a seafloor return, the emitted light energy must survive a number of limiting factors. First, the energy must propagate from air to water and not be reflected at the air-water interface. Next, the light is attenuated in the water column by absorption and scattering. Finally, the seafloor must reflect a minimum amount of energy for the light to travel back up the propagation path to the ALB sensor. To calculate air-to-water and water-to-air refraction, the system must also acquire or estimate the water surface. Without modeling the water surface, the system will not be able to compute bathymetric measurements. As a side note, sunlight can affect the noise level in the sensor receiver, and a greater depth range can be achieved by flying at night.
Because all of these factors potentially limiting sensor performance vary from site to site, and over time within a site, it can be difficult to predict the end result when ordering an ALB survey.
An overview of the advantages and disadvantages of using airborne LiDAR bathymetry versus traditional surveying methods.
- The only acquisition approach that produces a homogeneous terrain model consisting of both bathymetry and topography.
- Very efficient acquisition rate in shallow water. With a ~3 secci sensor operating at an altitude of 450m, we have experienced an concistent acquisition rate of 13 km2/h including line turns. Establishing bathymetry with ALB in shallow water will make complementary multibeam acquisition from surface vessels more efficient.
- Not prone to challenging sound profiles where fresh and salt water mix in estuaries and river inlets. These are areas where one must anticipate large spatial and temporal variations that introduce significant errors when mapping with MBES.
- A pure remote sensing acquisition method without the need for people or boats in remote, potentially dangerous or ecologically sensitive areas.
- Sensor performance, generally measured by the depth range achieved, depends on several physical and environmental factors that are difficult to assess and predict. First, the seafloor must have a reflectivity above a certain threshold and one must have a line-of-sight to the sensor free of obstructions and thick vegetation. Second, weather, sea state, amount of suspended sediment and rock/glacial debris in the water column will degrade performance.
- A successful mapping campaign requires good acquisition timing. When mapping in marine environments, large algal blooms must be avoided, and when mapping rivers and lakes, water quality, turbulence, whitewater and rapids, and biomass in the water will either absorb or scatter laser energy. Timing the perfect acquisition window with commercially available sensors can be challenging.
- Classifying a bathymetric point cloud is challenging, especially in low-density or noisy datasets where a geometric or statistical approach begins to fail and one must resort to more manual interpretation.
- Mapping the exact transition zone between "wet" and "dry" is challenging, and it can be difficult to distinguish the sea surface from the seafloor. This is due to both sensor design and how the system distinguishes between points in the water and points on the dry, but environmental factors such as waves and submerged vegetation can make the classification challenging.
Norwegian government agencies have been evaluating the potential of the ALB sensor for nearly 30 years. The Norwegian Hydrographic Service (NHS), together with Tenix LADS Corporation, conducted extensive tests in 1998 and 2002. NHS also investigated the performance of the then-new Riegl VQ-820 sensor in 2014 and the Optech Titan in 2017.
The evolution of ALB sensor technologies has been high and in 2021 the current generation of Riegl, Hexagon and Optech sensors were evaluated by an ALB working group over a range of river types. However, the delivery was not a direct comparative study of sensors, but rather an in-depth investigation into the use of ALB as a tool for more efficient management of rivers and freshwater bodies. The report "Validation and application of Airborne LiDAR Bathymetry (ALB) technology..." details the project scope and results.
Since 2019, NVE, Skagerak Energi and Hafslund Eco have mapped more than 30 rivers, and ALB is the de facto dataset that provides input for accurate hydraulic models and hydro-morphological analyses, such as monitoring sediment transport and erosion over time and simulating flood mitigation programs. Knut Alfredsen, professor at NTNU, has been a key resource in introducing the use of topobathy point clouds in various aspects of river management. His report Oversikt over bruk av batymetrisk LiDAR ved NTNU details the practical use of ALB in river studies.
To create a seamless elevation model and provide a dataset suitable for habitat and ecosystem management, ALB has been used to complement multibeam measurements. A team at NTNU, Ahmed, Bakken and Alfredsen, states in their report Assessment of the suitability of Green LiDAR in mapping lake bathymetry that ALB is "a dataset suited to identify potential areas for 7 spawning, feeding and rearing areas of lakes, if physical habitat preferences are known. It can also form the basis for physical habitat mitigating measures in lakes and reservoirs, potentially negatively affected by regulations or other physical modifications. As a basis for computational analysis in lakes and reservoirs, Green LiDAR will be very useful in establishing a bathymetric model for simulating commonly physical and chemical variables such as water velocities and depths, water temperatures and ice formation, sediment deposition and suspension, and basic water quality parameters (e.g. BOD, oxygen concentration, nitrogen and phosphorus concentration and turbidity)."
ALB has also been used to accurately map hydroelectric reservoirs to monitor sedimentation and facilitate volume estimation.
A number of ALB surveys have been carried out in the marine environment with the intention of providing a number of test areas from different parts of Norway where ALB can be studied as a source for mapping coastal habitats. ALB is also an essential tool for identifying and mapping hazard-prone areas, particularly for assessing the risks associated with rapid clay landslides near coastal regions. Through its precise elevation data and high-resolution shallow-water topobathy terrain models, ALB provides critical insight into surface morphology, slope stability and drainage patterns. This detailed information significantly improves the accuracy of hazard maps, enabling authorities and planners to better predict, prepare for and mitigate the devastating effects of fast clay landslides in vulnerable coastal areas.
Case: Fauske (2023)
Two areas in Fauske municipality were surveyed to produce a homogeneous topobathymetric dataset for modeling landslide >consequences in regions with known quick clay deposits. In Klungsetvika Bay—a nearly enclosed body of water with limited >circulation, muddy bottom, and a maximum depth of 17 meters—the ALB sensor successfully mapped to a depth of 14m. Near >Fauske town center, the bathymetric mapping extended to a depth of 25m, greatly improving the efficiency of >complementary surveys conducted with multibeam from surface vessels.
Case: Glomma Estuary (2021)
The estuarine section of the Glomma River was surveyed using both the Optech CZMIL Supernova and the Leica Chiroptera >4X. The aim was to compare sensor performance and generate a unified topobathy model for landslide modeling. The area >posed challenges due to high turbidity and low reflectivity from soft bottom sediments, resulting in a maximum mapping >depth of 1.8 meters for both sensors.
Multibeam echo sounder data were collected to supplement the ALB coverage and enable full modeling of the riverbed. >Despite limited depth penetration, the ALB data proved valuable for analyzing extensive shallow areas in the transition >zone between river and dry land—areas that would be impractical to survey with conventional methods.
Case: Helligvær and Bliksvær (2021)
As part of the NDH mapping program, an Teledyne Optech CZMIL Supernova sensor was deployed over the Helligvær and Bliksvær archipelagos in Nordland. These areas are characterized by sandy seabeds and calm, protected waters to the east, transitioning to deeper and more exposed conditions toward the Vestfjord in the west. The ALB sensor performed well, producing seamless topobathymetric models. The method proved efficient, with approximately 13 km² mapped per flight hour, including turns and cross-lines. Secchi depth measurements were logged prior to data acquisition, but the experience suggests that acquisition should be scheduled for early autumn to avoid algae blooms. Survey permissions from the Norwegian Armed Forces should be initiated at least six months in advance for areas as large as this to secure optimal data collection periods.
Good point density was achieved down to 25 meters below NN2000, with Secchi depths measured to 7 meters. Despite a rather large footprint, the sensor established a good DTM with good vegetation penetration.
Compared to local reference surfaces, the residual vertical datum shift in the point cloud was -13 cm (standard deviation: 4 cm), slightly exceeding the 10 cm requirement. However, once adjusted, the accuracy of the topographic model met the ALS specification for point cloud products. Establishing ground control points in such rural areas will be a challenge as the there is little infrastructure pushing the cost up.
With a 450 m flying altitude, the ALB sensor effectively mapped electrical and communication cables, outperforming traditional ALS systems that typically fly higher with smaller beam divergence. Topobathymetric mapping in such areas is well-suited for co-financing through Geovekst and potentially other non-member agencies with interest in the coastal zone.
Guidelines and recommendations on procuring ALB services, including tendering, specifications, and evaluation criteria.
WORK IN PROGRESS - ETA 2025
Step-by-step guide through the lifecycle of an ALB project, from planning and aquisition to quality control and project termination.
WORK IN PROGRESS - ETA 2025
The following technical specifications have been used by NVE and others over the past year and can be used as a template for bathymetry procurement. We have had mixed experience with the combination of ALB and MBES from surface vessels in a single contract, and we generally recommend that one first map with ALB and then bid out the infill survey with MBES in a separate tender. The specifications function as both ToR (Terms of Reference) and TS (Technical Specification) and are based on the requirements set by Kartverket standards.
- ALB_Technical_Spesification_2025_v1.docx (download using 'raw' button on right hand side)
A quality check of the delivered dataset should be performed close to acquisition in order to identify deficiencies in the raw dataset as well as errors in the delivery format, report, and metadata. A minimum QC regime based on QGIS and PDAL can be found here.
ALB (Airborne LiDAR Bathymetry)
A remote sensing technology using aircraft-mounted LiDAR sensors to measure seafloor and underwater terrain in shallow coastal and inland waters.
ALS (Airborne LiDAR ) A remote sensing technique that uses airborne laser scanning equipment to accurately measure distances from the sensor to the Earth's surface. This produces highly accurate three-dimensional point cloud data, where each point is classified according to a given classification scheme. ALS is widely used for topographic mapping, natural hazard assessment, forestry management, urban planning and infrastructure monitoring.
k Value (Diffuse Attenuation Coefficient)
The k value, or diffuse attenuation coefficient (𝐾_d), describes how quickly light diminishes with depth in water. It is a measure of light extinction and is widely used to quantify water clarity or turbidity.
- Low (<0.1 m⁻¹) Very clear (open ocean)
- Moderate (0.1–0.5 m⁻¹) Coastal/nearshore waters
- High (>0.5 m⁻¹) Turbid or sediment-rich water
Several remote sensing data sets, including Sentinel-3 OLCI, are particularly suitable for estimating or indicating the k-value in marine areas outside Norway and can give an indication of the expected performance of the ALB.
LiDAR (Light Detection and Ranging)
A remote sensing method that uses light in the form of a pulsed laser to measure distances from the sensor to the measured object.
LAS/LAZ (LAS/LAZ file format)
File format designed for the interchange and archiving of lidar point cloud data. LAZ is the preffered lossless compressed version.
MBES (Multibeam Echosounder)
Sonar system consisting of multiple sonar beams mapping the seafloor in swaths. Commonly operated from a surface vessel.
SECCI
Secchi depth is a measure of water transparency or clarity. It is determined using a circular, usually black-and-white disk, lowered into the water until it is no longer visible. The depth at which it disappears is recorded as the Secchi depth. The relationship to the k value can be approximated by the following formula: k = 1.7 / Zₛ where Zₛ is the Secchi Depth (m)
List of references, standards, and additional reading materials relevant to ALB practices.
- Produktspesifikasjon Punktsky (current version). Kartverket. https://sosi.geonorge.no/produktspesifikasjoner/Punktsky/
- Awadallah, M.O.M.; Malmquist, C.; Stickler, M.; Alfredsen, K. (2023). Quantitative Evaluation of Bathymetric LiDAR Sensors and Acquisition Approaches in Lærdal River in Norway. Remote Sensing. 2023, 15, 263. https://doi.org/10.3390/rs15010263
- Kastdalen, L., Stickler, M., Malmquist, C., & Heggenes, J. (2024). Evaluating methods for measuring in-river bathymetry: Remote sensing green LIDAR provides high-resolution channel bed topography limited by water penetration capability. River Research and Applications, 40(4), 467–482. https://doi.org/10.1002/rra.4245
- Mandlburger, G., Pfennigbauer, M., Schwarz, R., Flöry, S., & Nussbaumer, L. (2020). Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor. Remote Sensing, 12(6), 986. https://doi.org/10.3390/rs12060986
- Mandlburger, G. (2020). A review of airborne laser bathymetry for mapping of inland and coastal waters. Hydrographische Nachrichten, 116, 6–15. https://doi.org/10.23784/HN116-01
- Christopher E. Parrish, Jennifer A. Dijkstra, Jarlath P.M. O'Neil-Dunne, Lindsay McKenna, Shachak Pe'eri. Post-Sandy Benthic Habitat Mapping Using New Topobathymetric Lidar Technology and Object-Based Image Classification. Journal of Coastal Research, 76(sp1), 200-208, (1 December 2016). http://dx.doi.org/10.2112/SI76-017
- Mandlburger, G., Hauer, C., Wieser, M., & Pfeifer, N. (2015). Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habitats — A Case Study at the Pielach River. Remote Sensing, 7(5), 6160-6195. https://doi.org/10.3390/rs70506160
- Guenther, Gary C.; Cunningham, A. G.; LaRocque, Paul E.; Reid, David J. (2000). Meeting the Accuracy Challenge in Airborne Bathymetry. Proceedings of EARSeL-SIG-Workshop LIDAR, Dresden/FRG, June 16 – 17, 2000, https://apps.dtic.mil/sti/citations/ADA488934
- NTNU (2023). Assessment of the suitability of Green LiDAR in mapping lake bathymetry (https://hdl.handle.net/11250/3103489)
- NVE,Kartverket,Statens Vegvesen,Miljødirektoratet,Hafslund (2023). Validation and application of Airborne LiDAR Bathymetry (ALB) technology for improved management and monitoring of Norwegian rivers and lakes : a pilot study 2021-2022 (NVE Rapport nr. 2/2023). NVE. https://publikasjoner.nve.no/rapport/2023/rapport2023_02.pdf
- NORCE (2022). Application of airborne LiDAR bathymetry in Norway (LFI-rapport nr: 46) NORCE https://norceresearch.brage.unit.no/norceresearch-xmlui/handle/11250/3053017
- NTNU (2022). Oversikt over bruk av batymetrisk LiDAR ved NTNU (Rapport B1-2022-1). NTNU.[https://hdl.handle.net/11250/3027750
Please note that some datasets are MBES only. Most of the datasets are available over høydedata.no.
| Dataset | Acquisition Year | Contractor | Dataset Owner |
|---|---|---|---|
| Batymetri Fjøløy 2013 (MBES) | 2013 | Statens Kartverk Sjødivisjonen | Kartverket |
| Hafslund Elvekartlegging Geilo 2015 | 2015 | Statens Kartverk | Hafslund-Eco |
| Hafslund Elvekartlegging Gol 2015 | 2015 | Statens Kartverk | Hafslund-Eco |
| Hafslund Elvekartlegging Hallingdalselve Hol 2015 | 2015 | Statens Kartverk | Hafslund-Eco |
| Hafslund Elvekartlegging Hallingdalselve Torpo 2015 | 2015 | Statens Kartverk | Hafslund-Eco |
| Hafslund Elvekartlegging Hovet 2015 | 2015 | Statens Kartverk | Hafslund-Eco |
| Hafslund Elvekartlegging Nes 2015 | 2015 | Statens Kartverk | Hafslund-Eco |
| Hafslund Elvekartlegging Ål 2015 | 2015 | Statens Kartverk | Hafslund-Eco |
| NVE Driva 2016 | 2016 | Terratec AS | NVE |
| NVE Gaula 2016 | 2016 | Terratec AS | NVE |
| NVE Gudbrandsdalslågen 2016 | 2016 | Terratec AS | NVE |
| NVE Mandalselva 2016 | 2016 | Terratec AS | NVE |
| NVE Skiens og Porsgrunnselva 2016 | 2016 | Terratec AS | NVE |
| NVE Storelva Randselva Begna 2016 | 2016 | Terratec AS | NVE |
| Batymetri Søre Sunnmøre 2017 (MBES) | 2017 | Statens Kartverk Sjødivisjonen | Kartverket |
| Hornindalsvatnet dybde 2017 (MBES) | 2017 | Miljødirektoratet | |
| NDH Søre Sunnmøre dybdedata 2017 | 2017 | Terratec AS | Kartverket |
| NVE Eidselva 5pkt 2017 | 2017 | Terratec AS | NVE |
| NVE Etneelva 5pkt 2017 | 2017 | Terratec AS | NVE |
| NVE Figgjo 5pkt 2017 | 2017 | Terratec AS | NVE |
| NVE Nausta 5pkt 2017 | 2017 | Terratec AS | NVE |
| Selbusjøen dybde 2017 (MBES) | 2017 | Miljødirektoratet | |
| NVE Hååna (2017/2018/2019) | 2017/2018/2019 | TerraTec | NVE |
| NVE Kvina og Litleåna (2017/2018/2019) | 2017/2018/2019 | TerraTec og AHM | NVE |
| Eikesdalsvatnet dybde 2018 | 2018 | Statens Kartverk | Miljødirektoratet |
| NVE - Litleåna-Kvina 4pkt 2018 | 2018 | Terratec AS | NVE |
| NVE Flåmselvi 2018 | 2018 | Terratec AS | NVE |
| NVE Hååna 5pkt 2018 | 2018 | Terratec AS | NVE |
| NVE Jølstra-Anga 5pkt 2018 | 2018 | Terratec AS | NVE |
| NVE Lærdal 2018 | 2018 | Terratec AS | NVE |
| NVE Lærdalselva 5pkt 2018 | 2018 | Terratec AS | NVE |
| NVE Nidelva 2018 | 2018 | Terratec AS | NVE |
| NVE Numedalslågen 2018 | 2018 | Terratec AS | NVE |
| NVE Opo 2018 | 2018 | Terratec AS | NVE |
| NVE Rauma 2018 | 2018 | Terratec AS | NVE |
| NVE Tovdalselva 2018 | 2018 | Terratec AS | NVE |
| NVE Vosso 5pkt 2018 | 2018 | Terratec AS | NVE |
| Salvatnet dybde 2018 (MBES) | 2018 | Miljødirektoratet | |
| Hovsvatn dybde 13pkt 2019 | 2019 | Terratec AS | Nye Veier |
| NVE dronetest Figgjo (2020) | 2020 | Nordic Unmanned | NVE |
| SVV RV7 Lindelien - Kittilsviki (2020) | 2020 | Nearshore Survey / Terratec | SVV |
| Dybdedata Randselva-Storelva 2020 | 2020 | Ringerike kommune | |
| Fjøløy vår -MGK ALB Pilot 2021 | 2021 | Terratec AS | Kartverket |
| NDH Bøelva Chiroptera 2021 | 2021 | Hexagon | NDH |
| NDH Bøelva Riegl 840 (2021) | 2021 | Terratec | NDH |
| NDH Bøelva Riegl 880 (2021) | 2021 | AHM | NDH |
| NDH Glomma CZMIL 2021 | 2021 | Terratec AS | NDH |
| NDH Glomma Chiroptera 2021 | 2021 | Hexagon | NDH |
| NDH Glomma MBES 2021 | 2021 | Statens Kartverk | NDH |
| NDH Krøderen CZMIL 2021 | 2021 | Terratec AS | NDH |
| NDH Krøderen Riegl 840 (2021) | 2021 | Terratec | NDH |
| NDH Krøderen Riegl 880 (2021) | 2021 | AHM | NDH |
| NDH Lærdalselvi CZMIL 2021 | 2021 | Terratec AS | NDH |
| NDH Lærdalselvi Riegl 840 (2021) | 2021 | TerraTec | NDH |
| NDH Lærdalselvi Riegl 880 (2021) | 2021 | AHM | NDH |
| NDH Selbusjøen CZMIL 2021 | 2021 | Terratec AS | NDH |
| NDH Selbusjøen Chiroptera 2021 | 2021 | Hexagon | NDH |
| NDH Tangeelva Riegl 880 (2021) | 2021 | AHM | NDH |
| NDH Tangelva Chiroptera 2021 | 2021 | Hexagon | NDH |
| NDH Topobaty Bliksvær 2021 | 2021 | Terratec AS | NDH |
| NDH Topobaty Helligvær 2021 | 2021 | Terratec AS | NDH |
| NVE - 2023/2023/2024 (Several Rivers) | Field | NVE | |
| Hafslund Eco - 2023/2023/2024 (Several Rivers) | Field | Hafslund-Eco | |
| Skagerak Energi - 2023/2023/2024 (Several Rivers) | Field | Skagerak Energi | |
| Beiarelva Batymetri 2023 | 2023 | Field | Geovekst |
| Fauske Batymetri 2023 | 2023 | Field | Geovekst |
| Færder topobathy 2024 | 2024 | Hexagon | Geovekst |