Federal Airborne LiDAR Data Acquisition Guideline

• Acknowledgements
  • Notice
  • Context
  • Flood mapping framework
  • Federal flood mapping guidelines series
  • Guideline summaries
  • List of abreviations and acronyms
• 1.0 Introduction and purpose
• 2.0 Note on terminology
• 3.0 Target audience
• 4.0 Guideline structure
• 5.0 Summary requirements
• 6.0 Guideline
  • 6.1 Project planning
  • 6.2 Data collection
  • 6.3 Data processing and management
  • 6.4 Data validation
  • 6.5 Project deliverables
  • 6.6 Data ownership and copyright
• 7.0 Glossary
• 8.0 References
• 9.0 Revision history
• Appendix 1 – Forestry
• Appendix 2 – Flood mapping
• Appendix 3 – High relief terrain
• Appendix 4 – Urban infrastructure mapping
• Appendix 5 – Airborne topo-bathymetric LiDAR
• Appendix 6 – Contract

The Federal Flood Mapping Guidelines Series has been developed under the leadership of the Flood Mapping Committee, a partnership between Public Safety Canada (PSC), Natural Resources Canada (NRCan), Environment and Climate Change Canada (ECCC), National Research Council of Canada (NRC), Defence Research and Development Canada, Department of National Defence, Infrastructure Canada, Indigenous Services Canada, and Crown- Indigenous Relations and Northern Affairs Canada. A Technical Working Group on Flood Mapping formed in 2015 and comprised of parties from federal, provincial, and territorial jurisdictions, as well as the private sector and academia, has also contributed valuable input to the development of the Federal Flood Mapping Guidelines Series documents. Provincial and territorial government representatives also provided essential feedback for this publication.

The documents in the Federal Flood Mapping Guidelines Series are intended as a resource to support municipal, provincial, and territorial agencies and Indigenous communities working on flood hazard mapping. It is acknowledged that flood management in Canada is regulated at the provincial and territorial level of government, and that provinces and territories reserve the right to create their own guidelines specific to their jurisdictions.

Notice

Disclaimer of Liability

This technical documentation has been published by His Majesty the King in right of Canada, as represented by Natural Resources Canada (NRCan). No warranties or representations, express or implied, statutory or otherwise shall apply or are being made by NRCan in respect of the documentation, its effectiveness, accuracy, or completeness. NRCan does not assume any liability or responsibility for any damages or losses, direct or indirect, incurred or suffered as a result of the use made of the documentation, including lost profits, loss of revenue or earnings or claims by third parties. In no event will NRCan be liable for any loss of any kind resulting from any errors, inaccuracies, or omissions in this documentation. NRCan shall have no obligation, duty, or liability whatsoever in contract, tort or otherwise, including negligence.

Additional Information

For more information about this document, please contact the Canada Centre for Mapping and Earth Observation of Natural Resources Canada: geoinfo@nrcan-rncan.gc.ca.

Context

A community achieves an elevated level of resilience when its risks are proactively managed, it is adequately prepared for known and potential disaster events, and it demonstrates an ability to recover after such events have taken place. To become resilient, a community’s mitigation planners must first understand risks and ensure their capacity to manage those risks.

Floods are commonly occurring natural hazards in Canada and account for the largest portion of disaster recovery costs on an annual basis (Public Safety, 2022). Mitigating flood risks is key to increasing the resilience of affected communities. By proactively investing in flood risk mitigation activities, a community may improve its future growth and prosperity, reducing the risk of significant disaster recovery costs, productivity losses, economic losses, destruction of non- monetary cultural assets, environmental damage, injuries, and deaths.

Flooding is the temporary inundation by water of normally dry land, and it can occur in coastal and lake areas, along rivers, from stream blockages including ice jams, from failure of engineering works including dams, from extreme rainfall, rapid snow/ice/glacier melt or poor drainage characteristics, high groundwater levels, and other sources. Flood mapping that accurately delineates flood hazards, including those impacted by future conditions due to anticipated development or projected changes in climate, serves as the precondition for mitigation activities and is therefore the first step to increasing community resilience with regard to flooding. Establishing a national approach to flood mapping will facilitate a common national best practice and increase the sharing and use of flood hazard information. Considering the costs of damages and the vulnerabilities of infrastructure to the probability of the hazards provides an estimate of the flood risks, thereby improving the foundation from which further flood risk mitigation efforts can be initiated.

Circular diagram demonstrating the steps taken with community and stakeholder engagement when producing a flood map.

Flood mapping framework

The Flood Mapping Framework consists of all the components of the flood mitigation process, from flood hazard identification to the implementation of flood mitigation efforts. Figure 1 illustrates the relationship between these different components.

Federal flood mapping guidelines series

The following documents are intended to inform any individual or organization involved with flood management in Canada:

1. Federal Flood Mapping Framework
2. Federal Guidelines for Flood Hazard Identification and Priority Setting
3. Federal Airborne LiDAR Data Acquisition Guideline
4. Case Studies on Climate Change in Floodplain Mapping
5. Federal Hydrologic and Hydraulic Procedures for Flood Hazard Delineation
6. Federal Geomatics Guidelines for Flood Mapping
7. Federal Guidelines for Flood Risk Assessment
8. Federal Flood Damage Estimation Guidelines for Buildings and Infrastructure
9. Federal Land Use Guide for Flood Risk Areas
10. Bibliography of Best Practices and References for Flood Mitigation
11. Coastal Flood Hazard Assessment for Risk-Based Analysis on Canada’s Marine Coasts 

Guideline summaries

1. Federal Flood Mapping Framework
   This document provides background and context on flood mapping in Canada, describes a vision and principles for flood guidance, and introduces the Federal Flood Mapping Guidelines Series.  It provides a summary of each of the documents in the Series and explains how each document fits into the overall framework, including its role in the flood mapping cycle.
2. Flood Hazard Identification and Priority Setting
   This document outlines methods for determining where to conduct flood mapping and how to prioritize flood mapping projects.
3. Federal Airborne LiDAR Data Acquisition Guideline
   This document is a resource for the acquisition of base elevation data from airborne LiDAR data undertaken across Canada. This guideline provides technical specifications to federal, provincial and territorial departments, as well as individuals and organizations in Canada requiring information to understand and plan for airborne LiDAR data acquisition.
4. Case Studies on Climate Change in Floodplain Mapping
   This collection of documents describes projects from across Canada where climate change was incorporated into the floodplain mapping process. It provides examples for practitioners to draw upon and learn from others’ experiences and compliments the climate change-related information and resources included in the “Federal Hydrologic and Hydraulic Procedures for Flood Hazard Delineation” document.
5. Federal Hydrologic and Hydraulic Procedures for Flood Hazard Delineation
   This document provides guidance to municipal, provincial, and territorial agencies and Indigenous communities working to produce flood hazard maps. It provides technical information on the types of river and lake flooding, general practices, procedures for hydrologic and hydraulic analyses, procedures for incorporating climate change, and reporting best practices.
6. Federal Geomatics Guidelines for Flood Mapping
   This document contains information on the different types of flood maps and outlines technical specifications to consider when acquiring, managing and disseminating these maps and their associated geospatial data.
7. Federal Flood Risk Assessment Procedures
   This document provides technical guidance on conducting flood risk assessments in Canada.
8. Federal Flood Damage Estimation Guidelines for Buildings and Infrastructure
   This document provides guidance on how to evaluate potential economic losses, with a focus on buildings and infrastructure, incurred as a result of flooding.
9. Federal Land Use Guide for Flood Risk Areas
   This document provides guidance to communities in using risk-based methodologies for the purpose of land-use planning with attention to flood prone areas.
10. Bibliography of Best Practices and References for Flood Mitigation
    This document contains lists of Canadian and international references and case studies pertaining to hydrology and hydraulics, climate change, risk assessment and flood mapping. The purpose of this document is to provide a consolidated list of reference materials intended as further resources for practitioners involved in flood mapping.
11. Coastal Flood Hazard Assessment for Risk-Based Analysis on Canada’s Marine Coasts
    This document provides guidance on methodologies for coastal flood hazard assessments using risk-based approaches.

List of abreviations and acronyms

ANPD

Aggregate Nominal Pulse Density

ANPS

Aggregate Nominal Pulse Spacing

AOI

Area of Interest

ASPRS

American Society of Photogrammetry and Remote Sensing

CHA

Calculated Horizontal Accuracy

CCMEO

Canada Centre for Mapping and Earth Observation

CGG2013

Canadian Geoid 2013

CGVD

Canadian Geodetic Vertical Datum

CORS

Continuously Operating Reference Stations

CQL

Canadian Quality Level

CSRS

Canadian Spatial Reference System

DCAOI

Data collection Area of Interest

DEM

Digital Elevation Model

DSM

Digital Surface Model

DTM

Digital Terrain Model

EPSG

European Petroleum Survey Group

ESRI

Environmental Systems Research Institute

GLONASS

Globalnaya Navigazionnaya Sputnikovaya Sistema

GNSS

Global Navigation Satellite System

GPS

Global Position System

GRS80

Geodetic Reference System 1980

IMU

Inertial Measurement Unit

INS

Inertial Navigation System

ISO

International Standard Organization

LAS

LASer file format exchange

LAZ

LASzip

LiDAR

Light Detection and Ranging

NIR

Near Infrared

NPD

Nominal Pulse Density

NPS

Nominal Pulse Spacing

NRCan

Natural Resources Canada

NTS

National Topographic System

NVA

Non-Vegetation Vertical Assessment

OGC

Open Geospatial Consortium

PDOP

Position Dilution of Precision

PLS

Pulse(s)

PPP

Precise Point Positioning

RMSE_X

Horizontal Root Mean Square Error in the x direction (easting)

RMSE_Y

Horizontal Root Mean Square Error in the y direction (northing)

RMSE_R

Horizontal Root Mean Square Error in the radial direction (includes both x and y directions)

RMSE_Z

Vertical Root Mean Square Error in the z direction (elevation)

RMSD_Z

Vertical Root Mean Square Difference in the z direction (elevation)

RTK

Real Time Kinematic

SBET

Smooth Best Estimate Trajectory

TIN

Triangular Irregular Network

USGS

United States Geological Survey

UTM

Universal Transverse Mercator

VVA

Vegetation Vertical Assessment

WKT

Well Known Text

XML

eXtensible Markup Language

Alberta Environment and Sustainable Resource Development, General Specification for Acquisition of LiDAR Data, March 2015.

American Society for Photogrammetry and Remote Sensing (ASPRS), 2014, Positional accuracy standards for digital geospatial data – draft revision 7, version 1: American Society for Photogrammetry and Remote Sensing.

American Society for Photogrammetry and Remote Sensing (ASPRS), 2019, LAS specification version 1.4 – R15: Bethesda, Md., American Society for Photogrammetry and Remote Sensing, 47 p. Available at LASer-LAS-File-Format-Exchange-Activities.

GeoBC Specifications for LiDAR, version 5.0, Ministry of Forests, Lands and Natural Resource Operation, April 9, 2020.

Guideline for RTK/RTN GNSS Survey in Canada, Natural Resource Canada, Version 1.1, July 2013. (PDF, 245 kb)

High Resolution Digital Elevation Model (HRDEM) – CanElevation Series -Product Specification Edition 1.3, Natural Resources Canada, August 28, 2019 (PDF, 1.20 mb).

ICSM LiDAR Acquisition Specifications and Tender Template, Australia, Version 1.0, November 2010.

Levés Laser Aéroporté (LiDAR), Traitement et Livraison des Données Classifiées pour des Territoires Situés Dans la Région de L’Abitibi-Témiscamingue, Ministère de L’énergie et des Ressources Naturelles, 13 juillet 2016.

National Network of Regional Coastal Monitoring Programmes of England, Specification for LiDAR Surveys Version 1.0 August 2015.

Natural Resources Canada, Canadian Airborne LiDAR Acquisition Guideline, Version 1.0, May 01, 2014.

New Zealand National Aerial LiDAR Base Specification, Land Information New Zealand, December 2016.

Nova Scotia LiDAR Data Acquisition and Quality Assurance Specifications Version 1.0, Spatial energistics group April 11, 2012.

Ontario Specifications for LiDAR Acquisition version 1.1. Government of Ontario, April 15, 2016.

Provision of LiDAR Services for The Government of Manitoba Water Management, Planning and Standards Service Area: Souris River Basin Term: To March 31, 2017.

Province of New Brunswick Draft LiDAR Specifications, Government of New Brunswick, June 2016.

USGS, 2020, 3DEP LiDAR Base Specification 2020 rev. A, August 2020): NGL Standards and Specification, 48 p.

Webster, T. (2020) Applications of topo-bathymetric LiDAR to flood risk assessment. Technical report prepared for NRCan, 33 p.

Version 3.1

1. Extensive update of Appendix 1 – Forestry.
2. Fixed various typographical errors.
3. Included bookmarks for quick navigation inside the document.

Version 3.0

1. Addition of Appendix 5 regarding topo-bathymetric LiDAR acquisition.
2. In Appendices 1, 2 and 4, accuracy and density recommendations that were less demanding than the Canadian Quality Level (CQL1) have been modified to meet them.
3. In Appendices 1, 2 and 4, explanations for using the recommended accuracy and density values in the generic formulas in the guide have been added.
4. All references to first and only returns have been changed to first returns.
5. Modification of the formula for vegetated vertical accuracy (95th percentile) and consequently, adjustment of the recommended value for the CQL1.
6. Addition of the statement that acceptable data voids should be excluded from the density assessment.
7. Modification of the grid resolution for assessment of regularity (removal of the requirement to round up).
8. Correction of an error in the decimal values of the horizontal accuracy formula.
9. Correction of an error in the example used for horizontal accuracy (95%) of the CQL1 in Table 1.
10. Modification of the calculated horizontal accuracy value (CHA) to be equivalent to the Horizontal Root Mean Square error (RMSE_R) and corresponding adjustment of the recommended value for CQL1.
11. Addition of indication that the use of satellite-derived PPP corrections is permitted.
12. Rewording of the accuracy requirement for check points.
13. Addition of an alternative not based on the number of error points for the evaluation of the classification for large projects.
14. Addition of new types of classification errors that can lead to data rejection.
15. Addition of indication that only withheld points can be assigned to class 0 in the final point cloud dataset.
16. For the naming convention of LiDAR files, addition of the mention indicating not to insert a quality level if the acquisition is denser or more precise than CQL1.
17. Updating of versions/edits of reference documents.

Version 2.0

1. Addition of generic formulas to determine accuracy and density requirements for LiDAR acquisitions. The CQL1 remains the minimum standard in Canada and is used as an example for the use of generic formulas. The addition of generic formulas allows greater flexibility to establish denser and/or more precise acquisition specifications.

Introduction

Airborne light detection and ranging (LiDAR; also commonly referred to as airborne laser scanning or ALS), has become an important technology to provide simultaneously three-dimensional information on both the vertical structure of forest ecosystems and terrain morphology. LiDAR data are used for a range of natural resource applications related to fibre and non-fibre goods and services (e.g., wildlife habitat, clean water, recreation). The data are useful for estimating biophysical parameters (e.g., height, volume, aboveground biomass, vegetation density), as well as indicators of biological diversity (e.g., forest area coverage, structural assemblage, riparian habitat characterization), and indicators of ecosystem condition and productivity (e.g., post disturbance structure, canopy gaps, growth and yield forecasting). Forest inventory applications of LiDAR are typically either area-based or at the individual tree level. While the area-based approach (ABA; Næsset, 2002) is currently considered operational in a forest inventory context, individual tree approaches (ITA; e.g., Hyyppä & Inkinen. 1999) continue to be developed due to the inherent complexities in successfully delineating individual tree crowns. LiDAR can further be used for accurate and precise mapping of the terrain surface beneath the forest canopy. The derived bare-earth Digital Terrain Model (DTM) has utility for a broad range of applications (e.g. forest engineering, hydrological modelling, soil mapping).

The information provided in this appendix represents current recommended minimum specifications regarding airborne LiDAR acquisitions in support of forest applications. There are complex interactions between acquisition parameters, as well as between the laser pulses, the targeted forest environment, and complexity in topography, which require specifications to be assessed holistically, and tailored to suit project-specific information needs. Furthermore, these guidelines are not intended to be prescriptive, as sensor technology evolves rapidly and project-specific information needs vary and must also be considered.

Note: Currently, discrete return linear mode, discretized full waveform and more recently, single photon or photon-counting LiDAR data are being used in forest applications. The recommendations outlined in this appendix pertain solely to linear-mode and discretized full waveform LiDAR data, as recommended flight specifications and preprocessed point cloud data are similar. For details pertaining to single-photon LiDAR data, White et al. (2021a) and White et al. (2021b) evaluate in detail the use of these data for terrain characterization and operational implementation of enhanced forest inventory (EFI) using an ABA, respectively. For a model development and applications guide for generating an EFI using LiDAR and an ABA, see White et al. (2013 and 2017).

Data Considerations

The LiDAR data must be acquired consistently across the entire dataset to produce robust derivatives (i.e., metrics, predictions, surface models). The spatial distribution of useable points (LiDAR returns) must therefore be uniform and free from clustering, with consistent pulse densities throughout the project area. Ultimately, the uniformity in the sampling distribution across the target area is an important component of the data quality and helps assess its suitability for use. Multiple factors affect the distribution of pulse returns in a LiDAR dataset (e.g., instrument characteristics and parameterization, terrain, turbulence, environmental conditions, vegetation structure). Table A1 provides some guidelines regarding minimum pulse density ranges for different Canadian forest types. Below are the main data considerations:

• A mixture of sensors and sensor generations on a single project should be avoided as the optimal acquisition specifications will inevitably vary (beam divergence, wavelength, return digitization capability, etc.) causing non-uniformity in the resulting merged dataset, and consequently impacting any subsequent use of these data. It is advisable that sensors with similar characteristics and parameterization be used with coincident flight parameters for data acquisitions in support of a given project.
• LiDAR systems using a conical, rotating, or nutating mirror system yield uniform sampling patterns throughout the sampled swath, and hence minimize uneven spacing in sampling, namely at the swath’s edge. Uneven spacing in sampling can be greatly reduced using a 50% overlap with oscillating mirror systems. Additionally, certain oscillating mirror systems are equipped with technology that offers a dynamic field of view. The technology maintains fixed-width swaths, despite variations in terrain height, while sustaining regular point distributions.
• Opposing adjacent flight lines can increase the configurations from which the forest is sampled, although are not a requirement when using conical scan systems due to the two ground perspectives achieved in a single flight line.
• The targeted forest environments’ structure can be used to guide the scan angle limit. For example, the use of greater scan angles has been demonstrated to be inconsequential on the estimation of forest attributes for open canopies, or canopies composed of spike-like crowns on relatively uniform terrain, providing sufficient overlap (i.e., min. 20%) is maintained between flight lines (e.g., ±30° for balsam fir dominated stands; van Lier et al., 2021). Greater scan angles produces a wider swath width, however the probability of obtaining ground returns decreases at the swath’s edge, notably for dense and complex forest environments. In open terrain and sparse forest environments, a wider scan angle has less effect on the nominal point spacing (NPS) of ground returns.
• Requirements should consider the type of terrain, the complexity of the forest structure being sampled, the desired derived information products and their scale.
• The pulse density must be sufficient to reliably characterize the environment of interest throughout the project area. Greater pulse densities increase sampling of the forest canopy, increase the likelihood of obtaining ground returns, and increase confidence in identifying ground returns in forested areas, especially in complex environments. The majority of sensors made within recent years will achieve, and most often exceed, the recommended minimum of 2 pulses per square meter, even at the highest operating altitudes. As a general rule, the required pulse density will increase as the complexity of the forest environment sampled and/or associated terrain increase.
• Fall or early spring LiDAR acquisitions during leaf-off conditions in areas of significant deciduous tree cover, and where understory and other vegetation cover are in a dormant state, will decrease the NPS of ground returns, producing a more accurate terrain characterization and consequently a higher quality DTM. By contrast, acquiring data during leaf-on conditions may enable improved characterization of forest structure, although the requirement for a leaf-on or leaf-off data acquisition is ultimately dependent on the forest type and the information needs. White et al. (2015) provide a summary of literature that explore the use of leaf-on and leaf-off data using an area-based approach. Note that a mixture of leaf-on and leaf-off acquisitions on a single project should be avoided as the change in forest structure may necessitate additional sampling and modelling efforts to produce coherent products.
• A DTM grid with a spatial resolution of 2 m or less is suitable for height normalization of the point cloud. A finer resolution may be required for extremely steep terrain.

Note: required pulse densities increase with increases in either forest environment and/or terrain complexities.

Acquisition Parameter Specifications and Considerations

LiDAR sensors can be configured in a manner to be optimized for forest applications. Again, the parameters selected for forest applications are dependent on the intended project objectives and associated information needs. Table A2 provides a summary of recommended acquisition parameters based on current scientific knowledge and available sensors. No single parameter can be assed on it’s own as many are dependant on one another (e.g., increasing pulse repetition frequency will increase pulse density; increasing flight altitude will decrease pulse density). Detailed recommendations regarding LiDAR data suitable for the ABA are similarly further discussed in White et al. (2013).

The recommendations below should be viewed as absolute minimum requirements. Seeking expert opinion from service providers on appropriate parameters for specific forest conditions and project needs should always be undertaken. In general, end users should be particularly concerned with sufficient pulse density, swath overlap, scan angle, and acquisition timing (i.e. leaf-on versus leaf-off data).

• To establish all the specifications related to the density of a LiDAR data acquisition project, a value within the pulse density range associated to the appropriate application and forest type may be used as an Aggregate Nominal Pulse Density (ANPD) in the generic formulas in Sections 5 and 6 of the Federal Airborne LiDAR Data Acquisition Guideline.
• To establish all the specifications related to the accuracy of a LiDAR data acquisition project, the vertical (RMSE_Z) and horizontal (RMSE_R) accuracies recommended in the table above can be used in the generic formulas in Sections 5 and 6 of the Federal Airborne LiDAR Data Acquisition Guideline. A documented quality check and verification process should be included with the data delivery.
• Current LiDAR sensors have high repetition rates sufficient for providing adequate pulse densities. Sensor configuration of repetition rate should be largely determined by flight parameters, designed ANPD, scan angle, swath overlap for no gaps and sufficient pulse energy to avoid drop outs.
• LiDAR surveys should be designed and conducted with the objective of having no data voids, except in areas where low near infrared surface reflectance features are present, such as waterbodies. The spatial distribution of geometrically usable points should be uniform and regular except for data void areas. A specified minimum pulse density, as recommended in Table A1 or defined by the project objectives, should be present in 90% of cells of a uniform density grid within usable portion of a swath. See the Section 6.1.1 Project Method of the Federal Airborne LiDAR Data Acquisition Guideline for additional details on the acquisition planning considerations.
• A recommended minimum LiDAR point cloud classification will be conducted for forest applications compliant with ASPRS LAS 1.4 – R15 format and specification. Classification should be consistent across the entire project, void of noticeable variation in character, texture or quality of the classification between tiles, swaths, lifts or other non-natural division. Classification accuracy should be in compliance with the Federal Airborne LiDAR Data Acquisition Guideline in Section 4. Calibrated unclassified LiDAR returns (Class 0; Created, never classified) will be classified and assigned to Class 2 for bare earth ground returns, Class 7 for low noise, and Class 18 for high noise. Outliers, geometrically unstable pulse data, and blunders will be identified using the Withheld Flag and not be used in the classification process. The remaining points will be placed into Class 1 for processed but unclassified. Additional classification requirements will be based on project-specific requirements, which may include Class 3 for low vegetation, Class 4 for medium vegetation, Class 5 for high vegetation and Class 9 for water.

References

J. Hyyppä, M. Inkinen. 1999. Detecting and estimating attributes for single trees using laser scanner. The Photogrammetric Journal of Finland, 16(2): 27-42.

Laes, D., Reutebuch, S., McGaughey, B., Maus, P. Mellin, T., Wilcox, C., Anhold, J., Finco, M., Brewer, K. 2008. Practical LiDAR acquisition considerations for forestry applications. RSAC-0111-BRIEF1, Salt Lake City, Utah, US Department of Agriculture, Forest Service, Remote Sensing Applications Center. 32 p.

Næsset, E. 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 801): 88-99.

Rowe, J.S. 1972. Forest Regions of Canada. Fisheries and Environment Canada, Canadian Forest Service, Headquarters, Ottawa. 172 p.

van Lier, O.R., Luther, J.E., White, J.C., Fournier, R.A., Côté, J.-F. 2021. Effect of scan angle on ALS metrics and area-based predictions of forest attributes for balsam fir dominated stands. Forestry: An International Journal of Forest Research, cpab029.

White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D, Woods, M. 2013. A best practice guide for generating forest inventory attributes from airborne laser scanning data using the area-based approach. Information Report FI-X-10. Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre, Victoria, BC, Canada. 50 p.

White, J.C., Arnett, J.T.T.R., Wulder, M.A., Tompalski, P., Coops, N.C. 2015. Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with area based approach. Canadian Journal of Forest Research, 45: 1498-1513.

White, J.C., Tomplaski, P., Vastaranta, M., Wulder, M.A., Saarinen, N., Stepper, C., Coops, N.C. 2017 A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach. In Information Report FI-X-018. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, BC, Canada, 38 p.

White, J.C., M. Woods, T. Krahn, C. Papasodoro, D. Belanger, C. Onafrychuk, I. Sinclair. 2021a. Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions. Remote Sensing of Environment, 252, 112169.

White, J.C., Penner, M., Woods, M. 2021b. Assessing single photon LiDAR for operational implementation of an enhanced forest inventory in diverse mixedwood forests. Forestry Chronicle, 97, pp.78-96.

Introduction

LiDAR data is considered to be the primary source of Digital Elevation Model (DEM) data for various flood mapping applications, including specialized technical products such as inundation, flood hazard or flood risk maps, as well as public information maps. The primary goal of LiDAR applications in flood mapping is to obtain accurate elevation data, which are required for both the hydraulic modelling (deriving water elevations and extents for floods of various magnitudes) and the cartographic components of flood mapping projects. The elevation datasets include:

• Ground elevation data, which is built from last LiDAR returns, contains only topography, and is usually referred to as the Digital Terrain model (DTM).
• First return elevation data, which is built from first returns and includes structures (e.g. buildings and bridges) and tree canopy, and is usually called the Digital Surface Model (DSM).

Another use of LiDAR data is to facilitate flood zone area characterization, i.e. extraction of planimetric or basemap features useful for guiding hydraulic modelling and for creating flood maps, such as roads and pavement areas, stream banks, bridges, ditches, etc. Flood zone area characterization is best accomplished using LiDAR in combination with orthophotos. The level of detail in flood zone area characterization should be appropriate for the scale of the intended flood mapping (e.g. higher detail will be required for 1:2,000 scale maps compared to 1:5,000).

Data Acquisition Considerations

Low Flow Conditions

In addition to the environmental conditions typically recommended for LiDAR data collection (no snow or ice on the ground, shorelines and water courses free from significant ice buildup, no unusual flooding or inundation), LiDAR collection for flood mapping should take place during base flow (dry) conditions to maximize the area of water courses exposed; thus, maximizing the utility of the collected data for hydraulic modelling.

Stable Flows and Levels

Flow and levels in the water courses and water bodies should remain stable, i.e. free of significant fluctuations throughout the data collection period to maximize consistency of collected data. This is important in situations where water flows/levels are prone to rapid changes, e.g. rivers with flashy response or regulated rivers. When data collection spans a significant length of time (usually due to a large size of data collection area, which could potentially require data acquisition during several seasons), preference should be given to data collection during similar flow/level conditions.

Leaf-off Conditions

Leaf-off conditions are recommended to maximize canopy penetration thus maximizing ground point density and the quality of the resulting DTM. However, leaf-off acquisition is not required as long as the point density and vertical accuracy of non-canopy returns are sufficient to meet DTM and DSM accuracy requirements. In some cases leaf-on acquisition may be preferred or necessary to map and characterize vegetation types, for example for hydrological or detailed hydraulic (e.g. estimation of Manning’s n) modelling.

Acquisition Parameters

Table B1 summarizes the minimum required and recommended LiDAR acquisition parameters for flood mapping applications. Higher swath overlap is recommended for areas of high relief terrain.

Orthophoto Acquisition

Aerial images, particularly orthophotos, are extremely useful for flood mapping, providing valuable information for hydrologic, hydraulic and risk analyses, as well as a very useful basemap dataset for all types of flood maps. Orthophotos are also invaluable for hydro-enforcement of DTM, which is a recommended practice for post-processing LiDAR-derived DEMs (see below). If sufficiently high-quality and up-to-date orthophotos are unavailable for the planned LiDAR data collection area, consideration should be given to acquisition of orthophoto data within the same time frame as LiDAR to maximize data usability for flood mapping.

Data Quality Considerations

Data Density and Accuracy

Floods can have a variety of land cover types from open to low vegetation, brushland, forested or urban. Acquisition of LiDAR data in the flood zone area is therefore subject to a variety of conditions and should ultimately be guided by the need to collect sufficient ground returns for all cover types present in the flood zone area. For example, if a portion of the data collection area is covered in dense riparian vegetation, higher overall data collection density may be required in order to achieve sufficient ground point density in the riparian area.

The level of data collection effort (point density, vertical and horizontal accuracy) should generally reflect the requirements of the intended flood mapping application, which typically depend on the level of flood risk and the regulatory framework in place. Table B2 lists the recommended approximate LiDAR data accuracy and density specifications for flood mapping applications. These specifications meet the minimum requirement for airborne LiDAR data acquisition in Canada (CQL1) and are established according to the flood risk category, based on the review of existing provincial and territorial guidelines (Alberta Environment, 2011; Kerr Wood Leidal, 2011; Spatial Energistics Group, 2012; Elevation Coordination and Consultation Committee, Government of Ontario, 2016) and the National Floodplain Mapping Assessment report by MMM Group Limited (2014). The flood risk categories are defined following the MMM report, and are similar to the vertical accuracy classes adopted in the Ontario guidelines:

• High Flood Risk Category: All urban areas and rural areas that are protected by diking;
• Medium Flood Risk Category: All other rural areas that include settlements and agricultural lands;
• Low Flood Risk Category: Sparsely populated areas.

The accuracy and density specifications to be used depend on the flood risk category. To determine the risk category, refer to the definitions above. Once the category has been identified, values within the ranges of vertical (RMSE_z) and horizontal (RMSE_R) accuracies and density (DNGI) in the table above can be used in the generic formulas in sections 5 and 6 of the Federal Airborne LiDAR Data Acquisition Guideline.

Classification

The minimum required classes include ground, non-ground, bridge decks and water, which are necessary for creating a hydro-conditioned DTM. Buildings and other man-made structures class(es) will also typically be required to create a DSM, as well as to facilitate the removal of the building/structures returns from the DTM. It is recommended that the DTM should have at least 90-95% of buildings and structures removed. In addition, vegetation classes may be necessary in some cases for hydrotechnical modelling and base mapping.

Hydro-flattening

Hydro-flattening is a post-processing method applied to LiDAR-derived DEMs to ensure that water surfaces are flat in the bank-to-bank (perpendicular to the apparent direction of flow) direction, and non-increasing in the downstream direction. In some cases, further hydrological enforcement may be required for drainage features and flat river area with islands/channels where 2D flows may occur. Hydro-flattening is a recommended flood mapping application, the specific guidelines and specifications would be defined by the contracting authority

Other Considerations

Topo-bathymetric LiDAR

Consideration may be given to utilizing topo-bathymetric LiDAR systems for simultaneous and seamless collection of topographic and bathymetric data. Traditional methods for collecting bathymetric data (underwater portion of the river channel geometry), which involve echo sounding devices on boats or manual surveying, are expensive, time consuming and potentially hazardous. Topo-bathymetric systems include a green laser for penetrating the water and measuring the bathymetry and are able to generate a seamless above/below water elevation data. Details on using topo-bathymetric LiDAR are included in Appendix 5.

References

Alberta Environment, 2011. Flood Hazard Identification Program Guidelines. Alberta Environment, Water Management Operations, River Forecast Section, 92 p.

American Society for Photogrammetry and Remote Sensing (ASPRS), 2014. Positional accuracy standards for digital geospatial data, Edition 1, Version 1.0: American Society for Photogrammetry and Remote Sensing (PDF, 1.20 mb), 26 p.

Elevation Coordination and Consultation Committee, Government of Ontario, 2016. Ontario Specifications for LiDAR Acquisition, Version 1.1, 2016-04-15.

Federal Emergency Management Agency (FEMA), 2016. Guidance for Flood Risk Analysis and Mapping, Elevation Guidance (May 2016): Federal Emergency Management Agency, 21 p.

Kerr Wood Leidal, 2011. Coastal floodplain mapping – guidelines and specifications. Prepared for the Ministry of Forests, Lands and Natural Resource Operations, Government of British Columbia, 91 p.

Mapcon Mapping Ltd., 2009. Imagery and Elevation Acquisition Guidelines, Version 1.2. Prepared for the Government of Ontario, 18 p.

MMM Group Limited, 2014. National Floodplain Mapping Assessment, Final Report. Prepared for Public Safety Canada.

Natural Resources Canada, 2014. Canadian Airborne LiDAR Acquisition Guideline, Version 1.0, May 01 2014, 26 p.

Natural Resources Canada (NRCan): Federal geomatics guidelines for flood mapping

Spatial Energistics Group, 2012. LiDAR Data Acquisition and Quality Assurance Specifications, version 1.0. Prepared for Department of Fisheries and Aquaculture, Government of Nova Scotia, 61 p.

USGS, 2020, 3DEP LiDAR Base Specification 2020 rev. A, August 2020): NGL Standards and Specification, 48 p.

Introduction

Conducting topographic mapping and/or forest inventory in high-relief terrain areas using airborne LiDAR data requires an adjusted approach to data collection. High relief areas are typically mountainous areas where steep slopes occur e.g. > 35 degree and there is significant altitude change that results in implications to LiDAR collection e.g. surface conditions due altitude temperature difference or impacts on flying parameters due to terrain characteristics. High relief areas complicate typical data collection due to a mix of undulating terrain, steep slopes, remote access, and environmental conditions. The intent of this section is to provide recommendations and considerations when collecting airborne LiDAR data in high-relief areas.

References

Evans, Jeffery, S., Hudak, Andrew, T., Faux, Russ, and Smith, Alistair, M.S. Discrete Return LiDAR in Natural Resources: Recommendations for Project Planning, Data Processing and Deliverables. Remote Sens. 1, 776-794, 2009.

Hirata, Y. The Effect of Footprint Size and Sampling Density In Airborne Laser Scanning To Extract Individual Trees in Mountainous Terrain. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, Vol. XXXVI-8/W2.

Hopkinson, Chris, Demuth, Mike, Sitar, Mike, and Chasmer, Laura. Application of Airborne LiDAR Mapping in Glacierised Mountainous Terrain. Geoscience and Remote Sensing Symposium, 2001. IGARSS’01. IEEE 2001 International.

Hsu, Wei-Chen, Wu, Li-Wei, and Liu, Jin-King. Airborne LiDAR Survey in Cloudy and Extremely High Relief Mountainous Terrain of Taiwan. IGARSS 2012.

Jordan, Thomas, Marguerite Madden, Byungyun Yang, J. Sharma, and Sudhanshu Panda. "Acquisition of LiDAR for the Tennessee Portion of Great Smoky Mountains National Park and the Foothills Parkway." Center for Remote Sensing and Mapping Science (CRMS), Department of Geography, The University of Georgia, Athens, Georgia, USA, Tech. Rep. USGS Contract (2011).

Mitchell, Brent, Mike Walterman, Tom Mellin, Craig Wilcox, Ann M. Lynch, John Anhold, Donald A. Falk et al. "Mapping vegetation structure in the Pinaleno Mountains using LiDAR-phase 3: Forest inventory modeling." (2012).

Su, Jason, and Bork, Edward. Influence of Vegetation, Slope and LiDAR Sampling Angle on DEM Accuracy. Photogrammetric Engineering & Remote Sensing Vol 72, No 11, pp 1265-1274, 2006.

Introduction

The advancements in airborne LiDAR technology provide a multidimensional data source for 3D mapping at finer precision for urban environments. LiDAR applications for cities include mapping building features, urban forestry, defining floodplain areas, utility feature extraction, land cover classification and corridor assessment to name a few. Adjustments to LiDAR data collection and processing are needed to accommodate the variation in the urban landscape. This guideline aims to provide general guidance for LiDAR data collection for different types of urban applications.

Data Collection Considerations

Table D1 is general data collection consideration and recommendations for collecting in urban environments.

Data Processing Considerations

The LiDAR data acquisition process should include intensity image, point cloud classification and digital surface models. Digital surface models for cities include Digital Elevation Models with appropriate breaklines and hydro-flattening, Digital Surface Model and Canopy Height Model. Additional point cloud classification of pulse data based on LAS 1.4 – R15 classification would depend on project specific requirements but may include features such as rails, road surface, wires, and vegetation. Additional classes that could be assigned to class 64-255 for urban environments includes, light poles, road markings, street car tracks, electrical distribution poles, sidewalks, parking lots, and trails.

Pulse Density for Feature Extraction

The table below represents recommended LiDAR pulse density ranges (ANPD) for various urban applications for feature extraction. General recommendation for urban mapping is for a pulse density ≥ 10 pls/m², however depending on project specific objectives, the pulse density may range to accommodate the type of feature extraction.

Considerations, Limitations and Assumptions

• A value within the pulse density range associated to the appropriate application may be used as an ANPD in the generic formulas in Sections 5 and 6 of the Federal Airborne LiDAR Data Acquisition Guideline.
• Acquisition of airborne LiDAR data in urban areas may be performed during day or night and may be restricted due to air traffic control limitations. Planning for data collection should consider impacts to scheduling due to limitations in collecting the data near airports.
• In urban areas, certain building heights and structures can contribute significantly to LiDAR shadows and occlusions, which can cause a large number of data gaps in the LiDAR point cloud. From this perspective, multiple angles or narrow FOV of LiDAR systems may be necessary for urban applications. The decision should provide a balance between the building density, height and road network orientation in conjunction with financial considerations.
• In mapping buildings and the distribution of power lines, these may be obscured by trees; therefore, they may not be fully extracted from LiDAR data. Leaf off conditions can improve the detection of these features.
• Some building roofs and walls are made of glass which may be transparent or semitransparent. Consequently, there is a significant number of unexpected LiDAR points reflected from objects inside the building and under the rooftop. Many of these points may be considered as noise; thus, classified point clouds should take noise class into account.

References

Alexander, Cici, Kevin Tansey, Jörg Kaduk, David Holland, and Nicholas J. Tate. "Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas." ISPRS Journal of Photogrammetry and Remote Sensing 65, no. 5 (2010): 423-432.

Awrangjeb, Mohammad, Chunsun Zhang, and Clive S. Fraser. "Automatic extraction of building roofs using LIDAR data and multispectral imagery." ISPRS Journal of Photogrammetry and Remote Sensing 83 (2013): 1-18.

Awrangjeb, Mohammad, and Clive S. Fraser. "An automatic and threshold-free performance evaluation system for building extraction techniques from airborne LiDAR data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 10 (2014): 4184-4198.

Awrangjeb, Mohammad, Guojun Lu, and C. Fraser. "Automatic building extraction from LiDAR data covering complex urban scenes." The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40, no. 3 (2014): 25.

Clode, Simon, Peter J. Kootsookos, and Franz Rottensteiner. "The automatic extraction of roads from LIDAR data." In The International Society for Photogrammetry and Remote Sensing's Twentieth Annual Congress, vol. 35, pp. 231-236. ISPRS, 2004.

Dorninger, Peter, and Norbert Pfeifer. "A comprehensive automated 3D approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds." Sensors 8, no. 11 (2008): 7323-7343.

Hu, Xiangyun, Yijing Li, Jie Shan, Jianqing Zhang, and Yongjun Zhang. "Road centerline extraction in complex urban scenes from LiDAR data based on multiple features." IEEE Transactions on Geoscience and Remote Sensing 52, no. 11 (2014): 7448-7456.

Jarzabek-Rychard, M., and H. G. Maas. "Aerial image based geometric refinement of building models derived from airborne LiDAR data." (2016).

Jwa, Yoonseok, and Gunho Sohn. "A piecewise catenary curve model growing for 3D power line reconstruction." Photogrammetric Engineering & Remote Sensing 78, no. 12 (2012): 1227-1240.

Li, Zhengrong, Troy S. Bruggemann, Jason J. Ford, Luis Mejias, and Yuee Liu. "Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion." Journal of Field Robotics 29, no. 1 (2012): 4-24.

Marks, Kate, and Paul Bates. "Integration of high-resolution topographic data with floodplain flow models." Hydrological Processes 14, no. 11-12 (2000): 2109-2122.

McLaughlin, Robert A. "Extracting transmission lines from airborne LIDAR data." IEEE Geoscience and Remote Sensing Letters 3, no. 2 (2006): 222-226.

Meesuk, Vorawit, Zoran Vojinovic, Arthur E. Mynett, and Ahmad F. Abdullah. "Urban flood modelling combining top-view”.

Yan Wai Yeung, Shaker Ahmed, and El-Ashamwy Nagwa. “Urban land cover classification using airborne LiDAR data: a review.” Volume 158, 1 March 2015, Pages 295-310.

Introduction

Airborne topo-bathymetric LiDAR (TBL) systems typically utilize two lasers: a near infrared (NIR) laser for topographic data collection and a green laser for bathymetric data collection. These systems allow for the generation of high-resolution seamless digital terrain models (DTMs) that include land and submarine elevation. TBL works equally well in fresh or saltwater, however the water clarity is typically the limiting factor. Although shallow water (TBL) sensors are relatively new, the deeper water airborne laser bathymetry sensors were referred in the literature to ABL systems – airborne bathymetric LiDAR. For the rest of this Appendix, all systems will be referred to as TBL systems. The technology for TBL systems has been around since the late 80’s and early 90’s. A recent text entitled “Airborne Laser Hydrography II” provides a detailed history of the technology and a review of the current systems and application of TBL and is free to download (Philpot, 2019).

The intent of this Appendix is to concentrate on the bathymetric LiDAR component of TBL systems for specifications and guidelines. A focus is made on TBL for flood risks applications. For information related to the topographic component of TBL surveys, one should refer to section 6 of the current guideline and the appendix 2 on flood mapping. The topographic component of TBL surveys for flood risks applications must at least meet requirements presented in appendix 2.

Topo-bathymetric LiDAR general principles

TBL works by emitting a near-infrared (NIR) and a green laser from an aircraft, typically in an elliptical or circular scan pattern, and measuring the travel time of the laser pulses to and from the land, water surface, and seabed (Figure A1 A, B). The NIR laser pulse reflects off the land or water, while some of the green laser pulse is reflected at the air-water interface, the rest is refracted and attenuated as it passes through the water column and is reflected from the seabed to return to the detector (Figure A1-C, D). The travel path of the green laser is complex. As it passes from air to water, the speed of the light slows down by approximately 25% and thus is refracted. Once in the water column, the green light is scattered and loses its energy exponentially with depth until it reflects off the sea-river bed and returns to the detector. In order to compensate for the refraction angle and change in the speed of light from air to water, the system must be able to detect the water surface. The NIR laser returns and the green laser returns are used to detect the water surface in order to compensate for these optical effects of the green laser changing media from air to water and the return path back to the detector. The beam divergence of the NIR laser is typical of that of topographic LiDAR on the order of 0.5 mrad, whereas the green laser will have a larger beam divergence on many TBL sensors.

The main limiting factor affecting depth of penetration of TBL is water clarity. Thus, turbidity management is required for a successful survey. The main sources that can adversely affect the green laser from penetrating the water column come from suspended sediment, although algae blooms may also have negative impacts, as well as dissolved organic matter which can be common in some lakes and streams that originate in bogs. The water clarity fluctuations of a study site should be considered when planning a survey (See section ‘Guidelines for a Topo-Bathymetric LiDAR project). Moreover, the green laser of the TBL does not penetrate air bubbles (white caps, surf zone, etc.) as a result of the scattering. This has implications along coastlines with heavy surf that may need to be flown twice at different tide levels.

Flights operations, GNSS base stations set up and sensor calibration principles are similar to those of typical topographic LiDAR and thus are not presented here.

The processing of TBL data after a mission is like topographic data as the first step is to process the trajectory of the aircraft using a ground base station and the GNSS and inertial measurement unit in the sensor. During a TBL survey, the waveforms of the NIR and green laser are recorded and are related to the trajectory by GNSS time. Once related to the trajectory, the waveforms are then post processed into discrete points. During the processing of the green laser waveforms, the water surface is detected and modelled for the green laser pulse path to be refracted at the water surface and the range adjusted by the change of the speed of light in water. The typical recorded waveform from the green laser consists of a large amplitude return from the water surface followed by the volume backscatter of the laser pulse through the water column and then a peak representing reflection off the seabed or riverbed (Figure A1-C).

One distinct advantage of surveying hydrography with TBL when compared to echo sounding is that the speed of the light in water is only marginally affected by changes in salinity and temperature compared to how these properties effects on the speed of sound in water. This property makes TBL ideal for surveying in estuarine environments where fresh and saltwater mix in a sometimes-complex fashion.

Benefits of Topo-Bathymetric LiDAR for flood risk mapping

Most recent flood inundation maps were based on topographic LiDAR alone with no details of the bathymetry. Although the methods used to produce inundation maps from topographic LiDAR have been found to be reasonably accurate, these approaches do not calculate the speed of the flood water or the time and duration of inundation. In order to calculate these variables, one must utilize a hydrodynamic modelling software. One of the main parameters that controls the movement of water in a hydrodynamic model is the bathymetry. Other parameters also effect the movement of water including bed roughness, sometimes defined as Manning’s Roughness Coefficient, which effects the drag between the water and the bed. The results of TBL survey can provide information of topography and bed material (with LiDAR intensity) and have been used to construct benthic cover maps or habitat maps (Webster et al., 2016), which can be used to estimate bed roughness for hydrodynamic models.

Coastal flood risk is associated with elevated sea-levels associated with storm surges. In addition to elevated total water levels (tide+surge), waves and wave-runup can also cause additional flooding and erosion. In order to model the hydrodynamics of a storm surge event and possible wave set-up and run-up, a seamless DTM is required. In order to calculate wave set-up and wave run-up, near shore bathymetry is required (Olabarrieta and Warner, 2016). Nearshore bathymetry has been a challenge to map prior to TBL. Most techniques for mapping land elevation do not work in the coastal zone (e.g. photogrammetry, topographic LiDAR) and techniques to map the deep water such as boat-based echo sounding are dangerous and inefficient in shallow water. The ability of TBL to acquire seamless DTMs across the coastal zone into the near shore has enabled detailed hydrodynamic models to be constructed.

In the case of fluvial or inland flood risk mapping, many studies have used topographic LiDAR to map the exposed floodplain and estimate the river channel topography. Many fluvial models are based on 1-Dimensional hydrodynamic models, or 1-D models where cross-sections are extracted from the floodplain and river channel and used to calculate the momentum and mass of water moving through the section. Measuring riverbed cross-sections manually (e.g. Using GNSS, or boat-based echo sounding) is expensive and time consuming and in some cases dangerous to the operators. As a result, the channel topography is only measured in a few cross-sections and the remaining areas are estimated by interpolation or using an empirical relationship based on the exposed channel geomorphology. The discharge or flow (cubic meters per second) of a river is controlled by the channel bathymetry and gradient. The ability of TBL to acquire seamless elevation data of the floodplain and the river channel offers a great deal of potential to improve the accuracy of the cross-sections that are used in flood models.

Accuracy standards

The International Hydrographic Organization (IHO) specifies different Orders standard for bathymetric data in terms of total vertical or horizontal accuracy, at 95% confidence interval (IHO, 2008). The Order 1 standard applies for areas shallower than 100 m and Special Order is typically recommended for areas or applications requiring the highest accuracy of survey. The Total Vertical Uncertainty (TVU) varies with depth and is defined in the equation below, where ‘a’ represents the portion of uncertainty that does not vary with depth (e.g. airport position uncertainty from the trajectory processing), ‘b‘ represents the coefficient of the portion of the uncertainty that varies with depth ‘d’. All TBL system currently on the market can exceed the Order 1 requirements set out by IHO for depths less than 100 m (Saylam et al., 2018).

TVU = (a² + [b x d]²)^1/2

The following table describes the different IHO Orders and the horizontal and vertical accuracy expected.

In order to better appreciate how the TVU varies with depth for these different IHO orders, the TVU has been calculated at typical depths achieved by shallow water LiDAR sensors (15 m) and deep-water sensors (20 m and greater) (Table E).

Other bathymetric quality levels exist, such as the ones developed more recently through the “US’ National Coastal Mapping Strategy 1.0: Coastal Lidar Elevation for a 3-D Nation” (NSTC, 2016), but are not detailed in this appendix. Note that some of those quality levels are equivalent to the IHO Orders.

Acquisition Parameters Considerations

For TBL acquisitions for flood mapping and flood risks purposes, Table E3 provides summary of recommended acquisition parameters based on current scientific knowledge and available sensors.

• It is usually accepted that shallow water TBL are the sensors appropriate for flood risk studies and flood mapping. In cases where 2-3 Secchi depths need to be achieved at a lower point density for bathymetric coverage to support flood risk mapping, a combination of shallow and deep-water sensors may be suitable. Also, systems do exist that combine shallow and deep-water capabilities. Otherwise, the shallow water sensors provide the point density and penetration required to support most flood risk studies and can be supplemented with echo sounding data.
• For TBL acquisitions related to hydrographic charting, the Canadian Hydrographic Service specifies that the spatial accuracy of the bathymetric portion of the TBL must meet the IHO Order 1b standards. They also specify deep-water TBL sensors capable of 2-3 Secchi depth of penetration in optimal conditions.

Guidelines for a Topo-Bathymetric LiDAR project

This section aims to provide some general guidance for a TBL project. The details and methods were inspired from the Canadian Hydrographic Services specifications, the NOAA specifications and the existing literature (e.g. Webster et al., 2016; Saylam et al., 2018).

A) Project planning and collection

During the mission planning phase and execution of the survey, the contractor must account for, demonstrate and implement strategies to mitigate the impact of the following parameters:

• Topography of the survey area and surrounding area, such as mountainous terrain that could prevent or hinder flight operations.
• Weather, including but not limited to water clarity and turbidity management. See the sub-section ‘Water clarity considerations’ below for more details.
• Native phenomenon, such as kelp growth and algae blooms.
• Maximum data acquisition around low tide. This is described as two hours prior and two hours after.
• Include all bays, inlets, islands, peninsulas, inter tidal zones, flood plains, river deltas and land areas that are within the survey blocks.

The required point density is also important as it can influence how many flight lines are required and the level of overlap between survey lines. In general, if the desired point density can be achieved with a single flight line, then 20% overlap is sufficient to ensure there are no gaps during the collection process. A 50% overlap is recommended for flood-related TBL acquisitions to reduce data gaps and to ensure a more uniform distribution of points. It is understood that the requested density may not be met due to certain environmental conditions that cannot be controlled. Table E3 provides the recommended acquisition parameters for TBL acquisitions for flood-related applications.

The flight lines must be flown in alternate directions. They must also be planned to maximum expected depths, with the intention of collecting data to the point of extinction, not necessarily to the full extent of the survey blocks. Cross lines must be run across all planned LiDAR survey lines at angles ranging from 45 to 90 degrees more than the original survey line. Cross lines must be acquired and processed to the same accuracy and data quality standards as the main survey lines and may be included in the final product of the survey.

For shoreline mapping and modeling uses, it is particularly important to have good bathymetric data in the very shallow (0-4 m) areas. For this reason, the LiDAR systems, software, and processing procedures shall enable measurement of bathymetry in this very shallow region. The sensor used for this mapping shall have an operational measurement depth range equal to or greater than a 1.5 Secchi depth.

The time of acquisition must also be considered when conducting a TBL project. One challenge to TBL river surveys during full leaf-on conditions is the problem of overhanging vegetation obscuring the riverbed below. To overcome this, one could survey in the spring or fall during leaf-off conditions. The benefits of the leaf-off condition would have to be weighed against typical increased water levels and perhaps poorer water clarity conditions at that time of year.

Although not required, it is often desirable to have a field team on the water during the survey. The field team can collect Secchi depth readings to confirm the water clarity conditions (See the sub-section ‘Water clarity considerations’ for more details), and depending on the end application of the data, collect bottom samples and photographs to determine the benthic cover material. The field team would also collect check points both of hard surfaces on land and directly on the seabed to validate LiDAR data accuracy (see section ‘Data Validation’ below).

If coastal surveys are being conducted and any islands need to be surveyed, a twin-engine aircraft must be used in order to be able to safely make a landing if there is an engine failure. In addition to this safety requirement, most TBL surveys require a larger aircraft because of the increased power requirements for the green laser.

Water clarity considerations

For coastal and riverine TBL surveys, the weather proceeding a survey can greatly influence the conditions and probability of success. For example, in coastal areas, an onshore wind can cause waves which cause sediment to become suspended and thus high turbidity conditions where the particles in the water cause the light to scatter and not penetrate to the seabed. In the case of riverine environments, heavy rain can cause sediment and other organic materials to be transported into the river, thus degrading the water clarity. Turbidity management involves understanding the environmental conditions that cause high turbidity (e.g. wind and rain) and the length of time for the water to clear before it is suitable for a survey (Webster et al., 2016). A secondary factor affecting the depth of penetration of the green laser is the brightness of the seabed or riverbed. Darker objects have tendency to absorb the green light, while light objects, such as sand, reflect more of the green light and thus allow the signals to reflect back to the sensor at greater depths.

A Secchi disk is commonly divided into black and white quadrants in order to maximize contrast, and the depth at which the black and white disk disappears from view is called the "Secchi depth". Clear waters will have a larger Secchi depth than more turbid waters, and the size of the Secchi disk may need to be adjusted for different water clarity conditions (Philpot, 2019). TBL shallow water sensors typically have a depth rating of 1.5 times the Secchi depth, and deep water sensors have a rating of 2-3 times the Secchi depth.

Ideally when conducting TBL coastal surveys, there are multiple survey sites within the region that have different shoreline orientations such that not all sites will be turbid as a result of a regional wind event. It can be challenging to obtain Secchi depths leading up to a TBL survey and thus ensure the conditions are suitable. There are numerous terrestrial weather stations available on-line that can provide insights into the past and current wind and rain conditions; however it still can be challenging to predict the turbidity conditions, especially after a wind event when the water needs time to clear. This has led to the investigation of using real-time turbidity sensors that can be deployed in bays with different shoreline orientations or different geological and sedimentary conditions. These real-time turbidity buoys are equipped with a turbidity sensor and a communication package (e.g. cellular modem) that can transfer the readings to the internet to be checked remotely. This allows for a time-series of turbidity conditions to be measured and monitored prior to committing to a TBL flight.

Another approach to measure the water clarity is by using the Diffuse Attenuation Coefficient (Kd). The rate of change of the irradiance energy, E, is a function of the depth (Ed) and is a measure of water clarity that is similar in concept to the Secchi depth in that the rate of change will be faster in turbid waters than in clear waters. The rate of change of Ed, or logarithmic derivative of Ed, is the diffuse attenuation coefficient, Kd (Philpot, 2019).

Figure A2: Diffuse Attenuation Coefficient Kd, E(λ) radiance distribution over a hemisphere at a certain wavelength of energy (λ) at depth z (Philpot, 2019).

Kd values can change rapidly for a body of water, however the typical values of Kd range from 0.05 for clear water to 0.11 for turbid water for wavelengths used in TBL. For coastal areas, some medium resolution satellites, such as MODIS, can help estimate Kd or Secchi depth for certain wavelengths which can aid to determine if a TBL survey is feasible for an area or the best season to attempt a survey.

B) Data processing and management

Once the waveform data have been processed into discrete points (see section ‘Topo-bathymetric LiDAR general principles’ for more details), the classification is then typically performed. The points located in the bathymetric area must be classified according to the classes detailed in Table E4. We recommend that the points onshore be classified at least with the classes specified at section 5 of the current Guideline. Automatic and manual refinement of the classification are typically required using many of the standard tools that are used for topographic LiDAR processing. Most TBL system are equipped with a RGB or multispectral camera system that can be used to colorize the LiDAR point cloud and generate orthophotos. The coincident orthophotos can be very useful in assisting with the interpretation of some of the LiDAR returns.

During data processing, all blunders and artifacts (boats, fisheries, weeds, etc.) must be assigned to class 1.

The delivered point cloud data and/or derived DEMs must be a seamless product (i.e. topographic and bathymetric LiDAR data joined), referenced to common horizontal and vertical coordinate reference systems. For flood risk studies, we recommend using the same coordinate reference systems than those recommended for topographic LiDAR data in the current Guideline (See section 6.3.3).

For hydrographic charting, it may be required that the bathymetric data be referenced to the Chart Datum. In some case, a separation model between the Chart Datum and ellipsoidal heights can be provided by the technical authority to perform vertical transformation. If no such file is provided, the vendor is encouraged to use the Canadian Continuous Vertical Datum for Canadian Waters (CVDCW) process and the applicable Hydrographic Vertical Separation Surfaces (HySEP or SEP) for the survey area. The HySEP/SEP provides the separation between Chart Datum and the GRS80 (NAD83(CSRS)) ellipsoid.

The delivered point cloud data must be classified and in a LAS 1.4 point cloud format. Intensity values must be required for each multiple discrete return. The values recorded in the LAS files shall be normalized to 16 bit, as described in the LAS Specification.

C) Data validation

Wherever possible, it is always recommended that independent check points of a high precision be collected to compare to both the topographic and bathymetric LiDAR points. The standard approach is to utilize RTK GNSS to collect check points in the floodplain and across the river channel. For more information on the validation methods for topographic LiDAR data, see section 6.4 from the current Guideline.

For the bathymetric portion of a TBL survey, horizontal and vertical accuracy requirements are defined using the IHO Orders as stated earlier (Section ‘Accuracy standard’). However, there are no set guidelines in terms of the number of check points required or their distribution for TBL surveys. As with all validation techniques, the check points used to validate the LiDAR points or surface models must be at least 3 times more accurate than the expected accuracy of the LiDAR.

It is recommended that for river TBL surveys, cross-sections be taken using survey grade GNSS positioning directly of the riverbed and where that is not possible using echo sounding techniques. These check points should be distributed to include examples of variable depths and conditions in the river.

In the case of coastal TBL surveys, direct measurements of the seabed elevation are possible using a GNSS antenna connected to an extendable pole, on the order of 4 m. This ensures direct measurements of the seabed for check points in shallow water. These check points must be taken near the time of the TBL survey, unless the seabed is comprised of exposed bedrock and is not expected to change over time. In deeper areas, echo sounding techniques can be used if the vertical accuracy of the data is suitable. As with topographic LiDAR, the expected vertical accuracy of TBL surveys will be influenced by the conditions of the seabed and will vary based on the presence, density and height of submerged aquatic vegetation (SAV). The TBL systems utilize green light which will not penetrate a dense canopy of SAVs, similar to topographic LiDAR and dense vegetation on land. Thus, when collecting check points on the seabed, it is also desirable to measure the presence and height of SAV.

D) Deliverables

Prior to the acquisition period, it is recommended that the contractor provide the technical authority with a project planning report containing details on team mobilization, GNSS and base stations data, water clarity monitoring approach, calibration reports and methods, as well as a detailed project schedule. The project planning could be subject to acceptance by the technical authority.

During the acquisition period, it is recommended that the contractor provide the technical authority with progress reports at regular intervals or following acquisition days.

Typical final deliverables for a TBL project may include the following items:

• Seamless topo-bathymetric classified LiDAR files. In some cases, the combination of topographic and bathymetric point cloud may not be required. The raw point cloud could also be required in certain cases.
• Seamless topo-bathymetric digital elevation models. Note that for some projects, the combination of topographic and bathymetric digital elevation models may not be required.
• Final project report, including but not limited to data acquisition, data processing and quality assurance details, position techniques and ground control. This report should contain sufficient detail to demonstrate that the specifications have been met for each pulse data collected.
• Final index showing coverage of the data with complete explanation of possible data gaps (e.g. depth extinction, water clarity, etc.).
• Orthophotos taken simultaneously to the acquisition.
• Metadata compliant with the ISO 19115:2003 standard.
• Flight lines trajectory
• Check points with horizontal and vertical accuracy estimates.
• Depending on the variable uses of the data, flight line normalized LiDAR intensity model (NIR returns on land and green laser returns of the river-seabed).

References

Canadian Hydrographic Services (2013). Standards for Hydrographic Surveys. Edition 2.

International Hydrographic Organization (2008). IHO Standards for Hydrographic Surveys (Special Publication No. 44). Pp. 1-28. Monaco: International Hydrographic Bureau.

Klemas, V. (2011) Beach Profiling and LIDAR Bathymetry: An Overview with case studies. Journal of Coastal research 27(6), 1019-1028.

Legleiter, C. J., & Kyriakidis, P. C. (2008). Spatial prediction of river channel topography by kriging. Earth Surface Processes and Landforms, 33, 155–161.

McGuigan, K., Webster, T., Collins, K. (2015). A Flood Risk Assessment of the LaHave River Watershed, Canada using GIS Techniques and an Unstructured Grid Combined River- Coastal Hydrodynamic Model. Journal of Marine Science and Engineering, 3(3) ISSN 2077-1312; doi:10.3390/jmse3031093.

Olabarrieta, M. and Warner, J.C. (2016) Inwave: The Infragravity Wave Driver of Coast. COAWST Workshop. Woods Hole 2016.

National Oceanic and Atmospheric Administration Technical Memorandum NOS CS 32 (2013). A procedure for developing an acceptance test for airborne bathymetric lidar data application to NOAA charts in shallow waters.

National Science Technology Council (2016). National Coastal Mapping Strategy 1.0: Coastal Lidar Elevation for a 3-D Nation (PDF, 4.6 mb).

Philpot, William (2019). Airborne Laser Hydrography II.

Saylam, K., Hupp, J.R., Averett, A.R., Gutelius, W.F., and Gelhar, B.W. (2018). Airborne lidar bathymetry: assessing quality assurance and quality control methods with Leica Chiroptera examples. International Journal of Remote Sensing. Vo. 30 No. 8; doi: 10.1080/01431161.2018.1430916 pp. 2518-2542.

Webster, T., McGuigan, K., Crowell, N., Collins, K. and MacDonald, C. (2016). Optimization of data collection and refinement of post-processing techniques for Maritime Canada’s first shallow water topographic-bathymetric lidar survey. Journal of Coastal Research. Special issue on Advances in Topo-Bathymetric lidar. Vol 76: doi:10.2112/SI76-004, pp. 31-43.

The guideline reflects specifications for collecting airborne LiDAR data and contracts identify specific requirements, enhancements or changes to the guidelines. Key contract items included are listed below.

Definitions

Include a reference list of terms used in contract with definition.

Vendor Information

Provide information about the vendor including legal name, operational centre, and contact information.

Scope

Project specific requirements would be specified in this section. They would include data requests such waveform data, increase quality assurance where the vendor requires more sampling, and higher pulse density. A schedule and detailed deliverables would be clearly listed.

Some specific project scope contract items to be consider by the contracting authority are as follows:

• DCAOI
• Pulse density required
• Review of methodology
• Deviation from instrumentation
• Vertical and horizontal accuracy
• Request for intermediate or waveform pulse data
• Data collection period
• Check points consideration including the number and who will conduct this
• Data ownership and usage
• Contingency plan
• Time period for data validation and verification on project deliverables
• Raw data requirements
• Additional post classification fields
• Any derivative products i.e. DEM, contours, hydro-flattening
• Tile sizes, file format and naming convention
• Waivers or adjustment for high relief areas, snow and vegetation conditions
• Accepting compress LAZ files
• Use of Virtual Reference Systems
• Number of classification check
• Conditions for rejecting the data e.g. data voids or density

Insurance

Insurance coverage for aircraft and automobile is required. This would include Commercial General Liability and Errors and Omission. Specific liability amount would be identified. The contract authority would be identified as rider on the insurance.

Confirm Insurance coverage and requested certificate
Confirm Workplace Safety Insurance Coverage

Safety

Workplace safety plan would be provided to ensure the safety in data collection particularly in remote locations. This would include identification of hazards, risk assessment, mitigation plan and required safety gears. Safety consideration must follow all federal and provincial government regulations. Examples include: vehicle roadside safety, hours of data collection and wildlife hazards.

Request vendor health and safety plan

Termination Clause

Identify the conditions where a vendor fails to perform duties or breaches contract. A termination clause would be included in the contract identifying conditions for termination.

Dispute Resolution

Should a dispute in the terms and condition of the requirements and contract occur, this section would cover a detailed process for dispute resolution including notification by vendor or contracting authority, mediation and actions that can be taken.

Wavier

Any exceptions or adjustments by the contracting authority with the terms and conditions of the contract and data collection requirements are to be put into writing to be valid.

Deliverable Review Period

The vendor will provide as part of its deliverable early in the project a plan with methodology for meeting the guideline with respect to data collection, processing and deliverables. A time period is required for the review of the processing and to inspect and accept the deliverables. Terms would be included and a completion certificate for accepted work would be submitted by the vendor.

Payment Terms

Financial payments for the project would consider the upfront costs for airborne operations and the deliverables. Milestone payment and percentage of payment would be structured in the contract. Any withholding would be identified.

Subsuppliers and Subcontractors

Any additional changes of subsuppliers and subcontractors would require written consent by the contract authority for any changes. Workplace safety and all insurance requirements must be enforced with subsuppliers and subcontractors.

Force Majeure

A description outlining a breach of contract due to events such as war, riot, fire, sabotage, national security and other events or circumstances which are not reasonably foreseen and which have not been caused by an act, omission or negligence and is beyond the control of the vendor or contracting authority is included and what conditions and notification would transpire.

Entire Agreement

The contract is to contain all conditions, requirements and specifications. No other terms, agreements or conditions shall be binding.

Performance of Services

A clause indicating time of essence and agreement to perform the required task with the agreed schedule. The contracting authority may terminate the contract upon a default of agreed upon conditions.

Permits

A clause identifying any permits for flying, ground access or other requirements to provide the service and products is required by the Vendor at its own cost.

Compliance with Law

A clause is placed to bind the vendor and any subcontractors, agents, contractors to comply to all applicable laws and regulations at all levels of government. Vendor also indemnify the contract authority from any negligence occurring on behalf of the Vendor, its agents, contractors and subcontractors.

Expediting

A clause may be placed to expedite work and delivery of product should it become apparent that the schedule will not be met.

Warranty

Any warranty with the deliverables shall be provided. Warranty will cover compliance with specifications, quantity and quality. The warranty shall have a period of time and period in which non-conformity would be rectified. The warranty clause may contain any potential liability arising from defected product.

Governing Laws

The laws in which the contract applies can be provincial, federal and/or international laws.

Indemnity

The contract would contain clauses with respect to indemnification arising from not performing the service or delivering the product or negligence in the operation of collecting the data or any other breach of contract.

Confidentiality

Information provided to the vendor by the contracting authority identified as confidential information shall protect the information from unauthorized use, disclosure and duplication of content.

Successors and Assignments

A clause would restrict a vendor from transferring the contract to another party without the consent of the contracting authority. The clause would stipulate the contracting authority’s rights to accept or deny the request.

Data Ownership

This designates the assignment of rights and ownership of the data to the contracting authority. The data ownership model determined between the contracting authority and vendor is on a project by project basis.