Skip to main content
NSF NEON | Open Data to Understand our Ecosystems logo

Main navigation

  • About Us
    • Overview
      • Spatial and Temporal Design
      • History
    • Vision and Management
    • Advisory Groups
      • Science, Technology & Education Advisory Committee
      • Technical Working Groups (TWGs)
    • FAQ
    • Contact Us
      • Field Offices
    • User Accounts
    • Staff

    About Us

  • Data & Samples
    • Data Portal
      • Explore Data Products
      • Data Availability Charts
      • Spatial Data & Maps
      • Document Library
      • API & GraphQL
      • Prototype Data
      • External Lab Data Ingest (restricted)
    • Samples & Specimens
      • Discover and Use NEON Samples
        • Sample Types
        • Sample Repositories
        • Sample Explorer
        • Megapit and Distributed Initial Characterization Soil Archives
        • Excess Samples
      • Sample Processing
      • Sample Quality
      • Taxonomic Lists
    • Collection Methods
      • Protocols & Standardized Methods
      • Airborne Remote Sensing
        • Flight Box Design
        • Flight Schedules and Coverage
        • Daily Flight Reports
          • AOP Flight Report Sign Up
        • Camera
        • Imaging Spectrometer
        • Lidar
      • Automated Instruments
        • Site Level Sampling Design
        • Sensor Collection Frequency
        • Instrumented Collection Types
          • Meteorology
          • Phenocams
          • Soil Sensors
          • Ground Water
          • Surface Water
      • Observational Sampling
        • Site Level Sampling Design
        • Sampling Schedules
        • Observation Types
          • Aquatic Organisms
            • Aquatic Microbes
            • Fish
            • Macroinvertebrates & Zooplankton
            • Periphyton, Phytoplankton, and Aquatic Plants
          • Terrestrial Organisms
            • Birds
            • Ground Beetles
            • Mosquitoes
            • Small Mammals
            • Soil Microbes
            • Terrestrial Plants
            • Ticks
          • Hydrology & Geomorphology
            • Discharge
            • Geomorphology
          • Biogeochemistry
          • DNA Sequences
          • Pathogens
          • Sediments
          • Soils
            • Soil Descriptions
    • Data Notifications
    • Data Guidelines and Policies
      • Acknowledging and Citing NEON
      • Publishing Research Outputs
      • Usage Policies
    • Data Management
      • Data Availability
      • Data Formats and Conventions
      • Data Processing
      • Data Quality
      • Data Product Revisions and Releases
        • Release 2021
        • Release 2022
        • Release 2023
      • NEON and Google
      • Externally Hosted Data

    Data & Samples

  • Field Sites
    • About Field Sites and Domains
    • Explore Field Sites
    • Site Management Data Product

    Field Sites

  • Impact
    • Observatory Blog
    • Case Studies
    • Spotlights
    • Papers & Publications
    • Newsroom
      • NEON in the News
      • Newsletter Archive
      • Newsletter Sign Up

    Impact

  • Resources
    • Getting Started with NEON Data & Resources
    • Documents and Communication Resources
      • Papers & Publications
      • Document Library
      • Outreach Materials
    • Code Hub
      • Code Resources Guidelines
      • Code Resources Submission
      • NEON's GitHub Organization Homepage
    • Learning Hub
      • Science Videos
      • Tutorials
      • Workshops & Courses
      • Teaching Modules
      • Faculty Mentoring Networks
      • Data Education Fellows
    • Research Support and Assignable Assets
      • Field Site Coordination
      • Letters of Support
      • Mobile Deployment Platforms
      • Permits and Permissions
      • AOP Flight Campaigns
      • Excess Samples
      • Assignable Assets FAQs
    • Funding Opportunities

    Resources

  • Get Involved
    • Advisory Groups
      • Science, Technology & Education Advisory Committee
      • Technical Working Groups
    • Upcoming Events
    • Past Events
    • NEON Ambassador Program
    • Collaborative Works
      • EFI-NEON Ecological Forecasting Challenge
      • NCAR-NEON-Community Collaborations
      • NEON Science Summit
      • NEON Great Lakes User Group
    • Community Engagement
    • Science Seminars and Data Skills Webinars
    • Work Opportunities
      • Careers
      • Seasonal Fieldwork
      • Postdoctoral Fellows
      • Internships
        • Intern Alumni
    • Partners

    Get Involved

  • My Account
  • Search

Search

Learning Hub

  • Science Videos
  • Tutorials
  • Workshops & Courses
  • Teaching Modules
  • Faculty Mentoring Networks
  • Data Education Fellows

Breadcrumb

  1. Resources
  2. Learning Hub
  3. Tutorials
  4. What is a CHM, DSM and DTM? About Gridded, Raster LiDAR Data

Tutorial

What is a CHM, DSM and DTM? About Gridded, Raster LiDAR Data

Authors: Leah A. Wasser

Last Updated: Oct 7, 2020

LiDAR Point Clouds

Each point in a LiDAR dataset has a X, Y, Z value and other attributes. The points may be located anywhere in space are not aligned within any particular grid.

Representative point cloud data. Source: National Ecological Observatory Network (NEON)

LiDAR point clouds are typically available in a .las file format. The .las file format is a compressed format that can better handle the millions of points that are often associated with LiDAR data point clouds.

Common LiDAR Data Products

The Digital Terrain Model (DTM) product represents the elevation of the ground, while the Digital Surface Model (DSM) product represents the elevation of the tallest surfaces at that point. Imagine draping a sheet over the canopy of a forest, the Digital Elevation Model (DEM) contours with the heights of the trees where there are trees but the elevation of the ground when there is a clearing in the forest.

  • DSM and DTM

The Canopy height model represents the difference between a Digital Terrain Model and a Digital Surface Model (DSM - DTM = CHM) and gives you the height of the objects (in a forest, the trees) that are on the surface of the earth. *DSM, DTM and CHM

Free Point Cloud Viewers for LiDAR Point Clouds

  • Fusion: US Forest Service RSAC
  • Cloud compare
  • Plas.io website

For more on viewing LiDAR point cloud data using the Plas.io online viewer, see our tutorial Plas.io: Free Online Data Viz to Explore LiDAR Data.

Check out our Structural Diversity tutorial for another useful LiDAR point cloud viewer available through RStudio, Calculating Forest Structural Diversity Metrics from NEON LiDAR Data

3D Models of NEON Site: SJER (San Juaquin Experimental Range)

Click on the images to view interactive 3D models of San Juaquin Experimental Range site.

DTM DSM CHM
<figcaption> 3D models of a LiDAR-derived Digital Terrain Model (DTM;top), 
 Digital Surface Model (DSM; middle), and Canopy Height Model (CHM; bottom).
</figcaption>

Gridded, or Raster, LiDAR Data Products

LiDAR data products are most often worked within a gridded or raster data format. A raster file is a regular grid of cells, all of which are the same size.

A few notes about rasters:

  • Each cell is called a pixel.
  • And each pixel represents an area on the ground.
  • The resolution of the raster represents the area that each pixel represents on the ground. So, for instance if the raster is 1 m resolution, that simple means that each pixel represents a 1m by 1m area on the ground.
Raster or “gridded” data are stored as a grid of values which are rendered on a map as pixels. Each pixel value represents an area on the Earth’s surface. Source: National Ecological Observatory Network (NEON)

Raster data can have attributes associated with them as well. For instance in a LiDAR-derived digital elevation model (DEM), each cell might represent a particular elevation value. In a LIDAR-derived intensity image, each cell represents a LIDAR intensity value.

LiDAR Related Metadata

In short, when you go to download LiDAR data the first question you should ask is what format the data are in. Are you downloading point clouds that you might have to process? Or rasters that are already processed for you. How do you know?

  1. Check out the metadata!
  2. Look at the file format - if you are downloading a .las file, then you are getting points. If it is .tif, then it is a post-processing raster file.

Create Useful Data Products from LiDAR Data

Classify LiDAR Point Clouds

LiDAR data points are vector data. LiDAR point clouds are useful because they tell us something about the heights of objects on the ground. However, how do we know whether a point reflected off of a tree, a bird, a building or the ground? In order to develop products like elevation models and canopy height models, we need to classify individual LiDAR points. We might classify LiDAR points into classes including:

  • Ground
  • Vegetation
  • Buildings

LiDAR point cloud classification is often already done when you download LiDAR point clouds but just know that it’s not to be taken for granted! Programs such as lastools, fusion and terrascan are often used to perform this classification. Once the points are classified, they can be used to derive various LiDAR data products.

Create A Raster From LiDAR Point Clouds

There are different ways to create a raster from LiDAR point clouds.

Point to Raster Methods - Basic Gridding

Let's look one of the most basic ways to create a raster file points - basic gridding. When you perform a gridding algorithm, you are simply calculating a value, using point data, for each pixels in your raster dataset.

  1. To begin, a grid is placed on top of the LiDAR data in space. Each cell in the grid has the same spatial dimensions. These dimensions represent that particular area on the ground. If we want to derive a 1 m resolution raster from the LiDAR data, we overlay a 1m by 1m grid over the LiDAR data points.
  2. Within each 1m x 1m cell, we calculate a value to be applied to that cell, using the LiDAR points found within that cell. The simplest method of doing this is to take the max, min or mean height value of all lidar points found within the 1m cell. If we use this approach, we might have cells in the raster that don't contains any lidar points. These cells will have a "no data" value if we process our raster in this way.
Animation showing the general process of taking LiDAR point clouds and converting them to a raster format. Source: Tristan Goulden, National Ecological Observatory Network (NEON)

Point to Raster Methods - Interpolation

A different approach is to interpolate the value for each cell.

  1. In this approach we still start with placing the grid on top of the LiDAR data in space.
  2. Interpolation considers the values of points outside of the cell in addition to points within the cell to calculate a value. Interpolation is useful because it can provide us with some ability to predict or calculate cell values in areas where there are no data (or no points). And to quantify the error associated with those predictions which is useful to know, if you are doing research.

For learning more on how to work with LiDAR data, consider going through the Introduction to Working With Raster Data in R tutorial series.

Questions?

If you have questions or comments on this content, please contact us.

Contact Us
NEON Logo

Follow Us:

Join Our Newsletter

Get updates on events, opportunities, and how NEON is being used today.

Subscribe Now

Footer

  • My Account
  • About Us
  • Newsroom
  • Contact Us
  • Terms & Conditions
  • Careers

Copyright © Battelle, 2019-2020

The National Ecological Observatory Network is a major facility fully funded by the National Science Foundation.

Any opinions, findings and conclusions or recommendations expressed in this material do not necessarily reflect the views of the National Science Foundation.