Skip to main content

DSM DTM DEM CHM FHM

In Remote Sensing and GIS, DSM, DTM, DEM, CHM, and FHM are elevation-based digital surface representations derived from LiDAR, photogrammetry, stereo satellite imagery, or radar (e.g., InSAR). They are raster-based 3D models where each pixel stores an elevation (Z-value) relative to a vertical datum (e.g., Mean Sea Level).

DEM – Digital Elevation Model

Concept

A Digital Elevation Model (DEM) is a generic term for a raster grid representing elevation values of the Earth's surface.

  • It represents a continuous field surface

  • Each pixel contains a Z-value (elevation)

  • It may represent bare earth or surface, depending on data source

Terminologies

  • Raster resolution – spatial pixel size (e.g., 10 m, 30 m)

  • Vertical accuracy – elevation precision (± m)

  • Elevation datum – reference level (e.g., MSL, WGS84 ellipsoid)

  • Grid-based terrain model

  • Digital surface representation

Important Clarification

  • DEM is often used as an umbrella term

  • In many datasets, DEM ≈ DTM (bare earth)

  • Technically, DEM is the broader concept

Applications

  • Slope, aspect, curvature analysis

  • Watershed delineation

  • Terrain visualization

  • Contour generation

DSM – Digital Surface Model

Concept

A Digital Surface Model (DSM) represents the topmost reflective surface, including:

  • Buildings

  • Trees

  • Vegetation

  • Infrastructure

  • Ground

It captures the first return (top hit) in LiDAR data.

Terminologies

  • First return LiDAR

  • Top-of-canopy elevation

  • Surface elevation

  • Object-inclusive elevation model

Applications

  • 3D city modelling

  • Urban morphology studies

  • Shadow and solar radiation analysis

  • Telecommunication planning

DTM – Digital Terrain Model

Concept

A Digital Terrain Model (DTM) represents the bare-earth terrain, excluding:

  • Buildings

  • Trees

  • Vegetation

  • Man-made structures

It is generated using ground-classified LiDAR points or filtering algorithms.

Terminologies

  • Ground return (last return)

  • Bare-earth extraction

  • Terrain filtering

  • Morphological filtering algorithms

Applications

  • Hydrological modelling

  • Flood simulation

  • Slope stability analysis

  • Landslide susceptibility mapping

  • Geomorphological studies

CHM – Canopy Height Model

Concept

A Canopy Height Model (CHM) represents the height of vegetation or objects above ground level.

Mathematical Expression:

It removes terrain elevation and isolates object height.

Terminologies

  • Normalized height model

  • Vegetation height extraction

  • Vertical canopy structure

  • Relative height model

Applications

  • Forest biomass estimation

  • Carbon stock assessment

  • Precision forestry

  • Habitat structure analysis

FHM – Forest Height Model

Concept

A Forest Height Model (FHM) is a specialized version of CHM focusing specifically on:

  • Forest canopy height

  • Stand-level tree height variation

  • Forest structural parameters

Applications

  • Forest inventory

  • Growth monitoring

  • Timber volume estimation

  • Ecological modelling

Differences 

ModelRepresentsIncludes Objects?Data BasisPrimary Use
DEMGeneral elevation surfaceDependsAny elevation datasetGeneral terrain analysis
DSMTop reflective surfaceYesFirst returnUrban & surface modelling
DTMBare-earth terrainNoGround returnsHydrology & geomorphology
CHMHeight above groundOnly object heightDSM − DTMVegetation studies
FHMForest canopy heightVegetation onlyFiltered CHMForestry


Comments

Post a Comment

Popular posts from this blog

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...
Post a Comment
Read more

Platforms in Remote Sensing

In remote sensing, a platform is the physical structure or vehicle that carries a sensor (camera, scanner, radar, etc.) to observe and collect information about the Earth's surface. Platforms are classified mainly by their altitude and mobility : Ground-Based Platforms Definition : Sensors mounted on the Earth's surface or very close to it. Examples : Tripods, towers, ground vehicles, handheld instruments. Applications : Calibration and validation of satellite data Detailed local studies (e.g., soil properties, vegetation health, air quality) Strength : High spatial detail but limited coverage. Airborne Platforms Definition : Sensors carried by aircraft, balloons, or drones (UAVs). Altitude : A few hundred meters to ~20 km. Examples : Airplanes with multispectral scanners UAVs with high-resolution cameras or LiDAR High-altitude balloons (stratospheric platforms) Applications : Local-to-regional mapping ...
Post a Comment
Read more

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...
Post a Comment
Read more

Model GIS object attribute entity

These concepts explain different ways of organizing, storing, and representing geographic information in a Geographic Information System (GIS) . They include database design models (ER model), data structure models (Object and Attribute models), and spatio-temporal representations that integrate location, entities, and time . Together, they help GIS manage both spatial data (where things are) and descriptive information (what they are and how they change over time) . 1. Object-Based Model (Object-Oriented Data Model) The Object-Based Model treats geographic features as independent objects that combine spatial geometry and descriptive attributes within a single structure. Core Concept: Each geographic feature (such as a building, road, or river ) is represented as a self-contained object that stores both: Geometry – location and shape (point, line, polygon) Attributes – descriptive properties (name, type, length, capacity) Unlike older georelational models , which stored spatial ...
Post a Comment
Read more

GIS data continuous discrete ordinal interval ratio

In Geographic Information Systems (GIS) , data is categorized based on its nature (discrete or continuous) and its measurement scale (nominal, ordinal, interval, or ratio). These distinctions influence how the data is collected, analyzed, and visualized. Let's break down these categories with concepts, terminologies, and examples: 1. Discrete Data Discrete data is obtained by counting distinct items or entities. Values are finite and cannot be infinitely subdivided. Characteristics : Represent distinct objects or occurrences. Commonly represented as vector data (points, lines, polygons). Values within a range are whole numbers or categories. Examples : Number of People : Counting individuals on a train or in a hospital. Building Types : Categorizing buildings as residential, commercial, or industrial. Tree Count : Number of trees in a specific area. 2. Continuous Data Continuous data is obtained by measuring phenomena that can take any value within a range...
Post a Comment
Read more