Skip to main content

Datums Geodetic Vertical Global Local

A datum is a mathematical model that defines how the Earth's shape is represented for mapping and spatial data analysis. It serves as the foundation for geographic coordinate systems (GCS) and projected coordinate systems (PCS). Datums are crucial for accurate positioning, navigation, and geographic measurements.

1. Types of Datums in GIS

Datums are categorized into:

  1. Geodetic Datums (Horizontal Datums) – Define positions on the Earth's surface using latitude and longitude.
  2. Vertical Datums – Define elevations or depths relative to a reference surface (e.g., sea level).
  3. Global vs. Local Datums – Distinguish between datums that are globally applicable versus those optimized for a specific region.

2. Geodetic Datum (Horizontal Datum)

A geodetic datum defines a reference system for measuring positions (latitude, longitude) on the Earth's surface. It accounts for the Earth's ellipsoidal shape and is crucial for GPS and mapping applications.

Key Components of a Geodetic Datum

  1. Ellipsoid (Spheroid): An idealized mathematical model approximating the Earth's shape.
    • Example: WGS84, GRS80, Clarke 1866.
  2. Reference Point: A fixed point from which measurements originate.
  3. Coordinate System: Specifies how latitude and longitude are measured.

Examples of Geodetic Datums

  • WGS84 (World Geodetic System 1984) → Used by GPS and Google Maps.
  • NAD83 (North American Datum 1983) → Used in North America.
  • ETRS89 (European Terrestrial Reference System 1989) → Used in Europe.

Practical Use Case

  • When using GPS, your device references WGS84, ensuring global consistency in navigation.
  • A local GIS project in India may use Everest 1830 for better accuracy.

3. Vertical Datum

A vertical datum defines the reference surface for measuring elevation or depth. It is essential for terrain analysis, flood modeling, and coastal studies.

Types of Vertical Datums

  1. Tidal Datum: Based on sea level (e.g., Mean Sea Level - MSL).
  2. Geoid-Based Datum: Uses the geoid, a model of the Earth's gravity field (e.g., EGM96, NAVD88).
  3. Ellipsoidal Datum: Uses the reference ellipsoid for height measurements (e.g., WGS84 ellipsoidal height).

Examples of Vertical Datums

  • EGM96 (Earth Gravitational Model 1996) → Used globally.
  • NAVD88 (North American Vertical Datum 1988) → Used in the USA.
  • MSL (Mean Sea Level) → Used as a general reference for elevations.

Practical Use Case

  • Elevation data from NASA's SRTM (Shuttle Radar Topography Mission) is referenced to the EGM96 geoid.
  • Coastal flood risk mapping relies on Mean Sea Level (MSL) as a reference.

4. Global vs. Local Datums

Global Datums

A global datum provides a reference system that fits the entire Earth. It is optimized for worldwide accuracy but may introduce small errors at a local scale.

  • Example: WGS84 (World Geodetic System 1984) – Used for GPS globally.

Local Datums

A local datum is optimized for a specific country or region, providing higher accuracy within that area but not globally.

  • Example: Everest 1830 – Used in India.

Comparison Table: Global vs. Local Datums

FeatureGlobal DatumLocal Datum
CoverageWorldwideSpecific region
AccuracyGood globally, but minor local errorsHigh accuracy in a specific area
ExampleWGS84 (Global)NAD83 (North America), Everest 1830 (India)

Practical Example

  • Google Earth & GPS use WGS84 for global consistency.
  • A cadastral survey in Kerala, India may use Everest 1830 for precise local mapping.

5. Importance of Choosing the Right Datum in GIS

Selecting the correct datum is crucial to avoid coordinate mismatches and positional errors in GIS.

  • If a dataset in WGS84 is overlaid with data in NAD83, there might be offsets of several meters.
  • Elevation data based on ellipsoidal height may differ significantly from a geoid-based height.
  • Geodetic datums define horizontal positioning (latitude/longitude).
  • Vertical datums define elevation or depth.
  • Global datums (e.g., WGS84) are suitable for worldwide applications, while local datums (e.g., NAD83, Everest 1830) provide higher accuracy in specific regions.

Comments

Popular posts from this blog

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?"  Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination.  Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com

Energy Interaction with Atmosphere and Earth Surface

In Remote Sensing , satellites record electromagnetic radiation (EMR) that is reflected or emitted from the Earth. Before reaching the sensor, radiation interacts with: The Atmosphere The Earth's Surface These interactions control how satellite images look and how we interpret them. I. Interaction of EMR with the Atmosphere When solar radiation travels from the Sun to the Earth, four main processes occur: 1. Absorption Definition: Absorption occurs when atmospheric gases absorb radiation at specific wavelengths and convert it into heat. Main absorbing gases: Ozone (O₃) → absorbs Ultraviolet (UV) Carbon dioxide (CO₂) → absorbs Thermal Infrared Water vapour (H₂O) → absorbs Infrared Concept: Atmospheric Windows These are wavelength regions where absorption is very low, allowing radiation to pass through the atmosphere. Remote sensing depends on these windows. For example, satellites like Landsat 8 use visible, near-infrared, and thermal bands located in atmospheric windows. 2. Trans...

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes .  Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc.  Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...