Skip to main content

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

    • Disaster assessment (floods, landslides, forest fires)

    • Precision agriculture, urban planning

  • Strength: Flexible deployment, high resolution, cloud-free data (depending on altitude).

Spaceborne Platforms (Satellites)

  • Definition: Satellites carrying remote sensing sensors orbiting the Earth.

  • Altitude: Typically 200 km – 36,000 km.

  • Types of Orbits / Platforms:

    1. Low Earth Orbit (LEO)

      • Altitude: ~200–1000 km

      • Examples: Landsat, Sentinel, SPOT, IRS

      • Application: Land cover mapping, agriculture, disaster monitoring

    2. Medium Earth Orbit (MEO)

      • Altitude: ~1000–35,000 km

      • Example: Navigation satellites (GPS, GLONASS)

      • Application: Positioning and navigation

    3. Geostationary Orbit (GEO)

      • Altitude: ~36,000 km, orbit period matches Earth's rotation

      • Example: GOES, INSAT, Meteosat

      • Application: Weather and climate monitoring, continuous observation

  • Strength: Large area coverage, repetitive observations, long-term monitoring.


  • Ground-based → local, detailed, used for calibration.

  • Airborne → regional, flexible, high-resolution (e.g., UAV, aircraft).

  • Spaceborne → global/regional, systematic, used for large-scale monitoring.

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

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

Remote Sensing Technology

Remote sensing is a rapidly evolving geospatial technology used to collect information about the Earth's surface and atmosphere without direct physical contact . It involves detecting and measuring electromagnetic radiation (EMR) reflected or emitted from objects using sensors mounted on satellites, aircraft, or drones. Remote sensing systems are fundamentally classified based on (1) the energy source used for illumination and (2) the region of the electromagnetic spectrum utilized for sensing . 1. Types of Remote Sensing Based on Energy Source Remote sensing systems are commonly categorized according to whether the sensor generates its own energy or relies on naturally available radiation . Passive Remote Sensing Principle: Passive remote sensing relies on natural sources of electromagnetic energy , primarily solar radiation reflected from the Earth's surface or thermal radiation emitted by objects. Operation: Most passive sensors operate during daylight when sunlight is av...
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