Types of Remote Sensing

Remote Sensing means collecting information about the Earth's surface without touching it, usually using satellites, aircraft, or drones.

There are different types of remote sensing based on the energy source and the wavelength region used.

🛰️ 1. Active Remote Sensing

📘 Concept:

In active remote sensing, the sensor sends out its own energy (like a signal or pulse) to the Earth's surface.
The sensor then records the reflected or backscattered energy that comes back from the surface.

⚙️ Key Terminology:

Transmitter: sends energy (like a radar pulse or laser beam).
Receiver: detects the energy that bounces back.
Backscatter: energy that is reflected back to the sensor.

📊 Examples of Active Sensors:

RADAR (Radio Detection and Ranging): Uses microwave signals to detect surface roughness, soil moisture, or ocean waves.
LiDAR (Light Detection and Ranging): Uses laser light (near-infrared) to measure elevation, vegetation height, or buildings.

📍 Applications:

Mapping terrain and elevation (LiDAR).
Monitoring floods, forest biomass, and surface deformation (RADAR).
Measuring ice thickness or ocean waves.

💡 Example Satellite Missions:

Sentinel-1 (ESA) – C-band SAR (Synthetic Aperture Radar).
RISAT (India) – Radar Imaging Satellite.
ICESat-2 (NASA) – Laser altimeter (LiDAR).

☀️ 2. Passive Remote Sensing

📘 Concept:

In passive remote sensing, the sensor does not send any energy.
It detects natural energy (mostly sunlight) that is reflected or emitted by objects on Earth.

⚙️ Key Terminology:

Emitted radiation: energy given off naturally by an object (like thermal energy).
Reflected radiation: sunlight that bounces off the Earth's surface.
Spectral bands: ranges of wavelengths (like visible, infrared, etc.) recorded by sensors.

🌈 Examples of Passive Sensors:

Optical sensors on satellites like Landsat, Sentinel-2, and MODIS.
They detect visible, near-infrared, and shortwave infrared light.

📍 Applications:

Mapping vegetation (using NDVI).
Studying land use/land cover, water bodies, and soil.
Detecting forest fires or droughts.

💡 Example Satellite Missions:

Landsat series (NASA/USGS)
Sentinel-2 (ESA)
Resourcesat and Cartosat (ISRO)

🌡️ 3. Thermal Remote Sensing

📘 Concept:

This technique measures natural thermal infrared radiation emitted by the Earth's surface.
Every object with a temperature above absolute zero (0 K) emits infrared radiation.
Thermal sensors record this energy to estimate surface temperature.

⚙️ Key Terminology:

Thermal infrared region: wavelength between 3 µm and 14 µm.
Brightness temperature: temperature recorded by the sensor.
Thermal emissivity: ability of a surface to emit heat energy.

🌡️ Examples of Thermal Sensors:

Landsat Thermal Infrared Sensor (TIRS)
MODIS (Moderate Resolution Imaging Spectroradiometer)
ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)

📍 Applications:

Urban heat island studies.
Volcano and forest fire monitoring.
Soil moisture and evapotranspiration estimation.
Detecting ocean currents and water temperature.

📡 4. Microwave (RADAR) Remote Sensing

📘 Concept:

Uses microwave energy (longer wavelengths than visible light).
Can penetrate clouds, fog, and even vegetation, and works day and night.
Often used in active mode (RADAR), but can also be passive (radiometer).

⚙️ Key Terminology:

SAR (Synthetic Aperture Radar): advanced radar that produces high-resolution images.
Polarization: direction of the radar wave (VV, VH, HH, HV).
Backscatter coefficient (σ⁰): measure of the reflected microwave energy.

🌊 Examples of Microwave Sensors:

Active: Sentinel-1 (C-band SAR), RISAT (C-band SAR), TerraSAR-X.
Passive: SMOS (Soil Moisture and Ocean Salinity), AMSR-E.

📍 Applications:

Monitoring floods, landslides, and deforestation.
Measuring soil moisture and sea ice thickness.
Detecting ground deformation and earthquakes (InSAR).

Type	Energy Source	Wavelength Region	Works at Night	Cloud Penetration	Example Sensors
Active	Artificial (sensor-generated)	Microwave, Laser	✅	✅	Sentinel-1 (RADAR), LiDAR
Passive	Sunlight/Natural	Visible, Infrared	❌	❌	Landsat, Sentinel-2
Thermal	Natural (Earth-emitted heat)	Thermal Infrared (3–14 µm)	✅	❌	Landsat TIRS, MODIS
Microwave (RADAR)	Usually Artificial	Microwave (1 mm–1 m)	✅	✅	Sentinel-1, RISAT, TerraSAR-X

Comments

How to find drugs against the Corona. Covid 19

FOR SCIENTISTS (and others interested): How to find drugs against the coronavirus: First clues on how we can beat COVID-19. This shows the many ways we can interfere with its replication cycle by repurposing existing drugs - summarized in today's Science journal. LINK TO ARTICLE: https://science.sciencemag.org/content/367/6485/1412 .... Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://g.page/vineeshvc

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to 1832 , when Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in 1854 , when John Snow inv...

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...

Geographic phenomena fields objects boundaries.

In geography, geographic phenomena refer to features or processes that can be observed and studied on Earth's surface. These phenomena can be classified into three main categories: fields , objects , and boundaries . Each category has distinct characteristics, representations, and applications in Geographic Information Systems (GIS). 1. Fields A field represents continuous, spatially varying data where a value is present at every location within the study area. It describes conditions that exist across a geographic area. Characteristics : Continuity : Fields have no discrete boundaries; the data is continuous. Gradual Variability : The values of a field change gradually across space. Representation : Typically modeled using raster data in GIS, where a grid structure assigns a value (e.g., temperature or elevation) to each cell. Examples : Temperature Map : Shows temperature variation across a region. Rainfall Distribution : Displays rainfall levels over a large g...

Vineesh V, Geography

Search This Blog