Thermal Remote Sensing

Thermal remote sensing is a technique that measures the heat emitted by objects, often referred to as their radiant temperature. Unlike traditional photography, which relies on reflected sunlight, thermal remote sensing captures the infrared radiation emitted by objects based on their temperature.

Key Concepts and Terminology

Electromagnetic Spectrum: The range of all types of electromagnetic radiation, from radio waves to gamma rays. Thermal remote sensing primarily operates in the thermal infrared region of the spectrum.
Radiant Temperature: The temperature of an object as measured by its emitted thermal radiation. It may differ from the actual (kinetic) temperature due to factors like emissivity.
Emissivity: The ratio of an object's thermal radiation to that of a blackbody at the same temperature. A blackbody emits the maximum possible thermal radiation.
Thermal Infrared (TIR): A region of the electromagnetic spectrum where objects emit most of their thermal radiation. The primary bands used for thermal remote sensing are 3-5 µm and 8-14 µm.
Thermal Radiometer: A sensor designed to measure the radiant temperature of a specific point or area.
Thermal Imagery: Images created by capturing and processing thermal radiation. These images often appear in grayscale or pseudo-color, where warmer objects are represented by brighter or different colors.

Applications of Thermal Remote Sensing

Geology: Detecting volcanic activity, mapping mineral deposits, and monitoring geothermal areas.
Environmental Monitoring: Tracking wildfires, studying urban heat islands, and assessing water quality.
Agriculture: Monitoring crop health, detecting irrigation problems, and estimating crop yields.
Meteorology: Predicting weather patterns, tracking hurricanes, and studying ocean currents.
Security: Detecting concealed objects, identifying potential threats, and monitoring border security.

Advantages of Thermal Remote Sensing

Day and Night Capability: Unlike traditional photography, thermal remote sensing can operate 24/7, regardless of lighting conditions.
Non-Contact Measurement: It allows for measuring temperatures without physically touching the object.
Real-Time Monitoring: It can provide immediate information about temperature variations.
Wide Range of Applications: It has applications in various fields, from geology to meteorology.

Important Satellites for Thermal Remote Sensing

Earth Observation Satellites

Landsat Series: Operated by NASA and the USGS, Landsat satellites have a long history of providing multispectral and thermal infrared imagery for Earth observation.
MODIS (Moderate Resolution Imaging Spectroradiometer): A sensor aboard NASA's Terra and Aqua satellites, MODIS provides global coverage at moderate spatial resolution, including thermal infrared bands.
Sentinel-3: A European Space Agency satellite mission designed to provide a global ocean and land monitoring service, including thermal infrared data.
NOAA GOES Series: Geostationary Operational Environmental Satellites operated by the National Oceanic and Atmospheric Administration (NOAA) provide high-frequency thermal infrared imagery for weather forecasting and environmental monitoring.
Suomi NPP: A joint NASA-NOAA satellite carrying the Visible Infrared Imaging Radiometer Suite (VIIRS), which includes thermal infrared bands for environmental monitoring.

Other Notable Satellites

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer): A sensor aboard NASA's Terra satellite, ASTER provides high-resolution thermal infrared imagery for geological and environmental applications.
SMAP (Soil Moisture Active Passive): A NASA satellite mission designed to measure soil moisture globally using both active and passive microwave sensors, including thermal infrared bands.
Thermal Infrared Sensor (TIRS) on Landsat 8: A thermal infrared sensor designed to improve the accuracy and sensitivity of temperature measurements compared to previous Landsat missions.

Comments

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification: - You have prior knowledge about the c...

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...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

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...

Vineesh V, Geography

Search This Blog