Introduction To Remote Sensing

Stages In Remote Sensing

Remote sensing is the science and art of obtaining information about objects, areas, or phenomena through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under study. The process involves several key stages:

1. Energy Source/Target:

• Source: The most common energy source is the Sun, providing electromagnetic radiation (EMR). Artificial sources like radar (using emitted microwave energy) or lasers are also used.
• Target: The object or phenomenon being observed (e.g., Earth's surface, atmosphere, crops, buildings).

2. EMR Propagation Through the Atmosphere:

• As EMR travels from the source to the target, it interacts with the atmosphere.
• Atmospheric constituents (gases, aerosols, water vapor) can absorb or scatter the radiation, affecting the signal that reaches the target.

3. Energy Interaction with Target:

• When EMR reaches the target, it can be reflected, absorbed, or transmitted.
• The way a target interacts with EMR depends on its physical properties (material, surface roughness, moisture content) and the wavelength of the EMR.
• This interaction determines the spectral signature of the target.

4. Data Acquisition (Recording):

• Sensor: A sensor on a remote platform (satellite, aircraft, drone) records the reflected or emitted EMR from the target.
• Platforms: Satellites, aircraft, balloons, drones.
• Types of Sensors: Passive sensors (detect naturally available EMR like sunlight) and active sensors (emit their own energy and detect the return signal, like radar).

5. Data Transmission, Reception, and Processing:

• Transmission: The data acquired by the sensor is transmitted to a ground station.
• Reception: Ground stations receive the raw data.
• Processing: Raw data is processed to correct for geometric and radiometric errors, enhance image quality, and convert it into a usable format.

6. Data Analysis and Interpretation:

• Human or Machine: Trained analysts or specialized software analyze the processed data.
• Techniques: Visual interpretation (using elements like tone, texture, pattern) and digital image processing (using algorithms for classification, feature extraction, change detection).

7. Information Product Dissemination:

• Output: The interpreted information is presented in a usable format, such as maps, reports, statistics, or digital datasets.
• Application: Used for decision-making in various fields like environmental management, agriculture, urban planning, and disaster response.

Sensors

Sensors are the devices used in remote sensing to detect and record electromagnetic radiation (EMR) reflected or emitted from the Earth's surface. They are mounted on platforms like satellites, aircraft, or drones.

Multispectral Scanners

Description: These are passive sensors that simultaneously collect radiation in several (multiple) discrete and relatively narrow wavelength bands (spectral bands) of the electromagnetic spectrum. They capture the spectral signature of features.

How they Work: As the platform moves over the Earth, the scanner collects incoming radiation within each defined spectral band. This data is typically recorded digitally.

Types:

• Whiskbroom Scanners: Scan the Earth's surface by using a mirror that oscillates back and forth (sweeps) perpendicular to the direction of the platform's motion. Each detector records radiation from one point at a time.
• Pushbroom Scanners: Use an array of detectors, with each detector recording radiation from a single instantaneous field of view (IFOV). The entire array is electronically 'pushed' across the scene as the platform moves, building up the image line by line.

Advantages: Ability to capture spectral information, allowing for the identification and differentiation of various features (e.g., healthy vs. stressed vegetation, different soil types).

Whiskbroom Scanners

Description: A type of multispectral or hyperspectral scanner that uses a mirror to sweep the sensor's field of view across the scene perpendicular to the platform's direction of travel. As the platform moves forward, it builds up the image line by line, but each line is built point by point by the scanning mirror.

Mechanism: A single detector (or a small group) records the radiation from each point on the ground as the mirror sweeps. This is like scanning an image with a single line of pixels, one pixel at a time.

Examples: Landsat MSS (MultiSpectral Scanner) was an early example.

Pushbroom Scanners

Description: A type of scanner that uses an array of detectors, where each detector records radiation from a single instantaneous field of view (IFOV). The entire array is aligned perpendicular to the direction of flight, and the sensor builds the image line by line as the platform moves forward.

Mechanism: Imagine a broom 'pushing' across the scene. Each 'bristle' (detector) represents a pixel in a single line of the image. As the platform moves forward, a new line of pixels is captured electronically.

Advantages: More efficient and faster data collection compared to whiskbroom scanners; often have higher spatial resolution and better radiometric accuracy.

Examples: Modern satellites like the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and many high-resolution commercial satellites use pushbroom technology.

Resolving Powers Of The Satellites

The 'resolving power' of a satellite or its sensor refers to its ability to distinguish between closely spaced objects on the ground. This is a critical characteristic that determines the level of detail visible in satellite imagery. It is primarily determined by the sensor's design and the satellite's altitude, but more importantly, by the concept of "resolution" itself.

Understanding Resolution in Remote Sensing:

Resolution in remote sensing is not a single measure but encompasses several types, each describing a different aspect of the sensor's capability:

• Spatial Resolution: The size of the smallest object that can be resolved or distinguished on the ground.
• Spectral Resolution: The ability of a sensor to distinguish between different wavelengths of the electromagnetic spectrum.
• Radiometric Resolution: The ability of a sensor to distinguish between different levels of signal intensity (brightness or radiance).
• Temporal Resolution: The frequency with which a satellite passes over the same area on Earth.

When people refer to the "resolving power" of a satellite, they most commonly mean its spatial resolution, as this directly relates to the level of detail in the imagery.

Factors Influencing Spatial Resolution:

• Sensor Design: The size of the instantaneous field of view (IFOV) or the pixel size on the ground.
• Altitude of the Satellite: Generally, lower altitudes allow for higher spatial resolution (smaller pixel sizes), while higher altitudes result in lower spatial resolution (larger pixel sizes).
• Focal Length of the Optics: Similar to aerial photography, the focal length plays a role.

Examples:

• High Resolution: Satellites like WorldView-3 have spatial resolutions as fine as 0.3 meters, capable of distinguishing individual cars or even street furniture.
• Medium Resolution: Satellites like Landsat have resolutions of 30 meters, useful for mapping land cover and monitoring changes over larger areas.
• Low Resolution: Satellites like MODIS have resolutions of 250 meters to 1 kilometer, used for monitoring large-scale phenomena like vegetation health, cloud cover, and ocean temperatures.

Sensor Resolutions

Sensor resolution refers to the level of detail captured by a remote sensing instrument. There are four main types of resolution:

Spatial Resolution

Definition: The size of the smallest feature that can be detected or distinguished on the ground by a sensor. It is usually expressed as the ground dimension of one pixel in the image.

Examples:

• Very High Resolution (VHR): Less than 1 meter (e.g., 0.3 m for WorldView-3). Can see individual objects like cars, people, small buildings.
• High Resolution: 1-10 meters (e.g., 5 m for SPOT). Can distinguish roads, buildings, agricultural fields.
• Medium Resolution: 10-100 meters (e.g., 30 m for Landsat). Useful for land cover mapping, vegetation studies.
• Low Resolution: Greater than 100 meters (e.g., 250 m to 1 km for MODIS). Used for broad-scale studies like weather patterns, global vegetation monitoring.

Importance: Higher spatial resolution provides more detailed imagery, crucial for tasks like urban mapping, infrastructure monitoring, and precision agriculture. Lower spatial resolution is suitable for regional or global monitoring.

Spectral Resolution

Definition: The ability of a sensor to distinguish between different features based on their spectral reflectance or emission characteristics. It refers to the number, width, and location of the wavelength intervals (bands) in which the sensor collects data.

Types:

• Panchromatic: Collects data in a single, broad band (usually visible light). Gives a black and white image.
• Multispectral: Collects data in several (typically 3-15) relatively broad, discrete wavelength bands (e.g., visible, near-infrared, shortwave infrared). Landsat satellites are classic examples.
• Hyperspectral: Collects data in hundreds of very narrow, contiguous wavelength bands. This provides a very detailed spectral signature, allowing for precise identification of materials (e.g., specific minerals, crop types, water constituents).

Importance: Higher spectral resolution allows for better differentiation between features that have similar tones but different spectral properties, leading to more accurate classification and analysis.

Radiometric Resolution

Definition: The ability of a sensor to distinguish between different levels of signal intensity or brightness. It refers to the number of gray levels (or quantization levels) into which the recorded signal can be divided.

Examples:

• 6-bit sensors: Record 2^6 = 64 different levels of brightness.
• 8-bit sensors: Record 2^8 = 256 different levels of brightness (0-255).
• 11-bit, 12-bit, 16-bit sensors: Record a much larger range of brightness values (2^11=2048, 2^12=4096, 2^16=65,536 levels).

Importance: Higher radiometric resolution allows for the detection of subtle variations in radiance, which can be important for identifying fine details, subtle changes in vegetation health, or faint geological features.

Temporal Resolution: The frequency with which a satellite revisits the same area. High temporal resolution means frequent revisits (e.g., daily), while low temporal resolution means infrequent revisits (e.g., every 16 days). This is crucial for monitoring dynamic processes like crop growth or weather changes.

Data Products

The raw data collected by remote sensing sensors is processed and formatted into various data products that users can analyze. These products vary in their level of processing and the format in which the information is presented.

Photographic Images

Description: Historically, remote sensing data was captured on photographic film, similar to traditional cameras. These are analog records of the reflected or emitted EMR.

Characteristics:

• Format: Physical film negatives or positives.
• Resolution: Often had high spatial resolution but limited spectral information (e.g., panchromatic, limited color bands).
• Analysis: Interpreted visually using stereoscopes for 3D viewing or by direct observation.
• Processing: Limited processing capabilities compared to digital data.

Examples: Early aerial photographs taken by aircraft, some early satellite imagery (e.g., early Landsat films). While less common now for primary data, they are still used for historical analysis.

Digital Images

Description: Modern remote sensing data is almost exclusively captured and stored in digital format. This data consists of a grid of pixels, where each pixel has a numerical value representing the radiance or reflectance in a specific spectral band.

Characteristics:

• Format: Data files (e.g., TIFF, GeoTIFF, JPEG, ENVI formats) containing numerical pixel values.
• Resolution: Can have high spatial, spectral, and radiometric resolution.
• Analysis: Can be processed, analyzed, and visualized using specialized software (GIS and Remote Sensing software). Allows for sophisticated techniques like image classification, spectral analysis, change detection, and quantitative measurements.
• Processing: Easily manipulated for corrections, enhancements, and transformations.

Examples: Images from modern satellites like Sentinel, Landsat 8, ASTER, MODIS, commercial satellites (Maxar, Planet), and data from drones.

Data Formats: Common digital image formats include GeoTIFF (which includes georeferencing information), HDF (Hierarchical Data Format), NetCDF (Network Common Data Form), and vendor-specific formats.

Interpretation Of Satellite Imageries

Interpreting satellite imagery involves analyzing the visual and numerical characteristics of the images to extract meaningful information about the Earth's surface. This can be done visually or using computer-assisted digital image processing techniques.

Elements Of Visual Interpretation

These are the fundamental visual clues used by analysts to identify and differentiate features in an image.

Tone Or Colour

Description: The relative brightness or darkness of features in a grayscale image (tone), or the specific color hues in a color image.

Interpretation: Different materials reflect or emit EMR differently across the spectrum. For example, healthy green vegetation appears green in color infrared imagery, water appears dark blue/black, bare soil might appear brown or grey, and urban areas can have varied tones.

Texture

Description: The characteristic pattern of variation in tone or color within a feature. It describes the smoothness or roughness of a surface as perceived from the image.

Interpretation: A forest canopy might appear rough in texture, while a smooth water body appears uniform. Agricultural fields can show regular textures due to row patterns, while natural grasslands might have a more irregular texture.

Size

Description: The absolute size of an object on the ground (measured in meters or kilometers) or its relative size on the image.

Interpretation: Knowing the scale of the image, analysts can estimate the absolute size of features. For example, distinguishing between a large airport runway and a small road, or identifying large buildings versus smaller ones.

Shape

Description: The geometric form of an object as it appears in the image.

Interpretation: Certain features have characteristic shapes. Rectangular shapes might indicate buildings or agricultural fields, circular patterns could be irrigation systems or craters, and sinuous, winding shapes often represent rivers.

Shadow

Description: The dark areas cast by objects when illuminated by a light source (usually the sun). Shadows can provide information about the height and shape of objects.

Interpretation: The length and direction of a shadow can help estimate the height of tall objects like buildings, mountains, or bridges. Shadows also help in delineating the exact boundaries of objects.

Pattern

Description: The spatial arrangement or repetition of features within a scene.

Interpretation: Patterns can be indicative of human activity or natural processes. Examples include the regular pattern of agricultural fields, the dendritic pattern of rivers, the grid pattern of urban streets, or the clustered pattern of settlements.

Association

Description: The relationship between an object of interest and other features in the scene.

Interpretation: By recognizing the context, analysts can identify features. For instance, a factory is often associated with railways, roads, and often located near settlements or rivers. A school might be located within a settlement area.

Map Interpretation Procedure

Interpreting a topographical map involves a systematic approach to extract meaningful information about the landscape and human activities. It's a process of reading, analyzing, and synthesizing various map elements.

Step-by-Step Procedure:

1. General Information Analysis:

• Read the Title: Understand the area and subject matter.
• Identify the Scale: Note the scale to measure distances and areas accurately.
• Determine Direction: Locate the North arrow for orientation.
• Consult the Legend: Understand the meaning of all symbols, colors, and line types used.
• Note the Date of Survey/Publication: Check the recency of the information.
• Understand the Grid System: Use latitude/longitude or grid references for precise location.

2. Physical Feature Analysis:

• Relief: Analyze contour lines to identify elevation, slopes (steepness, concavity/convexity), and landforms (hills, valleys, ridges, plateaus).
• Drainage: Examine rivers, lakes, canals, and their patterns. Note perennial vs. intermittent water sources and man-made water features.
• Vegetation: Identify forests, grasslands, scrub, and barren land using symbols and colors.

3. Cultural Feature Analysis:

• Settlements: Observe the distribution, density, pattern, and size of villages and towns.
• Transportation: Analyze roads (types, network density), railways, tracks, and airports.
• Communication: Note the presence of post offices, police stations, etc.
• Other Features: Identify industries, mines, quarries, places of worship, and other man-made structures.

4. Synthesis and Inference:

• Interrelationships: Understand how physical and cultural features are connected (e.g., settlements near water, transport along gentle slopes).
• Land Use: Infer the main uses of the land (agriculture, forestry, urban, industrial).
• Occupation: Deduce the likely occupations of the inhabitants based on the observed land use and settlement patterns.
• Accessibility: Evaluate how accessible different parts of the area are based on the transport network.
• Potential Uses: Consider the suitability of the area for development, agriculture, or other purposes.

5. Drawing Conclusions: Summarize the key characteristics and potential of the area depicted on the map, forming an overall interpretation.