Types of image

1. Visible Image:

- A visible image is captured in the range of the electromagnetic spectrum that is visible to the human eye, typically spanning wavelengths from about 400 to 700 nanometers (nm). These images resemble what the human eye sees and are often used for visual inspection and interpretation.

2. Infrared Image:

- Infrared imagery captures radiation in the infrared portion of the electromagnetic spectrum, beyond what is visible to the human eye. It is useful for applications such as night vision, heat detection, and identifying temperature variations.

3. Multispectral Image:

- Multispectral imagery is captured in multiple distinct bands or channels across the electromagnetic spectrum. Each band provides information about a specific range of wavelengths, allowing for the analysis of different characteristics of the scene, such as vegetation health or land use.

4. Hyperspectral Image:

- Hyperspectral imagery captures data in numerous narrow and contiguous bands across the electromagnetic spectrum, often with hundreds of spectral bands. This high spectral resolution allows for detailed analysis of materials and their properties in a scene, making it valuable in fields like geology and agriculture.

5. LIDAR Image:

- LIDAR (Light Detection and Ranging) imaging uses laser pulses to measure the distance to objects or surfaces. It creates highly accurate three-dimensional maps of terrain, structures, or objects by measuring the time it takes for the laser pulses to return.

6. RADAR Image:

- RADAR (Radio Detection and Ranging) imaging uses radio waves to detect and locate objects or terrain. RADAR images are particularly useful in applications like weather forecasting, aircraft navigation, and military surveillance.

7. Thermal Imagery:

- Thermal imagery captures the infrared radiation emitted by objects based on their temperature. It is used to visualize variations in temperature, making it valuable in applications such as building inspections, search and rescue operations, and monitoring industrial equipment for overheating.

Each type of imagery has its own unique characteristics and applications, making them essential tools in various fields, including remote sensing, environmental monitoring, geology, agriculture, and more.

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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 ...

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl...

Raster Data Model

A raster data model represents geographic space as a grid of cells (called pixels ). Think of it like a chessboard covering the Earth. Each square = cell / pixel Each cell contains a value That value represents information about that location Example: Elevation = 245 meters Temperature = 32°C Land use = Forest The grid is arranged in: Rows Columns This structure is called a matrix . GRID Model (Cell-Based Matrix Model) 🔹 Concept The GRID model is the most common raster structure used in GIS for spatial analysis . It is mainly used for: Continuous data (data that changes gradually) Sometimes discrete/thematic data 🔹 Structure A 2D matrix (rows × columns) Each cell stores one numeric value Integer (whole number) Float (decimal number) 🔹 Key Terminologies Cell Resolution → Size of each pixel (e.g., 30m × 30m) Spatial Resolution → Level of detail DEM (Digital Elevation Model) → Elevation grid Raster Calculator → Tool for mathematical operations Overlay Analysis → Combining mu...

Landsat band composition

Short-Wave Infrared (7, 6 4) The short-wave infrared band combination uses SWIR-2 (7), SWIR-1 (6), and red (4). This composite displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation has lighter shades. Urban areas are blue and soils have various shades of brown. Agriculture (6, 5, 2) This band combination uses SWIR-1 (6), near-infrared (5), and blue (2). It's commonly used for crop monitoring because of the use of short-wave and near-infrared. Healthy vegetation appears dark green. But bare earth has a magenta hue. Geology (7, 6, 2) The geology band combination uses SWIR-2 (7), SWIR-1 (6), and blue (2). This band combination is particularly useful for identifying geological formations, lithology features, and faults. Bathymetric (4, 3, 1) The bathymetric band combination (4,3,1) uses the red (4), green (3), and coastal bands to peak into water. The coastal band is useful in coastal, bathymetric, and aerosol studies because...

DSM DTM DEM CHM FHM

In Remote Sensing and GIS, DSM, DTM, DEM, CHM, and FHM are elevation-based digital surface representations derived from LiDAR, photogrammetry, stereo satellite imagery, or radar (e.g., InSAR) . They are raster-based 3D models where each pixel stores an elevation (Z-value) relative to a vertical datum (e.g., Mean Sea Level). DEM – Digital Elevation Model Concept A Digital Elevation Model (DEM) is a generic term for a raster grid representing elevation values of the Earth's surface. It represents a continuous field surface Each pixel contains a Z-value (elevation) It may represent bare earth or surface, depending on data source Terminologies Raster resolution – spatial pixel size (e.g., 10 m, 30 m) Vertical accuracy – elevation precision (± m) Elevation datum – reference level (e.g., MSL, WGS84 ellipsoid) Grid-based terrain model Digital surface representation Important Clarification DEM is often used as an umbrella term In many datasets, DEM ≈ DTM (bare earth) Technically, DEM...

Vineesh V, Geography

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