Multispectral Imaging. Remote Sensing ensing

Multispectral imaging is a remote sensing technique that involves capturing data from multiple discrete bands of the electromagnetic spectrum. Each band corresponds to a specific range of wavelengths. The main idea behind multispectral imaging is to gather information about the Earth's surface by observing how different materials reflect or emit light at different wavelengths.

In multispectral imaging, satellite sensors are equipped with multiple detectors, each sensitive to a different wavelength range. By analyzing the data from these detectors, researchers and analysts can identify various features on the Earth's surface, such as vegetation, water bodies, urban areas, and more. This information can be used for tasks like land cover classification, environmental monitoring, and agricultural assessment.

Some important satellites with multispectral sensors include:

1. Landsat series: The Landsat satellites, operated by NASA and the USGS, have been providing multispectral data for decades. They offer a range of multispectral sensors, including the Thematic Mapper (TM) and Operational Land Imager (OLI), which capture data in different wavelength bands.

2. Sentinel-2: Operated by the European Space Agency (ESA), the Sentinel-2 satellites are part of the Copernicus program. They carry the MultiSpectral Instrument (MSI), which provides high-resolution multispectral imagery in 13 spectral bands.

3. MODIS (Moderate Resolution Imaging Spectroradiometer): A sensor on NASA's Terra and Aqua satellites, MODIS captures data in a range of spectral bands. While not as high-resolution as some other sensors, MODIS provides global coverage and is used for monitoring large-scale environmental changes.

4. WorldView-2 and WorldView-3: These satellites, operated by DigitalGlobe, offer very high-resolution multispectral imagery for various applications, including urban planning, disaster management, and agriculture.

5. Landsat-8: The latest addition to the Landsat series, Landsat-8 carries the Operational Land Imager (OLI) sensor, which provides improved capabilities for land cover monitoring and environmental assessment.

These satellites play a crucial role in monitoring and understanding our planet's changing environment, providing valuable data for research and decision-making across various fields.

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Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?" Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes . Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc. Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination. Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com

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...

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...

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