Skip to main content

Sources of spatial data

 Survey Data

Concepts and Terminologies:
Ground Survey: This is the direct measurement of features on the Earth using instruments such as total stations, theodolites, and modern Global Navigation Satellite Systems (GNSS, e.g., GPS).
Control Points: Fixed locations measured with high accuracy; these serve as reference points (or benchmarks) to georeference and tie together spatial datasets.
Coordinate Geometry (COGO): Techniques for calculating distances and angles from measured points, often used in legal and cadastral surveys.

Examples:
• A cadastral survey for establishing property boundaries typically involves collecting precise GNSS coordinates at the corners of parcels.
• Engineering projects rely on survey data to create high-accuracy maps where the relative positions of roads, utilities, and buildings must be known within decimeter or even centimeter accuracy.

Survey data is fundamental in creating the framework for maps and digital elevation models (DEMs) because it ensures that subsequent aerial or remote sensing data can be accurately aligned (georeferenced) with real-world coordinates.


2. Air Photos (Aerial Photography)

Concepts and Terminologies:
Aerial Photography: The process of capturing images from an airborne platform (aircraft, drones, or even kites/balloons).
Vertical vs. Oblique Imagery:
 – Vertical photographs are taken with the camera lens pointed straight down, minimizing distortion and ideal for mapping and photogrammetry.
 – Oblique photographs are taken at an angle, offering a perspective view useful for understanding terrain or structures, though they require additional correction to be used for accurate mapping.
Photogrammetry: The science of extracting 3D measurements from 2D images. This is commonly used to generate digital elevation models (DEMs) and to produce ortho-rectified images (orthophotos) that have been corrected for lens distortion and terrain relief.

Examples:
• Urban planners often use vertical aerial photographs to create up-to-date base maps of a city.
• Archaeologists may use oblique aerial photos to detect subtle crop marks or soil disturbances that reveal hidden archaeological sites.

Air photos provide high-resolution images that are excellent for detailed local mapping and are frequently used as a background in Geographic Information Systems (GIS) for further analysis.


3. Satellite Images

Concepts and Terminologies:
Remote Sensing: The process of collecting information about the Earth from a distance using sensors on satellites or aircraft.
Raster Data: Satellite images are typically stored as raster data (a grid of pixels), where each pixel carries a value representing the reflectance in one or more spectral bands.
Resolution Types:
 – Spatial Resolution: The size of the area each pixel covers on the ground (e.g., 10 m, 30 m).
 – Spectral Resolution: The ability to resolve wavelengths across the electromagnetic spectrum (e.g., multispectral vs. hyperspectral sensors).
 – Temporal Resolution: How frequently a satellite revisits the same location (e.g., every 5 days for Sentinel-2).
 – Radiometric Resolution: The sensor's ability to distinguish differences in energy (often expressed in bits, such as 8-bit or 12-bit).

Examples:
• The Landsat series provides imagery dating back decades at 30 m spatial resolution, making it invaluable for monitoring land use changes over time.
• Sentinel-2 satellites deliver 10 m resolution data in visible and near-infrared bands, suitable for precision agriculture and environmental monitoring.
• High-resolution commercial satellites (like those operated by Maxar) can provide sub-meter imagery useful for urban planning and disaster response.

Satellite images allow for large-area coverage and are indispensable for global monitoring of environmental changes, urban expansion, and natural disasters.


4. Field Data

Concepts and Terminologies:
In-Situ (Field) Data: Direct observations or measurements made on the ground. This includes everything from soil samples and vegetation surveys to geotagged photographs.
Ground Truthing: The process of validating remote sensing data with on-the-ground observations to improve the accuracy of classifications or measurements made from imagery.
Mobile GIS: The use of smartphones, tablets, or specialized devices that collect and sometimes process spatial data in real time during field surveys.

Examples:
• Environmental scientists may collect soil moisture, temperature, and nutrient data from specific sampling sites to validate satellite-derived indices (such as the Normalized Difference Vegetation Index, NDVI).
• Field crews using mobile GIS apps can quickly capture locations and attributes of features (like road conditions or infrastructure status) and update digital maps in real time.

Field data is crucial for both calibrating and validating spatial datasets from other sources. It provides the "ground truth" that ensures remote sensing images, aerial photos, and survey data accurately reflect the conditions on the ground.


Integration in a GIS

A modern GIS often integrates all these sources:
Survey Data provides the high-accuracy framework and control points.
Aerial Photos supply detailed, up-to-date visuals for a specific region.
Satellite Images deliver broad coverage and multi-temporal analysis capabilities.
Field Data offers direct measurements and verification for remote observations.


Comments

Popular posts from this blog

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 ...

Energy Interaction with Atmosphere and Earth Surface

In Remote Sensing , satellites record electromagnetic radiation (EMR) that is reflected or emitted from the Earth. Before reaching the sensor, radiation interacts with: The Atmosphere The Earth's Surface These interactions control how satellite images look and how we interpret them. I. Interaction of EMR with the Atmosphere When solar radiation travels from the Sun to the Earth, four main processes occur: 1. Absorption Definition: Absorption occurs when atmospheric gases absorb radiation at specific wavelengths and convert it into heat. Main absorbing gases: Ozone (O₃) → absorbs Ultraviolet (UV) Carbon dioxide (CO₂) → absorbs Thermal Infrared Water vapour (H₂O) → absorbs Infrared Concept: Atmospheric Windows These are wavelength regions where absorption is very low, allowing radiation to pass through the atmosphere. Remote sensing depends on these windows. For example, satellites like Landsat 8 use visible, near-infrared, and thermal bands located in atmospheric windows. 2. Trans...

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...

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...

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