Representing Geographic Space; Discrete and Continuous

Geographic data can be broadly classified into two categories - discrete and continuous. Discrete geographic data is information that is specific to particular locations, and can be represented by different types of values like nominal, ordinal, interval, and ratio. This type of data includes features like landownership, soils classification, zoning, and land use.

When represented on a map, discrete data typically has well-defined boundaries and is typically represented in polygon format. For example, the shape of a property boundary or a zoning district can be considered as discrete geographic data. In addition, point and line datasets such as tree locations, rivers, and streets are also considered as discrete data.

On the other hand, continuous data refers to information that does not have a clearly defined boundary and can be measured at every point on a map. Continuous data is often represented as surfaces and includes datasets like elevation, rainfall, pollution concentration, and water tables. When represented on a map, continuous data can be visualized as a continuous gradient or a smooth transition of values from one point to another.

In summary, discrete geographic data pertains to information that is specific to certain locations and has well-defined boundaries, whereas continuous geographic data pertains to information that is present throughout an area and has no clear boundaries. Understanding the difference between these two types of geographic data is essential for effective analysis and visualization of spatial data.

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Optical Sensors in Remote Sensing

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Radar Sensors in Remote Sensing

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Thermal Sensors in Remote Sensing

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Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types 1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

LiDAR in Remote Sensing

LiDAR (Light Detection and Ranging) is an active remote sensing technology that uses laser pulses to measure distances to the Earth's surface and create high-resolution 3D maps . LiDAR sensors emit short pulses of laser light (usually in the near-infrared range) and measure the time it takes for the pulse to return after hitting an object. Because LiDAR measures distance very precisely, it is excellent for mapping: terrain vegetation height buildings forests coastlines flood plains ✅ 1. Active Sensor LiDAR sends its own laser energy, unlike passive sensors that rely on sunlight. ✅ 2. Laser Pulse LiDAR emits thousands of pulses per second (even millions). Wavelengths commonly used: Near-Infrared (NIR) → land and vegetation mapping Green (532 nm) → water/ bathymetry (penetrates shallow water) ✅ 3. Time of Flight (TOF) The sensor measures the time taken for the laser to travel: from the sensor → to the sur...

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