Vineesh V, Geography

Unmanned Aerial Vehicles (UAVs) —commonly called drones —are pilotless aircraft used as remote sensing platforms to acquire very high-resolution geospatial data . They fly at low altitudes (typically 50–300 m), enabling them to record centimeter-level details of the Earth's surface. UAVs are increasingly used in remote sensing because they offer on-demand data acquisition , flexible sensor deployment , and the ability to fly under cloud cover , making them ideal for scientific, environmental, and disaster applications. Characteristics ✔ 1. High-Resolution Data Acquisition UAVs can collect imagery with spatial resolutions up to <1 cm . Suitable for detailed mapping of vegetation, buildings, hazards, and micro-topography. ✔ 2. On-Demand and Rapid Deployment Can be launched quickly anytime data is needed. Extremely useful after floods, landslides, earthquakes , or in inaccessible terrain. ✔ 3. Operational Flexibility Able to fly: in rugged ...

Scattering 

Hybrid classification is a combined classification approach that uses both supervised and unsupervised classification techniques together. It is designed to take advantage of the strengths of each method and to overcome their weaknesses. What Is Hybrid Classification? Hybrid classification blends: Unsupervised classification (e.g., ISODATA, K-means) Supervised classification (e.g., Maximum Likelihood, SVM) ✔ Concept First, an unsupervised algorithm groups pixels into spectral clusters without prior knowledge. These clusters are then labeled or merged into meaningful land-cover classes using supervised training data . ✔ Why use hybrid methods? Unsupervised classification captures natural spectral groupings. Supervised classification improves accuracy by using reference samples. Together, they reduce errors caused by poor training data or complex landscapes. ✔ Key Terminology Cluster : a group of pixels with similar spectral cha...

In remote sensing, a mixed pixel (also called a mixed cell or mixel ) is a pixel that contains more than one land-cover type inside its area. This is very common when: spatial resolution is coarse (e.g., 30 m, 250 m) land cover is patchy or complex boundaries between features exist (forest–agriculture, land–water edges) Traditional hard classification assigns each pixel to one single class only → either forest or soil or water. But mixed pixels contain fractions of several classes simultaneously, so hard classification produces errors. Two major solutions are: Spectral Mixture Analysis (SMA) Fuzzy Classification 1. Spectral Mixture Analysis (SMA) (Also called Linear Spectral Unmixing ) ✔ Concept SMA assumes that the reflectance recorded by a pixel is a linear combination of the reflectance of pure materials within that pixel. These pure materials are called endmembers . ✔ Endmembers Endmembers are spectrally pure classes found...

Assembling the Training Data Training data (also called training samples or signature sets ) are the foundation of supervised image classification in remote sensing. This is where the analyst selects representative examples of each land-cover class—such as water, vegetation, urban, soil, etc.—from the satellite image. To prepare training data properly, several analytical and interactive steps are used. These help ensure that the classes are well separated and that the classifier receives the correct spectral information. 1. Graphical Representation of Spectral Response Patterns ✔ What it means For each class (e.g., water, forest, built-up), the training pixels have a spectral signature —a pattern of reflectance values across the image's spectral bands. This pattern is visualized using: Spectral reflectance curves Band-by-band scatter plots Histograms for each band ✔ Purpose To understand how different classes behave in different bands To che...

Resolution 

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...

Jet stream  Warm regards. .. Vineesh V UGC Nodal Officer Assistant Professor of Geography, PG and Research Department of Geography, Government College Chittur, Palakkad.

Unmanned Earth resources satellites are satellites equipped with remote sensing instruments used to collect images and environmental data from the Earth's surface without a crew onboard. They help monitor: land use vegetation soil and water resources climate oceans atmosphere natural hazards These satellites are grouped based on the type of radiation they measure and the sensors they carry. Five Groups of Unmanned Earth Resources Satellites Remote sensing satellites can be categorized into five main groups , based on the wavelengths they record and the type of environmental information they collect. First-Generation Earth Resources Satellites Wavelength region: Visible and Near-Visible (VNIR) ✔ Characteristics Use multispectral scanners Record reflected sunlight Mainly for land use, vegetation, and surface mapping ✔ Example Landsat series (Landsat 1, 2, 3) These were the first generation of Earth resource sate...

Radar sensors are active remote sensing instruments that use microwave radiation to detect and measure Earth's surface features. They transmit their own energy (radio waves) toward the Earth and record the backscattered signal that returns to the sensor. Since they do not depend on sunlight, radar systems can collect data: day or night through clouds, fog, smoke, and rain in all weather conditions This makes radar extremely useful for Earth observation. 1. Active Sensor A radar sensor produces and transmits its own microwaves. This is different from optical and thermal sensors, which depend on sunlight or emitted heat. 2. Microwave Region Radar operates in the microwave region of the electromagnetic spectrum , typically from 1 mm to 1 m wavelength. Common radar frequency bands: P-band (70 cm) L-band (23 cm) S-band (9 cm) C-band (5.6 cm) X-band (3 cm) Each band penetrates and interacts with surfaces differently: Lo...