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Resolution of Sensors in Remote Sensing

Spatial Resolution 🗺️ Definition : The smallest size of an object on the ground that a sensor can detect. Measured as : The size of a pixel on the ground (in meters). Example : Landsat → 30 m (each pixel = 30 × 30 m on Earth). WorldView-3 → 0.31 m (very detailed, you can see cars). Fact : Higher spatial resolution = finer details, but smaller coverage. Spectral Resolution 🌈 Definition : The ability of a sensor to capture information in different parts (bands) of the electromagnetic spectrum . Measured as : The number and width of spectral bands. Types : Panchromatic (1 broad band, e.g., black & white image). Multispectral (several broad bands, e.g., Landsat with 7–13 bands). Hyperspectral (hundreds of very narrow bands, e.g., AVIRIS). Fact : Higher spectral resolution = better identification of materials (e.g., minerals, vegetation types). Radiometric Resolution 📊 Definition : The ability of a sensor to ...
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Platforms in Remote Sensing

In remote sensing, a platform is the physical structure or vehicle that carries a sensor (camera, scanner, radar, etc.) to observe and collect information about the Earth's surface. Platforms are classified mainly by their altitude and mobility : Ground-Based Platforms Definition : Sensors mounted on the Earth's surface or very close to it. Examples : Tripods, towers, ground vehicles, handheld instruments. Applications : Calibration and validation of satellite data Detailed local studies (e.g., soil properties, vegetation health, air quality) Strength : High spatial detail but limited coverage. Airborne Platforms Definition : Sensors carried by aircraft, balloons, or drones (UAVs). Altitude : A few hundred meters to ~20 km. Examples : Airplanes with multispectral scanners UAVs with high-resolution cameras or LiDAR High-altitude balloons (stratospheric platforms) Applications : Local-to-regional mapping ...

Satellite Remote Sensing: Concepts and Imaging Systems

  Satellite remote sensing relies on detectors (sensors) that measure reflected/emitted electromagnetic radiation from the Earth. How sensors collect data depends on: Spectral coverage → Multispectral (few bands), Hyperspectral (hundreds), Thermal, Microwave. Detector type → Discrete detectors, linear arrays, or area arrays. Scanning mechanism → Scanning mirrors (whiskbroom) vs. linear arrays (pushbroom). Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Whiskbroom scanners) Principle : A single detector (or a few detectors) measures radiation one pixel at a time. A scanning mirror sweeps across-track (perpendicular to the satellite path) to build up the image line by line. The forward motion of the satellite provides the along-track dimension. Known as a whiskbroom scanner . Characteristics : Good calibration stability. Narrow instantaneous field of view (IFOV). Susceptible to mechanical wear (moving ...

geostationary and sun-synchronous

Orbital characteristics of Remote sensing satellite geostationary and sun-synchronous  Orbits in Remote Sensing Orbit = the path a satellite follows around the Earth. The orbit determines what part of Earth the satellite can see , how often it revisits , and what applications it is good for . Remote sensing satellites mainly use two standard orbits : Geostationary Orbit (GEO) Sun-Synchronous Orbit (SSO)  Geostationary Satellites (GEO) Characteristics Altitude : ~35,786 km above the equator. Period : 24 hours → same as Earth's rotation. Orbit type : Circular, directly above the equator . Appears "stationary" over one fixed point on Earth. Concepts & Terminologies Geosynchronous = orbit period matches Earth's rotation (24h). Geostationary = special type of geosynchronous orbit directly above equator → looks fixed. Continuous coverage : Can monitor the same area all the time. Applications Weather...

Disaster Risk

Disaster Risk 

elements of interpolation of aerial photos

Location – Where the object is found on the map or photo. Knowing the place can give clues about what it is. Size – How big or small it appears, which helps identify objects (e.g., a football field vs. a garden). Shape – The outline or form of the object, such as round, rectangular, or irregular. Shadow – The dark area an object casts; it helps guess height, shape, and type of object. Tone/Color – Lightness, darkness, or color differences that help tell objects apart (e.g., blue water, green vegetation). Texture – How smooth or rough the surface looks in the image (e.g., forest appears rough, grassland appears smooth). Pattern – The arrangement or repetition of objects, like rows of trees or grid-like city blocks. Height/Depth – How tall or deep an object or landform is, often estimated from shadows or stereo images. Site/Situation/Association – The surroundings and relationships between objects (e.g., a swimming pool next to a house, or a f...

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...

Network data model

GIS, a network data model is used to represent and study things that are connected like a web — for example, roads, rivers, railway tracks, water pipes, or electric lines . It focuses on how things are connected and helps us solve problems like finding the best route, the nearest hospital, or where water will flow. Nodes → Points where things meet or end (e.g., road intersections, railway stations, pumping stations). Edges → Lines connecting the nodes (e.g., roads, pipelines, cables). Topology → The "rules" of connection — which node is linked to which edge. Attributes → Extra details about each part (e.g., road speed limit, pipe size, traffic volume). How It Works 🔍 Make the Network Model Start with a map of lines (roads, pipes, rivers) and mark how they connect. Run Analyses Routing → Find the shortest or fastest path. Closest Facility → Find the nearest hospital, petrol station, etc. Service Area → Find how far y...

Shortest Path Analysis, Time and Distance based Shortest Path in GIS

Shortest path analysis is about finding the best route between two places on a map. "Best" can mean shortest distance , least travel time , or lowest cost . It's used in transportation, logistics, urban planning , and many other fields. Key Ideas Network Dataset → A map of connected lines like roads, footpaths, railways, etc. Origin & Destination → Where you start and where you want to go. Impedance → The "cost" of traveling — could be distance, time, money, fuel, etc. Constraints → Rules or limits, like speed limits, traffic jams, toll roads, or road closures. How It Works Define the Network → GIS creates a model of roads or paths. Calculate Cost → For each road segment, GIS figures out the cost (time, distance, etc.). Run Algorithm → Uses formulas like Dijkstra's Algorithm or A* to find the lowest total cost from start to end. Show Results → The route is displayed on the map with info like total ti...