Vineesh V, Geography

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition.  Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...

Vector Geo processing 

Four Types of Resolution 

Layer Earth and Atmosphere 

Interior of the Earth 

Dating the Seafloor

Layers of the Ocean 

Carbon Cycle: Carbon moves between the atmosphere, plants, animals, and the soil. Plants take in carbon dioxide, animals eat plants, and carbon returns to the air through breathing and decay. This cycle helps regulate Earth's temperature! Water Cycle: Water continuously moves through evaporation, condensation, and precipitation. It rises as vapor, forms clouds, and falls back as rain—supplying fresh water to rivers, plants, animals, and us! Nitrogen Cycle: Nitrogen in the air gets converted by bacteria into forms plants can use. Animals get nitrogen by eating plants. When living things die, nitrogen returns to the soil—and the cycle repeats!

Forest  Vegetation

Magnetic field of the Earth 

Map elements 

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...