Skip to main content

Evolution of Indian Platform

The Indian Platform, also known as the Indian Shield or the Peninsular Shield, is a stable geological region that forms the core of the Indian subcontinent. It has a complex geological history that spans billions of years. Here's an overview of the evolution of the Indian Platform:


1. Archean Eon (4 billion to 2.5 billion years ago):

    The earliest geological history of the Indian Platform dates back to the Archean Eon, during which some of the oldest rocks on Earth were formed.

    The Dharwar Craton, located in the southern part of the Indian Platform, is one of the prime examples of Archeanage geological formations in India.


2. Proterozoic Eon (2.5 billion to 541 million years ago):

    During the Proterozoic Eon, the Indian Platform witnessed significant geological events.

    Sedimentary basins formed, leading to the accumulation of thick sequences of sedimentary rocks.

    The Vindhyan Supergroup, a prominent sedimentary rock formation, was deposited during this time.


3. Rodinia Supercontinent (1.3 billion to 750 million years ago):

    India was part of the supercontinent Rodinia during this period.

    It was situated near the southern margin of Rodinia.


4. Breakup of Rodinia and Formation of Gondwana (750 million to 540 million years ago):

    Rodinia began to break apart during the Neoproterozoic.

    India separated from the supercontinent and was positioned closer to Antarctica, forming part of the Gondwana supercontinent.


5. Cambrian to Devonian Periods (541 million to 358 million years ago):

    During this time, India experienced marine sedimentation and the deposition of sedimentary rocks in shallow seas.


6. Carboniferous to Permian Periods (358 million to 252 million years ago):

    India was located near the equator and experienced extensive coalforming swamps and glacial deposits.


7. Mesozoic Era (252 million to 66 million years ago):

    India remained part of Gondwana during the early Mesozoic, but it began drifting northward.

    This northward movement eventually led to its separation from Gondwana and initiated the formation of the Indian subcontinent.


8. Cenozoic Era (66 million years ago to present):

    The most significant phase in the evolution of the Indian Platform occurred during the Cenozoic.

    India continued to move northward and eventually collided with the Eurasian Plate around 50 million years ago.

    This collision resulted in the uplift of the Himalayan mountain range and the Tibetan Plateau, significantly impacting the Indian Platform's geology and topography.


The collision with the Eurasian Plate is a defining event in the evolution of the Indian Platform, shaping its current geological features and creating some of the world's most prominent mountain ranges, including the Himalayas. This collision also continues to influence seismic activity in the region. Overall, the geological evolution of the Indian Platform reflects its role in the assembly of the Indian subcontinent and its dynamic geological history.





Comments

Popular posts from this blog

Radar Remote Sensing SAR

1. Radar and Radar Remote Sensing RADAR stands for Radio Detection and Ranging . It is an active remote sensing system that transmits microwave energy toward the Earth's surface and records the energy that is returned to the sensor as an echo or backscatter . Unlike passive optical remote sensing, radar does not depend on sunlight. Therefore, it can operate day and night and, at suitable wavelengths, can acquire data through clouds, haze and light rain. Hence, radar is widely described as an all-weather, day-and-night remote sensing technology . Basic principle Microwave pulse → transmission → interaction with surface → backscatter/echo → antenna receives signal → signal processing → radar image 2. Microwave Energy Radar systems use microwave electromagnetic radiation , generally in wavelength ranges from approximately 1 mm to 1 m . Important radar bands include: Band Approx. wavelength Common applications ...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Lidar

LiDAR (Light Detection and Ranging) is an active remote sensing technology that measures distances by illuminating a target with laser pulses and analyzing the time it takes for the reflected light to return. Unlike passive systems (e.g., cameras, multispectral sensors), LiDAR provides its own energy source (laser), allowing it to operate both day and night and even penetrate through vegetation canopies . 🔹 How LiDAR Works (Step-by-Step Process) Laser Pulse Emission The system emits rapid, short pulses of laser light (commonly in the near-infrared wavelength, 1064 nm ). Some systems emit up to hundreds of thousands of pulses per second . Interaction with Target Surface The laser beam strikes objects such as vegetation, buildings, or bare ground. Depending on the object's structure, part of the pulse may scatter or reflect. Return Signal Detection The sensor records multiple returns : First Return → typically vegetation canopy tops. ...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...