Skip to main content

UGC NET Geography UPSC

Indus and Brahmaputra are examples of—
 (A) Subsequent drainage 
(B) Superimposed drainage 
(C) Antecedent drainage 
(D) Consequent drainage

The correct answer is (C) Antecedent drainage.

Let's explain all the options:

(A) Subsequent drainage: Subsequent drainage refers to a river system that develops and follows the slope of the land that has been formed by tectonic forces or other geomorphic processes. It occurs after the formation of the land and is not related to the initial development of the landscape. It is not applicable to the Indus and Brahmaputra rivers.

(B) Superimposed drainage: Superimposed drainage occurs when a river cuts through existing rock structures and maintains its course regardless of the underlying geological features. This type of drainage develops over previously formed landscapes and is often influenced by erosion-resistant rock layers. It is not applicable to the Indus and Brahmaputra rivers.

(C) Antecedent drainage: Antecedent drainage occurs when a river system develops in a region before the surrounding landmass undergoes uplift or tectonic changes. The river maintains its course despite the subsequent changes in the landscape caused by geological forces. This is the correct option for the Indus and Brahmaputra rivers. These rivers existed before the Himalayan mountain range was uplifted and have maintained their courses by eroding through the rising mountains.

(D) Consequent drainage: Consequent drainage develops in response to the existing topography or landform patterns. It occurs as a result of the initial structure and relief of the area. This option is not applicable to the Indus and Brahmaputra rivers because their courses were established before the formation of the Himalayas.

Therefore, the correct answer is (C) Antecedent drainage, as the Indus and Brahmaputra rivers existed before the formation of the Himalayas and have maintained their courses by eroding through the rising mountains.



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. ...

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...

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...