Overview of Disasters in India

India's Vulnerability to Natural Disasters

India's diverse geography and climate make it highly susceptible to a range of natural disasters. These events, including earthquakes, tsunamis, floods, droughts, cyclones, and landslides, can have devastating consequences for millions of people and the economy.

Major Natural Disasters Affecting India:

Earthquakes:
- Tectonic Setting: India's position on the Indian Plate, which is colliding with the Eurasian Plate, makes it prone to seismic activity.
- Impact: Earthquakes can cause widespread destruction, including building collapses, landslides, and tsunamis. The 2001 Gujarat earthquake is a prime example of such devastation.
Tsunamis:
- Oceanic Triggers: Underwater earthquakes and volcanic eruptions can generate tsunamis, as seen in the 2004 Indian Ocean Tsunami.
- Impact: Coastal areas are particularly vulnerable to tsunamis, which can lead to massive loss of life and property.
Floods:
- Monsoon Influence: India's monsoon system brings heavy rainfall, often leading to floods in river basins and urban areas.
- Impact: Floods can cause widespread damage to infrastructure, agriculture, and human settlements, as exemplified by the 2015 South India floods.
Glacial Lake Outburst Floods (GLOFs):
- Himalayan Threat: The Himalayan region is prone to GLOFs, which occur when glacial lakes breach their natural dams.
- Impact: GLOFs can cause rapid flooding, erosion, and landslides, as seen in the 2021 Uttarakhand disaster.
Cyclones:
- Coastal Vulnerability: India's long coastline is exposed to cyclones from both the Bay of Bengal and the Arabian Sea.
- Impact: Cyclones can bring strong winds, heavy rainfall, and storm surges, as demonstrated by Cyclone Amphan in 2020.
Droughts:
- Arid and Semi-Arid Regions: Large parts of India, especially in the western and central regions, are prone to droughts.
- Impact: Droughts can lead to crop failures, water scarcity, and economic hardship.
Forest Fires:
- Himalayan Threat: Forest fires, often triggered by human activities or lightning strikes, can devastate forests in the Himalayan region.
- Impact: Fires can lead to loss of biodiversity, soil erosion, and air pollution.

India's Response to Natural Disasters:

India has implemented various strategies to mitigate the impact of natural disasters:

National Disaster Management Authority (NDMA): Oversees disaster management efforts.
Early Warning Systems: Provides timely alerts for cyclones, tsunamis, and floods.
Community-Based Disaster Risk Reduction (CBDRR): Empowers communities to prepare and respond to disasters.
Infrastructure Development: Building resilient infrastructure, especially in disaster-prone areas.
Climate Change Adaptation: Addressing the impacts of climate change on disaster risk.

Fyugp note

Disaster Management

PG and Research Department of Geography,
Government College Chittur, Palakkad
https://g.page/vineeshvc

Comments

RADIOMETRIC CORRECTION

Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

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...

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...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Supervised Classification

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...

Vineesh V, Geography

Search This Blog