Cyclone Warnings 🌪️🌪️🌪️ Cyclone Amphan. ഉം പുൻ ചുഴലിക്കാറ്റ്

⚠️ Cyclone Warnings🌪️🌪️🌪️Cyclone Amphan

ഉം പുൻ ചുഴലിക്കാറ്റ്: ബംഗാൾ ഉൾക്കടലിൽ ന്യൂനമർദ്ദം ഇന്ന് വൈകുന്നേരത്തോടെ ചുഴലികാറ്റായി മാറും.

ശനിയാഴ്ച വൈകുന്നേരത്തോടെ അതിതീവ്ര ന്യൂനമർദ്ദത്തിലേക്കും തുടർന്നുള്ള 24 മണിക്കൂറിനുള്ളിൽ ചുഴലിക്കാറ്റിലേക്കും അതിശക്തമാകാൻ സാധ്യതയുണ്ട്.

അടുത്ത 48 മണിക്കൂർ കേരളത്തിൽ ഒറ്റപ്പെട്ട ശക്തമായ മഴയുണ്ടാകും.

അതേസമയം, തെക്കൻ ഇന്ത്യൻ ഉപദ്വീപിൽ അടുത്ത 4-5 ദിവസങ്ങളിൽ വ്യാപകമായ മഴ / ഇടിമിന്നലോടുകൂടിയ മിന്നലും കാറ്റും വീശും.

അടുത്ത 5 ദിവസങ്ങളിൽ കേരളത്തിലും, മെയ് 16 -17 മുതൽ തീരദേശ കർണാടക, ലക്ഷദ്വീപ് എന്നിവിടങ്ങളിലും ഒറ്റപ്പെട്ട കനത്ത മഴ പ്രതീക്ഷിക്കാം.

അടുത്ത 5 ദിവസങ്ങളിൽ വടക്കുകിഴക്കൻ സംസ്ഥാനങ്ങളിൽ വ്യാപകമായ മഴ പെയ്യാൻ സാധ്യതയുണ്ട്.

📢📢📢

Cyclone Amphan: Depression in Bay of Bengal to turn into severe storm by today evening.

Likely to intensify rapidly into a cyclonic storm by Saturday evening and then into a severe cyclonic storm during the subsequent 24 hours.

Next 48 hours is favourable for sporadic rain over Kerala Region.

Meanwhile, scattered to widespread rain & thunder showers accompanied with lightning and gusty winds is expected over south peninsular India during the next 4-5 days.

Isolated heavy rainfall activity over Kerala during the next 5 days, over Coastal Karnataka and Lakshadweep on late May 16 and 17.

Northeastern states are likely to experience scattered to widespread rain over the next 5 days.

imagery : NEPS / NCUM

....

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc

Comments

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...

Spatial Feature Manipulation and Filtering

1. Spatial Feature Manipulation: Spatial Frequency Spatial frequency describes how quickly brightness changes across an image. It can reveal different levels of detail, based on how often these changes occur: High Spatial Frequency : Areas where brightness changes quickly, like edges, fine textures, or detailed patterns. These parts contain high detail but can also include noise. Low Spatial Frequency : Areas with gradual changes in brightness, like smooth surfaces or broad homogeneous areas. These regions are usually featureless, such as water bodies or large agricultural fields. Zero Spatial Frequency : Represents a completely uniform area with no brightness variation. This area is very smooth and has no texture or detail. By manipulating spatial frequencies, we can enhance or suppress certain features in an image. Spatial filtering is one of the primary methods to perform this manipulation. 2. Spatial Filtering and Spatial Domain Spatial filtering is the process of applying mathe...

Vineesh V, Geography

Search This Blog