Zonal, neighbourhood and local operations in GIS

1. Local Operations in GIS

👀 Looks at one pixel at a time — like checking only one house on a map.

We do math or comparisons using just that pixel's value.

✅ Simple Examples:

Add two maps together: temperature map + rainfall map
Reclassify: Change values — e.g., if temperature > 30°C, mark as HOT

📌 Think of it like doing math problem by problem, not looking at neighbors.

Neighbourhood Operations in GIS

🏡 Looks at a pixel and all its surrounding neighbours — like checking your house and nearby houses.

We use this to smooth, sharpen, or highlight details in the map.

✅ Simple Examples:

Low Pass Filter: Makes the map look smooth (like blur in photos)
High Pass Filter: Makes sharp edges stand out (like outlines in drawings)
Edge Enhancement: Highlights boundaries between areas (like drawing a border between forest and farmland)

📌 Think of it like checking what your neighbours are doing before painting your own house!

Zonal Operations in GIS

🗺️ Looks at large zones or areas (groups of pixels with the same label), not just one or a few pixels.

We compare or summarize values inside a whole zone, like a city, forest, or lake.

✅ Simple Examples:

Calculate the average rainfall in each district
Find the highest temperature in each forest area
Compare population across different zones

📌 Think of it like checking how an entire neighbourhood or city is doing, instead of just one house.

Type of Operation	What it Looks At	Simple Meaning	Example
Local	One pixel	Checks and changes one spot only	Mark areas where temperature > 30°C
Neighbourhood	Pixel + nearby pixels	Looks around before making a decision	Blur or sharpen parts of a map
Zonal	Groups of pixels (zones)	Analyzes a whole area together	Average rainfall in each district

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...

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...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

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...

Vineesh V, Geography

Search This Blog