Architecture of GIS

GIS architecture encompasses the overall design and organization of a Geographic Information System (GIS).
The components of GIS architecture include hardware, software, data, people, and methods.
The architecture determines how these components interact and work together to create an efficient GIS system.
There are two main types of GIS architecture: client-server and web-based architecture.
In client-server architecture, GIS software runs on a server and is accessed by users through client computers.
The server is responsible for data storage, processing, and analysis, while the client is responsible for data visualization and user interaction.
Multiple users can work on the same dataset simultaneously, making it ideal for collaborative work.
In web-based architecture, the GIS software is accessed through a web browser, eliminating the need to install software on local machines.
The GIS data and software are stored on a server and accessed through a web interface, making it ideal for remote work and data sharing.
The hardware component of GIS architecture includes computer systems, storage devices, and input/output devices required to run and manage the GIS system.
The GIS software is the core component of the GIS architecture that enables users to capture, manage, analyze, and visualize geographic data.
The data component of GIS architecture includes various types of spatial and non-spatial data required to create and analyze maps.
The people component of GIS architecture includes GIS professionals, stakeholders, and end-users who use and maintain the GIS system.
The methods component of GIS architecture refers to the various techniques, procedures, and tools used to create, manipulate, analyze, and visualize geographic data.
The GIS architecture provides a framework for integrating the hardware, software, data, people, and methods to create a functional and efficient GIS system that meets the needs of the stakeholders.

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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...

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...

Isodata clustering

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Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...

Vineesh V, Geography

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