History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:

- Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.

- Utilizes halftone color gradients for visual representation.

2. 1854 - John Snow's Cholera Outbreak Analysis:

- Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.

- Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin.

3. Early 20th Century - Photozincography and Layered Mapping:

- Photozincography development allows maps to be split into layers for vegetation, water, etc.

- Introduction of layers, later a key feature in GIS, for separate printing plates.

4. Mid-20th Century - Computer Facilitation of Cartography:

- Waldo Tobler's 1959 publication details using computers for cartography.

- Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s.

5. 1960 - Canada Geographic Information System (CGIS):

- Roger Tomlinson develops the world's first operational GIS in Ottawa, Canada.

- CGIS used for Canada Land Inventory, incorporating data on soils, agriculture, wildlife, etc.

6. 1964 - Laboratory for Computer Graphics and Spatial Analysis:

- Howard T. Fisher establishes the Laboratory for Computer Graphics and Spatial Analysis at Harvard.

- Develops influential software code and systems distributed worldwide.

7. Late 1970s to Early 1980s - Commercialization of GIS:

- Public domain GIS systems MOSS and GRASS GIS in development.

- Commercial vendors (M&S Computing, ESRI, Intergraph, Bentley Systems, CARIS, ERDAS) emerge with features from CGIS.

8. 1986 - Desktop GIS Emerges:

- Mapping Display and Analysis System (MIDAS), the first desktop GIS, is released.

- Renamed MapInfo for Windows in 1990, marking the shift from research to business.

9. Late 20th Century - Consolidation and Standardization:

- Rapid growth in GIS systems consolidates on a few platforms by the end of the century.

- Users begin exploring GIS data over the Internet, requiring format and transfer standards.

10. 21st Century - Integration with IT and Internet Infrastructure:

- Integration of GIS with IT and Internet technologies like relational databases, cloud computing, SAAS, and mobile computing becomes a major trend.

- Growing number of free, open-source GIS packages customized for specific tasks.

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Supervised Classification

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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...

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 ...

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