Kuhn’s model in Geography

Thomas Kuhn (1922–1996)

Thomas Samuel Kuhn was an American philosopher and historian of science. In 1962, he published The Structure of Scientific Revolutions, introducing the concepts of paradigm and paradigm shift, which transformed the understanding of scientific progress.

Definition: A framework of assumptions, concepts, and values that guide a group or field.

Example (Science): Moving from a-earth-centered universe to a sun-centered solar system is a change in the scientific paradigm.

Example (Daily Life): A shared cultural belief or a standard way of doing business.

Kuhn's Model (1962)

Kuhn's Model explains that science develops through successive paradigms rather than by continuous, gradual progress.

Stages of Kuhn's Model

Pre-paradigm Stage – No common theory; different ideas exist.
Normal Science – Scientists work within an accepted paradigm.
Crisis – Anomalies (unexplained problems) accumulate.
Scientific Revolution – A new paradigm replaces the old one.
New Normal Science – Research continues under the new paradigm.

Key Terminologies: Paradigm, Normal Science, Anomaly, Crisis, Scientific Revolution, Paradigm Shift.

Kuhn's Model in Geography

In geography, Kuhn's model explains how geographical thought changes over time as new theories and methods replace older ones.

Examples

Environmental Determinism → Possibilism
Regional Geography → Quantitative Revolution (Spatial Science)
Spatial Science → Behavioural, Humanistic, Radical, and Critical Geography
Traditional GIS → GeoAI, Big Data, and Geospatial Data Science

Kuhn's Paradigm Shift

A Paradigm Shift is a fundamental change in scientific thinking, where an existing theory or framework is replaced by a new one that better explains observations.

Key Terminologies

Paradigm – An accepted scientific framework or model.
Anomaly – An observation that cannot be explained by the current paradigm.
Crisis – Failure of the existing paradigm to explain anomalies.
Scientific Revolution – Replacement of the old paradigm by a new one.
Paradigm Shift – The transition from one paradigm to another.

Paradigm Shift in Geography

A paradigm shift in geography occurs when new concepts, methods, or technologies fundamentally change geographical research and practice.

Major Paradigm Shifts in Geography (Timeline)

Period	Paradigm Shift
Late 19th Century	Environmental Determinism → Possibilism
1950s–1960s	Regional Geography → Quantitative Revolution (Spatial Science)
1970s	Spatial Science → Behavioural and Humanistic Geography
1970s–1980s	Emergence of Radical and Critical Geography
1990s–Present	GIS, Remote Sensing, and Geospatial Technologies
2020s–Present	GeoAI, Machine Learning, Big Data, and Digital Geography

Importance

Explains the evolution of geographical thought.
Encourages new theories and methods.
Supports scientific innovation in geography.
Forms the basis of modern GIS, spatial analysis, and GeoAI.

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...

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

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 ...

Vineesh V, Geography

Search This Blog