Application of Remote Sensing. Urban Planning ...

Urban land use and land cover mapping: Remote sensing can be used to map and monitor land use and land cover changes in urban areas, such as the expansion of housing developments, commercial areas, and transportation infrastructure.

Identification of areas of urban sprawl: Remote sensing can be used to identify areas of urban sprawl, which can help in planning for sustainable growth and managing land use.

Building inventory and change detection: Remote sensing can be used to create a detailed inventory of buildings and other structures in urban areas, and to detect changes in the built environment over time.

Transportation planning: Remote sensing can be used to map and monitor transportation infrastructure, such as roads, bridges, and rail lines, which can help in planning for new transportation projects and managing existing infrastructure.

Mapping of green spaces: Remote sensing can be used to map and monitor the distribution and health of green spaces in urban areas, such as parks, gardens, and open spaces, which can help in planning for sustainable urban environments.

Identification of potential hazards: Remote sensing can be used to identify and map potential hazards in urban areas, such as flooding, landslides, and wildfire risks, which can help in disaster risk reduction and mitigation.

Lighting and energy efficiency: Remote sensing can be used to assess the lighting and energy efficiency of buildings and other structures in urban areas, which can help in planning for sustainable urban environments.

Urban heat island effect: Remote sensing can be used to monitor the urban heat island effect, which can help in identifying areas with high temperatures, and in planning for cooling strategies.

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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...

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...

Applications of Remote Sensing

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition. Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...

Quantitative Revolution in Geography

The Quantitative Revolution was a major change in Geography during the 1950s and 1960s. It introduced mathematical, statistical and scientific methods to understand geographical phenomena. Before this revolution, geographers mainly described places, regions, landscapes and differences between areas. After the revolution, they increasingly used numerical data, statistical analysis, mathematical models and theories to explain why geographical patterns occur and how they may change. For example: Traditional approach: Describe the distribution of population in different regions. Quantitative approach: Use statistical methods to identify population patterns, explain the factors influencing population distribution and predict future population growth. The revolution helped establish Geography as a spatial science, focusing on the location, distribution, relati...

Vineesh V, Geography

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