Raster Analysis

Raster analysis is a powerful spatial analysis technique used in GIS to process and interpret grid-based datasets. It is widely applied in fields such as land cover classification, terrain modeling, hydrological studies, environmental monitoring, and spatial decision-making.

How Raster Analysis Works

Raster data is stored in a grid format where each cell (or pixel) represents a specific geographic location and contains a single value. The value can represent elevation, temperature, land cover type, or any other spatially continuous variable.

1. Spatial Resolution

The size of each cell in a raster dataset determines the level of detail.
Example: A 30m resolution DEM (Digital Elevation Model) means each cell represents a 30m × 30m area.

2. Extent

The geographic area covered by a raster dataset.
Example: A raster covering an entire country will have a larger extent than one covering a single city.

3. Cell Values and Data Types

Continuous Data: Represents smoothly varying phenomena (e.g., elevation, temperature, precipitation).
Categorical (Discrete) Data: Represents distinct classes (e.g., land use types, soil types).

Types of Raster Analysis in GIS

1. Overlay Analysis

Combines multiple raster layers to identify spatial relationships.

Example: Identifying flood-prone areas by overlaying elevation, rainfall, and land use rasters.

2. Suitability Analysis

Determines the best location for a specific activity based on multiple criteria.

Example: Finding a suitable site for a wind farm using layers like wind speed, land use, and proximity to roads.

3. Slope Analysis

Calculates the steepness of terrain from a DEM.

Example: Identifying areas with slopes greater than 30° for landslide risk assessment.

4. Aspect Analysis

Determines the direction a slope is facing.

Example: Finding south-facing slopes suitable for solar panel installation.

5. Distance Analysis

Measures the distance from a given feature.

Example: Mapping areas within 1 km of a river for ecological conservation.

6. Zonal Statistics

Summarizes raster values within defined zones (e.g., administrative boundaries).

Example: Calculating average rainfall within different watersheds.

7. Image Classification

Assigns land cover types to satellite images using supervised or unsupervised classification techniques.

Example: Classifying Sentinel-2 imagery into urban, forest, water, and agriculture classes.

8. Change Detection

Identifies changes in land cover or other raster datasets over time.

Example: Analyzing deforestation by comparing Landsat images from 2000 and 2020.

9. Terrain Analysis

Uses DEMs to derive hydrological and topographical features.

Example: Identifying valleys, ridges, and watershed boundaries.

10. Surface Modeling

Creates interpolated surfaces from point data.

Example: Generating a temperature surface from scattered weather station data.

Analysis Types Based on Cell Interactions

1. Local Operations (Per-Cell Analysis)

Each cell is analyzed independently without considering neighbors.
Example: Applying a mathematical function to all cells in a raster (e.g., converting elevation from meters to feet).

2. Neighborhood Operations (Focal Analysis)

A cell's value is determined based on surrounding cells.
Example: Applying a moving window filter (e.g., smoothing an elevation raster using a 3x3 mean filter).

3. Zonal Operations

Groups of cells belonging to the same zone are analyzed collectively.
Example: Calculating average elevation within different land use zones.

4. Global Operations

The entire raster dataset is used to compute an output.
Example: Calculating flow direction for an entire watershed.

Comments

Photogrammetry – Types of Photographs

In photogrammetry, aerial photographs are categorized based on camera orientation , coverage , and spectral sensitivity . Below is a breakdown of the major types: 1️⃣ Based on Camera Axis Orientation Type Description Key Feature Vertical Photo Taken with the camera axis pointing directly downward (within 3° of vertical). Used for maps and measurements Oblique Photo Taken with the camera axis tilted away from vertical. Covers more area but with distortions Low Oblique: Horizon not visible High Oblique: Horizon visible 2️⃣ Based on Number of Photos Taken Type Description Single Photo One image taken of an area Stereoscopic Pair Two overlapping photos for 3D viewing and depth analysis Strip or Mosaic Series of overlapping photos covering a long area, useful in mapping large regions 3️⃣ Based on Spectral Sensitivity Type Description Application Panchromatic Captures images in black and white General mapping Infrared (IR) Sensitive to infrared radiation Veget...

Photogrammetry – Geometry of a Vertical Photograph

Photogrammetry is the science of making measurements from photographs, especially for mapping and surveying. When the camera axis is perpendicular (vertical) to the ground, the photo is called a vertical photograph , and its geometry is central to accurate mapping. Elements of Vertical Photo Geometry In a vertical aerial photograph , the geometry is governed by the central projection principle. Here's how it works: 1. Principal Point (P) The point on the photo where the optical axis of the camera intersects the photo plane. It's the geometric center of the photo. 2. Nadir Point (N) The point on the ground directly below the camera at the time of exposure. Ideally, in a perfect vertical photo, the nadir and principal point coincide. 3. Photo Center (C) Usually coincides with the principal point in a vertical photo. 4. Ground Coordinates (X, Y, Z) Real-world (map) coordinates of objects photographed. 5. Flying Height (H) He...

Raster Data Structure

Raster Data Raster data is like a digital photo made up of small squares called cells or pixels . Each cell shows something about that spot — like how high it is (elevation), how hot it is (temperature), or what kind of land it is (forest, water, etc.). Think of it like a graph paper where each box is colored to show what's there. Key Points What's in the cell? Each cell stores information — for example, "water" or "forest." Where is the cell? The cell's location comes from its place in the grid (like row 3, column 5). We don't need to store its exact coordinates. How Do We Decide a Cell's Value? Sometimes, one cell covers more than one thing (like part forest and part water). To choose one value , we can: Center Point: Use whatever feature is in the middle. Most Area: Use the feature that takes up the most space in the cell. Most Important: Use the most important feature (like a road or well), even if it...

Photogrammetry

Photogrammetry is the science of taking measurements from photographs —especially to create maps, models, or 3D images of objects, land, or buildings. Imagine you take two pictures of a mountain from slightly different angles. Photogrammetry uses those photos to figure out the shape, size, and position of the mountain—just like our eyes do when we see in 3D! Concepts and Terminologies 1. Photograph A picture captured by a camera , either from the ground (terrestrial) or from above (aerial or drone). 2. Stereo Pair Two overlapping photos taken from different angles. When seen together, they help create a 3D effect —just like how two human eyes work. 3. Overlap To get a 3D model, photos must overlap each other: Forward overlap : Between two photos in a flight line (usually 60–70%) Side overlap : Between adjacent flight lines (usually 30–40%) 4. Scale The ratio of the photo size to real-world size. Example: A 1:10,000 scale photo means 1 cm on the photo...

Logical Data Model in GIS

In GIS, a logical data model defines how data is structured and interrelated—independent of how it is physically stored or implemented. It serves as a blueprint for designing databases, focusing on the organization of entities, their attributes, and relationships, without tying them to a specific database technology. Key Features Abstraction : The logical model operates at an abstract level, emphasizing the conceptual structure of data rather than the technical details of storage or implementation. Entity-Attribute Relationships : It identifies key entities (objects or concepts) and their attributes (properties), as well as the logical relationships between them. Business Rules : Business logic is embedded in the model to enforce rules, constraints, and conditions that ensure data consistency and accuracy. Technology Independence : The logical model is platform-agnostic—it is not tied to any specific database system or storage format. Visual Representat...

Vineesh V, Geography

Search This Blog