Skip to main content

Geographic phenomena fields objects boundaries.


In geography, geographic phenomena refer to features or processes that can be observed and studied on Earth's surface. These phenomena can be classified into three main categories: fields, objects, and boundaries. Each category has distinct characteristics, representations, and applications in Geographic Information Systems (GIS).


1. Fields

A field represents continuous, spatially varying data where a value is present at every location within the study area. It describes conditions that exist across a geographic area.

Characteristics:

  • Continuity: Fields have no discrete boundaries; the data is continuous.
  • Gradual Variability: The values of a field change gradually across space.
  • Representation: Typically modeled using raster data in GIS, where a grid structure assigns a value (e.g., temperature or elevation) to each cell.

Examples:

  • Temperature Map: Shows temperature variation across a region.
  • Rainfall Distribution: Displays rainfall levels over a large geographic area.
  • Elevation Data: Represents the height of land at every point in an area.

2. Objects

An object is a discrete geographic feature with a clearly defined location and boundaries. Objects are identifiable entities that exist as "whole" within a space.

Characteristics:

  • Discrete Entities: Objects are separate from their surroundings.
  • Defined Boundaries: Each object has clear limits.
  • Representation: Modeled using vector data in GIS, which represents objects as points, lines, or polygons.

Examples:

  • A City: Represented as a polygon or point on a map.
  • A River: Represented as a line feature in GIS.
  • A Building: Mapped as a point or polygon feature.

3. Boundaries

A boundary is the line or zone that marks the separation between different geographic features, fields, or phenomena. Boundaries can be sharp or gradual, natural or human-made.

Characteristics:

  • Sharp Boundaries: Distinct separation, such as a political border between two countries.
  • Gradual Transitions: Zones of change, such as a gradient from forest to grassland.
  • Representation: Typically represented as lines or polygons in GIS.

Examples:

  • Natural Boundary: Coastlines, rivers, or mountain ranges.
  • Human-Made Boundary: State or country borders, property lines.
  • Ecological Boundary: Transition zones between ecosystems, like a forest edge.

Key Differences Between Fields and Objects

AspectFieldsObjects
NatureContinuous data across spaceDiscrete entities with defined boundaries
RepresentationRaster dataVector data
ExamplesTemperature, elevation, rainfallCities, rivers, buildings
ChangeGradual changes across spaceFixed, defined locations


Fyugp note 
GIS second semester 

Comments

Popular posts from this blog

Radar Remote Sensing SAR

1. Radar and Radar Remote Sensing RADAR stands for Radio Detection and Ranging . It is an active remote sensing system that transmits microwave energy toward the Earth's surface and records the energy that is returned to the sensor as an echo or backscatter . Unlike passive optical remote sensing, radar does not depend on sunlight. Therefore, it can operate day and night and, at suitable wavelengths, can acquire data through clouds, haze and light rain. Hence, radar is widely described as an all-weather, day-and-night remote sensing technology . Basic principle Microwave pulse → transmission → interaction with surface → backscatter/echo → antenna receives signal → signal processing → radar image 2. Microwave Energy Radar systems use microwave electromagnetic radiation , generally in wavelength ranges from approximately 1 mm to 1 m . Important radar bands include: Band Approx. wavelength Common applications ...

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...

Lidar

LiDAR (Light Detection and Ranging) is an active remote sensing technology that measures distances by illuminating a target with laser pulses and analyzing the time it takes for the reflected light to return. Unlike passive systems (e.g., cameras, multispectral sensors), LiDAR provides its own energy source (laser), allowing it to operate both day and night and even penetrate through vegetation canopies . 🔹 How LiDAR Works (Step-by-Step Process) Laser Pulse Emission The system emits rapid, short pulses of laser light (commonly in the near-infrared wavelength, 1064 nm ). Some systems emit up to hundreds of thousands of pulses per second . Interaction with Target Surface The laser beam strikes objects such as vegetation, buildings, or bare ground. Depending on the object's structure, part of the pulse may scatter or reflect. Return Signal Detection The sensor records multiple returns : First Return → typically vegetation canopy tops. ...

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...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types  1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...