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

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...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...