Dasymetric Map. 🗺️

In cartography, a dasymetric map is a type of thematic map that represents and displays data using a combination of geographic boundaries and auxiliary information. It is designed to overcome the limitations of choropleth maps, which often assign a single data value to an entire geographic unit, such as a polygon or region.

Unlike choropleth maps, which divide the map into predefined polygons and color them based on the data value associated with that entire polygon, dasymetric maps aim to allocate the data more accurately within the polygons. This is achieved by utilizing auxiliary information, such as population density, land cover, or other relevant data sets, to refine the distribution of the primary data.

The term "dasymetric" itself refers to the process of partitioning a map into subregions or zones with different characteristics. This technique involves using ancillary data to estimate the distribution of the primary data within these subregions. By taking into account the varying characteristics of different areas within a polygon, dasymetric maps provide a more precise representation of the underlying data.

To create a dasymetric map, the following steps are typically involved:

1. Define the primary data: Determine the data that you want to represent on the map, such as population, income levels, or vegetation cover.

2. Identify auxiliary data: Select auxiliary data sets that are related to the primary data and can provide information about the distribution of the primary data within each polygon. For example, population density data can be used to allocate population values within different areas.

3. Divide the map into subregions: Based on the auxiliary data, partition the map polygons into subregions or zones that have distinct characteristics and can better represent the distribution of the primary data.

4. Allocate data values: Using statistical or modeling techniques, distribute the primary data values within each subregion according to the auxiliary data. This involves estimating how the primary data is likely to be distributed within the boundaries of each subregion.

5. Visualize the map: Display the dasymetric map by assigning colors or shading to the subregions based on the allocated data values. This provides a more accurate representation of the spatial distribution of the primary data.

Dasymetric maps are particularly useful when the primary data being represented exhibits significant variation within the boundaries of the polygons, such as population densities that vary across urban and rural areas. By incorporating additional information, dasymetric mapping allows for a more detailed and nuanced portrayal of data patterns, leading to improved cartographic representations and analysis.

Comments

RADIOMETRIC CORRECTION

Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

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...

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...

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...

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...

Vineesh V, Geography

Search This Blog