Value Attribute Table (VAT)
A table associated with raster datasets that stores attribute information for each unique cell value, including the value itself and a count of cells with that value. VATs are essential for classifying and analysing raster data.
.png)
Describe Value Attribute Table (VAT)
The attribute information for raster datasets, especially those with discrete (categorical) values like land cover, soil type, or land use classifications, is stored in a Value Attribute Table (VAT), a data structure in GIS.
Every cell (or pixel) in a raster has a value. The VAT associates cell values (commonly referred to as "value" or "class codes") with descriptive features in tabular form when a raster contains categorical data. A land cover raster, for instance, could have:
Value | Land Cover Type | Area (sq. km) |
1 | Forest | 150 |
2 | Water | 45 |
3 | Urban | 30 |
Related Keywords
A tabular form that associates each distinct value in a raster dataset with its associated attributes is called a Value Attribute Table (VAT) in GIS. It enables users to efficiently evaluate and classify raster data by storing information like pixel values, counts, and descriptive data. For tasks like statistical analysis, thematic mapping, and classifications, VATs are crucial.
An attribute table for each distinct pixel value in a raster, usually for categorical or thematic rasters, is called a VAT (Value Attribute Table) in GIS. It associates descriptive properties (such land cover type, area, or colour) with each raster value (like land use codes). VAT is crucial for categorization and thematic mapping since it enables analysis and queries on raster data without changing the actual pixel values.
An attribute table in a GIS contains comprehensive data about spatial features, including names, kinds, and measurements. Users can query, sort, and filter data, carry out statistical computations, and find patterns or relationships in the dataset by analysing this table. It effectively connects real-world attributes to physical places and is crucial for spatial analysis, mapping, and decision-making.
A GIS approach called Raster Value Attribute Mapping (RVAM) gives the numerical values of raster cells meaningful attributes or categories. Each value can indicate a particular class, such as soil type, elevation range, or land cover type, rather of just displaying the raw pixel values. By associating numerical values with actual attributes, this mapping facilitates improved raster data presentation, analysis, and interpretation.