Which of the following is an example of raster data analysis?

Raster data analysis primarily involves the manipulation and examination of data that is organized in a grid-like structure, which comprises cells or pixels. Each pixel holds a value representing information, usually pertaining to a physical quantity, such as temperature, elevation, or land cover type. This format enables various analytical processes, particularly those that relate to spatial phenomena spread across a continuous surface.

Surface modeling exemplifies raster data analysis, as it deals with creating representations of surfaces, such as Digital Elevation Models (DEMs), where the surface characteristics are defined using grid cells. This allows for the assessment of spatial relationships and analyses such as slope, aspect, and watershed calculations. Surface modeling techniques leverage the advantages of raster data to derive insights about topographic and geographic features effectively.

In contrast, other options like vector queries, network analysis, and database management refer to techniques primarily associated with vector data, which utilizes discrete points, lines, and polygons. Vector data is more suited for representing discrete features and often involves different analytical approaches that do not utilize the grid format characteristic of raster data.