Data Capture

The capture of massive quantities of spatial data, able to be distributed and shared in real time, provide for an ever-increasing range of environmental and societal applications. Data capture includes the principles, methods, technologies, applications, and institutional/programmatic aspects of spatial data acquisition. Sources of data include global navigation satellite systems, satellite and aerial sensing, field surveys, land records, socioeconomic data (e.g., census), volunteered geographic information, wireless sensor networks, and unmanned aerial systems.

Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized


Digital Data Sources & Capture Methods Land Surveying and Global Navigation Satellite Systems (GNSS)
Texts Professional Land Surveying
Historical (Paper) Maps Land records
Aerial Photography Global Navigation Satellite Systems
Ground Imagery  
Mobile Applications Field Data Collection
Social Media Platforms Field Data Capture Technologies
Remote Sensing Platforms & Sensors Sampling: Size Selection, Sample Types, Intervals
Mission planning U.S. Census Data
Nature of multispectral image data Volunteered Geographic Information (VGI)
Remote Sensing Platforms overview  
Landsat Data Coordinating Organizations
LiDAR Spatial Data Sharing Among Organizations
Hyperspectral Imagery State & Regional Coordinating Bodies
Unmanned Aerial Systems (UAS) Federal Agencies & National Organizations and Programs
Thermal Imagery International Organizations & Programs
Radar, Sonar, and Echolocation  
Processing Remotely-Sensed Data  
Image Interpretation: Aerial Photography & Satellites  
Spectral Properties of Terrestrial Surfaces  
Stereoscopy and orthoimagery  
Vector data extraction  
Algorithms and processing  
Ground verification and accuracy assessment  


DC-16 - Nature of Multispectral Image Data

A multispectral image comprises a set of co-registered images, each of which captures the spatially varying brightness of a scene in a specific spectral band, or electromagnetic wavelength region. An image is structured as a raster, or grid, of pixels. Multispectral images are used as a visual backdrop for other GIS layers, to provide information that is manually interpreted from images, or to generate automatically-derived thematic layers, for example through classification. The scale of multispectral images has spatial, spectral, radiometric and temporal components. Each component of scale has two aspects, extent (or coverage), and grain (or resolution). The brightness variations of an image are determined by factors that include (1) illumination variations and effects of the atmosphere, (2) spectral properties of materials in the scene (particularly reflectance, but also, depending on the wavelength, emittance), (3) spectral bands of the sensor, and (4) display options, such as the contrast stretch, which affect the visualization of the image. This topic review focuses primarily on optical remote sensing in the visible, near infrared and shortwave infrared parts of the electromagnetic spectrum, with an emphasis on satellite imagery.  

DC-26 - Remote Sensing Platforms

Remote sensing means acquiring and measuring information about an object or phenomenon via a device that is not in physical or direct contact with what is being studied (Colwell, 1983).To collect remotely sensed data, a platform – an instrument that carries a remote sensing sensor – is deployed. From the mid 1800’s to the early 1900’s, various platforms such as balloons, kites, and pigeons carried mounted cameras to collect visual data of the world below. Today, aircraft (both manned and unmanned) and satellites collect the majority of remotely sensed data. The sensors typically deployed on these platforms include film and digital cameras, light-detection and ranging (LiDAR) systems, synthetic aperture radar (SAR) systems, and multi-spectral and hyper-spectral scanners. Many of these instruments can be mounted on land-based platforms, such as vans, trucks, tractors, and tanks. In this chapter, we will explore the different types of platforms and their resulting remote sensing applications.

DC-06 - Sample size selection
  • Determine the minimum number and distribution of point samples for a given study area and a
  • Determine minimum homogeneous ground area for a particular application
  • Describe how spatial autocorrelation influences selection of sample size and sample statistics
  • Assess the practicality of statistically reliable sampling in a given situation
  • given statistical test of thematic accuracy
DC-21 - Spatial data sharing among organizations
  • Describe the rationale for and against sharing data among organizations
  • Describe the barriers to information sharing
  • Describe methods used by organizations to facilitate data sharing
DC-23 - State and regional coordinating bodies
  • Describe how state GIS councils can be used in enterprise GIS&T implementation processes
  • Explain the functions, mission, history, constituencies, and activities of your state GIS Council and related formal and informal bodies
  • Discuss how informal and formal regional bodies (e.g., Metro GIS) can help support GIS&T in an organization
  • Discuss the mission, history, constituencies, and activities of National States Geographic Information Council (NSGIC)
  • Determine if your state has a Geospatial Information Office (GIO) and discuss the mission, history, constituencies, and activities of a GIO
DC-13 - Stereoscopy and orthoimagery
  • Explain the relevance of the concept “parallax” in stereoscopic aerial imagery
  • Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data
  • Specify the technical components of an aerotriangulation system
  • Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph
DC-01 - Survey theory and electro-optical methods
  • Apply coordinate geometry to calculate positions in a coordinate system grid based on control point locations and measured angles and distances
  • Given the elevation of one control point, calculate the elevation of a second point by differential (spirit or direct) leveling
  • Given the elevation of one control point, calculate the elevation of a second point by trigonometric (indirect) leveling
  • Describe the differences between differential and trigonometric leveling
  • Explain how electronic distance measurement instruments work
  • Define the concepts ellipsoidal (or geodetic) height, geoidal height, and orthometric elevation
  • Illustrate the relationship between the concepts of ellipsoidal (or geodetic) height, geoidal height, and orthometric elevation
DC-28 - United States Census Data

The Census Bureau collects extensive numeric data on the residents of the United States as well ast the national economy.  This is accomplished both through a decennial census as well as numerous other more frequent surveys. The decennial census is a fundamental basis of American democracy, mandated by the U.S. Constitution and essential for the equal representation in a democratic government. Numeric census data are maintained in vast collections of tables and organized at many different levels of geographies. From the Census website, the geographic and tabular data can be downloaded and then joined for display and analysis within a GIS. Because of the nature of individual data aggregated over areas and other matters, care must be taken to avoid statistical errors when undertaking spatial analyses.

DC-24 - Unmanned Aerial Systems (UAS)

Unmanned Aerial Systems (UAS) are revolutionizing how GIS&T researchers and practitioners model and analyze our world. Compared to traditional remote sensing approaches, UAS provide a largely inexpensive, flexible, and relatively easy-to-use platform to capture high spatial and temporal resolution geospatial data. Developments in computer vision, specifically Structure from Motion (SfM), enable processing of UAS-captured aerial images to produce three-dimensional point clouds and orthophotos. However, many challenges persist, including restrictive legal environments for UAS flight, extensive data processing times, and the need for further basic research. Despite its transformative potential, UAS adoption still faces some societal hesitance due to privacy concerns and liability issues.

DC-14 - Vector data extraction
  • Describe the source data, instrumentation, and workflow involved in extracting vector data (features and elevations) from analog and digital stereoimagery
  • Discuss future prospects for automated feature extraction from aerial imagery
  • Discuss the extent to which vector data extraction from aerial stereoimagery has been automated