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 linked directly to either their original (2006) or revised entries; forthcoming, future topics are italicized. 


Digital Data Capture Land Surveying and GPS  
Data capture methods Survey theory and electro-optical methods  
  Land records  
Aerial Imaging Global Positioning System  
Nature of aerial photograph data    
Aerial photography image interpretation Field Data Collection  
  Spatial sample types  
Remote Sensing Platforms & Sensors Sample size selection  
Mission planning Sample intervals  
Nature of multispectral image data Census data  
Multispectral sensors and programs Field data technologies  
LiDAR data Volunteered Geographic Information (VGI)  
Unmanned Aerial Systems (UAS)    
  Data Coordinating Organizations  
  Spatial data sharing among organizations  
Processing Remotely-Sensed Data Federal agencies, National & International Organizations  
Stereoscopy and orthoimagery State and regional coordinating bodies  
Vector data extraction    
Algorithms and processing    
Ground verification and accuracy assessment    


DC-08 - Sample intervals
  • Identify the fundamental principle of the sampling theorem for specifying a sampling rate or interval
  • Discuss what sampling intervals should be used to investigate some of the temporal patterns encountered in oceanography
  • Propose a sampling strategy considering a variable range in autocorrelation distances for a variable
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-07 - Spatial sample types
  • Design point, transect, and area sampling strategies for given applications
  • Differentiate between situations in which one would use stratified random sampling and systematic sampling
  • Differentiate among random, systematic, stratified random, and stratified systematic unaligned sampling strategies
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-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