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Geographic Information Systems Applications in Natural Resource Management. Chapter 3 Acquiring, Creating, and Editing GIS Databases. Michael G. Wing & Pete Bettinger. Chapter 3 Objectives. Methods to acquire GIS databases, particularly via the Internet

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Geographic Information SystemsApplications in Natural Resource Management

Chapter 3

Acquiring, Creating, and

Editing GIS Databases

Michael G. Wing & Pete Bettinger


Chapter 3 Objectives

  • Methods to acquire GIS databases, particularly via the Internet

  • Methods to create new GIS databases

  • Processes to edit existing GIS databases

  • Types and sources of error potentially associated with GIS databases


Four general cases of GIS databases at most organizations

  • The data necessary for project work

    • Don’t exist

    • Do exist but were created for other general uses and may not be completely suitable for your project

    • Do exist but were created for other specific uses and may not be completely suitable for your project

    • The data are in place and in good order for your project!


Typically, you’ll have to acquire data

  • Hire a contractor to create or edit GIS data

  • Use a GPS or other device to capture data

  • Modifying an existing database

  • Create a new GIS database

    • Digitizing

    • Using/buying data from another organization

    • Downloading data from the Internet


Internet acquisition

  • Now a standard method of making data available

  • Many federal and state organizations make spatial data available

    • The U.S. Government is the largest producer of spatial data in the world

    • Manual of Federal Geographic Data Products

  • Freedom of information act allows the public some access to data created by public agencies

  • Use these data to save time


Oregon Data Sources

  • Oregon Geospatial Data Clearinghouse (OGDC)

    • Formerly the SSCGIS

    • Demonstration

  • OSU College of Forestry

  • Bureau of Land Management (BLM)*

  • National Forest Offices (SNF)

  • StreamNet*

  • Oregon Department of Fish and Wildlife (ODFW)*

  • Environmental Protection Agency (EPA)*


National Data Sources

  • US Geological Survey (USGS)*

  • Bureau of Land Management (BLM)*

  • National Park Service (NPS)*


Acquisition processes

  • Using an Internet browser to select and download

  • FTP- File Transfer Protocol

  • Transfer on hardware

    • Tape

    • External harddrive

    • CD or DVD

    • USB key

    • Floppy


Creating GIS databases

  • We’ve covered techniques in Chapter 1

    • Digitizing

    • Remote Sensing

      • Satellite imagery

      • LIDAR

    • Laser range finder

    • Total station

    • Scanning

    • GPS


Roads

Stands

Reference points

(with associated

X and Y

coordinates)

Figure 3.1. Measurement reference points for the Daniel Pickett forest to enable digitizing of additional landscape features or the creation of new GIS databases.


Figure 3.2. A landslide drawn on a map with a regular sharpened pencil (upper left), a marker (upper right), a sharpened pencil, yet in a sloppy manner – the landslide area is not closed (lower left), a marker, yet in a sloppy manner - the landslide area is barely closed (lower right).


(a)

(b)

(c)

Figure 3.3. A timber stand (a) in vector format, from the Brown Tract, scanned (b) or converted to a raster format using 25 m grid cells, then converted back to vector format (c) by connecting lines to the center of each grid cell.


Editing GIS databases

  • Reasons for editing

    • Changing a spatial projection

    • Edge matching GIS databases to other databases

    • Generalization and transformation processes necessary to convert a GIS database to a specific format or resolution

  • In natural resources, updates may occur annually to keep pace with timber inventory

    • Growth, disturbance, harvesting


Updating processes

  • Can be laborious and error-prone

  • Verification protocols can help

    • Assures that data variables are reasonable or meet some standard

    • Should be in place for spatial and attribute characteristics

    • Should involve multiple people


Inventory forester

Information systems

analysts

maps, data files

Delineate changes

to be made to

inventory

Check data for

mistakes and

omissions

maps, data files

Verification

process #1

maps, data files

Digitize changes

to spatial data,

encode inventory

maps, data files

GIS

databases

Check data for

mistakes and

omissions

Verification

process #2

maps, data files

Integrate into

GIS

database

GIS

databases

Check data for

mistakes and

omissions

Check data for

mistakes and

omissions

Verification

process #4

Verification

process #3

Figure 3.4. A generalized process for updating a forest inventory GIS database.


Editing attributes

  • Attributes are the values used to describe landscape features in a GIS database

    • Fields, variables, columns, data, etc.

  • Attributes may need to be updated overtime

    • Vegetation type, basal area, age, volume (mbf)

  • Easy to make mistakes, particularly with major updates

  • Verification processes can check whether values are in the appropriate range


Editing spatial position

  • As the locations or shapes of spatial features change, their coordinates will need to be changed within the GIS database

  • Editing procedures for this purpose vary widely among software products

    • Typically, a database is first made “editable”

    • The user then makes edits

      • Points, lines, and/or polygons moved, copied, created, or deleted

    • The edits are saved

    • Often, a time-consuming procedure


Consistency in spatial position

  • When updating or creating new data, inconsistencies may result as the data are incorporated into existing databases


Inconsistency

Roads

Timber

stands

Figure 3.5. A timber stand drawn more precisely (top) and less precisely (bottom). Note that the lines on the south and eastern portion of the figures are different.

Figure 3.6. Spatial inconsistency between a timber stand GIS database and a roads GIS database.


Error in GIS databases

  • Errors arise from many sources:

    • Editing, encoding, hardware, and others

  • Three primary sources of error in GIS data

    • Systematic

    • Human

    • Random


Systematic errors

  • Caused by problems in the processes and/or tools used to measure spatial locations or attribute data

  • Sometimes called cumulative errors since they add up during data collection

  • Sometimes called instrumental

  • Can be removed if identified and quantified


Human errors

  • Sometimes called gross errors or blunders

  • As the name suggests, these are introduced through carelessness or other inattention

  • Verification processes can be used to control human errors

  • Data collection and editing protocols can also assist in limiting human errors


Random errors

  • An almost unavoidable by-product of measuring and describing landscape data

  • No matter how careful we are in data collection procedures, there will almost always be some slight variance from the true measurement

  • Random errors are the errors that remain after systematic and human errors have been removed


Managing random errors

  • We assume that random errors follow a normal (Gaussian) distribution

    • They cluster around a mean value or center

  • Least squares adjustments can distribute and minimize the error among all measurements in a feature

  • More frequently, and especially in forestry, we assume that random errors will cancel each other out

    • For this reason, random errors are sometimes called compensating errors


Types of errors in GIS databases

  • Positional errors occur when things are in the wrong place

  • Can result from poor registration or inaccurate coordinate input during the digitizing process

  • Are sometimes handled with accuracy statements: “90 percent of landscape features are within 150 meters of their true position”

  • A root mean square error (RMSE) is sometimes used to set or describe an accuracy standard

    • A RMSE assesses the error between a mapped point and its on-the-ground (true) equivalent


Digitized road segment

Real-world representation #1

Real-world representation #2

Real-world representation #3

Figure 3.7. Uncertainty of the local shape of a road segment

(after Schneider 2001).


Other types of errors

  • Attribute errors

    • Incorrect values assigned to features

    • Can result from keyboard entry

    • Verification processes can help alleviate these

  • Computational errors

    • Can be introduced during procedures

      • Generalization

      • Vector-to-raster transformations

      • Interpolations

    • Results should be carefully considered to judge appropriateness of procedures


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