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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 systems applications in natural resource management l.jpg

Geographic Information SystemsApplications in Natural Resource Management

Chapter 3

Acquiring, Creating, and

Editing GIS Databases

Michael G. Wing & Pete Bettinger


Chapter 3 objectives l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
National Data Sources

  • US Geological Survey (USGS)*

  • Bureau of Land Management (BLM)*

  • National Park Service (NPS)*


Acquisition processes l.jpg
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 l.jpg
Creating GIS databases

  • We’ve covered techniques in Chapter 1

    • Digitizing

    • Remote Sensing

      • Satellite imagery

      • LIDAR

    • Laser range finder

    • Total station

    • Scanning

    • GPS


Slide10 l.jpg

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.


Slide11 l.jpg

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).


Slide12 l.jpg

(a) 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).

(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 l.jpg
Editing GIS databases 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).

  • 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 l.jpg
Updating processes 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).

  • 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


Slide15 l.jpg

Inventory forester 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).

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 l.jpg
Editing attributes 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).

  • 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 l.jpg
Editing spatial position 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).

  • 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 l.jpg
Consistency in spatial position 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).

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


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Inconsistency 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).

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 l.jpg
Error in GIS databases 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).

  • Errors arise from many sources:

    • Editing, encoding, hardware, and others

  • Three primary sources of error in GIS data

    • Systematic

    • Human

    • Random


Systematic errors l.jpg
Systematic errors 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).

  • 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 l.jpg
Human errors 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).

  • 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 l.jpg
Random errors 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).

  • 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 l.jpg
Managing random errors 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).

  • 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 l.jpg
Types of errors in GIS databases 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).

  • 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


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Digitized road segment 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).

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 l.jpg
Other types of errors 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).

  • 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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