This unit is part of the NCGIA Core Curriculum in Geographic Information Science. These materials may be used for study, research, and education, but please credit the author, Peter H. Schut, and the project, NCGIA Core Curriculum in GIScience. All commercial rights reserved. Copyright 1998 by Peter H. Schut.

Your comments on these materials are welcome. A link to an evaluation form is provided at the end of this document.

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Topics covered in this unit

• This unit provides a general overview of natural resources data, including:
• types of data
• typical applications
• common problems and limitations
• Specific kinds of data are described in greater detail in the accompanying subsections.

Learning Outcomes

• After learning the material covered in this unit, students should be able to:
• List the principal types of natural resource data
• Identify the principal sources of natural resource data
• Explain how natural resource data can be applied in a GIS context
• Identify the issues of concern in the application of natural resource data

Instructors' Notes

Full Table of Contents

Metadata and Revision History

1. Introduction

1.1. Purpose of natural resource databases

• natural resource-based data may be used
• as an inventory tool
• "what is here?"
• "where can this resource be found?"
• to better manage the marketing of the resource
• to protect the resource from improper development
• to model the complex interactions between phenomena so that forecasts can be used in decision-making

1.2. Contents of natural resource databases

• there are several different kinds of information needed in an environmental database
• the primary theme - geology, vegetation, hydrology, soils, etc
• however, to provide context, the environmental database may include several characteristics that are not generally perceived as "natural"
• transportation network
• political boundaries
• management unit boundaries
• other data may be needed for modeling will not form a part of the GIS dataset, e.g. variables relating to:
• erosion
• groundwater flow
• soil productivity

1.3. Sample Applications

• Sample geographic queries and applications of natural resource-based GIS:

2. Characteristics of Natural Resources Data

2.1 General Characteristics

• natural resource data in GIS is comparatively static
• update can be infrequent
• spatial resolution can be relatively low
• e.g. grid cells covering large areas
• historically, some natural resource GIS have been raster-based
• adequate for many planning and management applications
• can provide comprehensive coverage of a jurisdiction at reasonable cost
• could often run on existing mainframes - hardware requirements were modest for coarse rasters
• other resource databases have been vector based - soils, geology
• vector based data is easy to gather manually
• particularly useful where expert opinions define mapping units; where mapping is adequately or intuitively represented by groupings or associations

2.2. Spatial management units

• the actual management units of most natural resources in North America are pseudo-rasters
• square, forty acre parcels are the standard building block for PLSS areas (areas surveyed under the Public Land Survey System) of the Midwest, and Western United States, and much of Canada
• "forties" are frequently broken into ten acre units, or combined into:
• quarter sections (160 acres)
• sections (640 acres, 1 square mile)
• townships (6x6 miles)
• farms are managed in rectangular fields and forest resources are sold in similar acreage units
• however, natural resources do not commonly conform to these grids
• vector-based systems appear better able to accurately represent them
• on the other hand, satellite imagery, which is an important source of environmental data is raster-based,
• the raster may not be oriented the same way, or at a compatible scale.

2.3. Types of natural resource databases

• simple theme
• e.g. elevation, or stream hydrography,
• multiple theme
• e.g. soil type and properties (pH, texture, etc)
• numerous properties are mapped on a single set of polygons
• complex theme
• e.g. Ecoregions of North America

multiple unrelated attributes (such as geology, climate, and topography) are used to define a set of conceptual polygons

2.4. Lineage

 Databases can be populated by
• direct measurement (e.g. albedo)
• interpolated (e.g. precipitation)
• expert opinion (e.g. soil classification)
• takes years to create
• interpreted (e.g. risk of erosion)

3. Sources of Data

3.1. Thematic

• thematic map series are compiled by various agencies:
• soil maps (e.g. Soil Conservation Service)
• land use (e.g. USGS land use series, Canada Land Inventory )
• vegetation (forestry agencies, state governments)
• surficial geology (Federal and State/Provincial geological surveys)
• land ownership

3.2. Topographic

• topographic maps can supply:
• elevations
• roads and railroads
• cultural features
• streams and lakes
• political and administrative boundaries
• cadastral - "township and range"
• this type of data from USGS topographic maps is becoming available in digital form as DLG (digital line graph) files
• elevation data is available from the USGS in the form of DEMs, (digital elevation models) at various resolutions
• US Geological Survey supplies 30 m resolution data for much of US

3.3. Remote sensing

• remotely sensed imagery data can be interpreted to yield many layers
• e.g. urban/rural, vegetation, crops, surface geology, land use
• LANDSAT and TM (Thematic Mapper) are commonly used sources

4. Limitations

4.1. Completeness

• datasets are usually complete
• coverage is quite often incomplete

4.2. Precision

• normally reported reasonably
• errors reflect instrument or expert capability

4.3. Attribute accuracy

• direct measurements are subject to:
• instrument error
• error due to spatial resolution
• spatial referencing error
• interpolations subject to:
• spatial sampling artifact errors,
• errors due to interpolation algorithm,
• errors due to spatial autocorrelation
• expert opinion subject to:
• expert error,
• expert bias,
• impacts of expediency
• interpreted data subject to:
• algorithm problems,
• source data problems,
• severe error propegation

4.4. Logical consistency

• frequently different datasets will provide conflicting data or can be used to produce conflicting interpretations
• datasets gathered at different times or by different authorities may not be compatible.

4.5. Using remotely sensed data in GIS

• often difficult or time consuming to develop systematic products of known accuracy
• complex operations are required to force images to correspond to a known map projection and/or to have a consistent scale
• difficult to go from image (varying reflectance or emissivity in different wavelength bands) to interpreted features and objects
• however, since the value of a GIS is directly related to the quality and currency of its internal data
• remote sensing offers a suite of tools for quickly creating current, consistent datasets for input to a GIS
• conversely, remotely sensed data is best interpreted when additional spatial datasets (representing other dates, other scales, other sensors, other methods for acquiring data about the earth) are employed
• such data may be obtained from a GIS
• thus, strong links between remote sensing and GIS can improve both technologies

5. Summary

• to be provided

6. Print References

Marble, D.F. et al., 1983. &QUOTGeographic information systems and remote sensing," Manual of Remote Sensing. ASPRS/ACSM, Falls Church, VA, 1:923-58. Reviews the various dimensions of the relationship between the two fields.

Niemann, Jr., B.J., et al, 1988. &QUOTThe CONSOIL project: Conservation of natural resources through the sharing of information layers," Proceedings GIS/LIS '88, San Antonio, TX, pp. 11-25. Reviews a multi-agency project in Wisconsin to design and evaluate an LIS for soil conservation.

Star, J.L., and J. Estes, 1990. Geographic Information Systems: An Introduction, Prentice-Hall, Englewood Cliffs, NJ. Chapter 5 reviews data sources.

Sullivan, J.G., and B.J. Niemann, Jr., 1987. &QUOTResearch Implications of eleven natural resource GIS applications," Proceedings, IGIS '87, Arlington, VA, 3:329-341. A short review of several LIS for natural resource applications, discusses common themes, problems and techniques.

7. Exam and Discussion Questions

1. Review the difficulties inherent in obtaining interpreted features and objects from remotely sensed images.
2. Assume that you have access to remotely sensed images of your city with a resolution of 80 m (roughly the pixel size of Landsat). What functions of city government or local business would be able to make use of this resolution?
3. Discuss the range of errors which may exist in a soils map.
4. Discuss each of the types of data mentioned in this class in terms of required frequency of update.
5. How does a soil map become outdated?
6. What layers might you want for siting a waste incinerator?

Evaluation

Citation

Peter H. Schut (1998) Natural Resources Data,  NCGIA Core Curriculum in GIScience, http://www.ncgia.ucsb.edu/giscc/units/u090/u090.html, DRAFT, posted October 6, 1998.