Land Cover Classification for Planning and Management
Integrated Geospatial Technology and Training (iGETT)
Curriculum Guide
Learning Unit Title: Land Cover Classification for Planning and Management
Focus Area: Environmental Management
Learning Unit Description
This learning unit will guide students through image processing for classifying and
evaluating land cover. Essential skills in the acquisition, processing and
interpretation of imagery will be developed. Students will incorporate reference
data, including existing high resolution imagery and GIS vector data sets. Land
cover classification will be optionally field verified using a GPS-based protocol.
Results will be presented in a Powerpoint presentation..
Background
Humans have altered or modified most of Earth’s land surfaces. Population
increase, coupled with technologies that increase our capacity to extract
resources and ‘develop’ the land, make the issue of assessing land cover and
land use (hereafter referred to as LCLU) of paramount importance. Applications
for LCLU typing include resource management, hazard mitigation, and planning.
A significant body of research provides a foundation for understanding LCLU
and methods for analysis, including the use of remotely sensed imagery (see
References and Resources on Land Cover Classification and Imagery section of
this document).
The concepts of land cover and land use are related yet distinct. Land cover
refers to observable surface features that may be grouped in categories (i.e.,
classified) such as 'forest', 'crops', and 'residential areas'. Land use, while it can
often be inferred from land cover, is less tangible and more abstract. Supporting
reference materials, such as ownership maps, are often needed to determine
land use. For example, a 'forest' land cover type may actual represent a range
of different land uses such as 'open space/recreation', 'private timberlands', and
'restricted watersheds'. The focus of this learning unit is on land cover
classification.
Medium resolution satellite imagery may be well suited to assessing land cover,
depending on the application and area of study. Such imagery covers large
geographic areas (e.g., a Landsat Thematic Mapper scene covers an area of
over 30,000 square kilometers), and are often available for a range of different
time periods (in the case of Landsat, imagery has been available dating back to
the 1970s). Image bands, defined by ranges of electromagnetic wavelengths
measured in micrometers, are the most common means of differentiating
spectral characteristics (see accompanying Support Document entitled
Characteristics of Landsat and Aster Imagery). In addition to understanding the
spectral characteristics of imagery, spatial characteristics must be evaluated for
appropriate application. For example, Landsat imagery has a spatial resolution
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Land Cover Classification for Planning and Management
(i.e., resolving power) of 30 meters. As such, it is appropriate for global and
regional applications, but is generally of insufficient resolution for local
applications. Aster imagery, by contrast, has a spatial resolution of 15 meters (i.e.,
twice the resolving power of Landsat) for the visible and very near infrared
bands, and is therefore more appropriate for local analysis. For many
applications it may be necessary to acquire high resolution imagery, such as
Quickbird or Ikonos, with sub-meter spatial resolution (such imagery is costly and
not addressed in this learning unit). Evaluating the spatial and spectral
characteristics of imagery is an important aspect of learning effective use of
remotely sensed imagery (USGS's EROS website is an excellent source for
exploring this topic; http://eros.usgs.gov/products/satellite.html).
Image processing for classifying land cover has important applications in the
workplace. Resource management and planning organizations, both in the
public and private sector, depend on accurate land cover maps. The U.S.
Federal government manages vast areas of the western United States (roughly
half of California's 100 million acres is federally owned). Therefore agencies such
as the U.S. Forest Service, Bureau of Land Management, and National Park
Service are potential employers. State agencies, such as the California
Department of Water Resources, are also important users of land cover data. In
the private sector, forestry, mining, transportation, engineering, and related
companies require land cover information, and are therefore potential clients for
remote sensing. Regional and global concerns, such as urbanization and climate
change, require accurate land cover data for impact assessment and
mitigation. Workers trained in remote sensing and GIS will be increasingly valued
in coming years, as the need for accurate land cover mapping and related
spatial data increases.
Evaluation/Assessment
 Students will complete each of part of the Learning Unit (each Part
includes a deliverable; see Student Guide)
 Student maps and charts will be evaluated for accuracy, documentation,
and cartographic quality
 Presentation of resulting maps to a community organization or
conference will be accompanied by an evaluation form.
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Land Cover Classification for Planning and Management
Timeline
Learning unit to be completed over 16 or 20 hours (typically over four or five 4
hours labs sessions). The field validation is optional, since access to the
geographic area of the learning unit (i.e., Redding), may not be feasible.
Instructors may wish apply the basic procedures using data sets from their own
geographic locales.
 Part 1 – Basics of imagery; viewing, characteristics, acquisition
 Part 2 – Image Processing with Unsupervised Classification; stack, subset,
perform unsupervised classification, combine and clump classes, derive
cell statistics
 Part 3 – Image Processing with Supervised Classification; delineate training
sites, perform supervised classification, derive cell statistics
 Part 4 – Reference Data Integration and Interpretation of Results;
evaluated classified images based on reference data, georeference
images with vector data, produce Powerpoint with image maps, graphs,
results
 Part 5 - Field validation (optional); locating sampling sites, implementing
field protocol
Scientific or Geographic Concepts
Land use
Land cover
Raster
Vector
Unsupervised classification
Supervised classification
Spatial extent
Spectral bands
Pixel location
Pixel value
Sampling
Field validation
Key words
Land cover
Land use
Image processing
Image enhancement
Vegetation indices
Unsupervised classification
Supervised classification
Training sites
GPS Field validation
GIS integration
Relevant Disciplines
Geography
Geographic Information Systems
Environmental Science
Fire Science
Natural Resources
Engineering/Surveying
Author: Dan Scollon
Institution: Shasta College
Email: dscollon@shastacollege.edu
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