Land Use and Land Cover
Chapter 20
Land use – defined by economic terms
Land cover – visible features
Both are important and are really
We depend on accurate LU/LC data for
scientific and administrative purposes
What are some examples of why
knowledge of LU/LC is important?
Predictive Land Use Modeling
A Basin-Scale Econometric Model for
Projecting Future Amazonian Landscapes was
developed to predict forest loss associated
with development scenarios in the Amazon
Given the scenarios, projections follow from results of
econometric modeling based on economic theory and
detailed local observation (led by Alexander Pfaff of
Columbia University).
As an example, this image shows a situation in
which deforestation precedes road-building.
It depicts in red several settlement roads in 1988;
deforested areas, as of 1988, are shown by the yellow
polygons extending beyond the roads.
Since the roads now pass through these old
deforested areas, the figure suggests reverse
causality, in which deforestation actually leads
to road-building.
This situation is probably common in areas of
smallholder colonization.
Air Photos
Most LU/LC data are derived from air
• Used as early as 1930 by the TVA
USGS later developed a classification
USGS Classification System
A Land Use And Land Cover Classification
System For Use With Remote Sensor Data
Geological Survey Professional Paper 964
A revision of the land use classification system as
presented in U.S. Geological Survey Circular 671
Typical data characteristics
LANDSAT (formerly ERTS) type of data
High-altitude data at 40,000 ft (12,400 m) or
above (less than l:8O,OOO scale)
Medium-altitude data taken between 10,000
and 40,000 ft (3,100 and 12,400 m)
(1:20,000 to 1:80,000 scale)
Low-altitude data taken below 10,000 ft
(3,100 m) (more than 1:20,000 scale)
Level I
Level II
1 Urban or Built-up Land
11 Residential
12 Commercial and Services
13 Industrial
14 Transportation, Communications,
and Utilities
15 Industrial and Commercial
16 Mixed Urban or Built-up Land
17 Other Urban or Built-up Land
2 Agricultural Land 21 Cropland and Pasture
22 Orchards, Groves,
Vineyards, Nurseries, and
Ornamental Horticultural
23 Confined Feeding
24 Other Agricultural Land
3 Rangeland
31 Herbaceous Rangeland
32 Shrub and Brush Rangeland
33 Mixed Rangeland
4 Forest Land
41 Deciduous Forest Land
42 Evergreen Forest Land
43 Mixed Forest Land
Visual Interpretation
Interpreters look at imagery and draw
boundaries to mark categories
• Use the numeric symbols of the classification
Remember chapter 5 about visual
interpretation cues
Visual Interpretation
Cropped agricultural land is
recognized by systematic division of
fields into rectangles or circles, with
smooth even textures.
• Tone varies with growth stage
Pasture is usually more irregular in
shape, a mottled texture, medium tone
with possibly some isolated patches of
Visual Interpretation
Transportation is often seen as linear
patterns that cut across the landscape,
and by distinctive loops of interchanges
Visual Interpretation
Some parcels are delineated as multiple
• Airports – include runways, hangars,
terminals, roads, etc.
Land Use Change by Visual
Two maps representing the same region
are prepared for different dates, to depict
land use/land cover
• Must use the same classification system
• One can’t be “forest” if the other splits into “pine”
and “deciduous”
• Must be compatible with respect to scale,
geometry, and level of detail
• Should be able to identify points on both maps
Historical and Environmental
Aerial photos are helpful where
hazardous materials may have been
• Disposal sites include ponds, lagoons,
landfills, etc.
These may now be in populated areas due to
urban sprawl
Other LU/LC classification
The Anderson system described earlier
is a general-purpose classification
• Land Utilization Survey of Britain
• TVA land use
• New York Land Use and Natural Resources
Other LU/LC classification
Special Purpose Classification Systems
• Wetlands Classification (Cowardin, 1979)
Land-Cover Mapping by Image
Appears straightforward, but many
factors are hidden
• Selection of images – what season, what
dates are of most significance
Processing – accurate registration and
atmospheric corrections
• Subsetting needs to be done carefully
• Classification algorithm – needs to be
chosen based on region
• “grow” homogenous training fields
Land-Cover Mapping by Image
Assignment of spectral classes to
informational classes – “deciduous
forest” may require spectral classes that
reflect slope aspect.
Display and symbolization –
consistency of color choices
Digital Compilation of Land-Use
Probably no one set of techniques works
in all situations. Digital change (Jensen
• Image algebra – image subtraction where
values near zero are no change, usually
applied to single band
• Must select threshold for change/no change
• Image ratios can also be applied
Digital Compilation of Land-Use
• Postclassification comparisons –
independent classification of scenes which
are then compared
• Accurate classification is required
• Multidate composites – assemble all bands
from multiple dates into a single composite.
The entire set is then analyzed by principle
components or other techniques
• Pretty unwieldy
Digital Compilation of Land-Use
Spectral change vector analysis – examines a
pixel’s position in multispectral data space. If the pixel
occupies roughly the same position in the two data
sets, it has not changed.
Binary change mask – classify first date, do image
algebra on original images from both dates, create a
binary mask representing only changed/unchanged
pixels. Classify the second date, but use only the
pixels identified as changed in the mask.
Digital Compilation of Land-Use
• On-screen digitization – software is used to
view both images side-by-side and visually
interpret them.
Change detection by image display –
corresponding bands from different dates are
used as separate overlays in RGB display
Broad-scale Land-Cover studies
AVHRR and other data has been used to
look at continental or hemispheric
changes over time
Multiresolution Land Characteristics
Gap Analysis

Land Use and Land Cover