Remote Sensing for Land UseLand Cover

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Remote Sensing for Land Use/Land Cover
April 2005

• Sally J. Westmoreland
• University of Redlands, California
• Tim Foresman
• International Center for Remote Sensing Education
  and
• University of Maryland

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Land Cover

• Biophysical physical skin of the planet
• Vegetation type/community
• Rock/Soil type
• Water Condition includes snow and ice
• Man-made concrete, asphalt, building materials,
  etc.
• Dynamic condition in space and time
• Seasonal
• Human change
• Other disturbance

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Land Use

• Functional/Anthropogenic Use
• The human activities on the planets surface
• Described by major uses
• E.g., urban, agriculture, timber, transportation
• Highly influenced by land cover
• Dynamic in space and time

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Conceptually, we may see land use as residing on
the land cover foundation
Land Use
Land Cover
Not a One-to-One Relationship
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Grassland Land Cover Class Many Land Use
Classes Pasture Park Golf Course Lawn
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Land Use and Land Cover Global to Local Scale
International Agencies must cooperate to create
geo-referenced data National and local agencies
and organizations help validate data
e.g. Land Use/Land Cover Demographics Global
Climate Sea Surface Temperature Digital Elevation
Global Scale
Regional Scale
e.g. Land Use/Land Cover Food and Fiber Disaster
Preparedness Biodiversity Coastal Sensitivity
e.g. Land Use/Land Cover Precision
Agriculture Hydrologic Modeling Transportation
Planning
National Scale
For All Nations Solve practical
problem Contribute to informed decision
making Communicate with citizens
e.g. Land Use/Land Cover Smart Growth Public
Health Disaster Response Weather
Local Scale
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Mapping Land Use/Land Cover

• Remote Sensing Instruments are efficient for
  capturing land cover/use
• Large area coverage
• Synoptic
• Automated classification/visual interpretation
• Geographic Information Systems
• Capture, Store, Manage, Analyze results of land
  use/land cover mapping

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Global Scale

• Suitable for land cover
• Based on seasonal patterns of vegetation
• Limited use for land use, especially urban
• Captures large area climate/physiographic
  patterns
• Dynamic
• MODIS/AVHRR sensors
• Coarse spatial resolution 1 km
• High temporal frequency daily

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Regional Land Use/Land Cover

• Distinguish general land use classes in addition
  to land cover
• Urban
• More discrimination of vegetation cover and type
  e.g., broad-leaf deciduous vs. conifer trees
• While seasonally dynamic, land use tends to be
  less variable
• Lower requirement for high temporal coverage

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Regional Scale Sensors

• Good archive of data and continuing collection
• US Landsat
• 30m resolution from 1982 to present
• 80m resolution from 1972-1992
• SPOT (French/European)
• 10m/20m from 1986 to present
• 5m introduced 2002
• Indian Remote Sensing
• Ranges from 72.5m originally to 5.8m now

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Honduras North Coast
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Honduras North CoastAutomated Land Use
Classification
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Local Land Use/Land Cover

• Detailed land use
• Urban categories
• Intensity of use
• E.g., single family vs. multi-family residential
• Agricultural practices
• Generally low frequency requirement

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Local Scale

• Aerial Photography
• High spatial resolution sensors launched
  beginning in 1999
• IKONOS, QuickBird, OrbView3
• Resolution
• Panchromatic (black white) 0.62 to 1m
• Multispectral (color) 2.7 to 4m
• Source for Google Earth

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Digital Orthophoto

• Digitize land use
• boundaries
• Visual Interpretation
• Heads-up digitizing

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Digital Land Use File
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Change

• Compare image data or land use files from
  multiple dates
• Change in associated cover may reflect change in
  use
• Other applications
• Storm damage
• Disturbance (removal of cover)
• Vegetation growth/condition

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Rio San Juan/Lean North Coast
2001 vs. 2003
Red Vegetated in 2001, cleared 2003 Cyan
Clear in 2001, cover in 2003
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2001 2003 Change
Change in North Coastal and Wetland Environment
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Summary

• Land use/Land Cover is Complex
• Multiple scales and Driving Forces

Social Driving Forces
Biophysical Driving Forces
Proximate Agents and Processes
Global Regional National Sub-national Local
Global Regional National Sub-national Local