Remote Sensing Applications

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Remote Sensing Applications in Land Cover
Mapping
By JWAN M. ALDOSKI
Geospatial Information Science Research Center
(GISRC), Faculty of Engineering, Universiti
Putra Malaysia, 43400 UPM Serdang, Selangor
Darul Ehsan. Malaysia.
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1. Remote Sensing Data
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Remote Sensing Images

• Landsat
• Spot
• DOQQs
• Ikonos

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Landsat ETM Image
The Enhanced Thematic Mapper Plus (ETM) is a
multispectral scanning radiometer that is carried
on board the Landsat 7 satellite. The sensor has
provided continuous coverage since July 1999,
with a 16-day repeat cycle.
Spatial resolution 30 meters for band 1-5, and 7
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Landsat 7 and E-Spectrum
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Landsat ETM imagery in Ohio

• Through OHIOVIEW program LANDSAT-7 data
  available free of charge, to all but the
  corporate world, within 48 hours of the satellite
  passing over state
• OhioLink purchases images
• Image must have 30 or less cloud cover
• Access www.ohioview.org and http//www.ohiolink.ed
  u

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Landsat ETM imagery
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Landsat ETM imagery Example
Landsat 7 Path 19/Row 32, September 2, 2002
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Landsat ETM imagery Example
Landsat 7 Path 19/Row 32 August 14, 2001
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SPOT Imagery
The is a multispectral scanning radiometer that
is carried on board the Ariane 4 satellite. The
sensor has provided continuous coverage with a
26-day repeat cycle.
Spatial resolution Spot 5 10 meter Spot 1-4 20
meter
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SPOT and E-Spectrum
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SPOT Imagery

• State purchased SPOT 2000/2001 coverage for the
  entire state
• Available free of charge from OGRIP
• 3 CDs available upon request

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SPOT Imagery
Image Year
1998
1999
2000
2001
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SPOT 4 Image Example
Spot 10 meter pan image (Northwest Columbus)
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Digital Orthophoto Quarter Quads (DOQQ)

• State covered by digital orthophoto quarter
  quadrangles under the USGS NAAP program in 1993/9
  time period -- 3084 DOQQs
• A DOQQ is an aerial photograph with camera and
  terrain distortions removed
• 1 meter pixel resolution

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DOQQ Availability

• Available U.S. Geological Survey
• USGS distributes in native format
• Meets National Map Accuracy Standards at 112,000
  (/- 33 feet)
• UTM Base on NAD 83
• File size approx 45 mb

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Ohio DOQQ availability

• http//www.state.oh.us/DAS/dcs/Gis/doqq/index.htm
• GIS Service Bureau distributes in enhanced format
• Uses MrSid compression
• Output is on State Plane Coordinate Base
• File size approx 2mb

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DOQQ Example
(1 meter resolution) NW Columbus
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IKONOS Image
Ikonos, launched on September 24th 1999, is the
first commercial high-resolution satellite,
collecting 1-meter panchromatic and 4-metre
multi-spectral imagery.
Spectral Range1-meter black-and-white
(panchromatic) 0.45 - 0.90 mm. 4-meter
multispectral Blue 0.45 - 0.52 mmGreen 0.51 -
0.60 mmRed 0.63 - 0.70mmNear IR 0.76 - 0.85
mm
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IKONOS Availability

• Not publicly available
• IKONOS imagery is expensive 32 per km2.

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IKONOS Image of Ohio State University
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National Gap Analysis Program
Geographical Approaches to Planning
Supported by Biological Resources Division,
United States Geological Survey
National Gap Analysis Program http//www.gap.uida
ho.edu
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National GAP -Objective

• Objective keep common species common
• Methods
• identify those species and plant communities that
  are not adequately represented in existing
  conservation lands.
• give land managers, planners, scientists, and
  policy makers the information they need to make
  better-informed decisions when identifying
  priority areas for conservation
• Advantage
• Gap Analysis is superior to a species-by-species
  approach because it identifies and protects
  regions rich in habitat, therefore the animal
  species that inhabit them can be adequately
  protected.

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National GAP - Status
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GAP Background
The land cover map for Ohio is generated as part
of the Gap Analysis project (GAP). This is the
fourth of a planned five years of activities.
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Objective of -GAP

• 1. Map the existing land cover of the state using
  current standards specified in the Gap Analysis
  Program handbook.
• Produce maps showing the predicted distributions
  of each indigenous bird, mammal, reptile, and
  amphibian species of the state
• Map the ownership of all public lands and private
  conservation lands
• Categorize all lands according to the GAP
  management status categories.

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Priorities Areas
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ODNR Priorities Areas
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Land Cover Map Major Steps

• Photo acquisition
• Photo backup
• Photo geo-referencing
• Unsupervised classification of TM images
• Field work
• Supervised vegetation classification of TM images
• Quality evaluation
• Vector map generation

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Photo Acquisition- Image Characteristics

• Camera Used Nikon D1 X
• Average Ground Level Flying Height 1200m
• Pixel resolution 3000 x 1800
• Dimension of Pixel 0.3636 x 0.3673 sq.m

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Photo Acquisition
Wrong Focal Length
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GoodPicture
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Photo Acquisition
Over 60,000 digital aerial photographs in flight
lines 4 kilometers apart, approximately 1 foot
resolution
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Photo Acquisition -Status
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Photo Backup

• All photographs obtained are saved onto a DVD
  recordable disc in their native format (NEF)
• Each DVD holds up to 4.7 GB of space where nearly
  550 images can be stored
• Presently we have about 25,654 images to date,
  out of which 6,700 images have been stored in 12
  DVDs

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Photo Backup
A single DVD looks similar to a CD and can store
at least 4.7 gigabytes GB) of data, which equals
over 7 CDs.
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Photo Backup Internet access
Digital aerial photographs will be available at
ohioupclose.cfm.ohio-state.edu But not finish yet
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Photo Geo-Referencing

• Major Steps ( For each DVD containing images)
• Photographs obtained are in Nikon NEF Format
• ERDAS Imagine does not read photographs in the
  NEF format
• The Nikon Image Capture 2.0 software is used to
  convert the Nikon raw data (NEF format) to TIFF
  format

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Photo Geo-Referencing
4. Geo-referencing of the photographic images
are done using ERDAS Imagine software, using
DOQQs as the reference images 5. Program created
at Center for Mapping called GPS Converter
obtains the time and GPS co-ordinates of the
image from the Nikon raw data 6. Each TIFF
image and its corresponding data file is stored
in the server for easy access for geo-processing
purposes
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Photo Geo-Referencing Status

• Direction Correction
• Flight direction is either from east to west or
  the opposite direction. Thus, a counter-clockwise
  / clockwise rotation is performed using ERDAS
  Imagine geometric correction function

Photograph Rotation
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Geo-Referenced Photographs
Galloway , Northwest DOQQ
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Unsupervised Classification

• Major Steps (1)
• Define a priority area
• Import Landsat ETM images to ERDAS Image
• Put images into a mosaic for the priority area
• Evaluate cloud cover and replace heavily clouded
  areas with additional data
• Perform principal component (PC) analysis on both
  leaf-on and leaf-off images
• Group the first three PCs of leaf-on and leaf-off
  images into a layered image with 6 bands

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Unsupervised Classification

• Major Steps (2)
• 7. Classify urban area with supervised
  classification with three classes urban,
  vegetation and soil, and water
• 8. Perform another supervised classification
  but for the delineated urban areas with three
  classes high density, low density, and others.
• 9. Cross out urban areas and put the class
  others back to the PC image for future
  classification
• 10. Digitize cloudy areas using screening
  digitizing
• 11. Obtain additional data for these cloudy areas
• 12. Perform unsupervised classification on the PC
  image with 60 classes

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Unsupervised Classification Example
Priority Area1
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Unsupervised Classification Example
step 2 Import ETM images (leaf-off)
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Unsupervised Classification Example
Step 3 Put images into a mosaic
Leaf-on image

• Leaf-off image

Step 4 replace heavily clouded areas with
additional data

• Leaf-on image

• Leaf-off image

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Unsupervised Classification Example
Step 5 Perform principal component (PC)
analysis
First three PCs for leaf-on image
First three PCs for leaf-off image
Step 6 Group first three PCs of leaf-on and
leaf-off images
Layer stacked image with first three PCs of
leaf-on and leaf-off images
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Unsupervised Classification Example
Step 7. Classify urban area with supervised
classification
PC image for urban area
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Unsupervised Classification Example
High density
Low density
Step 8. Classified high and low density urban
areas
others
High density
Low density
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Unsupervised Classification Example
Step 9. Cross out urban areas from the PC image
Step 10. Digitize cloudy areas using screening
digitizing
Entire image with cloud digitized
Example of cloudy areas
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Unsupervised Classification Example
Step 10. PC image after removing urban and clouds
Step 11. Obtain additional data for cloudy areas
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Unsupervised Classification Example
Step 12. Perform unsupervised classification on
the PC image
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Field Work Status
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Field Work -Prior to Field Trip

• 1. Identify area of interest
• 2. Within the area of interest, sort through
  digital photos taken during flight and identify
  photos with the following
•      a) Large areas of Vegetation, for example,
  forested, wetland, or grassland
•      b) Easily accessible field areas
•      c) Notable Landmarks such as, homes, fences,
  roads
• d) Varying vegetation (not all field areas
  should look alike)
• Print out photos on Color printer
• Mark the GPS coordinates of the photos in the
  field areas in a MapSource document

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Field Work -Prior to Field Trip

• 5. Double check to ensure your field areas are
  randomly spread out within the area of interest
• 6. Create routes with the routing tool in
  MapSource
• 7.  Download both the maps and the waypoints into
  the GPS by connecting the GPS to the computer
  (ensure the GPS is in the GARMIN mode not the
  NMEA mode).
• 8.  Print out zoomed in maps of MapSource of the
  Area of Interest.
• 9.  Print out Field Sheets and bring any and all
  vegetation books needed to aid in the
  identification process.

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Field Work in the field

1. Go to the field areas by finding the waypoint of
   the image desired using the GPS instrument. This
   will ensure that you are in the right field area.
   
2. Locate your exact field area by noting specific
   landmarks in the photo.
3. Fill out the field sheet noting diagnostic and
   dominant species in the area. To do this, it may
   be helpful to make notes directly on the photo.
4. Take at least three waypoints for each field area
   if possible.

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Supervised Vegetation Classification

1. Perform a Supervised Classification with the
   LANDSAT images to the Alliance level of the
   United States National Vegetation Classification
   (USNVC) and define signatures for the relevant
   alliances by using the mosaicked digital photos
   and DOQQ's as reference.
2. Use the library of digital photos developed from
   previous ground-truthing to aid in the aerial
   photo interpretation.
3. Evaluate signatures created and delete, rename,
   or merge with other signatures from previous
   images.
4. Run the Supervised Classification for the entire
   image.

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Supervised Vegetation Classification
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Supervised Vegetation Classification
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Supervised Vegetation Classification
Raster format
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Vector Map Generation
Vector format ESRI Arc/Info Coverage