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Remote Sensing

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... that is not in physical or intimate contact with the object or phenomena under study' ... aerial photography based on cameras on aircraft. satellite-based ... – PowerPoint PPT presentation

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Title: Remote Sensing


1
Remote Sensing
2
Remote Sensing
  • is the measurement or acquisition of information
    of some property of an object or phenomena by a
    recording device that is not in physical or
    intimate contact with the object or phenomena
    under study

3
Remote Sensing
  • includes aircraft, spacecraft and satellite based
    systems
  • products can be analog (e.g., photos) or digital
    images
  • remotely sensed images need to be interpreted to
    yield thematic information (roads, crop lands,
    etc.)

4
Principles
  • sensors measure the amount of energy reflected
    from the earths surface

5
The remote sensing process
6
Principles
  • energy sources and radiation principles
  • different sensors measure different parts of the
    electromagnetic spectrum

7
Electromagnetic spectrum
8
Visible light
  • optical systems use photographic film or
    electro-optical scanners
  • also work in near-infraredaerial photography
    based on cameras on aircraft
  • satellite-based systems

9
Aerial photography
  • important for updating large scale topographic
    maps (e.g., new roads, urban areas)
  • stereo-effect pairs of images that are displaced
    produce 3-D effect
  • allows for measuring elevation

10
Satellite based systems
  • Landsat , SPOT, etc.
  • also Russian, Indian, Japanese, European, and
    Canadian
  • panchromatic versus multispectral
  • Landsat 7-8 spectral bands some in visible
    spectrum
  • new and planned systems have many more
    (hyper-spectral images)

11
Landsat TM image ofHongkong
(bands 7,4,3 - 60m resolution) shows vegetation
in green, urban areas in purple/ white, water in
blue/black
Source Eosat
12
Panchromatic image of Atlanta
from the Russian KVR 1000 camera (2-5 m
resolution)
Source Eosat
13
Washington D.C.
Merged Image of Washington D.C. combining Landsat
TM and KVR 1000 data (resampled to 5m resolution)
Source Eosat
14
Satellite based systems
  • data recorded for pixels picture elements
  • size on-ground of a pixel varies from 1m to 60m
    or more for commercial systems
  • images are sent back from satellite as very large
    raster data sets

15
The remote sensing process
16
Digital image processing
  • digital satellite data usually need considerable
    processing
  • registration and atmospheric correction
  • analysis - measurement- classification-
    estimation

17
Measurement
  • temperature
  • vegetation biomass- Normalized Difference
    Vegetation Index (NDVI)
  • elevation
  • crop condition
  • urbanized area

18
Classification
  • identify and map areas with similar
    characteristics
  • assign meaningful categories such as land-use or
    land-cover classes to pixel values
  • need training areas (ground-truth)
  • statistical approaches

19
Classification
  • reflectance varies with time of day
  • often large uncertainty in classification-
    pixels may contain several classes
  • despite good image processing systems requires
    lots of experience(part art, part science)

20
Estimation
  • objective is to estimate total amounts of a
    quantity, or areas under cultivation for an
    administrative or management area
  • examples crop areas, forest resources

21
Other systems
  • meteorological satellites
  • e.g., Advanced Very High Resolution Radiometer
  • coarser resolution but higher frequency and
    larger areas covered
  • designed for meteorology but used for many other
    purposes (e.g., NDVI)

22
Other systems
  • radar remote sensing (e.g., microwave)
  • advantages in areas where cloud cover is frequent
    (e.g., tropical areas close to the equator)
  • difficult to interpret

23
Other systems
  • aerial video
  • visible light
  • using off-the-shelf video cameras and
    post-processing systems
  • cheap, rapid data collection for monitoring and
    data capture

24
Survey costs
25
Socioeconomic applications
  • delineation of newly urbanized areas (e.g.,
    Quito, Manila)
  • mapping of villages for population estimation
    (e.g., Sudan, W-Africa) with Landsat (rooftop
    surveys)
  • Defense Meteorological Satellite Programs (DMSP)
    nighttime visible light emissions

26
DMSP data
(water areas masked)
27
Population Distribution 1980
Source U.S. Bureau of the Census
28
Census from heaven?
  • DMSP data good for delineating urban/non-urban
  • too little variation to link to population
    densities
  • higher resolution data usually too expensive
    and not accurate enough for census-type
    activities
  • but useful source of base maps for EA delineation

29
Remote sensing and GIS
  • remotely sensed data is an important data source
    (currency, frequency)
  • large scale e.g., cities revealed
  • medium scale framework data, urban/non-urban,
    crop conditions, etc.
  • small scale NDVI, global land cover data sets

30
Remote sensing and GIS
  • requires considerable processing to achieve high
    accuracy products
  • image rectification and registration with GIS
    data sets difficult with raster systems
    (resampling)
  • image interpretation guided by GIS data
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