Remote%20Sensing%20of%20Snow

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Remote Sensing of Snow

• Presented to ENSC 454/654
• Presented by Jinjun Tong
• Date January 22, 2009

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Outline

• Fundamentals of remote sensing
• Satellites and sensors
• Application of remote sensing
• Remotely sensed snow distribution in the Quesnel
  River Basin (QRB)

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Definition of Remote Sensing
Several of the human senses gather their
awareness of the external world almost entirely
by perceiving a variety of signals, either
emitted or reflected, actively or passively, from
objects that transmit this information in waves
or pulses.
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• Remote Sensing is a technology for sampling
  electromagnetic radiation to acquire and
  interpret non-immediate geospatial data from
  which to extract information about features,
  objects, and classes on the Earth's land surface,
  oceans, and atmosphere (and, where applicable, on
  the exteriors of other bodies in the solar
  system, or, in the broadest framework, celestial
  bodies such as stars and galaxies).

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• Energy Source or Illumination (A)
• Radiation and the Atmosphere (B)
• Interaction with the Target (C)
• Recording of Energy by the Sensor (D)
• Transmission, Reception, and Processing (E)
• Interpretation and Analysis (F)
• Application (G)

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Electromagnetic Radiation
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ultraviolet
Visible
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Infrared
Microwaves
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Interactions with the Atmosphere
Scattering
Absorbing
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• Those areas of the spectrum which are not
  severely influenced by atmospheric absorption and
  thus, are useful to remote sensors, are called
  atmospheric windows

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Target Interactions

• Absorption (A) occurs when radiation (energy) is
  absorbed into the target while transmission (T)
  occurs when radiation passes through a target.
  Reflection (R) occurs when radiation "bounces"
  off the target and is redirected.

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• water and vegetation may reflect somewhat
  similarly in the visible wavelengths but are
  almost always separable in the infrared.

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Passive vs. Active Remote Sensing
Passive Sensing
Active Sensing
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Satellites and Sensors

• In order for a sensor to collect and record
  energy reflected or emitted from a target or
  surface, it must reside on a stable platform
  removed from the target or surface being
  observed. Platforms for remote sensors may be
  situated on the ground, on an aircraft or balloon
  (or some other platform within the Earth's
  atmosphere), or on a spacecraft or satellite
  outside of the Earth's atmosphere. Although
  ground-based and aircraft platforms may be used,
  satellites provide a great deal of the remote
  sensing imagery commonly used today.

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Satellite Orbits
Geostationary orbits
Sun-synchronous orbits
Near-polar orbits
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Weather Satellites/Sensors

• TIROS-1(launched in 1960 by the United States)
• GOES (Geostationary Operational Environmental
  Satellite)
• -GOES-1 (launched 1975), GOES-8
  (launched 1994)
• Advanced Very High Resolution Radiometer(NOAA
  AVHRR)(sun-synchronous, near-polar orbits)
• FengYun-1, FengYun-2, FengYun-3, FengYun-4
  (China)
• GMS (Japan)
• Meteosat (European)

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Land Observation Satellites/Sensors

• Landsat (Landsat-1 was launched by NASA in 1972,
  near-polar, sun-synchronous orbits).
• -Return Beam Vidicon (RBV), MultiSpectral
  Scanner (MSS), Thematic Mapper (TM)
• SPOT(SPOT-1 was launched by France in 1986,
  sun-synchronous, near-polar orbits)
• -Twin high resolution visible (HRV)
• Multispectral Electro-optical Imaging
  Scanner(MEIS II)
• Compact Airborne Spectrographic
  Imager(CASI)(airborne sensors)(Canada)
• CBERS-1 (China Brazil)

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Marine Observation Satellites/Sensors

• Nimbus-7 satellite (launched by NOAA in 1978)
• -Coastal Zone Colour Scanner (CZCS)
• Marine Observation Satellite (MOS-1)( launched by
  Japan in February, 1987)
• -a four-channel Multispectral
  Electronic Self-Scanning Radiometer (MESSR),
• -a four-channel Visible and Thermal
  Infrared Radiometer (VTIR),
• -a two-channel Microwave Scanning
  Radiometer (MSR)
• SeaWiFS (Sea-viewing Wide-Field-of View Sensor),
  SeaStar spacecraft, (NASA)
• HY-1 (launched by China in 2001)

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Data Reception, Transmission, and Processing
In Canada, CCRS operates two ground receiving
stations - one at Cantley, Québec (GSS), just
outside of Ottawa, and another one at Prince
Albert, Saskatchewan (PASS)
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Applications of Remote Sensing

• Agriculture
• Forestry
• Geology
• Oceans Coastal Monitoring
• Mapping
• Hydrology
• Land Cover Land Use
• Snow Ice

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Agriculture

• crop type classification
• crop condition assessment
• crop yield estimation
• mapping of soil characteristics
• mapping of soil management practices
• compliance monitoring (farming practices)

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Forestry

• reconnaissance mapping
• -forest cover type discrimination
• -agroforestry mapping
• Commercial forestry
• -clear cut mapping / regeneration
  assessment
• -burn delineation
• -infrastructure mapping / operations
  support
• -forest inventory
• -biomass estimation
• -species inventory
• Environmental monitoring
• -deforestation (rainforest, mangrove
  colonies)
• -species inventory
• -watershed protection (riparian strips)
• -coastal protection (mangrove forests)
• -forest health and vigour

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Geology

• surficial deposit / bedrock mapping
• lithological mapping
• structural mapping
• sand and gravel (aggregate) exploration/
  xploitation
• mineral exploration
• hydrocarbon exploration
• environmental geology
• geobotany
• baseline infrastructure
• sedimentation mapping and monitoring
• event mapping and monitoring
• geo-hazard mapping
• planetary mapping

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Oceans Coastal Monitoring

• Ocean pattern identification
• Storm forecasting
• Fish stock and marine mammal assessment
• Oil spill
• Shipping
• Intertidal zone

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Mapping

• planimetry
• digital elevation models (DEM's)
• baseline thematic mapping/topographic mapping

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Hydrology

• wetlands mapping and monitoring,
• soil moisture estimation,
• snow pack monitoring / delineation of extent,
• measuring snow depth,
• determining snow-water equivalent,
• river and lake ice monitoring,
• flood mapping and monitoring,
• glacier dynamics monitoring (surges,
  ablation)
• river /delta change detection
• drainage basin mapping and watershed
  modelling
• irrigation canal leakage detection
• irrigation scheduling

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Land Cover Land Use

• natural resource management
• wildlife habitat protection
• baseline mapping for GIS input
• urban expansion / encroachment
• routing and logistics planning for seismic /
• exploration / resource extraction activities
• damage delineation (tornadoes, flooding,
• volcanic, seismic, fire)
• legal boundaries for tax and property evaluation
• target detection - identification of landing
  strips,
• roads, clearings, bridges, land/water
  interface

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Sea Ice

• ice concentration
• ice type / age /motion
• iceberg detection and tracking
• surface topography
• tactical identification of leads navigation
  safe
• shipping routes/rescue
• ice condition (state of decay)
• historical ice and iceberg conditions and
• dynamics for planning purposes
• wildlife habitat
• pollution monitoring
• meteorological / global change research

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Remotely sensed snow distribution and its
relationships with the hydrometeorology in the
QRB, Canada
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Outline

• Research background and area
• Data processing methods
• Evaluation of Moderate Resolution Imaging
  Spectroradiometer (MODIS) data
• Snow distribution in the QRB
• Relationships between snow cover extent (SCE),
  snow cover fraction (SCF), snow cover duration
  (SCD), topography, streamflow, and climate
  change.
• Conclusions

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DEM in the QRB

• Snow plays a vital role in the energy and water
  budgets of drainage basins.
• the SCE and snow water equivalent (SWE) are
  important parameters for various hydrologic
  models.
• The QRB is one of 13 main sub-watersheds in
  Fraser River Basin, which is one of the world's
  most productive salmon river systems with five
  salmon species and 65 other species of fish.

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Data

• MODIS daily and 8-day SCE
• Global land one-kilometer base elevation (GLOBE)
  DEM
• Daily streamflow of Quesnel River
• Daily snow depth, temperature and precipitation
  of nine ground stations

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EOS-MODIS
Lets watch the video about the EOS-MODIS
instruments first
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(No Transcript)
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Snowmap

• The snow-mapping algorithm (Snowmap) employs a
  Normalized Difference Snow Index (NDSI) to
  identify and classify snow on a pixel-by-pixel
  basis.

Reflectance of snow and ice
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NDSI
A Normalized Difference Snow Index (NDSI) is
computed from Band 4 (green) and Band 6 (SWIR)

• Snow is determined if NDSI 0.4, and the
  reflectance in Band 2 (near-IR) 0.11, and Band
  4 (green) 0.10, to eliminate water and other
  dark surfaces from being classified as snow. A
  Normalized Difference Vegetation Index (NDVI) is
  computed from MODIS Band 1 (Red) and Band 2, and
  the NDSI and NDVI are used together to map snow
  in dense forests.

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• The NDSI is also used for MODIS sea ice products.
  In regions illuminated by the sun, the NDSI is
  used to differentiate sea ice from open water. A
  second method, one based on Ice Surface
  Temperature (IST), is also used for detection of
  sea ice. This is especially useful in areas
  lacking solar illumination. MODIS Bands 31 and
  32, near 11.6 µm, are used in a split-window
  technique to derive IST, utilizing coefficients
  specific to sea ice.
• Lets watch the animation shows the global
  advance and retreat of daily snow cover along
  with daily sea ice surface temperature over the
  Northern Hemisphere from September 2002 through
  May 2003.

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Data Processing
Spatial filter method points
Flow chart of spatial filter method
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Comparison of snow maps of MOD10A1, MOD10A2, and
SF in the QRB within the same period and 8-day
annual average cloud coverage of MOD10A1,
MOD10A2, and SF from 2000-2007 in the QRB.
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Evaluation of MODIS data
MODIS
Snow No snow
Snow a b
No snow c d
Ground
Accuracy of different MODIS snow data
Stations Elevation m MOD10A1, MOD10A2, SF,
Horsefly Lake/Gruhs Lake 777 88.31 88.92 91.49
Boss Mountain Mine 1460 71.14 81.25 82.72
Yanks Peak East 1670 62.17 73.85 74.15
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Relationships between topography, SCF and SCD
Annual cycle of the SCF distribution in
different elevation bands, 2000-2007.
The SCD for different periods across the QRB
based on the MOD10A2 (left) and SF (right)
products, 2001-2007. The SCD days for the entire
year equal 3 times the values in the legend.
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The mean (left) and standard deviations (right)
of SCDs for 10-m elevation bands for 3 seasons
based on the MOD10A2 and SF products, 2001-2007.
The correlation coefficients between SCDs and
elevations within different periods (plt0.001) and
the corresponding d(SCD)/dz (days (100 m)-1) in
parentheses.
  Snow melt season Snow accumulation season Entire year
SF 0.986 (4.31) 0.961 (3.76) 0.965 (11.51)
MOD10A2 0.976 (3.94) 0.933 (3.42) 0.938 (11.26)
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(a) The mean elevational dependence of snow cover
fraction (SCF) for the months of February to
July, 2000-2007. (b) The mean (points) and
standard deviation (bars) of the rate of change
in SCF at different elevations, 26 February to 26
June, 2000-2007.
The average annual cycle of snow cover fraction
distribution in different slope and aspect bands,
2000-2007.
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Relationships between runoff, SCF and SCE
Lagged correlation coefficients between 8-day
maximum SCE in the QRB and streamflow of QR
during snow melt seasons from 2000-2007.
The 8-day MODIS maximum snow cover fraction of
the QRB and the corresponding 8-day runoff at
Quesnel gauge station from February 26,2000 to
December 31,2007.
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Scatter plots between (normalized) SCE and
(normalized) streamflow during snow ablation
seasons from 2000-2007 in the QRB
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MOD10A2
Relationships between SCF, runoff and climate
change
Correlation coefficients (a) and scatter plot (b)
between average temperature within different
periods and SCF50 and scatter plot (c) between
SCF50 and R50 during snow melt seasons from
2000-2007 in the QRB.
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Conclusions

• Spatial filter method can decrease the cloud
  cover fraction from average 15 to 10 with
  increasing the accuracy of the MODIS snow
  products.
• Spatial filter method can improve the analyses
  between the MODIS snow products and other
  characteristics such as streamflow, SCE, SCF and
  SCD.
• There are significant correlations between the
  SCE and streamflow of QRB during the snow melt
  seasons with a correlation coefficient -0.8
  (plt0.001).

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• The snow melt process is highly correlated with
  the mean temperature in the QRB with a
  correlation coefficient -0.85 (plt0.01). The
  runoff has significant linear relationship with
  SCF with a correlation coefficient 0.82 (plt0.01).
• The SCD is correlated with the elevations
  significantly in QRB with correlation
  coefficient over -0.95 (plt0.001). There is
  perennial snow over 2500 m in the QRB.

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Thank You!!!