Introduction to Satellite Remote Sensing SeaWiFS, June 27, 2001
Miles Logsdon, Univ. of Washington Oceanography
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My agenda Show you pretty pictures Introduce Remote Sensing
terms and concepts Get the language down Think about the
future
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Acknowlegements!! Mark Abbott: Oregon State University MODIS
highlights, data, and images Seely Martin: University of Washington
Illustrations, and explanations Robin Weeks: University of
Washington Graphics and data Leon Delwiche: University of
Washington Illustrations Most of what you see here is explained
better by these people
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Lets start with some nice pictures
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What is Remote Sensing and Image Classification? Remote Sensing
is a technology for sampling radiation and force fields to acquire
and interpret geospatial data to develop information about
features, objects, and classes on Earth's land surface, oceans, and
atmosphere (and, where applicable, on the exterior's of other
bodies in the solar system). Remote Sensing is detecting and
measuring of electromagnetic energy (usually photons) emanating
from distant objects made of various materials, so that we can
identify and categorize these object by class or type, substance,
and spatial distribution Image Classification has the overall
objective to automatically categorize all pixels in an image into
classes or themes. The Spectral pattern, or signature of surface
materials belonging to a class or theme determines an assignment to
a class.
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Specifically, we measure radiation produced in three ways: 1.
Emitted from the surface (thermal IR) 2. Reflected from the surface
(solar) 3. Reflected from energy pulses directed at the surface
(RADAR)
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Landsat-ETM 30m resolution 16-day repeat
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MODIS Terra - Daytime Descending Orbits MODIS 1k resolution
Daily repeat Best response in a Ph.d. oral exam: Ill give up space
to get time. Miles Logsdon
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One goal is to produce a: Classified Product
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MOD13 NDVI (16-day) 500m resolution June 2002 (bright
photosynthesizing vegetation) LowHigh A second goal might be to
produce a: Derived Product
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MOD11 Daytime (8-day averaged) Land Surface Temperature June
2002 ~3 o C~50 o C Temperature ( o C)
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AprMayJun JulAugSep Remote Sensing as a Time Series SeaWifs,
1999, 1km monthly mean chlorophyll-a estimates Current Collections
Pacific Northeast, Apr Sep, 1999 - 2003
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Additive Subtractive
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First: A few Simple Reminders about Spectral Signatures Thanks
to Robin Weeks
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Coordinate system used with satellite sensors Z Zenith angle
Look or incidence angle S Solar zenith angle
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When radiation interacts with the atmosphere, then depending on
the wavelength, the three things that happen are - Absorption, -
Scattering, - Emission.
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The Effect of the Atmosphere on Spectral Data Path Radiance (L
p ) Atmospheric Transmissivity (T) Thanks to Robin Weeks
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Scattering There are two kinds of scattering, Rayleigh or
molecular scatter, which only matters in the visible; and Mei or
aerosol scatter (scatter from raindrops, sulfuric acid droplets,
salt particles) which matter at much longer wavelengths.
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Rayleigh Scatter
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Solar scattering generates a Rayleigh path radiance
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Two kinds of solar reflection in the visible: Direct surface
reflection, diffuse sub-surface backscatter
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Once the Light hits the surface we are concerned with
reflection
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The PIXEL
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Wavelength (Bands)
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Terrestrial and Ocean Color Sensors Ocean Color Sensors
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Comparison of Ocean Color Instruments InstrumentSatelliteDates
of OperationSpatial ResolutionSwath Width CZCSNimbus-710/24/78-
6/22/86 825 m1556 km MOSIRS P33/18/96-520 m200 km
MOSPriroda4/23/96-650 m85 km OCTSADEOS8/17/96-700 m1400 km
SeaWiFSSeaStar5/971100 m2800 km OCI ROCSAT-1 4/98 (Delayed) 800
m690 km MODIS EOS AM-1 6/981000 m2330 km GLIADEOS-2 2/99 (Delayed)
1000 m1600 km MERIS ENVISAT-1 7/99 (Delayed) 1200 m1450 km Low
Resolution Camera KOMPSAT 1999 (Delayed) 1000 m800
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Band Combinations 3,2,1 4,3,2 5,4,3 R,G,B R G B Landsat band 5
4 3 2 1
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We approach RS in two ways To classify or group thematic land
surface materials To detect a biophysical process
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Cluster and Classify
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Spectral Profile
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Spatial Profile
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Spectral Signatures
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1d classifier
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Spectral Dimensions
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3 band space
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Clusters
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Dimensionality N = the number of bands = dimensions . an (n)
dimensional data (feature) space Measurement Vector Mean Vector
Band A Band B 190 85 Feature Space - 2dimensions
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Spectral Distance * a number that allows two measurement
vectors to be compared
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Classification Approaches Unsupervised: self organizing
Supervised: training Hybrid: self organization by categories
Spectral Mixture Analysis: sub-pixel variations.
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Clustering / Classification Clustering or Training Stage:
Through actions of either the analysts supervision or an
unsupervised algorithm, a numeric description of the spectral
attribute of each class is determined (a multi-spectral cluster
mean signature). Classification Stage: By comparing the spectral
signature to of a pixel (the measure signature) to the each cluster
signature a pixel is assigned to a category or class.
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terms Parametric = based upon statistical parameters (mean
& standard deviation) Non-Parametric = based upon objects
(polygons) in feature space Decision Rules = rules for sorting
pixels into classes
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Unsupervised Clustering Minimum Spectral Distance ISODATA I -
iterative S - self O - organizing D - data A - analysis T -
technique A - (application)? Band A Band B Band A Band B 1st
iteration cluster mean 2nd iteration cluster mean
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ISODATA clusters
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Supervised Classification
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Classification Decision Rules If the non-parametric test
results in one unique class, the pixel will be assigned to that
class. if the non-parametric test results in zero classes (outside
the decision boundaries) the the unclassified rule applies either
left unclassified or classified by the parametric rule if the pixel
falls into more than one class the overlap rule applies left
unclassified, use the parametric rule, or processing order
Non-Parametric parallelepiped feature space Unclassified Options
parametric rule unclassified Overlap Options parametric rule by
order unclassified Parametric minimum distance Mahalanobis distance
maximum likelihood
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Band A Band B Parallelepiped Band A Band B cluster mean
Candidate pixel Minimum Distance Maximum likelihood (bayesian)
probability Bayesian, a prior (weights)
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Parametric classifiers
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Classification Systems USGS USGS - U.S. Geological Survey Land
Cover Classification Scheme for Remote Sensor Data USFW USFW - U.S.
Fish & Wildlife Wetland Classification System NOAA CCAP NOAA
CCAP - C-CAP Landcover Classification System, and
DefinitionsDefinitions NOAA CCAP NOAA CCAP - C-CAP Wetland
Classification Scheme Definitions PRISM PRISM - PRISM General
Landcover King Co. King Co. - King County General Landcover
(specific use, by Chris Pyle) Level 1 Urban or Built-Up Land 11
Residential 12 Commercial and Services 13 Industrial 14
Transportation, Communications and Utilities 15 Industrial and
Commercial Complexes 16 Mixed Urban or Built-Up 17 Other Urban or
Built-up Land 2 Agricultural Land 21 Cropland and Pasture 22
Orchards, Groves, Vineyards, Nurseries and Ornamental Horticultural
Areas 23 Confined Feeding Operations 24 Other Agricultural
Land
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Detecting a Process: Two examples Using band math
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Laboratory Spectral Signatures II Common Urban Materials
Healthy grass Concrete Astroturf wavelength Thanks to Robin
Weeks
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Vegetation: Pigment in Plant Leaves (Chlorophyll) strongly
absorbs visible light (0.4 to 0.7 m) Cell Structure however
strongly reflects Near-IR (0.7 1.1 m) Thanks to Robin Weeks
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(courtesy
http://earthobservatory.nasa.gov)http://earthobservatory.nasa.gov
NDVI Band 3 Band 4 Band 4 - Band 3 Band 4 + Band 3 Simple Ratio
NDVI When using LANDSAT:
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Ocean Color Lets begin with phytoplankton Phyton = plant;
planktos = wandering. These reproduce asexually, are globally
distributed, consist of 10s of thousands of species and make up
about 25% of the total planetary veg. These are the grass that the
zooplankton graze upon. And, they fix carbon as well.
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Chaetoceros species of diatoms: cells are 20-25 mm in diameter.
Chloroplasts contain pigments
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1.Water provides an internal standard shape for spectral
comparison with other variable components 2.Slopes for pigments and
CDOM similar from 440 to 600 nm, but are opposite from 400 to 440
nm 3.Note that detritus is include with CDOM since shapes are
similar 4.Spectral de-convolution of pigment absorption from CDOM
absorption is straight-forward 5.Shapes of phytoplankton or pigment
absorption are not constant (next slide) 6.For Case 2 waters, ratio
of CDOM to chlorophyll a is not constant Strategy for Spectral
Separation of Absorption Components with Semi-Analytic Algorithm
Ken Carder: University of South Florida
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Colored Dissolved Organic Material (CDOM) Organic Sources
Terrestrial CDOM decay vegetation from river and nearshore Ocean
CDOM detritus - cell fragments, zooplankton fecal Inorganic Sources
Sand & Dust => Errosion rivers, wind, wave or current
suspension
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Whats the difference between MODIS chlorophylls? Case 1
waters:Chlor_MODIS (Clark) This is an empirical algorithm based on
a statistical regression between chlorophyll and radiance ratios.
Case 2 waters:Chlor_a_3 (Carder) This is a semi-analytic
(model-based) inversion algorithm. This approach is required in
optically complex case 2 (coastal) waters and low-light,
nutrient-rich regions (hi-lats). A 3 rd algorithm was added to
provide a more direct linkage to the SeaWiFS chlorophyll:
SeaWiFS-analog Chlor_a_2 (Campbell) SeaWiFS algorithmOC4.v4
(OReilly) Ken Carder: University of South Florida
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Chl-a increasing Florescence Independent of Chl-a R( )
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Case 1 Rrs Model with superimposed MODIS bands 8-14: All
variables co-vary with chlorophyll a Note that slopes between blue
and green wave lengths decrease with increasing chlorophyll,
explaining the strategy of empirical algorithms Case 2 waters are
more complicated Ken Carder: University of South Florida
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Slide 62
SeaWiFS empirical OC4 algorithm for Chl-a; Called a
maximum-band ratio alg.
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Slide 64
MODIS Ocean Products MODIS Instruments: Terra (1030 morning),
Aqua (1330 afternoon) 40 products: 4 SST, 36 Ocean Color
Resolution: Spatial: Level 2 - 1km, ~2000km x 2000km; Level 3 -
4km, 39 km, 1 deg [all products are global] Temporal Resolution:
Level 2 - 5 minute granule; Level 3 - daily, 8 day week, monthly,
yearly
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MODIS Ocean data products There are 86 ocean parameters
available in over 100 categories of MODIS Ocean data types archived
by (and may be obtained from) the NASA Goddard Distributed Active
Archive Center. The three basic groupings of MODIS ocean data
parameters are: ocean color sea surface temperature ocean primary
production Ocean Parameter categories: 36 Ocean Color parameters 4
Sea Surface Temperature parameters 8 Primary Productivity
parameters (including 2 Primary Production indices) 38 Quality
Control parameters.
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Processing levels Ocean color and sea surface temperature are
available at a variety of processing levels: Level 1 - Unprocessed
top of the atmosphere radiance/reflectance At 1-km spatial
resolution 5 minute granule time resolution Level 2 swath data At
1-km spatial resolution 5 minute granule time resolution Level 3
global binned or mapped data spatial resolutions of 4.63km, 39km,
or 1 degree Time resolutions of one day, 8 days, a month or a year.
The binned data products use an integerized sinusoidal equal area
grid (ISEAG). The mapped products use a Cylindrical Equidistant
Projection.
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Level 4 Productivity Ocean primary production data is available
only as binned or mapped Level 4 (i.e. L4) data. Ocean Productivity
outputs are averaged weekly or yearly. Like the L3 data, the L4
data is organized spatially as either 4km ISEAG gridded bins or as
maps using a Cylindrical Equidistant Projection. The mapped data
products are available in a choice of 4km, 39km, or 1 degree
spatial resolutions. More than one model is used for deriving these
data products and some quality statistics are available.
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DAILY PGE20 8-DAY PGE54 MONTHLY PGE73 YEARLY PGE74 PGE51 L2 L3
L4 L3 L2 Sat 1 km ISEAG 4.63 km CED 4.88 km CED 39 km CED 1 o L4
PGE52 L4 OPP s.a. model (opp_wk) OPP mapping (opp_map) Time binning
(mtbin) L3 binning (mspc/mmap) L2L3 binning (msbin) OPP stat model
(opp_hv) DATA BINNING PATHWAYS Ocean Color & SSTOcean Primary
Productivity 1 km 4.6 km 4.9km 39km 1 =111 km Swath ISEAG Linear
Linear Linear Binned Maps
