1. Introduction to Satellite Remote Sensing SeaWiFS, June 27, 2001 Miles Logsdon, Univ. of Washington Oceanography

2. My agenda Show you pretty pictures Introduce Remote Sensing terms and concepts Get the language down “Think” about the future

3. 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

4. Let’s start with some nice pictures

5. 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.

6. 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)

7. Landsat-ETM 30m resolution 16-day repeat

8. MODIS Terra - Daytime Descending Orbits MODIS 1k resolution Daily repeat Best response in a Ph.d. oral exam: “I’ll give up space to get time”. Miles Logsdon

9. One “goal” is to produce a: Classified Product

10. MOD13 – NDVI (16-day) 500m resolution June 2002 (“bright” photosynthesizing vegetation) LowHigh A second “goal” might be to produce a: Derived Product

11. MOD11 – Daytime (8-day averaged) Land Surface Temperature June 2002 ~3 o C~50 o C Temperature ( o C)

12. AprMayJun JulAugSep Remote Sensing as a Time Series SeaWifs, 1999, 1km monthly mean chlorophyll-a estimates Current Collections Pacific Northeast, Apr – Sep, 1999 - 2003

13. Additive Subtractive

14. First: A few Simple Reminders about Spectral Signatures Thanks to Robin Weeks

15. Coordinate system used with satellite sensors  Z Zenith angle  Look or incidence angle  S Solar zenith angle

16. When radiation interacts with the atmosphere, then depending on the wavelength, the three things that happen are - Absorption, - Scattering, - Emission.

17. The Effect of the Atmosphere on Spectral Data Path Radiance (L p ) Atmospheric Transmissivity (T) Thanks to Robin Weeks

18. 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.

19. Rayleigh Scatter

20. Solar scattering generates a Rayleigh path radiance

22. Two kinds of solar reflection in the visible: Direct surface reflection, diffuse sub-surface backscatter

23. Once the Light “hits” the surface we are concerned with reflection

24. The “PIXEL”

25. Wavelength (Bands)

26. Terrestrial and Ocean Color Sensors Ocean Color Sensors

27. 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

28. Band Combinations 3,2,1 4,3,2 5,4,3 R,G,B R G B Landsat band 5 4 3 2 1

29. We “approach” RS in two ways To classify or group thematic land surface materials To detect a biophysical process

30. Cluster and Classify

31. Spectral Profile

32. Spatial Profile

33. Spectral Signatures

34. 1d classifier

35. Spectral Dimensions

36. 3 band space

37. Clusters

38. 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

39. Spectral Distance * a number that allows two measurement vectors to be compared

40. Classification Approaches Unsupervised: self organizing Supervised: training Hybrid: self organization by categories Spectral Mixture Analysis: sub-pixel variations.

41. Clustering / Classification Clustering or Training Stage: –Through actions of either the analyst’s 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.

42. 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

43. 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

44. ISODATA clusters

45. Supervised Classification

46. 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

47. Band A Band B Parallelepiped Band A Band B cluster mean Candidate pixel Minimum Distance Maximum likelihood (bayesian) probability Bayesian, a prior (weights)

48. Parametric classifiers

49. 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

50. Detecting a Process: Two examples Using “band math”

51. Laboratory Spectral Signatures II Common Urban Materials Healthy grass Concrete Astroturf wavelength Thanks to Robin Weeks

52. Vegetation: Pigment in Plant Leaves (Chlorophyll) strongly absorbs visible light (0.4 to 0.7 μm) Cell Structure however strongly reflects Near-IR (0.7 – 1.1 μm) Thanks to Robin Weeks

53. (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:

54. Ocean Color Let’s 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.

55. Chaetoceros species of diatoms: cells are 20-25 mm in diameter. Chloroplasts contain pigments

56. 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

57. 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

58. What’s 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 (O’Reilly) Ken Carder: University of South Florida

59. Chl-a increasing Florescence Independent of Chl-a R( )

60. 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

62. SeaWiFS empirical OC4 algorithm for Chl-a; Called a maximum-band ratio alg.

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

65. 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.

66. 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.

67. 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.

68. 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) L2→L3 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

69. SeaWifs, April 24, 1999 Thanks to Seelye Martion

70. July 17 th, 2003 Aug 3 rd, 2003

71. http:/sal.ocean.washington.edu (my lab web-site) http://learn.arc.nasa.gov/ http://www.earth.nasa.gov/

72. Flying