Remote Sensing and how to use d ata

1. RemoteSensing and how to use data Peter Fox(Geo I lecture; Nov. 11 2013)

2. What is remote sensing? • Observing or measuring or inferring a quantity remotely, i.e. not in direct contact (cf. in-situ) • Sense – light, sound – what about other human senses? • Taste, smell, touch? • Questions – are the following remote sensors? • Camera, radar, thermometer, accelerometer

3. Light = electromagnetic spectrum

4. What is this?

5. Where/ what is this?

7. And this?

8. Using remote sensing products • http://en.wikipedia.org/wiki/Remote_Sensing_Satellite_and_Data_Overview • Basic idea: • A direct measurement of what you want • Not a direct measurement • Many factors to account for: • Instrument, device … • Observing position … • Things to correct for …

9. Fields vs. objects classic geophysics “Coverage” viewpoint classic geology “Feature” viewpoint • simple data structures • collated/gridded ready for analysis • complex data • database insertion • complete feature interpretations Via Cox/2005 AGU Spring

10. Images … • A raster image is a type of computerized image that consists of row after row of tiny dots (pixels) • There are many different raster image file formats

11. One common correction = “registering” • Registration consists of specifying the coordinates of a minimum number of points (3) on the image to orient it in geographic space – they are called control points • Three points are needed to determine the axes and scale • You should also try to determine the type of projection used in the original image • Note, this is different from geocoding

12. Example – registration points If you don’t know the projection, you can get the registration by a best-fitting procedure. In this case you should provide as many registration points as possible.

13. Sources • Sources of registration points: GPS, other maps, bench marks, geographic data, recognizable points on ground (state boundaries), other spatially enabled data (road vector file) • Using: Scan or download image, or get aerial photo – read into GIS application as table, register, set up as layer

14. Overall intent is re-projection! • During a raster re-projection process, the software recalculates the pixel values of the source image to make them display correctly in the destination image. • In this resampling process, the idea is to restore every pixel value of the image based on the pixels around it • Many methods for calculating the pixel values of the destination image, e.g.: • Cubic Convolution • Nearest Neighbor

15. Example USGS: (L) An elevation image classified from a satellite image of Minnesota exists in a different scale and projection than the lines on the digital file of the State and province boundaries. (R) The elevation image has been re-projected to match the projection and scale of the State and province boundaries.

16. Where is this?

17. So what’s this?

18. Map projections • Representation of the surface of a 3D spherical or ellipsoidal body on a 2D planar map • Who has used • Google Earth/Maps? • Microsoft Visual Earth? • NASA WorldWind? • Another?

20. 3D worlds • Great circle - path of minimum distance on surface of sphere, intersection of sphere with plane passing through center of sphere, radius is that of sphere • Small circle - intersection of any plane with surface of sphere, has radius equal to or less than radius of sphere • Map projections: • Always have distortions • User chooses which characteristics to be correct • Distortions depend on scale

21. Properties** to conserve • Area – coin covers the same area anywhere on map (equal area, equivalent, homolographic, authalic, equiareal) • Shape – relative local angles are correct (conformal, orthomorphic) • Scale – no projection conserves scale throughout, equidistant projections give true distances from one point to all others • Direction – azimuthal projection conserves direction from center of map to all other points

22. Projections

24. Other projections • Mercator – lines of constant direction are straight • Gnomonic – great circles are straight lines • Stereographic – great/small circles are circles • Many different types – cylinders, cones, planes

25. References for Coordinates • Equator – latitude = 0, positive northward, negative southward • Prime meridian (Greenwich UK) longitude = 0, positive eastward, negative westward • Grids, often made of lon-lat • Cartesian coordinate systems, usually local, designate points by perpendicular distances from axes on a flat map • Y – meridians, positive North (northings) • X – parallels, positive East (eastings)

26. United States • In US we use UTM (Universal Transverse Mercator) and SPCS (State Plane Coordinate Systems) Large regions are divided into zones to decrease distortion.

27. Oblate spheroid Earth is flattened by about f » 1/300 f = (a-b)/a » 1/300 » 22 / 6370 km; a – b » 22 km Earth’s flattening is sufficient to distort maps at 1:100,000 and larger scales.

28. In reality – it’s a Geoid • Earth is not an exact ellipsoid and its real shape is called the geoid. The geoid is the shape the Earth’s surface would have if it was entirely covered with water, in other words, sea level defines the geoid and is an equipotential surface. Local variations in the gravity field cause the geoid to stray from the ellipsoid by ~ ± 100 meters in height. • The geoid is important in surveying because it locally defines vertical (by gravity). Elevations estimated by traditional leveling are relative to the geoid and not to the ellipsoid. Elevations estimated by GPS are relative to the ellipsoid.

29. Latitude for an ellipsoid • A = geographic (geodetic) latitude, • B = geocentric latitude • A is slightly greater than B, at poles and (recall) equator A = B

30. Huh? Geodetic/geocentric? • It is important to note that geodetic latitude is different from geocentric latitude. • Geodetic latitude is determined by the angle between the normal of the spheroid and the plane of the equator, whereas geocentric latitude is determined around the center

31. Channels… • Many remote sensing instruments have many observing modes – often called channels • Different wavelengths / frequencies • Very Large Array (radiotelescope): • 74 MHz to 50 GHz (400 to 0.7 cm) • NASA spectrometers • 36 channels on Moderate Resolution Imaging Spectrometer (MODIS)

32. MODIS, partial list

33. RGB as an example

34. Color (intensity) correction

35. Now… using these data

36. Using the data Producers Consumers Quality Control Quality Assessment Fitness for Purpose Fitness for Use Trustor Trustee 36

37. Data quality needs: fitness for use • Measuring Climate Change: • Model validation: gridded contiguous data with uncertainties • Long-term time series: bias assessment is the must , especially sensor degradation, orbit and spatial sampling change • Studying phenomena using multi-sensor data: • Cross-sensor bias is needed • Realizing Societal Benefits through Applications: • Near-Real Time for transport/event monitoring - in some cases, coverage and timeliness might be more important that accuracy • Pollution monitoring (e.g., air quality exceedance levels) – accuracy • Educational (users generally not well-versed in the intricacies of quality; just taking all the data as usable can impair educational lessons) – only the best products

38. Definitions • Quality • Is in the eyes of the beholder – worst case scenario… or a good challenge • Uncertainty • has aspects of accuracy (how accurately the real world situation is assessed, it also includes bias) and precision (down to how many digits)

39. Quality Control vs. Quality Assessment Quality Control (QC) flags in the data (assigned by the algorithm) reflect “happiness” of the retrieval algorithm, e.g., all the necessary channels indeed had data, not too many clouds, the algorithm has converged to a solution, etc. Quality assessment is done by analyzing the data “after the fact” through validation, intercomparison with other measurements, self-consistency, etc. It is presented as bias and uncertainty. It is rather inconsistent and can be found in papers, validation reports all over the place.

40. Definitions • Bias has two aspects: • Systematic error resulting in the distortion of measurement data caused by prejudice or faulty measurement technique • A vested interest, or strongly held paradigm or condition that may skew the results of sampling, measuring, or reporting the findings of a quality assessment: • Psychological: for example, when data providers audit their own data, they usually have a bias to overstate its quality. • Sampling: Sampling procedures that result in a sample that is not truly representative of the population sampled.

41. Satellite ~ level 2 data

42. Level 2 data • Swathfor MISR, orbit 192 (2001)

43. Factors contributing to uncertainty and bias in Level 2 • Physical: instrument, retrieval algorithm, aerosol spatial and temporal variability… • Input: ancillary data used by the retrieval algorithm • Classification: erroneous flagging of the data • Simulation: the geophysical model used for the retrieval • Sampling: the averaging within the retrieval footprint Borrowed from the SST study on error budget

44. The quality of data can vary considerably Version 5 Level 2 Standard Retrieval Statistics

45. Percent of Biased Data in MODIS Aerosols Over Land Increase as Confidence Flag Decreases *Compliant data are within + 0.05 + 0.2Aeronet Statistics from Hyer, E., J. Reid, and J. Zhang, 2010, An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals, Atmos. Meas. Tech. Discuss., 3, 4091–4167.

46. The effect of bad qualitydata is often not negligible Hurricane Ike, 9/10/2008 Total Column Precipitable Water Quality Best Good Do Not Use kg/m2

47. Replace bad-quality pixels with fill values Original data array (Total column precipitable water) Mask based on user criteria (Quality level < 2) Good quality data pixels retained Output file has the same format and structure as the input file (except for extra mask and original_data fields)

48. Level 3 data

49. Why can’t we just apply L2 quality to L3? Aggregation to L3 introduces new issues where aerosols co-vary with some observing or environmental conditions – sampling bias: • Spatial: sampling polar areas more than equatorial • Temporal: sampling one time of a day only (not obvious when looking at L3 maps) • Vertical: not sensitive to a certain part of the atmosphere thus emphasizing other parts • Contextual: bright surface or clear sky bias • Pixel Quality: filtering or weighting by quality may mask out areas with specific features

50. Addressing Level 3 data “quality” • Quality aspects (examples): • Completeness: • Spatial (MODIS covers more than MISR) • Temporal (Terra mission has been longer in space than Aqua) • Observing Condition (MODIS cannot measure over sun glint while MISR can) • Consistency: • Spatial (e.g., not changing over sea-land boundary) • Temporal (e.g., trends, discontinuities and anomalies) • Observing Condition (e.g., exhibit variations in retrieved measurements due to the viewing conditions, such as viewing geometry or cloud fraction) • Representativeness: • Neither pixel count nor standard deviation fully express representativeness of the grid cell value