1. Remote Sensing Technology for Scalable Information Networks Douglas G. Goodin Kansas State University Geoffrey M. Henebry University of Nebraska - Lincoln

2. Ecological Remote Sensing enables recurrent observation… What is the role of remote sensing in ecological research?

3. …at vast but variable spatial extents…

4. …at multiple spatial scales… Konza Prairie – 4 m resolutionKonza Prairie – 1000 m resolution Konza

5. …and provides regional context *Konza

6. Elements of Remote Sensing

7. Remote Sensing Technology is…  Hardware – sensors, computers, storage, distribution networks  Software – commercial, public domain, user-created  “Wetware”– scientists, data managers

8. What are the Elements of Remote Sensing Technology (from an ecological perspective)?  Orbital, airborne, near-ground sensor systems  Ranges of spatial, temporal, & spectral resolutions  System for data acquisition, processing, distribution, & archiving  Algorithms to retrieve biogeophysical variables  Theory for interpretation & prediction

9. Observed Phenomenon Spectral Region Biogeophysical Variables Representative Sensors Ranges of Resolutions Solar Reflectance Visible, Near-IR, Mid-IR Albedo fPAR Land Cover NPP AVHRR SeaWiFS MODIS MERIS TM/ETM+ ALI IKONOS AVIRIS MASTER 1 m – 1 km <1 d – 18 d 1–228 bands Terrestrial Emission Mid-IR, Thermal-IR, Microwaves Surface temperature Surface moisture SMMR SSM/I AVHRR MODIS ASTER TIMS 25 m - 25 km <1 d – 3 d 1 – 50+ bands Anthropogenic Radiation RADAR, LIDAR, [SONAR] Surface roughness Soil moisture Terrain RADARSAT ASAR JERS SIR-C VCL LVIS 8 m – 150 m 18 d <10 bands Types of Earth Observing Sensors

10. Orbital Remote Sensing Systems

11. Landsat  US – Private/Gov’t  Moderate spatial resolution  1972-Present

12. IKONOS  US – Private  1999 – present  Very fine spatial resolution (1-4m)

13. NOAA – Polar Orbiter  US Government  Coarse spatial resolution, global coverage  1982 - Present

14. RADARSAT  Canada – Gov’t/private  Imaging radar  1996 - Present

15. Terra/EO-1 “Next-Generation” – Earth Observation Multi-instrument platform Multispectral, hyperspectral Coordinated observation With Landsat - 7

16. Aircraft Sensing Systems Flexible mission planning Selectable spatial resolution High cost (?)

17. AVIRIS US Gov’t (NASA) Hyperspectral (224 bands) Multiple Aircraft (ER-2, Twin Otter)

18. Other Aircraft Systems Multiple (light) aircraft platforms (Relatively) modest cost Researcher control!

19. Close Range Remote Sensing A wide variety of multi/hyper spectral instruments Not just “ground truth” Researcher control

21. What are the Elements of Remote Sensing Technology (from an Ecological perspective)?  Orbital, airborne, near-ground sensor systems  Ranges of spatial, temporal, & spectral resolutions  System for data acquisition, processing, distribution, & archiving  Algorithms to retrieve biogeophysical variables  Theory for interpretation & prediction

22. Observed Phenomenon Spectral Region Biogeophysical Variables Representative Sensors Ranges of Resolutions Solar Reflectance Visible, Near-IR, Mid-IR Albedo fPAR Land Cover NPP AVHRR SeaWiFS MODIS MERIS TM/ETM+ ALI IKONOS AVIRIS MASTER 1 m – 1 km <1 d – 18 d 1–228 bands Terrestrial Emission Mid-IR, Thermal-IR, Microwaves Surface temperature Surface moisture SMMR SSM/I AVHRR MODIS ASTER TIMS 25 m - 25 km <1 d – 3 d 1 – 50+ bands Anthropogenic Radiation RADAR, LIDAR, [SONAR] Surface roughness Soil moisture Terrain RADARSAT ASAR JERS SIR-C VCL LVIS 8 m – 150 m 18 d <10 bands Types of Earth Observing Sensors

23. Spatial Resolution Coarse FineModerate

24. Spectral Resolution Panchromatic: 1 spectral band - very broad Multispectral: 4-10 spectral bands - broad Superspectral: 10-30 spectral bands - variable Hyperspectral: >30 spectral bands - narrow The challenge of hyperspectra is to reduce dense, voluminous, redundant data into a compact, effective suite of superspectral bands and indices for retrieval of biogeophysical fields.

25. What are the Elements of Remote Sensing Technology (from an Ecological perspective)?  Orbital, airborne, near-ground sensor systems  Ranges of spatial, temporal, & spectral resolutions  System for data acquisition, processing, distribution, & archiving  Algorithms to retrieve biogeophysical variables  Theory for interpretation & prediction

26. Acquisition Processing Distribution/Storage Data Handling System - Hardware

27. Data analysis system – linkages are critical Archiving/Distribution Researchers/ Groups

28. The MODIS system An example

29. What are the Elements of Remote Sensing Technology (from an Ecological perspective)?  Orbital, airborne, near-ground sensor systems  Ranges of spatial, temporal, & spectral resolutions  System for data acquisition, processing, distribution, & archiving  Algorithms to retrieve biogeophysical variables  Theory for interpretation & prediction

30. NDVI = (  NIR -  Red )/(  NIR +  Red ) R  =  f ( ,  ) sin  cos  d  d   0 = [(  (i=1..N) x i 2 )/N] * [(C/k) * (sin  )/(sin  ref )] Retrieval of Biogeophysical Quantities & Indices EVI =2.5*(  NIR -  Red )/(L+  NIR +C 1 *  Red -C 2 *  Blue )    

31. Calibration to derive physical quantities: an engineering problem  Does the instrument give the correct physical data?  Is the instrument’s range & sensitivity appropriate for the application?  Cross-sensor calibration

32. Calibration to derive ecological quantities: a scientific problem  Can the sensor data yield ecologically relevant relationships?  NOT ground “truth” – ground level observation  RESCALING  Empirical relationships are site & time specific but reflectance, emission, and backscattering are interactions not intrinsic properties of observable entities

33. Calibration to derive ecological quantities: a scientific problem  Top-down vs. bottom-up modeling perspectives  Model invertibility  Model robustness

34. Empirical Model – Top down

35. Analytical Models – Bottom up

36. What are the Elements of Remote Sensing Technology (from an Ecological perspective)?  Orbital, airborne, near-ground sensor systems  Ranges of spatial, temporal, & spectral resolutions  System for data acquisition, processing, distribution, & archiving  Algorithms to retrieve biogeophysical variables  Theory for interpretation & prediction

37. To enable ecological forecasting, we need monitoring strategies for change detection: perceiving the differences change quantification: measuring the magnitudes of the differences change assessment: determining whether the differences are significant change attribution: identifying or inferring the proximate cause of the change

38. Observations Ground segment Acquisition, processing, storage, & archiving Ground segment Acquisition, processing, storage, & archiving Retrieval of biogeophysical variables Spatio-Spectral- Temporal analysis Definitions of nominal trajectories and estimates of uncertainty Assimilation of current observational datastreams Change detection Change quantification Change attribution Change assessment Ecological Questions & Hypotheses Information for Ecological Forecasting

40. ACKNOWLEDGMENTS DGG acknowledges support from NASA EPSCoR subcontract 12860. GMH acknowledges support from NSF #9696229/0196445 & #0131937.