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

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

…at vast but variable spatial extents…

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

…and provides regional context *Konza

Elements of Remote Sensing

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

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

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

Orbital Remote Sensing Systems

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

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

NOAA – Polar Orbiter  US Government  Coarse spatial resolution, global coverage  Present

RADARSAT  Canada – Gov’t/private  Imaging radar  Present

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

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

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

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

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

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

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

Spatial Resolution Coarse FineModerate

Spectral Resolution Panchromatic: 1 spectral band - very broad Multispectral: 4-10 spectral bands - broad Superspectral: 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.

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

Acquisition Processing Distribution/Storage Data Handling System - Hardware

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

The MODIS system An example

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

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 )    

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

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

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

Empirical Model – Top down

Analytical Models – Bottom up

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

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

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

ACKNOWLEDGMENTS DGG acknowledges support from NASA EPSCoR subcontract GMH acknowledges support from NSF # / & #