1. Applications of the Terrestrial Observation & Prediction System
Forrest Melton
CSU Monterey Bay, Seaside, CA
Ecological Forecasting Lab
NASA Ames Research Center, Moffett Field, CA
With contributions from:
Rama Nemani, Petr Votava, Andrew Michaelis, Christina Milesi, Hirofumi Hashimoto,
Weile Wang
William Reisen & Chris Barker, UC Davis
Support from:
NASA Applied Sciences Program: REASoN Award, Decision Support through Earth Science Research Results Award
MODIS NDVI, Mesoamerica, Jan. 2-16
SERVIR Workshop Panama City, Panama, Mar. 1, 2007

2. Outline Ecological monitoring and forecasting
Types of ecological forecasts
Modeling framework for producing EFs
the Terrestrial Observation and Prediction System (TOPS)
Examples of ecological forecasts and TOPS products
TOPS-SERVIR datasets
EF Applications for Mesoamerica
Anomaly detection and landscape monitoring
NPP and agricultural production monitoring and forecasting
Vector and disease risk mapping and ecological forecasting
MODIS Terra Image of Panama, February 24, 2004 (MODIS Rapid Response)

3. What is Ecological Forecasting?
Ecological Forecasting (EF) predicts the effects of changes in the physical, chemical, and biological environments on ecosystem state and activity.
TOPS daily soil moisture forecast, Dec 30, 2006

4. Changing Surface Temperatures
Why we need ecological forecasting?

5. Why are Ecological Forecasts Important?
Ecological forecasts offer decision makers estimates of ecological vulnerabilities and potential outcomes given specific natural events, and/or management or policy options.
Ecological forecasting is critical in understanding potential changes in ecological services, before they happen (early warning), and are critical in developing strategies to off-set or avoid catastrophic losses of services.
Goal is to develop management strategies and options to prevent or reverse declining trends, reduce risks, and to protect important ecological resources and associated processes.
Bruce Jones, NCSE, Forecasting Environmental Changes, 2005
Foster interdisciplinary activity

6. Short-term Monitoring and Forecasting
Sacramento river flooding, California
Irrigation requirements
Based on weather forecasts, conditioned on historical ecosystem state
Days to a week

7. Mid-term/Seasonal Forecasts: Water resources, Fire risk, Phenology
ENSO-Rainfall over U.S
El Nino
La Nina
Based on ENSO forecasts
Weeks to months

8. Long-term Projected Changes
Rizzo & Wilken, Climatic Change, 21(1), pp , 1992
Based on GCM outputs
Decades to centuries

9. TOPS: Common Modeling Framework
Monitoring
Modeling
Forecasting
Multiple scales
Predictions are based on
changes in biogeochemical
cycles
Nemani et al., 2003, EOM
White & Nemani, 2004, CJRS

10. Access to a variety of observing networks
Fluxnet
Weather network
Streamflow network
Soil moisture network

11. Access to a variety of remote sensing platforms
MODIS, Landsat, AMSR-E, AVHRR, Meteosat, GOES and others
Integration across: Platforms, Sensors, Products, DAACs is non-trivial

12. Retrospective to real time Operational remote sensing
Satellites: MODIS on Terra & Aqua
Retrospective to real time
Operational remote sensing
Terra Launch: Dec. 18, 1999
First Image: Feb. 24, 2000
Aqua Launch: May 04, 2002
First Image: June 24, 2002

13. Multiple Instruments per Mission
Example: MODIS on Terra & Aqua
Terra Satellite Launched Dec. 18, 1999 with five instruments (ASTER, CERES, MISR, MODIS, MOPITT)
Aqua Satellite Launched May 4, 2002 with six instruments (AIRS, AMSR-E, AMSU, CERES, HSB, MODIS)
MODerate resolution Imaging Spectroradiometer
Orbit: 705 km, 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua), sun-synchronous, near-polar, circular
Swath Dimensions: 2330 km (cross track) by 10 km (along track at nadir)
Data Rate:10.6 Mbps (peak daytime); 6.1 Mbps (orbital average)
Spatial Resolution: 250 m (bands 1-2), 500 m (bands 3-7), 1000 m (bands 8-36)
Design Life: 6 years

14. Multiple Products per Instrument: MODIS Measurements
MOD01 Level-1A Radiance Counts
MOD02 Level-1B Calibrated Relocated Radiances
MOD03 Relocation Data Set
MOD04 Aerosol Product
MOD05 Total Precipitable Water
MOD06 Cloud Product
MOD07 Atmospheric profiles
MOD08 Gridded Atmospheric Product (Level-3)
MOD09 Atmospherically-corrected Surface
Reflectance
MOD10 Snow Cover
MOD11 Land Surface Temperature & Emissivity
MOD12 Land Cover/Land Cover Change
MOD13 Vegetation Indices
MOD14 Thermal Anomalies, Fires & Biomass
Burning
MOD15 Leaf Area Index & FPAR
MOD16 Surface Resistance & Evapotranspiration
MOD17 Vegetation Production, Net Primary
Productivity
MOD18 Normalized Water-leaving Radiance
MOD19 Pigment Concentration
MOD20 Chlorophyll Fluorescence
MOD21 Chlorophyll_a Pigment Concentration
MOD22 Photosynthetically Active Radiation (PAR)
MOD23 Suspended-Solids Conc, Ocean Water
MOD24 Organic Matter Concentration
MOD25 Coccolith Concentration
MOD26 Ocean Water Attenuation Coefficient
MOD27 Ocean Primary Productivity
MOD28 Sea Surface Temperature
MOD29 Sea Ice Cover
MOD31 Phycoerythrin Concentration
MOD32 Processing Framework & Match-up
Database
MOD35 Cloud Mask
MOD36 Total Absorption Coefficient
MOD37 Ocean Aerosol Properties
MOD39 Clear Water Epsilon
MOD43 Albedo 16-day L3
MOD44 Vegetation Cover Conversion
MODISALB Snow and Sea Ice Albedo

15. Ability to integrate a variety of models
Biogeochemical Cycling
Crop growth/yield
Pest/Disease
Global carbon cycle
Prognostic/diagnostic models

16. Ability to work across different scales of time and space
Hours
Years/Decades
Days
Weeks/Months
Weather/Climate Forecasts at various lead times
Downscaling

17. Standard TOPS Outputs DATA PROPERTIES Spatial Resolution: 30m to 1km
MODIS PRODUCTS (8 days/Annual) 1
LAI 2
FPAR 3
GPP/NPP 4
LST-TERRA/AQUA 5
NDVI 6
EVI 7
LANDCOVER* 8
ALBEDO 9
SNOW 10
FIRE
METEOROLOGY (Daily) 11
MAX TEMPERATURE 12
MIN TEMPERATURE 13
RAINFALL 14
SOLAR RADIATION 15
DEW POINT/VPD 16
DEGREE DAYS
* Once a year
TOPS-NOWCASTS (daily) 17
TOPS-SNOW 18
TOPS-SOIL MOISTURE 19
TOPS-ET 20
TOPS-OUTFLOW 21
TOPS-GPP/NPP 22
TOPS-PHENOLOGY 23
TOPS-VEG STRESS
TOPS-FORECASTS (5 days to 180 days) 24
BGC-LAI/PHENOLOGY 25
BGC-SOIL MOISTURE 26
BGC-OUTFLOW 27
BGC-ET 28
BGC-VEG STRESS 29
BGC-SNOW 30
BGC-GPP/NPP
DATA PROPERTIES
Spatial Resolution: 30m to 1km
Temporal Resolution: 1 to 30 days Data Presentations: Nowcast, forecast, anomaly, cumulative, current average
Data Formats: Binary, GeoTIFF, JPEG, PNG
Metadata: ESML & OGC compliant
Delivery Mechanisms: FTP, WMS, Web

18. Standard TOPS Outputs: Local to Global Scales
Global NPP Anomalies
U.S. Gross Primary Productivity
California Daily Soil Moisture Estimates
Napa Valley Forecasted Vineyard Irrigation Demands
Yosemite Minimum Temperatures
Spatial scales from 0.5 degrees to 4m. Temporal scales from yearly to daily.

19. TOPS/SERVIR Products for Mesoamerica

20. TOPS/SERVIR Products for Mesoamerica
TOPS MODIS Ecosystem Products
1km spatial resolution products
8 / 16 day composites
Land Surface Temperature (LST)
Leaf Area Index (LAI)
Fraction of Photosynthetically Active Radiation (FPAR) absorbed
Normalized Difference Vegetation Index (NDVI)
Enhanced Vegetation Index (EVI)
Gross Primary Productivity (GPP)

21. TOPS MODIS Products for Mesoamerica: LST
Land Surface Temperature (LST)
Land surface temperature at time of satellite overpass
Degrees Kelvin
Composited from the MODIS MOD11A1 daily LST values
Derived from MODIS bands: 31 (11.03 µm) 32 (12.02 µm)
MOD11 algorithm incorporates information from MODIS cloud mask, atmospheric profile, land cover, and snow cover

22. Normalized Difference Vegetation Index (NDVI)
Provides a measure of vegetation density and health.
Used in studies of landscape change, crop monitoring, and risk mapping for vector-borne diseases.
Calculated from the visible and near-infrared light reflected by vegetation
Healthy vegetation absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light.
NDVI values for a given pixel always result in a number that ranges from minus one (-1) to plus one (+1)
Credit: Robert Simmon

23. TOPS MODIS Products for Mesoamerica: NDVI
Normalized Difference Vegetation Index
16-day values composited from the MODIS MOD13A1 daily NDVI values
Derived from MODIS bands: 1 (Red; nm) 2 (NIR; nm)
The reflectance values are the surface bidirectional reflectance factors for MODIS bands 1 ( nm) and 2 ( nm)
Tends to saturate over high biomass regions; sensitive to atmosphere and canopy variations.
A main disadvantage of the NDVI is the inherent non-linearity of ratio-based indices and problem in scaling ratios. The NDVI also exhibits asymptotic (saturated) signals over high biomass conditions and is very sensitive to atmosphere and canopy background variations with NDVI degradation particularly strong decreasing atmosphere visibility and with higher canopy background brightness.

24. Enhanced Vegetation Index (EVI)
EVI developed to provide improved vegetation signal in high biomass regions.
De-couples the canopy background signal and corrects for residual atmospheric influences.
Input reflectances may be atmospherically-corrected or partially atmospheric corrected for Rayleigh scattering and ozone absorption.
Tracking vegetation condition with MODIS EVI in the Amazon Basin
Credit: Huete et al GRL 33.

25. TOPS MODIS Products for Mesoamerica: EVI
Enhance Vegetation Index (EVI)
Composited from the MODIS MOD13A1 daily EVI values
Derived from MODIS bands: 1 (Red; nm) 2 (NIR; nm) 3 (Blue; ) nm
Better performance than NDVI in the tropics and other regions with high biomass.
The enhanced vegetation index (EVI) was developed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences.

26. TOPS MODIS Products for Mesoamerica: LAI & FPAR
Leaf Area Index (LAI)
Measure of plant canopy structure.
One sided leaf area per unit ground area.
Unitless index; values of <1.0 indicate incomplete canopy closure.
Typical values range from 0 to 7.
Highly related to a variety of canopy processes, such as water interception, evapotranspiration, photosythesis, respiration, and leaf litterfall.
Used in ecological and climate models as a representation of canopy structure.
Fraction of Photosyntethically Active Radiation (FPAR) absorbed
Measure of the proportion of available radiation in the photosynthetically active wavelengths of the spectrum ( microns) that is absorbed by the canopy.
Radiation term; more directly related to remotely sensed variables (such as NDVI) than LAI.
Can be used to translate direct satellite data such as NDVI into simple estimates of primary production.
Both FPAR and LAI are used in biogeochemical models to estimate primary productivity.

27. TOPS MODIS Products for Mesoamerica: LAI & FPAR
8-day composites
Provide measures of canopy structure and photosynthetic activity

28. TOPS MODIS Products for Mesoamerica: GPP
Gross Primary
Productivity (GPP)
Measure of gross CO2 assimilation in vegetation.
Estimates of GPP from satellite data based on the concept of radiation use efficiency (RUE)
RUE is a measure of how effective vegetation is in using PAR to converting solar radiation in the wavelength band from micrometers to fix CO2 from the atmosphere as carbohydrate for growth and respiration
Varies depending on vegetation condition and environmental conditions.
Net primary productivity (NPP) is difference between GPP and amount of CO2 lost to respiration.

29. TOPS MODIS Products for Mesoamerica: GPP
Gross Primary Productivity
(GPP)
Composited from the MODIS MOD17A1 daily GPP values
Derived from MODIS FPAR and LAI, and utilizes GMAO surface meteorology and a biome properties look-up table to produce model-derived estimates.

30. Mesoamerican Anomalies
Other examples:

31. Mesoamerican Anomalies
Using anomaly persistence to assess significance

32. Mesoamerican Anomalies

33. TOPS/SERVIR Climate Products for Mesoamerica
TOPS Climate Products
Daily, 1 km spatial resolution demonstration products
Gridded meteorological surfaces derived from station observations using modified Daymet algorithm (Thornton et al. 1997, 2000)
Derived from 90 meteorological stations in Mesoamerica that report to NOAA Global Summary of the Day (GSOD)
Maximum temperature (C◦)
Minimum temperature (C◦)
Precipitation (mm)
Vapor Pressure Deficit (Pa)
Shortwave Radiation (watts/m2)

34. TOPS/SERVIR Products for Mesoamerica
Minimum Temperature
Shortwave Radiation
Precipitation
Vapor Pressure Deficit

35. Reporting Stations used by TOPS for NA / MA
Accuracy of gridded meteorological surfaces directly related to density of meteorological staitons
3000 – 6000 stations in U.S. (depending on time period)
700 stations in California
90 stations in Mesoamerica
Sample data set prepared for SERVIR for

36. Examples of TOPS Applications
Landscape monitoring and trend analysis
Soil moisture estimates and irrigation demand forecasting
Ecosystem monitoring for protected area management
Mapping of insect vectors for vector-borne diseases

37. Long-Term Monitoring and Trend Analysis

38. Agricultural Management Applications of EF
Short-term: Vineyard Irrigation Forecasts
Irrigation Forecast
for week of July 19-26, 2005
Tokalon Vineyard,
Oakville, CA
CIMIS Measured Weather Data
through July 18, 2005
NWS Forecast Weather Data
July 19-26, 2005
1000 N
meters
Forecast Irrigation (mm) 30
Seasonal
Fully automated web delivery to growers

39. Agricultural Management Applications of EF
Mid-range: Forecasting the onset of growing season
Based on White and Nemani, RSE, 2006

40. Agricultural Management Applications of EF Mid-range: Forecasting crop yields
Lobell, Cahill, and Field (2006) recently demonstrated the use of climate data (temperature and precipitation) to predict seasonal yields for 12 major crops in California
Lead time of weeks to months
Forecasts capture more than 50% of the variability in yield anomalies, and as much as 89%
Forecasted versus observed yields for 12 California crops (from Lobell et al., 2006, California Agriculture, 60(4):

41. Long-range: Net Primary Productivity Anomalies
56% of global population lives in regions where water availability strongly influences NPP. Significant correlations between MEI and NPP were found over 63% of the vegetated surface, inhabited by 3.3 billion people.
* MEI = multivariate enso index
* Over half of the global human population is presently living in areas with above average NPP of 490 g C m2 year or greater.
* By 1998, nearly 56% of global population lived in regions where water availability strongly influences NPP. Per capita NPP declined over much of Africa between 1982 and 1998, in spite of the estimated increases in NPP over the same period.
* On average, NPP over 40% of the total vegetated land surface has shown significant correlations with ENSO-induced climate variability affecting over 2.8 billion people.
Population(millions) 7 12
Milesi et al., Glob. Pl. Change, 2005
Hirofumi et al., JGR-Atm, 2004

42. Key Questions for Protected Area Managers
What is the current status of ecosystems in and adjacent to the park/protected area?
How are they changing?
How will they change in the future (in response to changes in climate and land use)?
How do these changes impact resource management?
MODIS Direct Broadcast image of a fire event in Yosemite National Park, September, 2005.

43. TOPS EF Tools for Protected Area Management
Monitoring and forecasting of ecosystem conditions
Automated event and anomaly detection
Monitoring and forecasting of summer streamflow, soil moisture, and vegetation stress conditions for fire risk forecasting
Monitoring and modeling of GPP, total aboveground carbon, and current aerosol levels to assess potential air quality impact of management initiated burns
Snowpack monitoring and forecasting for runoff prediction
Long-term simulations for analysis of potential impacts of climate change on ecosystem conditions
Observed vs. predicted snow cover, Merced Watershed, Yosemite National Park,

44. Anomaly Detection for Resource Monitoring
Automated anomaly detection and trend analysis assist resource managers in identifying significant events and focusing ground-based monitoring and management efforts.

45. Interpreting Anomalies
Ground-based observations key to validating and interpreting anomalies.

46. TOPS Data Fusion: Trend Analysis for Features of Interest

47. Ecological Forecasting and Public Health
Potential Areas of Contribution:
Air quality (fire frequency, land cover change / desertification and particulates)
Water quality (flooding, drought)
Food security
Vector-borne disease
PATHOGEN
HOST
CLIMATE
HYDROLOGY
HABITAT
VECTOR

48. Ecological Forecasting: Lyme Disease
CDC National Lyme Disease Risk Map
Predictive risk map of habitat suitability for Ixodes scapularis in Wisconsin and Illinois.
Fish & Howard. Morbidity and Mortality Weekly Report,48, pp 21-24, 1999
Guerra et al, Emerging Infectious Diseases, Vol. 8(3), 2002

49. Ecological Forecasting: Malaria
Soil moisture
Patz, J et al. Tropical Medicine & International Health, 3.10, (1998):
Modeled soil moisture / surface-water availability in Kenya to predict biting rates (climate, land cover, and soil type as inputs to model)
Soil moisture was a better predictor than precipitation, and comparable to NDVI from AVHRR
Land cover change
Vittor, A. et al. Am. J. Trop. Med. Hyg., 74.1, (2006): 3-11
In deforested sites in Peruvian Amazon, A. darlingi had a biting rate > 278 times higher than the rate determined for areas that were predominantly forested.
Regression of the log of An. Gambiae and An. Funestus HBR and modeled soil moisture. Source: Patz et. al. 1998

50. Tracking parameters related to mosquito abundance:
Regional Nowcasts: California
Tracking parameters related to mosquito abundance:
Hydrology
Vegetation
Ecosystem
Meteorology

51. Land use and seasonal patterns of mosquito abundance
Effect of land use on seasonal patterns of mosquito abundance in Sacramento, CA.
100
200
300
400
500
600
May
Jun
Jul
Aug
Sep
Oct
Month
Cx. tarsalis per trap-week
Average seasonal profiles for Cx. tarsalis counts per New Jersey light trap-week by bioregion,
Figures courtesy of CM Barker

52. Summary of TOPS Applications for Mesoamerica
Landscape
Monitoring
Daily / weekly / monthly satellite- and model-based measures of ecosystem condition
Identification of anomalies and trends
Potential use as inputs to annual ‘state of the nation’ assessments
Ecosystem
Modeling
Soil moisture
Evapotranspiration
Watershed outflow
Accuracy and spatial resolution determined by availability of ground-based observations (soil classification map, hydrologic data, meteorological data)
Research &
Application
Development
Inputs to other models
Crop yield monitoring
Irrigation demand forecasting
Fire risk mapping
Flood forecasting
Requires collaboration with MA research teams

53. Summary
TOPS is a modeling framework that uses ecosystem models to ingest satellite observations, meteorological observations and forecasts, and ancillary data to monitor ecosystem conditions and produce ecological forecasts. TOPS has been used to develop an initial suite of ecosystem products for SERVIR.
Remote sensing & ecological forecasts provide an important supplement to ground-based monitoring and climate forecasts for protected area management, agricultural management, and public health decision support / disease risk mapping. EF can assist with translation of climate variables into measures of ecosystem conditions associated with disturbance events, agricultural productivity, and pathogen-vector-host interactions.
Rapidly growing number of successful examples of applications that utilize ecosystem models to integrate satellite, climate, and ground-based observations to develop predictive models. Characterizing and communicating uncertainty remains a key issue.
Further progress depends on…
Improved in-situ monitoring networks.
Better linkages among models.
Comprehensive framework for data access and management.