1 Applications of the Terrestrial Observation & Prediction System Forrest Melton CSU Monterey Bay,...

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Applications of the Terrestrial Observation & Prediction System

Forrest MeltonCSU Monterey Bay, Seaside, CA

Ecological Forecasting LabNASA 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

SERVIR Workshop Panama City, Panama, Mar. 1, 2007

MODIS NDVI, Mesoamerica, Jan. 2-16

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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)

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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

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Changing Surface Temperatures

Why we need ecological forecasting?

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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.

Foster interdisciplinary activity

Bruce Jones, NCSE, Forecasting Environmental Changes, 2005

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Short-term Monitoring and Forecasting

Sacramento river flooding, California Irrigation requirements

Based on weather forecasts, conditioned on historical ecosystem stateDays to a week

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ENSO-Rainfall over U.S

El Nino

La Nina

Based on ENSO forecastsWeeks to months

Mid-term/Seasonal Forecasts: Water resources, Fire risk, Phenology

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Long-term Projected Changes

Based on GCM outputsDecades to centuries

Rizzo & Wilken, Climatic Change, 21(1), pp. 37-55, 1992

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Monitoring

Modeling

Forecasting

Multiple scales

Nemani et al., 2003, EOM White & Nemani, 2004, CJRS

TOPS: Common Modeling Framework

Predictions are based onchanges in biogeochemicalcycles

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Streamflow network Soil moisture network

FluxnetWeather network

Access to a variety of observing networks

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Access to a variety of remote sensing platforms

Integration across: Platforms, Sensors, Products, DAACs is non-trivial

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Terra Launch: Dec. 18, 1999First Image: Feb. 24, 2000

Aqua Launch: May 04, 2002First Image: June 24, 2002

Satellites: MODIS on Terra & AquaRetrospective to real

timeOperational remote

sensing

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Multiple Instruments per Mission

Terra SatelliteLaunched Dec. 18, 1999 with five instruments (ASTER, CERES, MISR, MODIS, MOPITT)

Aqua SatelliteLaunched 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

Example: MODIS on Terra & Aqua

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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

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Ability to integrate a variety of models

Biogeochemical CyclingCrop growth/yieldPest/Disease Global carbon cycle

Prognostic/diagnostic models

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Ability to work across different scales of time and space

Hours

Days Weeks/Months

Years/Decades

Weather/Climate Forecasts at various lead timesDownscaling

http://www.nasa.gov/images/content/105582main_GlobalWarming_2060_lg.jpg

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Standard TOPS Outputs

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

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Standard TOPS Outputs: Local to Global Scales

Global NPP Anomalies U.S. Gross Primary Productivity

California Daily Soil Moisture Estimates

Yosemite Minimum Temperatures

Napa Valley Forecasted Vineyard Irrigation Demands

Spatial scales from 0.5 degrees to 4m. Temporal scales from yearly to daily.

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TOPS/SERVIR Products for Mesoamerica

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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)

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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

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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

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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; 620-670 nm) 2 (NIR; 841 - 876 nm)

• The reflectance values are the surface bidirectional reflectance factors for MODIS bands 1 (620 - 670 nm) and 2 (841 - 876 nm)

• Tends to saturate over high biomass regions; sensitive to atmosphere and canopy variations.

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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.

http://tbrs.arizona.edu/project/MODIS/evi.php

Tracking vegetation condition with MODIS EVI in the Amazon Basin

Credit: Huete et al. 2006. GRL 33.

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TOPS MODIS Products for Mesoamerica: EVI

Enhance Vegetation Index (EVI)

• Composited from the MODIS MOD13A1 daily EVI values

• Derived from MODIS bands: 1 (Red; 620-670 nm) 2 (NIR; 841 - 876 nm)3 (Blue; 459-479) nm

• Better performance than NDVI in the tropics and other regions with high biomass.

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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.

http://www.ntsg.umt.edu/remote_sensing/leafarea/

• Measure of the proportion of available radiation in the photosynthetically active wavelengths of the spectrum (0.4 - 0.7 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.

Fraction of Photosyntethically Active Radiation (FPAR) absorbed

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TOPS MODIS Products for Mesoamerica: LAI & FPAR

• 8-day composites• Provide measures of canopy structure and photosynthetic activity

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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 0.4 - 0.7 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.

http://www.ntsg.umt.edu/remote_sensing/netprimary/

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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.

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Mesoamerican Anomalies

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Using anomaly persistence to assess significance

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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)

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Precipitation

Shortwave Radiation

Vapor Pressure Deficit

Minimum Temperature

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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 2000-2005

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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

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Long-Term Monitoring and Trend Analysis

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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

0 1000meters N

Agricultural Management Applications of EF

Short-term: Vineyard Irrigation Forecasts

Fully automated web delivery to growersSeasonal

0Forecast Irrigation (mm)

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Mid-range: Forecasting the onset of growing season

Based on White and Nemani, RSE, 2006

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Mid-range: Forecasting crop yields

Forecasted versus observed yields for 12 California crops (from Lobell et al., 2006, California Agriculture, 60(4): 211-215.

• 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%

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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.

Zimbabwe Cereals Total Yield and Total NPP

Anomalies

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-8000

-6000

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-2000

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1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

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Milesi et al., Glob. Pl. Change, 2005Hirofumi et al., JGR-Atm, 2004

Long-range: Net Primary Productivity Anomalies

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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.

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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, 2000-2004

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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.

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Interpreting Anomalies

Ground-based observations key to validating and interpreting anomalies.

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TOPS Data Fusion: Trend Analysis for Features of Interest

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Ecological Forecasting and Public Health

CLIMATE

HYDROLOGY

HABITAT

VECTORHOST

PATHOGENPotential Areas of Contribution:

• Air quality (fire frequency, land cover change / desertification and particulates)

• Water quality (flooding, drought)

• Food security

• Vector-borne disease

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Ecological Forecasting: Lyme Disease

Predictive risk map of habitat suitability for Ixodes scapularis in Wisconsin and Illinois.

Guerra et al, Emerging Infectious Diseases, Vol. 8(3), 2002

CDC National Lyme Disease Risk Map

Fish & Howard. Morbidity and Mortality Weekly Report,48, pp 21-24, 1999

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Ecological Forecasting: Malaria

Soil moisture• Patz, J et al. Tropical Medicine &

International Health, 3.10, (1998): 818-827– 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

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Regional Nowcasts: California

Meteorology Hydrology Vegetation Ecosystem

Tracking parameters related to mosquito abundance:

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Land use and seasonal patterns of mosquito abundance

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Average seasonal profiles for Cx. tarsalis counts per New Jersey light trap-week by bioregion, 1981-2000.

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Effect of land use on seasonal patterns of mosquito abundance in Sacramento, CA.

Figures courtesy of CM Barker

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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

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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.

1 Applications of the Terrestrial Observation & Prediction System Forrest Melton CSU Monterey Bay,...

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