# Presentation: 666602

## Terrestrial ecosystem analysis using MODIS data

**Terrestrial ecosystem analysis using MODIS data**
- 			Ramakrishna Nemani
- 		     NASA Ames Research Center
- 			   Moffett Field, CA
- With contributions from:
**Alfredo Huete, kamel Didan, U. of Arizona**
**Ranga Myneni and Wenze Yang, Boston University**
**Steve Running and Maosheng Zao, University of Montana**
**Charles Keeling and Steve Piper, Scripps Inst. Oceanography**
**Petr Votava, Andy Michaelis, Kazuhito Itchii, CSU Monterey Bay**
**Marcus Reichstein, Potsdam Institute, Germany**
**Feihua Yang, University of Wisconsin**
**Chris Field, Carnegie Inst., Stanford University**
**Chris Potter, ARC**
**Clark Glymour and Brian Bonnlander, Carnegie Mellon U.**
- MODIS Science team meeting, March 22, 2005

## Outline

- _Outline_
**Prognostic/Diagnostic Analysis of Ecosystem dynamics**
**              **monitoring/modeling/forecasting System
- 	   snow cover (Snotel/MODIS)
- 	   gross primary production (Fluxnet/MODIS)
**Data-driven models**
- 	carbon/Water fluxes (Fluxnet/MODIS)
- 	wildland fire risk
**MODIS data in global carbon cycling studies**
- 	mapping near-real time NPP anomalies
- 	tropical ecosystems – new insights from MODIS
**MODIS data in modeling: issues needing further attention**

## The Grand Vision

** ****Monitoring, modeling and prediction**

**The Grand Vision**

## enabling information integration

**NASA Satellites **

**~1.5 TB/day**

**EOSDIS**

**Ground Obs. Networks**

**Ancillary Data****Topography, River Networks, Soils, Biodiversity . . .**

**250+ products, ****> 2 Petabytes**

**Causal Discovery****Autonomous analysis of data for discovery of novel causal models; integrated with TOPS for model validation**

**10-100 MB/day**

**TOPS****Biospheric models for ecological nowcasting / forecasting from data**

**Ecocast Architecture**

_**Knowledge:**_**100K to 10 MB****Daily nowcast and forecast maps, integrated datasets, images, causal models**

_**Data Overload:**_
**1-100 TB****Massive data sets, multiple products, heterogeneous data types**

**Terrestrial Observation and Prediction System**

**.1-10 TB**

## Prognostic/Diagnostic Analysis

- nemani/ARC

## Gridding climate data

- RAWS

- Modular

- Unattended

- Tmax / Tmin
- VPD, precipitation
- Solar radiation
- Daylength

- Any user
- Defined grid

- Jolly, nemani, Running.... 2004. Envi. Modeling and Software

- Daily weather data from Over 6000 stations worldwide

## Regional Application :Columbia River Basin

_**Regional Application :Columbia River Basin**_

- Satellite
- Data

- Ground Obs.
- Data

- Ecosystem Models (TOPS, Biome-BGC)

- Soil Water, Snow, GPP, NPP, NEE

- e.g. Land Cover
-        LAI, FPAR

- e.g. T, Prec,
-        VPD, Rad

## Improvement of Snow Model

_**Improvement of Snow Model**_

**Ground Obs.**** ****(SNOTEL)**
**SnowSeasonLength (days)**

**Model**
**SnowSeasonLength (days)**

- Empirical Model

- Process-based Model

- Itchii/Votava/Michaelis CSUMB

## Improvement of Snow Model

**MODIS**

**MODEL**

_**Improvement of Snow Model**_

- Itchii/Votava/Michaelis CSUMB

## Original

**Original**

**New Model**

- Snow Cover Area (105 km2)

- Snow Cover Area (105 km2)

**Snow Cover Area : Model vs MODIS**

- Itchii/Votava/Michaelis CSUMB

## European eddy covariance network

- European eddy covariance network
- on MODIS Land-cover grid

- Markus Reichstein, Carboeurope

## Updating MODIS GPP algorithm parameters LUEmax

**Updating MODIS GPP algorithm parameters LUE****max**

## Data-driven models

**Data-driven models**
**Extrapolating fluxtower observations using MODIS**

**Feihua Yang, University of Wisconsin**

## Data-driven models

- Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC

- Train the algorithms on all the
- non-arson fires during 2000-2002
- _Methods include_:
- Support Vector Machines
- Artificial Neural Networks
- Logistic Regression

**Data-driven models**
- MODIS data in mapping wildland fire risk

## Predicting fire risk

**Predicting fire risk**

- Brian Bonnlander/Clark Glymour/Votava, IHMC/ARC

## Biospheric activity and atmospheric CO2

**Sensor transitions: AVHRR to MODIS**

- Myneni/Cliveg group/BU

- Biospheric activity and atmospheric CO2

## TOPS enables Biospheric Monitoring

- Near Realtime

- Based on Running, S.W and R.R. Nemani et al., Bioscience, 2004

## Summer 2003 European Heatwave

**Warmest summer in 500 years**
**Large scale declines in plant growth**
**High elevation Alps did better**
**May have contributed to the record CO****2**** increase in 2003 (2.54ppm)**

- Markus Reichstein, Carboeurope

## Tropical regions dominate global carbon cycling

## Global Net Primary Production (MOD17) Anomalies 2000-2003

- Year    NPP       Trop. NPP      CO2 growth rate
-               (Gt/y)         (Gt/y)           (ppm/y)
**2000       56.06        32.88              1.17**
**2001      57.74         33.21              1.56**
**2002      55.53         31.97              2.04**
**2003      54.80         31.25              2.54**

- Zhao/Running/NTSG

## ENSO as a possible mechanism 

- ENSO as a possible mechanism
- for the enhanced behind CO2 growth rate s during 2002-2003

- Hirofumi et al., JGR-atm, december, 2004

- 2002-2003 a mild
- El Nino

- Global distribution of
- El Nino impacts on
- NPP based on data
- from 1982 to 1999

- China!
- Increased fire activity!

## Potential Climate Limits for Plant Growth

## Evidence from field studies

- Asner et al., PNAS (April 2004)

- Saleska et al., Science (Nov, 2003)

**Evidence from field studies**

## What MODIS show?

- Location of study area showing (a) TRMM-based measurements of annual rainfall and (b) LBA site-intensive and climate transect areas analyzed.

- Huete et al., U. Arizona

**What MODIS show?**

**Notes:**
FIGURE 1.

## Land use and ecosystem dynamics

- Comparison of four-year averaged phenology profiles at  two primary forest sites with nearby forest converted, pasture sites (Ikonos imagery below figure display extent of land conversion).

- 2000-2003 Average Enhanced Vegetation Index

- Huete et al., U. Arizona

**Notes:**
FIGURE 2.

## Amazonia (Dry – Wet) season Enhanced Vegetation Index

**Increases in dry season activity (positive values) were found throughout the basin. The deforested portions of the Amazon tothe east and south, as well as near the Tapajos area, had negative values indicating less activity in the dry season.**
- .

- based on 2000-2004 monthly composites

- Huete et al., U. Arizona

**Notes:**
FIGURE 4.

## Amazon Basin Percent Tree Cover from 500m MODIS data

**Amazon Basin Percent Tree Cover from 500m MODIS data**

- Trees with deep root systems are primarily responsible
- for the seasonal dynamics

## Dry vs Wet season changes in MODIS Leaf Area Index 

- Dry vs Wet season changes in MODIS Leaf Area Index

- based on 2000-2004 monthly composites

- Wenze/Myneni/Nemani BU/ARC

## Tropical Asia

- ??

- Wenze/Myneni/Nemani BU/ARC

**Tropical Asia**

## Tropical Africa

- Wenze/Myneni/Nemani BU/ARC

**Tropical Africa**

- Low seasonal rainfall variability
- Low biodiversity
- Low interannual variability in cloudiness

## MODIS use among modelers

**MODIS use among modelers**
- Importance of QA
- 	goading users to pay attention
- Produce high quality monthly average (2000-2004) products
- 	not all users care about interannual variability
- Interdisciplinary studies – e.g., coastal ecosystems
- 	needs a lot more effort
- Uncertainty – may be characterized by biome or climate zone
- 	guidance to users on how to use the product in modeling

## Access to deep water

- No access to deep water

_**Modeled Wet and Dry season differences in NPP**_

- Resembles
- EVI/LAI behaviour

- CASA model results using monthly average
- LAI/FPAR from 1982-1999

- CASA- Field et al., Potter et al.,

## Updating MODIS GPP algorithm parameters Keyp. LUEmax

- Markus Reichstein, Carboeurope