U.S. - Japan - SE Asia Workshop on Monsoon Asia Tropical Forest Carbon Dynamics and

Sustainability

SummaryAlfredo R. Huete, Univ Arizona

Xiangming Xiao, Univ. Oklahoma

Scott R. Saleska, Univ. Arizona

Dennis Dye, JAMSTEC, Japan

Nobuko Saigusa, NIES, Japan

WORKSHOP OBJECTIVES“Basic” Objectives

• Facilitate/enhance international communication among researchers/students concerning Monsoon Asia tropical forest carbon dynamics and sustainability

• Survey of recent/ongoing/planned research activities and results in tropical Monsoon Asia, reports from other regions (Amazon, etc.)

• Identify key science questions and research priorities (including societal benefits), particularly those that require integrated approaches (field observations, process modeling, remote sensing)

Workshop on Monsoon Asia Tropical Forest Carbon Dynamics and SustainabiityJanuary 8-11, 2009, Khon Kaen, Thailand

Potential Objectives

• Plan/proposal for new international research collaborations

• A framework for addressing the science questions and research priorities, for example:

• simple: individual, ad hoc projects

• ambitious:

• substantial bilateral/multilateral project(s)

• broad, “umbrella” mission (similar to STORMA, GAME, LBA?)

• basis for seeking research support from respective funding agencies

Workshop on Monsoon Asia Tropical Forest Carbon Dynamics and SustainabiityJanuary 8-11, 2009, Khon Kaen, Thailand

Potential Objectives, cont.

• Outline or “scope” specific project(s) that:

• can be conducted effectively within our proposed framework

• targets the key science questions and research priorities

Workshop on Monsoon Asia Tropical Forest Carbon Dynamics and SustainabiityJanuary 8-11, 2009, Khon Kaen, Thailand

Broader Impacts: Tropical forest regions have social, cultural and economic significance, as home to large and growing human populations and as sources of important renewable and non-renewable resources.

This workshop will address a number of challenging, cross-discipline research topics integrating in-situ observations, remote sensing, land use, and modeling approaches to advance our understanding of tropical forests and their sustainability in the face of increasing human and environmental pressures.

These issues are among the research and educational priorities identified by the 11th Japan-U.S. Workshop on Global Change in 2005 and this proposal is in response to recommendations for Japan-U.S. joint research that emerged from the Workshop.

Workshop on Monsoon Asia Tropical Forest Carbon Dynamics and SustainabiityJanuary 8-11, 2009, Khon Kaen, Thailand

Workshop encompassed a few key themes, structured around a set of

motivating science questions;

(1) What can we learn about Monsoon Asia tropical forest functioning by integrating ground-based carbon flux datasets spanning a range of vegetation conditions and climate?

(2) How can we use local site carbon fluxes to scale to and calibrate spatially extensive satellite data to enable regional modeling and prediction of carbon fluxes across Monsoon Asia?

(3) What is the role, extent, and effects of human interactions (land use activities, resource use, forest disturbance and conversion, etc.) on landscape functioning and sustainability?

(4) What is the seasonality of carbon and water fluxes in Monsoon Asia forests, how do they compare with seasonal patterns in the neotropics, and why is this seasonality so difficult to model?

(5) What role do environmental controls on seasonality have in explaining vegetation dynamics in response to inter-annual climate variability.

field trip

Asian Institute of Technology

• Previous answer: photosynthesis and/or transpiration decline in dry seasons:

Dickenson & Henderson-Sellars (1988)

Nobre et al. (1991)

Tian et al. (1998) [TEM]

Botta et al. (2002) [ IBIS]

Werth & Avissar (2002) [ GISS GCM ]

Lee et al. (2005) [ NCAR CLM]

Climate and/or ecosystem models

What is the seasonality of ecosystem

metabolism in Amazônia?

Saleska

Early LBA results from Tapajos National Forest showed unexpected seasonality…

Jan Apr Jul Oct

500

1000

1

2

3

Models

Data

GP

P (

Mg

C h

a-1

mo

-1)

PA

R

(µm

ol m

-2 s

-1)

Dry Season

(GPP = Gross Primary Production)

Saleska et. al. (2003) Science; Hutyra et al. (2007) JGR

What is the seasonality of ecosystem

metabolism in Amazônia?

• … generality of unexpected seasonality

confirmed for equatorial Amazon by

remote sensing.

• These data are powerful suited for vegetation dynamics and for deriving relationships between carbon fluxes and key driving variables.

• Use to test model/remote sensing estimates of carbon exchange and seasonality.

Green-up Brown-down

EVI was well correlated with tower-flux GPP in intact rainforest and pasture sites and provides a useful tool for models in scaling this flux to the Amazon region.

Huete et al. GRL 2006

Seasonal Cycle Shapes Amazon’s

Response To Environmental Change

… generality of unexpected

seasonality confirmed for

equatorial Amazon

by remote sensing.Huete et al. (2006)

!EVI

Basin-wide greening in dry season October EVI (dry season) minus June EVI (wet season)

!Questions" Is this typical across the Amazon? If not, what are the differences?

" What mechanisms matter for controlling seasonality of metabolism?

" How do we get the models right for the right reasons?

Santarem PastureTapajos Forest

Huete et al. GRL 2006

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

EV

I

Start of Dry Season (rainfall <100 mm/month)

Cerrado

Tropical rainforest

Amazon transitional ecosystems (Ecotones)

Ratana et al.

NOAA- Advanced Very High Resolution Radiometer (AVHRR) in the Amazon

rainforest drying?

0.1

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JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Month of Year

AV

HRR

- N

DV

I (2

0 y

ear)

0

50

100

150

200

250

300

350

400

Prec

ipit

atio

n, m

m (

20

yea

r)

Brasilia Araguaia-1 Araguaia-2 Tapajos

Brasilia, precip Araguaia, precip. Tapajos, precip

Precip.

Dry season

Not only models, but

also satellite vegetation

data products show

inconsistent patterns of

seasonality and

“vegetation responses”

to drought and

disturbance.

Phenology peak

month

Huete et al.,

2008

Huete et al.,

2008

Satellite Enhanced Vegetation Index (EVI) relationships with eddy covariance tower measurements of gross primary productivity (GPP) are very high and linear, with consistent cross-site relationships in temperate and tropical biomes. (Huete et al., Agric. & For. Met., 2008).

- this greatly facilitates the scaling and extension of tower data of ET and Carbon fluxes to regional and global scales. Huete et al.,

2008

Scott R. Saleska Luis Gustavo de Goncalves, Ian Baker, Marcos Costa, Ben Poulter, Brad Christoffersen, Scott Denning, Humberto da Rocha, Kamel Didan, Lindsey Gulden, Alfredo Huete, Kazuhito Ichii, Hewlley Imbuziero, Akihiko Ito, Bart Kruijt, Antonio Manzi, Celso von

Randow, Natalia Restrepo-Coupe, Enrique Rosero, Rodrigo da Silva, Julio Tota, Xubin Zeng

Seasonality of carbon and water fluxes across the Amazon basin: synthesizing results from remote

sensing, towers, and models: the LBA-MIP project

Saleska

The LBA-Model Intercomparison Project (LBA-MIP)

Initial Results

#Focus on 3 sites:- Central Eastern Amazon Forest (Santarem K67)- Southern Amazon forest (RJA)- Non Amazon Cerrado (Savanna) (PDG)

#Focus on 4 models:

Caxiuana

São Gabriel

Cachoeira

Sinop

Equator

10 ° S

20 ° S

Amaz ô nia

(A) Atlantic Ocean

Caxiuana

São Gabriel

Cachoeira

Sinop

Equator

10 ° S

20 ° S

Amaz ô nia

(A) Atlantic Ocean

Equator

10 ° S

20 ° S

Equator

10 ° S

20 ° S

(A) Atlantic Ocean

(K67)

(RJA)

(PDG)

- 2 variants of NCAR’s Community Land Model (CLM): (CLM3, CLM3.5)

-Integrated Biosphere Simulator (IBIS) (Foley et al)-Simple Biosphere, ver 3 (SiB3)

# Focus on Evapotranspiration Saleska

1.Finish the LBA-MIP! (model runs collated, then jointly analyzed, special issue of Ag. Forest Meteorology)

2.LBA-MIP, phase 2: Large-scale, spatially continuous driver and model output fields

3.Beyond seasonality: ability to correctly model effects of interannual variation, such as ENSO response, and ultimately, climate change

4.Generalize: apply similar integrated approach to Asian tropical forests to better understand similarities and differences?

Future directions

Saleska, Didan, Huete, Rocha (2007), Science

Forests green-up in during 2005 Amazon drought

[Resilience]

Little is known regarding the resilience of tropical forest

ecosystems to increasing human pressures and climate

variability, despite the large consequences to carbon cycling,

fires, ecosystem services, and the socioeconomic welfare of

the region. Curran et al. (Science, 2004) found strong linkages of ENSO

events on tropical forest functioning in the Kalimantan rainforests of Borneo, in which intact rainforests were able to survive ENSO events, however, disturbed areas became drought susceptible, triggering widespread fires and a drastic reduction in the regenerative capacity of the rainforests.

The combination of land use pressures with climate variability

may result in forest collapse,

Disturbance & Land Use Pressures

Mekong Region Economic Corridor Development

Figure 2. Development of economic corridors. All weather paved road infrastructure as

of 1992 (A); 2006 (B); and planned for 2015 (C).

1992 2006 2015

Little is known regarding the resilience of Monsoon Asia tropical ecosystems to increasing human pressures and climate variability, despite the large

consequences to carbon cycling, fires, ecosystem services, and the socioeconomic welfare of the region.

!"#$"%&'(%&)%(*+,-.&/(%"0$&1,+"#,"2&3.(4-.&5%(6%-7&('&)%(*+,-.&/(%"0$&1,+"#,"&8&9(#+$(%+#6

::&*.($0&+#&;<&,(=#$%+"0>&?&7+..+(#&$%""0&7(#+$(%"@ Shirley Dong

Smithsonian Global Forest Carbon Research Initiative

Quantifying and explaining variation in carbon pools and fluxes in tropical and temperate forests

Objective 1: Quantify current forest carbon pools

- Tree trunks and branches (~40-50%)

- Woody necromass (~5-10%)

- Soil and fine roots (~30-40%)

- Liana (~1-3%)

Objective 2: Quantify major forest carbon fluxes

- Annual tree growth and mortality rates

- Litter fall: leaf, fruit, flower production

- Soil respiration: below-ground production and allocation

- Input and output of coarse woody debris

Objective 3: Investigate the mechanisms

- Meteorological monitoring: temperature, rainfall, humidity, wind speed and direction, solar insolation

- Soil characterization: structure, fertility, water-holding capacity

- Plant functional traits: wood specific gravity, mature height, leaf size, seed mass

• Huai Kha Khaeng Wildlife Sanctuary, Thailand

• Khao Chong Wildlife Refuge, Thailand

• Pasoh Forest Reserve, Peninsular Malaysia

U.S. Department of the InteriorU.S. Geological Survey

Cloud-induced Dynamics ofGlobal and Diffuse PARat a Moist Tropical Site:Implications for Forest Photosynthesis

Dennis G. Dye

USGS Southwest Geographic Science Center

Flagstaff, Arizona

Building an Asian network of PAR sensors through international collaboration

Phimai, Thailand

Putussibau,

Indonesia,Pontianak,

Indonesia

Sapporo, Japan,

Haibei, China

Ulanbator, Mongolia (planned)

Lambir Hills, Malaysia(potential)

Yokohama, Japan (planned)

Tsukuba, Japan (planned)

Coincident with CO2 flux towers

on opportunity basis

Sulawesi, Indonesia(potential)

Sumatra, Indonesia(potential)

Central Kalimantan(potential)

Chiang Mai, Thailand(potential)

Balikpapan,

Indonesia

Dennis Dye

Putussibau Observation Site" Delta-T BF3 sensor

" total (global) & diffuse PAR (!mol m-2 s-1)

" 1-min. interval

" 15 days (14-28 Mar 2007)

Dennis Dye

Sample Images (natural color) from prototypeHDR-SISrgb for PAR at Putussibau, W. Kalimantan

Dennis Dye

Current SE Asia flux towers and research in tropical forests and peatlands

Takashi Hirano (Hokkaido Univ., Japan)

Kohn Kaen, January 9-11, 2009

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1. Kog-Ma2. Mae Moh3. Mae Klong4. Sakaerat5. Pasoh6. Lambir7. Palangkaraya18. Palangkaraya29. Palangkaraya310.Bukit Soeharto（interruption）11.Kampong Chhnang12.Kampong Thom

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Takashi Hirano

Environmental conditions of tropical forest in SE Asia

• Large variability in precipitation pattern (ENSO) • Land-use change (plantation, oil palm, rubber et al.）• Large-scale fires and haze (smoke) in El Niño years

Kuala Lumpur (Sep. 2005)Sep. 1997

Networking tower sites

Synthesis researches on:" Geographical distribution of CO2 flux (Fc) and

evapotranspiration (ET)" Seasonal and inter-annual variations in Fc and ET" Controlling factors of variations in Fc and ET

Kalimantan (Sep. 2002)

Haze

Takashi Hirano

Tropical peatland in SE Asia

"In SE Asia, pealtlands are widely distributed in the area of 270,000 km2, which accounts for about 10% of total land area there.

"The carbon stored in tropical peatland soil of Indonesia is estimated at 55±10 Pg (Jaenicke et al., 2008).

"Although tropical petalands naturally coexists with swamp forest, the peatlands have been deforested at 1.5% every year. The area of 120,000 km2 has been already deforested and drained (Hooijer et al., 2006).

Distribution of tropical peatlands in SE Asia (Page et al., 2004)Takashi Hirano

Tower in DF

Large canal for drainage

Tower in UDF

Takashi Hirano

BC site• Drained and deforested in the mid-1990’s.• Regrowing vegetation and surface peat soil were

burnt twice in 1997 and 2002 (El Niño years).

Vegetation recovery

April 2004 December 2004

March 2008Takashi Hirano

Forest Carbon Monitoring Program

Yoshiki Yamagata yamagata@nies.go.jp

Hozuma Sekine : hozuma@mri.co.jp

Workshop onMonsoon Asia Tropical Forest Carbon Dynamics and SustainabilityJANUARY 8 to 11, 2009, KHON KAEN, THAILAND

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Yoshiki Yamagata yamagata@nies.go.jp

Hozuma Sekine : hozuma@mri.co.jp

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Subtropics Limit (+/- 35 deg)

Country Eligibility of REDD

GLC2000 Yoshiki Yamagata yamagata@nies.go.jp

Hozuma Sekine : hozuma@mri.co.jp

Subtopic 2 [Shimada JAXA]Analysis of SAR Images

Subtopic 4 [Oguma NIES]Estimating Forest Structure

Parameters using Microwave

Subtopic 5 [Takeuchi UoT]Using Remote Sensing to Understand

Vegetation Disturbance

Subtopic 3 [Sekine MRI]Analysis of Forest Inventories

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Bottom-up approach（Plot & Site Level)

Subtopic 6 [Ito NIES]Evaluating Carbon Balance

with Ecosystem Models

Development of SARMosaic Dataset

Forest Carbon Monitoring Approach

Yoshiki Yamagata yamagata@nies.go.jp

Hozuma Sekine : hozuma@mri.co.jp

South-East Asia Mosaic Dataset 1 (PALSAR;50m)

FBD 34.3 HH / HV, R=HH, G=HV, B=HH-HV

http://www.eorc.jaxa.jp/ALOS/kc_mosaic/kc_mosaic.htm

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Task main activities (in brackets potential contributors)a. Consolidation of observational requirements and associated products (NIES, JRC,

WHRC)

b. Coordinated assessment of tools and methodologies (GOFC-GOLD, JRC, WHRC)

c. Coordination of observations, including securing their continuity (JAXA, Norway,

CSIRO-Australia, CEOS?)

d. Coordination of the production of reference datasets (FAO, GTOS, JRC)

e. Demonstration or pilot initiatives to show capabilities (Australia, Norway, FAO, ESA,

Brazil, Niger)

f. Improvement of access to observations, datasets, tools and expertise and associated

Capacity Building activities. (Australia, Norway, Rep. of Korea, FAO .)

Yoshiki Yamagata yamagata@nies.go.jp

Hozuma Sekine : hozuma@mri.co.jp

Rubber plantation at Chachengsao(RFC)

Hill evergreen forests at Kog-Ma (KMW)

Dry evergreen forests at Sakaerat (SKR)

Mixed deciduous forests at Mae Klong (MKL)

Teak plantation at Mae Moh (MMP)

observation sites(ThaiFlux network)

Registered

ThaiFlux

Dr. Kasemsap

Rubber plantation at Chachengsao(RFC)

Hill evergreen forests at Kog-Ma (KMW)

Dry evergreen forests at Sakaerat (SKR)

Rice (rain-fed)at Sukothai

Teak plantation at Mae Moh (MMP)

Casava Plantationat Nakorn Ratchasima

Dry dipterocarp forest(2008)

Sugarcane plantation at Bureerum (2007)

Observation sites (ThaiFlux network)

Mixed deciduous forests at Mae Klong (MKL)

Land use monitoringat Tak

Dr. Kasemsap

Background The Sakaerat Environmental Research Station (SERS) was established by the Thai government on

September 19, 1967.(!.". 2510; #$%&'()*+

,-./01 ,23%4.5).

It was administered by the Thailand Institute of Scientific and

Technological Research as a facility for ecological and

environmental research

UNESCO Biosphere Reserve Thailand Long-term Ecological Research

Sakaerat TowerNakhonratchsima, Thailand14°29’N,101°55’E, 535 m aslMixed seasonal evergreen foresta 47 m tower

in

Dr. Kasemsap

Dr. Kasemsap

Rubber Flux at Chachoengsao (RFC)Position

Latitude: 13º 39' 56" N

Longitude : 101º 26' 29" E

Elevation 69 m

Slope Negligeable

Terrain Type Flat

Area 6 ha plot in a 200 ha plantation

Climate Tropical monsoon climate

Mean annual air

temperature28.0degC

Mean annual

precipitation1276mm

Vegetation Type Mono-specific rubber tree (Hevea brasiliensis) plantation

Dominant Species

(Overstory)Hevea brasiliensis

Dominant Species

(Understory)Scarce shrubs and remains of previous intercrop (rattan, bamboo...)

Canopy height 20 m

Age 13 years old

LAI 3.2

Soil typeSandy-clay-loam (Kabin Buri series) characterized by a compact lateritic layer with ferralitic

concretions at ca 1 m deepDr. Kasemsap

OCEAN AND CLIMATE RELATED PROGRAMS TO GLOBAL WARMING IN INDONESIA

Fadli Syamsudin Agency for the Assessment and Application of Technology (BPPT)

Workshop on Monsoon Asia Tropical Forest Dynamics and Sustainability , Thailand 8-11 January, 2009

• Technological roadmap on global warming• Research facilities• Ongoing programs • HARIMAU• Concluding remarks

BPPT TECHNOLOGY ROADMAP ON GLOBAL WARMING

•Weather, Flooding, and Dryness Prediction Models•Research Vehicle for Atmospheric Aerosol

Tech

nolo

gy C

onte

nt

2007 2008 2009 2010 - 2014

•Flooding Prediction Model

•Weather, Flooding, and Dryness Prediction Models

•Weather, Flooding, and Dryness Prediction Models

CH4 Monitoring &

Capture in Landfill

Atmospheric aerosol

deposition

Air Pollution

Monitoring by

FTIR

Study of emision &

desposition of aerosol

CO2 reduction by

air-sea interaction

CO2 reduction by

phytoplankton

Global warming induced Sea level rise

CO2 Budget

Fadli Syamsudin

4. Development and Application of Remote Sensing Model for Seaweed Distribution Mapping and Measurement of

Harvesting Potential in Indonesia

Activities (2008-2009)

$ Seaweed mapping using high resolution images of Formosat-2

$ Algorithm development using hyperspectral satellite and in situ

$ measurements for Sargassum sp. identification

$ Seasonal distribution mapping for Sargassum sp.

$ Estimation on harvesting potency of Sargassum sp.

$ Satellite validation using in situ method

BPPT in cooperation with BRKP

Fadli Syamsudin

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Thank you