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Integrating ground observation, satellite remote sensing, and terrestrial ecosystem model for future carbon monitoring systems Nobuko Saigusa National Institute for Environmental Studies (NIES), Japan AsiaFlux Tsukuba Office Slides Contributed

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Integrating ground observation, satellite remote sensing, and terrestrial ecosystem model for future carbon monitoring systems

Nobuko Saigusa

National Institute for Environmental Studies (NIES), Japan AsiaFlux Tsukuba Office Slides Contributed by Dr. R. Hirata & M. Hayashi (NIES)

  • 1. Networking cross-disciplinary ground observations

and their capacity building programs

  • 2. Integrating ground observation, satellite remote

sensing, and terrestrial ecosystem model for better estimates of regional C-budget

  • 3. Summary
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SLIDE 2

Integrating ground observation, satellite remote sensing, and terrestrial ecosystem model for future carbon monitoring systems

  • 1. Networking cross-disciplinary ground observations

and their capacity building programs

  • 2. Integrating ground observation, satellite remote

sensing, and terrestrial ecosystem model for better estimates of regional C-budget

  • 3. Summary

Nobuko Saigusa

National Institute for Environmental Studies (NIES), Japan AsiaFlux Tsukuba Office Slides Contributed by Dr. R. Hirata & M. Hayashi (NIES)

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Carbon, water and energy budget FLUXNET AsiaFlux International Long Term Ecological Research (ILTER) GEO BON JapanFlux ILTER Asia-Pacific Network: ILTER-EAP

Networking cross-disciplinary ground observations to monitor changes in terrestrial ecosystems

IGBP-iLEAPS IGBP&IHDP- GLP GEO Biodiversity observation AP BON JaLTER Monitoring sites 1000 (MOE) J-BON

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FLUXNET

http://fluxnet.ornl.gov Location of FLUXNET sites

World-wide network for monitoring CO2, H2O, and energy exchanges between terrestrial ecosystems and the atmosphere (> 500 sites) (1996~)

Archiving CH4, N2O flux data (started)

Eddy covariance method

Networking cross-disciplinary ground observations to monitor changes in terrestrial ecosystems

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SLIDE 5

AsiaFlux: a regional network in FLUXNET

Norway Antarctica

92 sites

AsiaFlux sites Information

12th AsiaFlux International Workshop Philippines, August 18-23, 2014

forests grasslands croplands

NEW! Indonesia India Thailand Vietnam Data sharing:

Networking cross-disciplinary ground observations to monitor changes in terrestrial ecosystems

http://asiaflux.net/

supported by:

Information sharing:

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JapanFlux, JaLTER, JAXA & JAMSTEC select several common sites to share data and technical skills for advanced multi-scale, long-term, and consistent ecosystem observations on the ground and from space.

Japan Long-Term Ecological Research Japan Aerospace Exploration Agency

Global Change Observation Mission (GCOM)

(Oct. 2008) Long-term ground

  • bservation

Remote sensing Terrestrial modeling

Networking cross-disciplinary ground observations to monitor changes in terrestrial ecosystems

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CO2, water vapor and energy flux data

Saigusa et al. (2010) (2013)

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Tropical Forests Evergreen Conifer Deciduous Conifer (Larch) Mixed Evergreen & Deciduous Alpine Grassland Crop (Rice) Crop (Wheat & Maize)

Seasonal patterns of C-budget

Total photosynthesis (GPP) Total Respiration (RE) Net CO2 Exchange (NEE) (negative: uptake)

Deciduous Broadleaved

Model – data integration (so far mainly CO2 & ET)

Ichii et al. (2010) (2013)

Monthly GPP (total photosynthesis) at 24 sites & simulated by 8 models Regional & continental C-budget estimations

Anomaly in GPP (gC m -2 day -1 ) July-August 2003

A A B B C C

(base period 2001-2006)

D D E E F F

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SLIDE 8

YPF 2004

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Networking ground- based observations Cross-site analysis, model-data synthesis

Capacity Building (CB) for Asian Terrestrial Ecosystem Carbon Budget Monitoring Network

Regional-, country-, continental-scale C- budget estimations (Bottom-up) in Asia- Pacific (AP) region Comparison between Top-down (inversion with satellite data) & Bottom- up approaches in AP Detection of C-cycle climate hotspots in AP

Anomaly in GPP (gC m -2 day -1 ) July-August 2003

A A B B C C

(base period 2001-2006)

D D E E F F Productivity

Research flow Effective CB for AP will bring: (1) Reduction of blank area in Asia (2) Sustainable high-quality data sharing (3) Better prediction for terrestrial C-cycle climate feedbacks Effective CB can help solve bottlenecks in research flow. For beginners: Observation, data quality control, paper writing, data sharing For experts: Promotion of integrative studies for global (Asian) C- budget estimations

CO2 flux C-budget Country-scale Continental-scale Point-scale

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SLIDE 9

Capacity building and technology transfer

Supported by LI-COR Co. Supported by LI-COR Co.

Learning to Learn Together

Open-path CH4 flux sensor

TC for methane flux monitoring in S-Asia

Financially supported by:

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  • Promoting data sharing with JaLTER DB and

data paper (Ecological Research)

Data Registration Camp (JaLTER)

(Miyagi, Japan, Sep. 2012)

  • ne page abstract in

the print product &

  • nline data archive
  • Invite young scientists or

technical staffs and instruct data registration

  • Encourage submission of

data paper

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SLIDE 11

Integrating ground observation, satellite remote sensing, and terrestrial ecosystem model for future carbon monitoring systems

  • 1. Networking cross-disciplinary ground observations

and their capacity building programs

  • 2. Integrating ground observation, satellite remote

sensing, and terrestrial ecosystem model for better estimates of regional C-budget

  • 3. Summary

Nobuko Saigusa

National Institute for Environmental Studies (NIES), Japan AsiaFlux Tsukuba Office Slides Contributed by Dr. R. Hirata & M. Hayashi (NIES)

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Larch Plantation (14ha) Tower

Monitoring CO2 uptake after artificial disturbance

Teshio CC-LaG Site

Clear-cut & plantation in 2003

(Hokkaido Univ., NIES, Hokkaido Electric Power Co., Inc.)

Teshio Carbon Cycle & Larch Growth Experiment Site

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Hokkaido (Northern Japan)

Monitoring C-budget in two larch forests with different age distribution

Mature mixed →Young larch forest

Larix gmelinii × L. kaempferi TSE (Teshio CC-LaG experiment site) Takagi et al. (2009) GCB Observation period : 2002(mix) → 2003-2012 (larch) Tree Age : 200 years (mix)→1 – 12 years Tree Height: 20 m →1 – 2.5 m Annual temperature: 5.7℃ Annual precipitation: 1000mm Soil type: Gleyic Cambisol Disturbance: Bring out 55%, residual 45%

Mature larch forest

Larix Kaempferi Sarg. TMK (Tomakomai flux research site) Hirata et al. (2007) AFM Observation period : 2001-2003 Tree Age : 42 – 44 years old Tree Height: About 15 m Annual temperature: 6.2℃ Annual precipitation: 1043mm Soil type: Volcanogenous regosol

Disturbed Non-disturbed

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Changes in (a) C-stock and (b) C-flux in a primary rainforest and in an oil palm plantation converted from the primary forest

Adachi, Ito et al. (2011) Biogeosciences Rainforest  Oil palm

Integration using a terrestrial ecosystem model VISIT (Vegetation Integrative Simulator for Trace Gases) to estimate changes in GHGs flux and carbon stock caused by climate change as well as natural and artificial disturbances.

VISIT (Vegetation Integrative Simulator for Trace Gases)

(provided by A Ito)

Simulating C-budget in two larch forests with different age distribution

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SLIDE 15

Young larch forest Mature larch forest Mixed forest NEP (Net ecosystem production) GPP (Total photosynthesis) RE (Ecosystem respiration)

Carbon absorption Carbon release Hirata, Takagi, Ito, Hirano, Saigusa (2014) Biogeosciences Discuss.

Simulating C-budget in two larch forests with different age distribution

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SLIDE 16

Young larch forest Mature larch forest Mixed forest NEP (Net ecosystem production) GPP (Total photosynthesis) RE (Ecosystem respiration)

Carbon absorption Carbon release Hirata, Takagi, Ito, Hirano, Saigusa (2014) Biogeosciences Discuss.

Simulating C-budget in two larch forests with different age distribution

Effects of disturbances (recovery processes) are essential for better estimation of regional C- uptake.

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Spaceborne LiDAR (ICESat / GLAS)

［NASA webpage］

Spaceborne LiDAR (I CESat / GLAS) I CESat ： I ce, Cloud, and land Elevation Satellite GLAS ： Geoscience Laser Altimeter System

100 200 300 400 500 50 100 150

Time Laser return 60 m

Hayashi et al. (2013) ISPRS J. Photogram. Remote Sens.

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Canopy height estimation in Hokkaido

GLAS footprints (13,586 points) Canopy height estimation

Airborne LiDAR point cloud Estimation accuracy

10 20 30 10 20 30 GLAS canopy height (m) Airborne LiDAR canopy height (m)

RMSE = 3.5 m

Hayashi et al. (submitted)

Hokkaido (Northern Japan)

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SLIDE 19

Forest disturbances monitoring

Canopy heights before & after ‘Typhoon Songda’ on 8 September 2004

［Takao, 2006］

Hayashi et al. (submitted)

Larch (Larix kaempferi)

Typhoon Trajectory

(Ito, 2010)

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SLIDE 20

Forest disturbances monitoring

Canopy heights before & after ‘Typhoon Songda’ on 8 September 2004

19.2 19.5 18.2 15.3 5 10 15 20 25 Pre-typhoon No damage Light damage Heavy damage Average canopy height (m) Post-typhoon Pre-typhoon m m m m

［Takao, 2006］

5 10 15 20 25 Sakhalin fir Yezo spruce Sakhalin spruce Japanese larch Broadleaved trees Average canopy height (m)

Japanese larch

Hayashi et al. (submitted)

Broadleaved

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Forest biomass in Hokkaido

Histogram Area average

200 400 600 800 1,000 1,200 50 100 150 200 250 300

Frequency Aboveground Biomass (Mg ha-1) Ave = 104.5 Mg ha -1

115.5 112.5 110.2 109.8 102.2 101.6 90 95 100 105 110 115 120

Dounan Okhotsk Douou Douhoku Tokachi Konsen Average aboveground biomass (Mg ha-1)

Aboveground biomass estimation

Hayashi et al. (in preparation)

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Forest biomass estimation in Borneo

  • 1. AGB estimation

@ 110,743 points

  • 2. Average AGB

@ 20 km mesh

  • 3. Interpolated AGB

(Kriging method)

Hayashi et al. (submitted)

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SLIDE 23

Integrating ground observation, satellite remote sensing, and terrestrial ecosystem model for future carbon monitoring systems

  • 1. Networking cross-disciplinary ground observations

and their capacity building programs

  • 2. Integrating ground observation, satellite remote

sensing, and terrestrial ecosystem model for better estimates of regional C-budget

  • 3. Summary

Nobuko Saigusa

National Institute for Environmental Studies (NIES), Japan AsiaFlux Tsukuba Office Slides Contributed by Dr. R. Hirata & M. Hayashi (NIES)

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SLIDE 24

Summary

  • 1. Networking interdisciplinary ground
  • bservations has a great potential for

predicting future ecosystem responses.

  • 2. Effects of disturbances (ages) are

indispensable for accurate estimation of regional C-uptake.

  • 3. Space-borne LiDAR has a high potential

for regional estimates of C-stocks