National Carbon Project (NCP) National Carbon Project (NCP) : ISRO - - PowerPoint PPT Presentation

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National Carbon Project (NCP) National Carbon Project (NCP) : ISRO - - PowerPoint PPT Presentation

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National Carbon Project (NCP) National Carbon Project (NCP) : ISRO Geosphere Biosphere Program Initiative V K Dadhwal and NCP Team V K Dadhwal and NCP Team National Remote Sensing Centre (ISRO) (S P S Kushwaha*, N R Patel*, S Singh*, R Nayak, M S R

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National Carbon Project (NCP) National Carbon Project (NCP)

: ISRO Geosphere Biosphere Program Initiative

V K Dadhwal and NCP Team V K Dadhwal and NCP Team

National Remote Sensing Centre (ISRO)

(S P S K h h * N R P t l* S Si h* R N k M S R M th G R j kh G S (S P S Kushwaha*, N R Patel*, S Singh*, R Nayak, M S R Murthy, G Rajasekhar, G S Pujar, CS Jha, N Sharma, S Kumar, P Patil, M Kaul, & others ; *: IIRS)

Asia Pacific Advanced Network Meeting , New Delhi, 24 Aug 2011

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Carbon Cycle : Key Science Questions

What controls atm CO2 ? How much rise in future ? How is natural C cycle altered by human activities ? How much rise in future ? y

Where are missing C sinks on land ?

VK DADHWAL , Terrest C Cycle India ‐Understanding with RS, Shimla, 2009 November

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Introduction Introduction

I t t t i t i t di l b l b l

  • In contrast to very intensive studies on global carbon cycle,

covering global, sectoral and regional C pools, fluxes & net C balances the Indian efforts (total and forest C cycle) are quite balances, the Indian efforts (total and forest C cycle) are quite modest.

  • Inherently, C cycle studies are multi‐disciplinary and thus must

include – biosphere and anthropogenic components, – observations, synthesis and modelling y g – different techniques and approaches

  • Under ISRO Geosphere Biosphere Program, a “National Carbon

Project (NCP)” has been taken up since 2007, as a collaborative effort with other institutions

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National Carbon Project National Carbon Project : Goals & sub : Goals & sub‐projects projects

  • GOALS

GOALS – Assessment of C Pools, Fluxes & Net C balance for terrestrial biosphere in India – To establish a observational network and remote sensing‐based spatial databases for assessment & modeling of C cycle id i l i i d i l – To provide support to national activity under National Communication to UNFCCC

  • SUB‐PROJECTS
  • SUB‐PROJECTS

– Vegetation C Pool Assessment – Soil C Pool Assessment Soil C Pool Assessment – Soil‐Vegetation Atmosphere C fluxes

  • FOCUS

– Use of Remote Sensing, new RS data & techniques, and development of national spatial data sets – Supported by field observations, instrumented observatories and modeling ctivity

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Vegetation Carbon Pool Vegetation Carbon Pool

  • Assessment of terrestrial vegetation biomass in the country using

ground sampling and satellite remote sensing data

  • To generate geospatial data of the terrestrial phytomass Carbon
  • f India along with estimates of uncertainty

g y

  • APPROACH

b b f f ld k l d ll

  • Estimate by a combination of field work, geospatial modelling,

grid‐wise C density (biomass contribution) of forests, trees

  • utside forest (TOF) crop & other vegetation
  • utside forest (TOF), crop & other vegetation.
  • Grid‐wise area under each of land cover category above for

refernce year is obtained by remote sensing. y y g

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10000 sample plots envisaged

VCP : Proposed Field Plot sample locations

2500 permanent plots for monitoring biomass/carbon increment

20 zonal sites i.e. AWiFS ~ 100 samples sites per quadrant (2000 super 4 samples plots at each site i.e. AWiFS quadrants quadrant (2000 super plots of 250×250 m) nearly ~8000 sample plots

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Forest Forest Phytomass Phytomass C Density using RS C Density using RS

  • Using zonation sample field data and local spectral
  • Using zonation, sample field data and local spectral

models, assessment of forest phytomass density is carried out carried out

  • Three Approaches Exist

– Plot means in RS data based strata Plot means in RS data based strata – Plot data & RS spectral Model

  • 1st approach : Plot‐pixel model, scaling Errors

pp p , g

  • 2nd approach : Up‐scaled mean
  • Average biomass grid‐models

– Geostatistical Approach

  • Future : New RS‐based approach

Future : New RS based approach

– Microwave, LIDAR and High spatial resolution

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SLIDE 10
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Assessment of TOF Assessment of TOF

B k d

  • Background

– FSI estimates TOF Growing Stock (GS) to be 1/3 of forest GS in India and provides estimates for 14 physiographic zones – However, fast change monitoring at smaller level requires small area TOF GS/ phytomass C contribution

  • VCP Approach

– To add the variable TOF component in output grid

Spatially modeled estimates of TOF using infrastructure information and high resolution image samples Telangana sub‐region, AP

co po e t

  • utput g d

– A typology and sampling strategy and integration plan has been worked out and been worked out, and – Field measurements planned ca. 500 TOF sites of 5x5 km grids located on HR data

Water bodies Canal/Drain – Lined, unlined Water bodies – reservoirs/tanks – dry/perennial Water bodies – river/stream ‐ perennial

located on HR data

Forest mask

Road layer (non‐forest) Built Up – Urban – Vegetated area

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Historical Soil C density data set

  • Locations of published forest SOC

data sets (> 1000)

  • New samples (NCP * SNC) will be

p ( ) added for a large database (+ 1500 * 600)

S. Forest Type Mean SE No yp 0-100 0-30 30-50 50-100 0-30 30-50 50-100 1 Him Dry Temperate 103 78.57 24.28

  • 15.60

3.35

  • 2

Him Moist Temperate 183 104.22 32.7 47.13 8.20 3.51 6.65 3 Sub Alpine and Alpine 143 78.61 27.82 37.71 9.60 3.35 5.83 4 Subtropical Broadleaved hill 207 92.91 44.11 70.6 8.97 5.36 11.33 5 Subtropical dry Evergreen 16 7.35 1.9 7.13 1.58 0.53 1.68 6 Subtropical Pine 147 63.96 28.03 54.9 5.26 3.75 9.64 7 Tropical Dry Evergreen 141 80.52 25.32 34.95 7.62 4.25 5.95 8 Tropical Wet Evergreen 165 71.37 34.3 61.51 4.80 2.90 7.97 9 Tropical Dry Deciduous 94 43.29 18.98 32.05 2.89 1.56 2.91 10 Tropical Moist Deciduous 124 60.92 24.88 38.34 2.51 1.13 2.24 11 Tropical Semi Evergreen 148 81.44 25.15 41.81 13.61 5.45 12.13 12 Tropical Thorn forest 105 42.82 20.8 42.04 7.08 3.70 7.97 13 Littoral and Swamp Forest 137 65.54 33.3 37.7 6.25 6.93 6.09

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Soil Vegetation C Fluxes : Objectives

  • Study spatio‐temporal C source‐sink variability

– Using ground measurements and RS‐based columnar Using ground measurements and RS based columnar atmospheric CO2 retrievals – Measurement of eddy covariance and meteorological y g parameters using flux towers – RS‐based upscaling and validation of modeled regional C fluxes

  • Measurement and spatial modeling of soil CO2

p g

2

flux N ti l l lti l t i t d li

  • National‐scale multiple constraint modeling

and analysis for spatial estimation of net carbon balance

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Atmospheric CO2

  • Measurements technique and use of atmospheric CO2

for

– Surface CO2 for diurnal and seasonal CO2 variability

  • Based on sensors 1% accuracy, 30 min continuous measurements

– Fluxes of CO2 measured for vegetation and soil for calibrating component models

S il CO2 fl h t th i t

  • Soil CO2 fluxes, photosynthesis rates

– Eddy covariance net CO2 exchange to calibrate and validate models and upscale to large area using remote sensing models and upscale to large area using remote sensing – Satellite retrieval of columnar CO2 study spatial, intra‐ and inter‐seasonal variability for land‐ocean, surface exchange‐ inter seasonal variability for land ocean, surface exchange transport and cliamtic control on national C exchange

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Other CO2 Observing Sites

Nainital (ARIES), 2009 Jul Gadanki (NARL), 2010 Mar Planned : SHAR, Mt Abu, Shillong

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AIRS CO AIRS CO2

2 over India and surrounding oceans

  • ver India and surrounding oceans

1.CO2 is increasing at 2.14 ppm per yr 2 Detrended CO2 over India for land 2.Detrended CO2 over India for land has sharper/ bigger peaks than ocean 3.Terrestrial biosphere larger role in determing seasonal variability of atm CO2

Large spatial latitudinal variability Annual Cycle: CO2 Increases (Winter

CO2

Annual Cycle: CO2 Increases (Winter,

Spring) declined (summer , Autumn)

Monsoonal effect visible (transport and

terrestrial sink)

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Surface CO Surface CO2 , Flux Tower Measurement : Steps & Status , Flux Tower Measurement : Steps & Status

I l t ti St

  • Flux Tower Status
  • Implementation Steps

– Site Selection – MOU / Forest Dept

  • Flux Tower Status

– Haldwani : Operational – Meerut : Operational

/ p – AAI/DGCA Approval – Site Infrastructure T

p – Barkot : Operational / * * – Betul : Operational

– Tower – Instrument installation – Operational with field data

– Sundarbans / WB/ Mang / Procurement – Khurda / Orissa : Site Selected Khurda / Orissa : Site Selected

Surface CO2 Measurements Dehradun, Nainital Mt Abu Gadanki

Grassland Forest

Mt Abu , Gadanki

Operational Under Installation Plann ed Grassland Cropland

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Flux Flux‐based based wheat crop C wheat crop C exchange exchange

Crop : Wheat

GPP & RESP

1 2 1.4 1.6 1.8

2 s-1)

Crop : Wheat Location : Meerut

CALIBRATED

0 4 0.6 0.8 1 1.2 GPP (mg CO2 m

  • 2

0.2 0.4 500 1000 1500 2000 APAR (mmol m-2 s-1) G y = 0.0788e0.074x R2 = 0.704 0.25 0.30 0.35 O2 m-2 s-1)

Q10 = 2.1

APAR (mmol m s ) 0.10 0.15 0.20 Night Re (mg CO 0.00 0.05 0.00 5.00 10.00 15.00 20.00 Ai (oC) N

FGPP = -FNEE + Re

Air temperature (oC)

FGPP = Gross primary productivity Re = ecosystem respiration FNEE = Net ecosystem Exchange Patel, Dadhwal, Mitra, Saha, 2011, Agric For Met (under review)

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FLUX TOWERS, HALDWANI, MEERUT

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Flux Tower : Net Carbon Flux Tower : Net Carbon Exhange Exhange, , Haldwani Haldwani

2000 3 2000 3

Dry stage (JD 18 - 23) Flush stage (JD 141 - 145)

400 800 1200 1600 IPAR (umol m -2s-1)

  • 9
  • 6
  • 3

NEE (umol m-2s-1) IPAR NEE 800 1200 1600 IPAR (umol m -2 s-1

  • 9
  • 6
  • 3

NEE (umol m-2 s-1) PAR NEE

Diurnal variation in net ecosystem CO2 exchange

400 600 730 900 1030 1200 1330 1500 1630 Time (h)

  • 15
  • 12

400 0600 0730 0900 1030 1200 1330 1500 1630 time (hr)

  • 15
  • 12

2000 3 2000 3 IPAR

Peak growth (JD 222 - 226) Full growth (JD 278-282)

2

at key pheno‐phases over mixed forest plantation

800 1200 1600 PAR (umol m -2 s-1)

  • 9
  • 6
  • 3

EE

(umol CO2 m -2 s-1)

PAR NEE 800 1200 1600 IPAR (umol m-2 s-1) 11

  • 9
  • 7
  • 5
  • 3
  • 1

1 NEE (umol m-2 s-1) IPAR FCO2 400 0600 0730 0900 1030 1200 1330 1500 1630 time(hr) I

  • 15
  • 12

NE 400 6:00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 time (hr)

  • 15
  • 13
  • 11

6 9

  • 3

3 6 umol m-2 s-1)

  • 12
  • 9
  • 6

FCO2 (u

  • 15

140 140 141 141 142 142 143 143 144 144 145 145 146 146 147 147 148 148 149 149 150 150 151 151 152 152 153 153 Julian Day

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Open Fires & Biomass Burning Open Fires & Biomass Burning

74 8 83 34873 088 35000 40000

  • ns

100

20664 2637 20863 24668 2648 21759 28 2 10000 15000 20000 25000 30000

  • . of Fire Detectio

94 96 98

% Fire Detections

615 961 630 1116 1157 1273 2532 1690 5000 2003 2004 2005 2006 2007 2008 2009 2010

N Daytime Fire Detections Nighttime Fire Detections

90 92 2003 2004 2005 2006 2007 2008 2009 2010

% Nighttime Fire Detections Daytime Fire Detections

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RS Model estimate of Indian NPP by Land cover

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Mean and trends in NPP and SOC

(CASA, NOAA‐AVHRR 1981‐2003)

Trends (p > 0.10) Annual Climatology of NPP and SOC

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Model‐based Forest C Balance studies

  • Wide variation in model formulation, spatial and temporal scope,

Wide variation in model formulation, spatial and temporal scope, data needs & results

– Indian Modelling Component is weak

  • Haripriya, 2003 (Climate Change)

– Uses model adopted from Canada CBM‐CFS I (Kurz), applied to India (1993‐94)

  • Chhabra & Dadhwal, 2004 (Climate Change)

– Uses MBL Model for 1880‐1995 annual Net C release

  • Kaul, Fritz, Dadhwal, 2009 (For. Ecol. Manag.)
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vkdadhwal@nrsc.gov.in dadhwalvk@hotmail.com