sar data for forestry and agriculture - home - earth...

September 5,2007 Lecture D3L3 SAR data for agriculture and forestry Thuy Le Toan

SAR data for forestry and agriculture

Thuy Le Toan

September 5, 2007 Lecture D3L3


Assets of radar data for vegetationmonitoring?

All weather capability to secure data acquisition during growing season

Information content on : - biomass- vegetation structure- soil moisture


• Forest biomass• Rice monitoring

Context

Use of SAR data in studies related to the Carbon cycle


The terrestrial carbon balanceMass balance equation (Inventory approach)

Δ C= Δ Above Ground Biomass + Δ Below Ground Biomass + Δ Litter + Δ Soil Carbon

Process equation (Dynamic Vegetation Model Approach, Productivity Efficiency Approach)

Δ C= Gross Primary Production- Autotrophic respiration-Heterotrophic respiration - Loss by disturbances

(GPP and Ra: depend on biomassRh and Loss by disturbances depend on past biomass)


Austrian pine VHFλ > 3 m

P bandλ= 70 cm

L bandλ= 27 cm

X bandλ= 3 cm

Radar signal related to biomass of leaves, branches, trunk, depending on the SAR frequency band, polarisation and incidence

Why a SAR for forest biomass?


Biomass vs Stem volume and height

Height (m)Stem volume (m3)

Bio

mas

s(to

ns/h

a)

Vol

ume

(m3/

ha)

( from Saatchi et al.)


-30

-25

-20

-15

-10

0 40 80 120 160

Above-ground biomass (tons/ha)

Sigm

a° (d

B)

L - HVP - HV

HF - VHF

Landes forestFrance

Sensitivity of SAR data to forest biomass

HV data


Ecological studies

Landscape modelling(inventory)

Dynamic Vegetation modelling

Biomass0.1-1 ha

day-100s years

Biomass0.01-109ha1-100 years

BiomassGrid-109ha

day-100s years

Carbon Budget

Biomass informationin terrestrial carbon modelling

Light Use Efficiency modelling

BiomassGrid-109ha

day-10s years

Input

Input

Test

ForcingTest

Input


NBP

LEACHED

Litter Disturbance

ATMOSPHERICCO2

Biophysics

Soil

Photosynthesis

Growth

Biomass

GPP

NPPThinning

Mortality

Fire

The Dynamic Vegetation Model (CTCD)


Map of biomass using L-band ESARComparison with output fromSheffield Dynamic Vegetation ModelC flux prediction using SDVM

Use of radar-retrieved biomass on temperate forests (localscale) for:. Local or regional carbon budget ( Kyoto protocol etc..). Forest management

Research results to illustrate:

Büdingen forest (Germany)


< 12 t/ha > 12 t/ha - 24 t/ha> 24 - 42 t/ha> 42 t/ha

Mapping of biomass using L-band data

Pixel-wise inversion

Stand-wise inversion

Büdingen forest, Land Hessen, Germany.


0102030405060708090

100

0 20 40 60 80 100

SDVGM estimated biomass (m3/ha)

Rad

ar re

triev

ed b

iom

ass

(m3/

ha)

SpruceDouglasOakBeech

Comparison biomass predicted by model and biomass by SAR


Use of Dynamic Vegetation Model to predict Timber Volume for scenarios of climate (temperature, precipitation, CO2)

Timber volume up to 2020


Use of Dynamic Vegetation Model to predict Carbon fluxesNet Primary Productivity (red) and soil respiration (blue)


BIOMASS

A P-band SAR for Carbon Assessment


The annual flux of C to the atmosphere 1850 -1990, in which the unidentified sink is assigned to uptake by the land (Houghton, 1999).

The The ‘‘missing carbon sinkmissing carbon sink’’


Current status of global Current status of global biomassbiomass informationinformation

1. Biomass from inventory data2. Biomass from remote sensing

-->no biomass information suitable for carbon cycle and Earth system models

-> no consistent mapping of forest area, structure, and change


Spatial distribution of biomassin the Amazon:

Comparison of current methods(Houghton et al., 2001)

Uncertainty in estimates of biomass at regional scale


LPJSDGVM

Carbon Carbon sources sources and sinks predicted and sinks predicted by modelsby models

( Hybrid, IBIS, LPJ, SDGVM, Triffid, Vecode)


90°E

Le Toan et al., 2004J. of Climatic Change

Sheffield Dynamic Vegetation Model

Biomass estimatesBiomass estimates:: ComparisonComparison ModelModel--EOEO

1

2

100°E

3

4 5

60° N

55° N

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

kg C m-2

Siberia-I radar forest map


2003 Burn 1988 Burn

Mapping Disturbance and Recovery

Yellowstone National Park

Pine Beatle Disease

( from Saatchi et al.)


500 100 200150

Biomasse (T/ha)

• Image optiqueP-band SAR Image

Forêt de Nézer

CESBIO

Le Toan et al., 1992,Beaudoin et al.,1994

Biomass mapping: temperate conifer plantations


Biomass map for BOREAS test sitefrom P-Band SAR

(Saatchi et Moghaddam, 2000)

0

50

100

150

200

0 50 100 150 2

Fiel

d S

tem

Bio

mas

s (to

ns/h

a)

SAR Predicted Stem Biomass

P-band AlgorithmR=0.954

Saatchi & Moghaddam 2000

Biomass mapping in boreal forest


P-band

Intensity

Coherence

Phase

Polarimetric interferometr

Garestier et al., 2006

Advanced techniques to measure tree height

BIOMASS proposal


Rice monitoring using SAR data


Methane global concentrations (SCIAMACHY):


Rice field: a major source of methane

(Denier van der Gon, 1996)


Role of rice fields in the terrestrial carbone cycle? (CH4 & CO2)

Changes in Green House Gases (GHG) due to changes in land use and farming practices ?

Projection in the future and solution for mitigation?

Questions


Rice Monitoring in China

ESA-China Dragon Project


LPJmL grid cell- crops- managed grasses- land use change

Crop specificparameterisation:

- Phenology: sowingdates, maturity, determination of somevarietal parameters

- Carbon allocation to the yield-storage organ.

The LPJ Dynamic Global Vegetation Modeladapted to managed land (Bondeau et al., GCB, 2007)

C budget, H20 & CO2 Fluxes,Vegetation CompositionCrop production

Modelling approach


GISGIS

CH4MODCH4MODCropCrop--CC

Field measurements & literature reports

Model validation

Process levelUnderstanding

C model from Institute of Atmospheric Physics, CAS, Beijing


LPJ Model inputs

Farming practices:• Cropping system (single-crop rice, double-crop rice, rice-wheat, rice-maize,etc)• Crop calendar: sowing date, harvest dates for each crop variety• Flooding and drainage calendar• Which variety class (e.g. high-yield variety: harvest index)• Sowing density (plant density)• Fertilizer inputs calendar • Residues management (straw taken out, burned, left over the field and flooded, ploughted into the soil ?)• TillageLand use / land cover• % crops cover (rice and other crops) within the grid cell• History (land use change, needed for soil carbon building)


Ear

th O

bser

vatio

n

Ric

e ca

rbon

Mod

elsRice mapping

Rice varieties

Crop calendarWatermanagement

LAI/FAPAR

Biomass.

Initiation

Assimilation

Run time control

Testing

Approach: use of remote sensing & models


Rice MappingLocal & regional

Rice Parameters

Yieldestimation

ENVISATASAR & MERIS,

VGT

Rice and BiochemistryModels

Methane & CO2 Fluxes

Ancillary& ground data

RiceProduction

Rice in Carboncycle

Methane Measurement

ENVISATSCIAMACHY

Meteorological data

Remote Sensing Methods


Remote Sensing results

Development and validation of methods for mapping of paddy fields at local and regional scales using ASAR data

Retrieval of rice biomass using ASAR

Mapping of rice varieties using ASAR

Determination of rice and other crop calendar using SPOT VGT

Detection of mid season drainage using ASAR


Water

ASAR for rice monitoringAt C band, HH and VV: the dominant scattering mechanism is thedouble bounce vegetation-waterHH>VV because of the stronger attenuation of VV by verticalstems

HH and VV increases with the plantbiomass.The increase is very important (up to 10 dB during the growth season) (Le Toan et al., 1997).

HH/VV is related to biomass


-30-28-26-24-22-20-18-16-14-12-10-8-6-4

0 10 20 30 40 50 60 70 80 90 100Days (days after sowing)

Bac

ksca

tteri

ng co

effic

ient

(dB

)

Scatterring mechanisms

ERS Model

-20-18-16-14-12-10-8-6-4-20

0 500 1000 1500 2000 2500 3000 3500 4000Moist Biomass (g/m²)

Bac

ksca

tteri

ng co

effic

ient

(dB

)

Volume-Surface

Volume Scattering

Basic principle of using C-band SAR for rice monitoring

Strong increase (>10 dB)during the growth cycle

Main scattering mechanism is volume-surface interaction

Le Toan et al., 1997, Wang et al., 2004


Bac

ksca

tter

ing

Coe

ffic

ient

(dB

)

-20-18-16-14-12-10-8-6-4-20

0 500 1000 1500 2000 2500 3000 3500 4000Moist Biomass (g/m²)

Effect of Polarisation

ERS (VV)

RADARSAT (HH)

Solid line: modelling result

• Strong increase as a function of biomass, from transplanting to maturity• HH>VV• Smaller increase at higher incidence

Effet of Incidence angle

-16-15-14-13-12-11-10-9-8-7-6-5-4

0 20 40 60 80 100 120Age (# days after sowing)B

acks

catt

erin

gC

oeff

icie

nt (d

B)

C-HH-23°

C-HH-43°

Solid line: modelling result


Example of Chaindeveloped using:

•Gamma ASAR (Gamma RS)

•Multi-image filtering(Quegan et al., 2000)

• Temporal change (Le Toan et al., 1997)

Mapping algorithmInitial images

Calibrated coregistered

Filtered

Filteredgeocoded

1 2 M…………….

.

.

1 2 M…………….date 1 date Mdate 2

1 2 M…………….

1 2 M…………….

Analysis , RetrievalClassification

Spatial filteringGeocoding

Multi image filtering

CalibrationRegistration


Jiangsu Province

Rice monitoring using ENVISAT


Small field: powerful filtering

Filtering using 20 images (2 polarisations, 10 dates)


HH VVHongze (Jiangsu) 2004 09 06


Magenta=HH, Green=VV 34km*38km

September 6th, 2004 , Hongze area

yellow=rice, red=urban, black=other

Rice mapping at a single date using HH/VV


In situ mapping of 1km x 1km samples with DGPS

Ground campaign for validation

Overall accuracy: 80-88%


pixel size : 75 m210 km * 127.5 km

magenta : August 18green : October 27

Funing

HuaiAn

Chuzhou

Suqian

Shuyang

Yancheng

ASAR WSM region North of Qingjiang, Jiangsu province

Rice mapping using WS ASAR data


XinqiRiver

Guboshanhou River

JieyuRiver

Guangriver

QiangweiRiverHuaimu

xinRiver

Lianyungang

Guannian

Guanyun

Regional rice mapping


Mapping of rice varieties

Hybrid rice

Japonica rice

Dragon projectCAAS, Nanjing


Continuous flooding

Mid-season drainage

Wuxian, Jiangsu province

Mid-season drainage reduces methane emissions

Li et al.,JGR,2001

30 to 60%


Magenta: 20060705 Green: 20060809Magenta: 20060702 Green: 20060806

WSM VVMapping of mid season drainage in rice fields

Fields in magenta are flooded rice fields and fields in green are rice fields with temporary drainage that occurs approximately 3 weeks after transplantation (in July)


Dates 2004

28-june

20-july

2-august

18-august

8-sept.

24-sept.

11-oct.

HH/VV vs Wet biomass of a test field in Gaoyou, 2004

0

1

2

3

4

5

0 1 2 3 4 5 6

Wet biomass (kg/m²)

HH/V

V (d

B)

flowering

tillering

jointing

heading

ripening

ready for harvest

Retrieving of biomass


Hongze and Gaoyou - Beginning of Tillering to Flowering2004 (Blue), 2005 (Magenta)

y = 1,0266x - 0,1058R2 = 0,7968

-1

0

1

2

3

4

5

6

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5

Wet Biomass

HH

/VV

Relation HH/VV and rice wet biomass


Determining rice and other crop cycle and calendar using SPOT VGT (1 km, 10 days)

Wheat Rice

RiceRice

SPOT VGT-NDVI


triple-cropping farmland

double-cropping farmland

forest

urban

water

2005

Landuse/cover and number of crop/year using VGT


Transplanting date (min NDVI) 2005

Light green: 1-10 June

Green: 11-20 June

Dark green: 21-30 June

TransplantingTransplanting datedate


Ricepercentage

2004


Jun

80 90 100 110 120 130

2025

3035

4045

50

0

2

4

6

8

10

Jul

80 90 100 110 120 130

2025

3035

4045

50

0

2

4

6

8

10

Aug

80 90 100 110 120 130

2025

3035

4045

50

0

2

4

6

8

10

GgCH4

Rice monthly CH4 1991−2000

Total annualCH4 emissionfrom Rice fieldsin China:

4.9 Tg CH4

Initial modelling result using LPJmL


In June and August, the extreme high tropospheric CH4 enrichment appeared over central China with the values of 9.2 ppb and 11.5 ppb.

IAP-CAS

Modelling results by IAP


A peak increment of 11.9 ppb in July occurred in Sichuan

IAP-CAS


In June and August, the extreme high tropospheric CH4enrichment appeared over central China

IAP-CAS

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