N. Ackermann - Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery - 1 Nicolas Ackermann Supervisor:

N. Ackermann - Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery - 1

Nicolas Ackermann

Supervisor: Prof. Christiane Schmullius

Co-supervisors: Dr. Christian Thiel, Dr. Maurice Borgeaud

FSU Jena, the 30th November 2010

Biomass retrieval in temperate forested areas with Biomass retrieval in temperate forested areas with a synergic approach using SAR and Optical a synergic approach using SAR and Optical

satellite imagery: state November 2010satellite imagery: state November 2010

PhD Colloqium 2010


Context

Objectives

Application: Biomass retrieval in the Thuringian Forest (Germany) Test site and data Pre-processing Analysis of the data Biomass retrieval

Schedules

Presentation outlinePresentation outline


Biomass – Carbon assessment: 1/3 of land surface is covered by forests Temperate forests : ~1/4 of world’s forests =>

Pool of Carbon Kyoto Protocol: “quantify emission limitation

and reduction commitments”

ENVILAND2: Objective:

automated processing chain land cover products optical and SAR synergistic approach

Status ENVILAND1 : scale integration + spatial

integration (2005-2008) ENVILAND2: level 3 products (kick-off:

November 2008)

ContextContext

World forest distribution (National Science Foundation)

Temperate terrestial biome


ObjectivesObjectives

Forested areas in Thuringian Forest

SPOT-5 ALOS-PALSAR

Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery

Priorities: Algorithms simple and robust Algorithms spatially and temporally transferable Global /regional scale Automatisation


Fusionprocessing

Results

Validation

Methodology

Processing phasesProcessing phases

Biomass retrieval

Results

Validation

Methodology

Analysis of the data

Regions of interests

SAR and Optical data analysis

Ground dataanalysis

SAR data

Pre-processing of the data

Optical data

Ground data

Test site selection

Test site

Data availability

Analysis of the dataTest site selection Pre-processing of the data Biomass retrieval

95%Completed: 80% 50% 40% 10%


Test site selectionTest site selection



Tree Species

Scots Pine (Kiefer)

European Larch

Norway Spruce (Fichte)

Oak

Sessile Oak

European Beech (Buche)

European Ash

Thuringia Forest (Germany) Surface: 110 km x 50 km Terrain variations

90% of forest over hilly areas range: 800m - 900m

Forest proprieties: main species: Scots pines, Norway

Spruce, European Beech large biomass dispersion

Climate cool and rainy frequently clouded

Peculiarities logging for forest exploitation Kyrill storm (February 2007)

0

200

400

600

800

1000

1200

1400

0 -5

0

50 -1

00

100 -

150

150 -

200

200 -

250

250 -

300

300 -

350

350 -

400

400 -

450

450 -

500

500 -

550

550 -

600

600 -

650

Freq

uenc

y

Stem Volume [m3/ha]

Stem Volume

25 km

Test siteTest site



Initial test site limitations Only contains Norway Spruce Not well covered by RapidEye and

PALSAR Not sufficiently reliable forest stands Mostly located topographic area

25 km

Extended test siteExtended test site

Extended test site overcomes these limitations



SAR data ALOS PALSAR (L-Band, 46 days) TerraSAR-X (X-Band, 11 days) Cosmo-SkyMed (X-Band, 1 day)

Optical data RapidEye Kompsat-2

Ancillary data DEM: SRTM 25[m], LaserDEM 5[m] Laser points (2004), Orthophotos (2008) HyMap (2008,2009) Forest inventory (1989-2009) Photos with GPS coord. (2009) Weather data Field work

Available Data (state November 2010)Available Data (state November 2010)

Interferometric coherence

SAR topographic normalisation, SAR analysis

SAR analysis, Forest inventory validation

Multispectral radiometric normalisation



Satellite data - Thuringia Forest test site (state November 2010)

Mission Sensor Radar-Frequence Beam Polarisation Incident

angle# scenes available

ALOS PALSAR L-Band FBS HH 34.3° 43

ALOS PALSAR L-Band FBD HH/HV 34.3° 58

ALOS PALSAR L-Band PLR HH/HV/VH/VV 21.5° 13

TSX TSX X-Band HS HH, VV 21°-45° 41

TSX TSX X-Band SL HH, VV, HH/VV 23°-45° 9

TSX TSX X-Band SM HH/HV, VV/VH 23°-45° 18

CSK CSK X-Band Himage HH 40° 12

RapidEye RapidEye R,G,B, Red-edge, NIR 25

Kompsat2 Kompsat2 R,G,B, NIR, PAN 6

Available Satellite DataAvailable Satellite Data

Total: 225 scenes



Available DataAvailable Data

Temporal overview satellite data

ALOS PALSAR FBS

ALOS PALSAR FBD

ALOS PALSAR PLR

TSX SM

TSX SL

TSX HS

CSK Himage

Kompsat2

RapidEye



Pre-processing of the dataPre-processing of the data



RapidEyeRapidEye

RapidEye, R, G, B, 13th June 2009 (atmosphere corrected)

Multispeectral data - RapidEye L1B -

Orthorectification(manual GCPs)

Orthorectified-Radiance

-[W m-2 sr-1 µm-1] -

Atmosphere correction

• Calibrated 16 Bit product [W m-2 sr-1 µm-1]

Atmosphere corrected

- Reflectance [%] -

• Orthoengine PCI Geomatica

• ATCOR PCI Geomatica• Sun/Surface/Sensor normalisation• Atmospheric corrections• Relief radiometric normalisation• Scaling



Validation(JM distance)

RapidEyeRapidEye

• Definiens ecognition• Multisegmentation (scale 10)• Brightness ratio ((Bmax-B)/(Bmax-Bmin))• NDVI

Clouds/Clouds shadow masking

Cloud/shadow masked- Reflectance [%] -

Radiometric refined- Reflectance [%] -

Hyperspectral data - HyMap L2 -

Atmosphere corrected

- Reflectance [%] -

Empirical line correctionMAD normalisation

• MAD and Empirical line correction are based on linear regressions

• Validate with Jeffries-Matusita distance (JM) => require a low spectral separability

• External reference data: HyMap 2008, 2009 (DLR)

Training ROIs

Testing ROIs

Reference refined- Reflectance[%] -

Validation(JM distance)

Testing ROIs

1.

2.

3.4.

5.

6.

Training ROIs(automatic)



RapidEyeRapidEye

RapidEye (25th Mai 2009)

R: NIRG: Red-edgeB: R

RapidEye – Clouds mask

RapidEye – Clouds shadow mask

Non forest elements can be neglected

• + Performs well the masking• - Manual approach for each scene



PALSAR / TSXPALSAR / TSX

Crown Optical depth considerations n coefficient takes different values according to the investigated surface In forested areas, n can be related to the crown optical depth, which

in turns depends of two physical parameters : Ke and Hc Ke can be a function of the tree species composition and the weather

conditions (frozen, humid, dry, etc.) Hc can be a function of the local slope and the crown height

Analysis methodology Define a reference and perform a sensibility analysis by comparing

the coefficent of variation (CV) for different environnemental conditions

Estimate the optimal n coefficient by iteration using as a criteria the minimum of CV



PALSAR / TSXPALSAR / TSX

Example Reference parameters Number of stands may influence the statistics (min fixed: 25-30 stands) Incident angle = 38.7[°]

Intensity Parameter Reference test1 test2 test3 test4 test5 test6 test7 test8 test9 test10 test11σ0 (Hc) Local slope θloc = θref θloc < θref θloc > θref θloc = θref θloc = θref θloc = θref θloc = θref θloc = θref θloc = θref θloc = θref θloc = θref θloc = θrefσ0 (Hc) Tree height 20-23 20-23 20-23 10-13 30-33 20-23 20-23 20-23 20-23 20-23 20-23 20-23σ0 (Ke) Tree struct N.Spruce N.Spruce N.Spruce N.Spruce N.Spruce Beech Scot pine N.Spruce N.Spruce N.Spruce N.Spruce N.Spruceσ0 (Ke) Weather dry dry dry dry dry dry dry humid frozen thaw dry dry

σ0 Stem Volume Dense Dense Dense Dense Dense Dense Dense Dense Dense Dense Sparse Denseσ0 Forest Layers 1 1 1 1 1 1 1 1 1 1 1 2 or 3

cv = 0.136754

Ga

mm

a n

ou

gh

t [

dB

] -

FB

S

Local incident angle [°]

= -7.6 [dB]

n = 1

Ga

mm

a n

ou

gh

t [

dB

] -

FB

S

Local incident angle [°]


cvmin = 0.135138n optimal= 0.21

= -7.7 [dB]


Weather dataWeather data

Weather data DWD: Deutsche Wetter Dienst Acquisition period: 2006-2010 Parameters: Precipitation, Snowdepth, Water-equivalent, Wind,

Temperature, Sunshine duration, relative Humidity Pre-processing

Collaboration with FSU geoinformatic institute JAMS (Jena Adaptable Modelling System) Software 2 temporal scales : daily / hourly

Weather data[ .xml ]

Daily outputgeneration

HourlyRegionalised

[ .txt ]

Daily[ raster ]

Hourly Inputconversion

Daily inputconversion

HourlyRegionalisation

DailySelected[ .dat ]

HourlySelected[ .dat ]

DailyRegionalisation

DailyRegionalised

[ .txt ]

Hourly outputgeneration

Hourly[ raster ]

JAMS Software

90m

Station 1

Station 2

Station 5

Station 3

Station 4



Weather dataWeather data

Weather parameters outputs Raster data

90m and 300m spatial resolution Initial site / extended site

Excel table Describe mean weather values of the

overlapping selected forest stands and satellite data

Figure depicting the raster data (31mar08 - 03apr08)

Temperature [°C]Precipitations [mm]

High: 36

0

High: 10

0

Table summarizing weather data (simplified version)

Sensor Acquisition-date Daily (4 days meteorological conditions) Hourly (4 hours meteorological conditions)

Frame T°air_mean [°C]

T°air_min [°C]

T°air_max [°C]

Precipitation [mm]

Humidity [%]

Sunshine [hrs]

Wind speed [m/s) T°air_mean

[°C]Precipitation

[mm]Humidity

[%]Wind speed

[m/s}PALSAR 02/04/2008 21:29 1000 5.7 3.1 9.1 20.3 81.7 2.1 3.9 2.9 2.8 93.9 4.7PALSAR 02/04/2008 21:29 1010 7.6 4.6 11.5 16.7 76.4 2.1 4.0 5.1 2.0 88.4 4.4

… … … … … … … … … … … … … …PALSAR 06/05/2008 21:33 1000 10.9 4.5 16.7 0.1 69.8 9.6 3.3 11.4 0.0 75.3 3.8



Analysis of the dataAnalysis of the data



Processing chart-flowProcessing chart-flow

SAR data - ALOS PALSAR, TSX -

InSAR Phase / Coherence

Optical data - RapidEye -

DEM

Weather data- Precipitation,

T°, Wind -

SAR backscatter

Spectral Reflectance

SAR backscatter

InSAR Phase / Coherence

InSAR Height, Bands ratio, Textur,

Thresholding

Bands ratio, Textur,Thresholding

NDVI, ThresholdingSpectral Reflectance

Forest/non Forest Tree speciesCrown cover

Forest/non Forest Forest layersTree Height

Forest/non Forest Tree speciesCrown cover

IWCM

WCM

Biomass map

Pre-processing Algorithms to retrieve biophysical parameters

Algorithms to retrieve Forest biomass

Data analysis Biomass retrieval



RapidEye preliminary investigationsRapidEye preliminary investigations

RapidEye (R,G,B, Red-edge, NIR) – Test site Schmiedefeld

Color composite: R (NIR), G (Red-edge), B (R)

RE R, Red-edge, NIR, 5m, 13th June 2009

Tree species compositionRed: European BeechBlue: Norway Spruce

- The forest stands are well overlapping over the satellite image.

- Good separation between Beech and Spruce with a higher reflectance for Beech.

- Generally, very high reflectance in Red-edge and NIR for open area (grass).

- Urban show more reflectance in R than the other surface, which depicts the bright blue color.

- The wavelength are, as expected, absorbed by water in R, Red-edge and NIR channels (Dark blue).




RapidEye (R,G,B, Red-edge, NIR) – Test site Schmiedefeld

RE B, G, R, Red-edge, NIR, 5m, 13th June 2009

- The reflectance can be better differenciate for the different classes in high wavelength spectrum (Red-edge, NIR channel).

- Open area, Beech and Spruce have respectively ~60 [%],~40 [%] und ~20 [%] reflectance in NIR => potential of discrimination of these classes

- Slight differences bewtween signatures from low stem volume in comparison to high stem volume => maybe lead to a small sensitivity to forest biomassWavelength [nm]

Ref

lect

ance

[%

]



Stem volume [m3/ha]


RapidEye (NIR)

RE NIR, 5m, 25th September 2009

- Important physical considerations:

• Tree structure : shadow and additive reflectance

• Tree species composition: chlorophylle pigments

• Abiotic factors : soil moisture, air relative humidity, atmosphere effects

- Little negative linear correlation between RE NIR and stem volume for Beech (r2=0.25), Spruce (r2=0.1) and Pines (r2=0.04)

- The dispersions of the points are relatively homogeneous. Islotated high and low reflectance values are still occuring => clouds and clouds shadow => affect the statistics

- Good separation of Norway Spruce and Beech.

Ref

lect

ance

[%

] –

RE

NIR

ReflectanceBlue: Norway Spruce (S)Yellow: European Beech(B)Red: Scot Pine (P)

R2S=0.10

R2B=0.25

R2P=0.04

Y(x) = A * x + B



ALOS PALSARALOS PALSAR

ALOS PALSAR coherence

Stem Volume [m3/ha]

Inte

rfe

rom

etr

ic C

oh

ere

nc

e

Fra

me

1F

ram

e 2

R2S=0.19

R2B=0.038

R2P=0.044

R2S=0.10

R2B=0.009

R2P=0.009

R2S=0.22

R2B=0.012

R2P=0.06

R2S=0.35

R2B=0.12

R2P=0.08

R2S=0.36

R2B=0.15

R2P=0.088

R2B=0.002

R2S=0.29

Y(x) = A * x + B

Spatial baseline

Precipitations:23jul09: 28.6mm07sept09: 3.9mm

- Little negative correlation between ALOS PALSAR coherence and Stem Volume

- Higher coherence and higher correlation for Spruce in comparison with Beech and Pines (branches structure differs but stem volume distribution also)

- Weather effect (precipitations) and high perpendicular baseline can affect the correlation and the level of coherence

PALSAR FBD, 38.7°, HH, A, 25m

Frame location

1 2

Interferometric coherenceBlue: Norway Spruce (S)Yellow: European Beech(B)Red: Scot Pine(P)



Biomass retrievalBiomass retrieval



3. 5.


ALOS PALSAR coherence – multitemporal approach

25% stands - Training -

SANTORO et al., 2000; SANTORO, et al., 2002b

5.

4.

3.

2.

1.

Linear Non-linearCoherence stack

Select andfit model

Scatterplot + model

Inverse modeland retrieve Stem volume

Estimated Growing stock

Volume

Compute RMSEand weights

Weights

75% stands - Testing -

Compute Growing Stock volume map

Growing stock Volume map

1., 2.4.

Forest inventory




ALOS PALSAR coherence – Growing stock Volume map

Frame location

1 2

- Similarly to spatial averaging, the multitemporal combination act as a filter and decreases the noise.

- RMSEi>200 [m3/ha] is very high, in particular due to the high dispersion of the coherence. The methodology should be tested for each species separately and by inversing testing and training stands.

- Water and urban can be recognized, with respectively low (dark green) and high (gray) coherence

PALSAR FBD, 38.7°, HH, A, 25m

Multitemporal coherence biomass map

RMSE>200 [m3/ha]



Field campagn 2010Field campagn 2010

Forest campagn objective: collect information on the undergrowth in order to

interpret some of the obtained results Focus on low stem volume forest stands


Undergrowth in a young regenerating forest stand


Schedules – next stepsSchedules – next steps DECEMBER-JANUARY

Data analysis:

- Process SAR intensity and coherence 2010

- Validate obtained results using ancillary data (weather, forest campagn)

Pre-processing:- Complete pre-processing Multispectral data

- Pre-processing weather parameter snowdepth and water equivalent

JANUARY-APRIL Data analysis:

- Complete analysis multispectral data

- Complete analysis SAR data

Modeling:- Develop modeling approach for SAR data

APRIL-AUGUST Fusion:

- Develop fusion approach for SAR – Multispectral data

- Complete modeling with the SAR data

PhD Dissertation:- Final results

- Papers


Thuringia Forest – July 2010

Vielen Dank für Ihre Aufmerksamkeit!

Documents

N. Ackermann - Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery - 1 Nicolas Ackermann Supervisor: