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

Biomass retrieval in temperate forested Biomass retrieval in temperate forested areas with a synergetic approach using SAR areas with a synergetic approach using SAR

and Optical satellite imageryand Optical satellite imagery

Nicolas Ackermann

Supervisor: Prof. Christiane Schmullius

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

Jena, the 11th June 2009

Context

Objectives

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

Schedules

Presentation outlinePresentation outline

ContextObjectivesApplication: Biomass retrieval in the Thuringian Forest (Germany)Schedules

N.A - Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery -N.A 2

3

Biomass – Carbon assessment: 1/3 of land surfaces are 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

Idenfitication of forested areas in South-East China with satellite imagery Ackermann Nicolas


- Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery -N.A 3

World forest distribution (National Science Foundation)

Temperate terrestial biome

4

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

AutomatisationGlobal regional scaleRobust

Fusionprocessing

Results

Validation

Methodology

5

Processing phasesProcessing phases




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

Biomass retrieval

Results

Validation

Methodology

Fusion

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

Tree Species

Scots Pine (Kiefer)

European Larch

Norway Spruce (Fichte)

Oak

Sessile Oak

European Beech (Buche)

European Ash

6Idenfitication of forested areas in South-East China with satellite imagery Ackermann Nicolas



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

Thuringia Forest (Germany) Surface: 110 km x 50 km (5500km2) Terrain variations

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

Forest proprieties: main species: Scots pines,

Norway Spruce, European Beech

large Stem Volume variance Climate

cool and rainy frequently clouded

Peculiarities logging for forest exploitation Kyrill storm (February 2007)

Test siteTest site

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Freq

uenc

y

Stem Volume [m3/ha]

Stem Volume

Thuringia Forest (GoogleTM Earth)

25 km

Scots pines

Norway Spruce

European Beech





Available DataAvailable Data SAR data

ALOS PALSAR (L-Band) TerraSAR-X (X-Band)

Additionnal SAR data Cosmo-SkyMed (X-Band, constellation of 4 satellites) ? E-SAR (L-Band and X-Band, DLR campagns) ?

Mission SensorRadar-

Frequency Beam Pol.

Incident angle

# Scenes

# Scenes Overlapping

Status

ALOSPALSA

RL-Band FBS HH 34.3° 29 3 received

ALOSPALSA

RL-Band FBD HH/HV 34.3° 32 3 received

ALOSPALSA

RL-Band PLR HH/HV/VH/VV 21.5° 6 2 received

TSX TSX X-Band HS HH 23° 16 8 acquisition

TSX TSX X-Band SL HH/VV 45° 12 1 acquisition

TSX TSX X-Band SM HH/HV , VV/VH 37° 16 2 acquisition

SAR data - Thuringia Forest test site

PALSAR PLR

PALSAR FBD

PALSAR FBS

Thüringer Wald - 2006-2009

TSX HS

TSX SL

TSX SM

Thüringer Wald - 2009

Jan. 06 Jul. 06 Jan. 07 Jul. 07 Jan. 08 Jul. 08 Jan. 09 Jul. 09

Jan Feb Mrz Apr Mai Jun Jul Aug Sep Okt Nov Dez





Available DataAvailable Data Acquisition dates





Available DataAvailable Data

Optical data RapidEye

Additionnal Optical data Kompsat-2 ? JAS (Push broom Digital

Camera, Jena-Optronik) ?

Mission Sensor Channels # Bilder Status

RapidEye RapidEyeRGB, IR, NIR (5 Channels)

8 acquisition

Ancillary data DEM: SRTM 25[m], LaserDEM 5[m] Laser points (2004), Orthophotos (2008) Ground data: In-situ data (1989-2009), photos with GPS coord. (2009), weather data

RapidEye


RapidEye


RapidEye

Thüringer Wald - 2009






Pre-processing phasesPre-processing phases

SAR Data pre-processing Calibration Multi-looking Geocoding Normalization Co-registration

Slope

Aspect?

Normalization specific to forest regions:

Volume scattering: a) Tilted surface facing the radar, b) flat surface, tilte surface opposite to the radar (Castel, 2001)

Ground data pre-processing Forest stands selection Forest stands buffer Graphe correlation with Satellite data

Optical data pre-processing Calibration Atmospheric corrections Topographic normalization Co-registration

Biomass retrieval ALOS PALSAR RapidEye





Data analysis perspectiveData analysis perspective

Forest identification ALOS PALSAR TSX RapidEye

Species identification TSX RapidEye

Tree and vegetation density TSX RapidEye

Forest stress and chlorophyll activity RapidEye

ASAR WS Biomass map (Santoro, et al., 2006)





Regions of interest (ROIs)Regions of interest (ROIs)

ROIs definition Land cover: Crops, Forest, Urban, Water Forest category: Sparse, Dense Forest type: Broadleaf, Needle Forest species: Scots pines (Kiefer), Norway Spruce (Fichte), European Beech (Buche)

ROIs selection procedure Orthophotos observations Areas > 100 pixels (~6 ha) Homogeneity (low variance intra-class)

Multitemporal PALSAR :

FBD : 3 - 9 scenes FBS: 2 - 10 scenes FBD - FBS: 5 - 16 scenes

TSX: HS: 9 scenes HS - SL: 9 - 10 scenes





SAR data analysisSAR data analysis

Frequency L-band vs X-band

PALSAR HH - TSX HH PALSAR HV - TSX HV

Polarisation HH vs HV

PALSAR HH - PALSAR HV TSX HH - TSX HV

HH vs VV TSX HH - TSX VV

VV vs VH TSX VV - TSX VH

VH vs HV TSX VH - TSX HV

Incident angle Radiometric analysis

TSX 23° - TSX 37° - TSX 45°

Geometric distortions analysis TSX 23° - TSX 37° - TSX 45°

The data analysis takes into consideration comparable data, namely for an analyzed parameter between two sets of data, all other parameter of this data must remain the same.

Multitemporal RapidEye

7 scenes ?

Bio-physical parameters RapidEye

Absorbed Photosynthetically Active Radiation (PAR)

Leaf Area Index (LAI)





Optical data analysisOptical data analysis

Descriptive statistics Frequency Parameters correlations

Bio-physical analysis Relative stocking Forest growth





Ground truth data analysisGround truth data analysis

Understand the forest





Additional data analysisAdditional data analysis

Texture and Structure Filters

TSX RapidEye JAS

Variogram TSX RapidEye JAS

Bio-physical parameters JAS

Height derivation Absorbed Photosynthetically Active Radiation Leaf Area Index Bi-directional reflectance distribution function

(BRDF)

InSAR Coherence

PALSAR (35 days r.p.) TSX (11 days r.p.) Cosmo-SkyMed (~1 day r.p. or better) E-SAR (~1 hour r.p. or better)

Interferometric Height TSX Cosmo-SkyMed E-SAR

Image processing Pixel-based segmentations

ISODATA, K-Means, Fuzzy K-Means, …

Object-based segmentations Decision Tree, …





Biomass retrievalBiomass retrieval

Remote sensing systems ALOS PALSAR RapidEye

Processing Empirical models

Linear regressions Non-linear regressions

Semi-empirical models Water Cloud Model (WCM) Branching Model …

Satellite[-DN-]

Ground dataStem Volume

y = ax + b

y = aebx

WCM(Attema and Ulaby (1978)

Vegetation component Ground component





Fusion perspectiveFusion perspective

Map accuracy improvement Biomass base map

Transferability concept Spatial: clouds, mosaicing Temporal: lack of data

SAR

Optical

Possible cases1. Optical image clear

2. Optical image cloud covered

3. SAR image clear

4. SAR image cloud covered

5. Superimposed Optical and SAR images clear

6. Superimposed Optical and SAR image cloud covered

Quality Factor (max=1)

0,8

0,5

0,7

0,7

0,9

0,8

1.

2.

3.

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6.

5.

t

t

Spatial transferability

Temporal transferability

Optical data

SAR data





Fusion methodologyFusion methodology

Fusion

Segmentation – Oriented-object

Data merge – Pixel-based

Biomass product(SAR or Optical)

Model combination

Fusion Levels

…

…Optical

Pre-processing

Forest Biomassmap

SAR

Pre-processing

SAR- Pre-processed -

Optical- Pre-processed -

Automatic Efficient Robust

Fusion

Segmentation – Oriented-Object


Biomass product (SAR and Optical)

Model combination

Fusion Levels

…

…

Fusion



Biomass product(SAR and Optical)

Model combination

Fusion Levels

…

…

SAR Optical - Segmented -

x

- Segmentation -

Biomass

ND

VI

YSAR = cx + d

σ0

Biomass

Yoptisch= ax + b x

- Model combination -

Fusion




Model combination

Fusion Levels

…

…

Optical - Biomass product -

x

SAR- Biomass product -

- Forest Biomass map -

Fusion




Model combination

Fusion Levels

…

…

SAR

x

- Data merge -

Optisch- Pixels -

What? Where? When? How?




1. Data pre-processing

2. Data analysis & Biomass retrieval

3. Paper review

Fusion

SAR data

Optical systems

Optical data

SAR systems

Forest parameters

SAR data

Optical data

Forest stands

JUNE-JULY

AUGUST

SEPTEMBER




Thank you for your attention!Thank you for your attention!

Thuringia Forest

Documents

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