N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 1 Nicolas Ackermann Supervisor: Prof. Christiane Schmullius.

Slides:



Advertisements
Similar presentations
Poster template by ResearchPosters.co.za Effect of Topography in Satellite Rainfall Estimation Errors: Observational Evidence across Contrasting Elevation.
Advertisements

On The Use of Polarimetric Orientation for POLSAR Classification and Decomposition Hiroshi Kimura Gifu University, Japan IGARSS 2011 Vancouver, Canada.
On Estimation of Soil Moisture & Snow Properties with SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa.
Forest Monitoring of the Congo Basin using Synthetic Aperture Radar (SAR) James Wheeler PhD Student Supervisors: Dr. Kevin Tansey,
The Effects of Site and Soil on Fertilizer Response of Coastal Douglas-fir K.M. Littke, R.B. Harrison, and D.G. Briggs University of Washington Coast Fertilization.
New modules of the software package “PHOTOMOD Radar” September 2010, Gaeta, Italy X th International Scientific and Technical Conference From Imagery to.
ReCover for REDD and sustainable forest management EU ReCover project: Remote sensing services to support REDD and sustainable forest management in Fiji.
An artificial neural networks system is used as model to estimate precipitation at 0.25° x 0.25° resolution. Two different networks are being developed,
Akira Kato 1, Manabu Watanabe 2, Tatsuaki, Kobayashi 1, Yoshio Yamaguchi 3,and Joji Iisaka 4 1 Graduate School of Horticulture, Chiba University, Japan.
What is RADAR? What is RADAR? Active detecting and ranging sensor operating in the microwave portion of the EM spectrum Active detecting and ranging sensor.
IGARSS 2011 Classification of Typhoon-Destroyed Forests Based on Tree Height Change Detection Using InSAR Technology Haipeng Wang 1, Kazuo Ouchi 2, and.
Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping Z-S Zhou, P. Caccetta, E. Lehmann, A. Held – CSIRO,
N. Ackermann - Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery - 1 Nicolas Ackermann Supervisor:
Remote Sensing and Active Tectonics Barry Parsons and Richard Walker Michaelmas Term 2011 Lecture 4.
earthobs.nr.no Land cover classification of cloud- and snow-contaminated multi-temporal high-resolution satellite images Arnt-Børre Salberg and.
Microwave Remote Sensing Group 1 P. Pampaloni Microwave Remote Sensing Group (MRSG) Institute of Applied Physics -CNR, Florence, Italy Microwave remote.
On the Retrieval of Accumulation Rates on the Ice Sheets Using SAR On the Retrieval of Accumulation Rates on the Ice Sheets Using SAR Wolfgang Dierking.
The University of Mississippi Geoinformatics Center NASA RPC – March, Evaluation for the Integration of a Virtual Evapotranspiration Sensor Based.
On Estimation of Surface Soil Moisture from SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.
Biomass retrieval algorithm based on P-band BioSAR experiments of boreal forest Lars Ulander 1,2, Gustaf Sandberg 2, Maciej Soja 2 1 Swedish Defence Research.
DOCUMENT OVERVIEW Title: Fully Polarimetric Airborne SAR and ERS SAR Observations of Snow: Implications For Selection of ENVISAT ASAR Modes Journal: International.
SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.
Summer Colloquium on the Physics of Weather and Climate ADAPTATION OF A HYDROLOGICAL MODEL TO ROMANIAN PLAIN MARS (Monitoring Agriculture with Remote Sensing)
- Microwave Remote Sensing Group IGARSS 2011, July 23-29, Vancouver, Canada 1 M. Brogioni 1, S. Pettinato 1, E. Santi 1, S. Paloscia 1, P. Pampaloni 1,
An Application of Field Monitoring Data in Estimating Optimal Planting Dates of Cassava in Upper Paddy Field in Northeast Thailand Meeting Notes.
N. Ackermann - Investigations of TSX and CSK backscatter intensity and interferometric coherence over temperate forested areas - 1 Nicolas Ackermann Supervisor:
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High Resolution Snow Analysis for COSMO
Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.
Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery Nicolas Ackermann Supervisor: Prof. Christiane.
Monitoring Tropical forests with L-band radar: lessons from Indonesian Peat Swamps Matt Waldram, Sue Page, Kevin Tansey Geography Department.
1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.
Quality control of daily data on example of Central European series of air temperature, relative humidity and precipitation P. Štěpánek (1), P. Zahradníček.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
BIOPHYS: A Physically-based Algorithm for Inferring Continuous Fields of Vegetative Biophysical and Structural Parameters Forrest Hall 1, Fred Huemmrich.
IEEE IGARSS Vancouver, July 27, 2011 On the potential of TanDEM-X for the retrieval of agricultural crop parameters by single-pass PolInSAR Juan M. Lopez-Sanchez.
SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS Anca Popescu, Inge Gavat University Politehnica Bucharest (UPB)
FSU Jena – Department of Earth Observation CREATION OF LARGE AREA FOREST BIOMASS MAPS FOR NE CHINA USING ERS-1/2 TANDEM COHERENCE Oliver Cartus (1), Christiane.
Christian N. Koyama University of Cologne IGARSS 2011 Vancouver, July 26 Soil Moisture Retrieval Under Vegetation Using Dual Polarized PALSAR Data Christian.
Comparison of L and P band radar time series for the monitoring of Sahelian area P.-L. Frison, G. Mercier, E. Mougin, P. Hiernaux.
An Improved Global Snow Classification Dataset for Hydrologic Applications (Photo by Kenneth G. Libbrecht and Patricia Rasmussen) Glen E. Liston, CSU Matthew.
BioSAR 2010 – A SAR campaign in support to the BIOMASS mission
On Estimation of Soil Moisture with SAR Jiancheng Shi ICESS University of California, Santa Barbara.
N. Ackermann - Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery - 1 Nicolas Ackermann Supervisor:
SWOT Hydrology Workshop Ka-band Radar Scattering From Water and Layover Issues Delwyn Moller Ernesto Rodriguez Contributions from Daniel Esteban-Fernandez.
N. Ackermann - Biomass retrieval in temperate forested areas with a synergetic approach using SAR and Optical satellite imagery - 1 WSL Davos Colloquium.
SEA ICE CHARACTERISTICS IN THE SOUTHERN REGION OF OKHOTSK SEA OBSERVED BY X- AND L- BAND SAR Hiroyuki Wakabayashi (Nihon university) Shoji Sakai (Nihon.
0 Riparian Zone Health Project Agriculture and Agri-Food Canada Grant S. Wiseman, BS.c, MSc. World Congress of Agroforestry Nairobi, Kenya August 23-28,
How does InSAR work? Gareth Funning University of California, Riverside.
A Temporal Filtering Algorithm to Reconstruct Daily Albedo Series Based on GLASS Albedo product Nanfeng Liu 1,2, Qiang Liu 1,2, Lizhao Wang 2, Jianguang.
Classification Method Validation for Rice Mapping Using ENVISAT APS Data Erxue CHEN(1), Zengyuan LI(1), BingxiangTan(1) , Wei He(1), Bingbai LI(2) (1)Institute.
IGARSS-2011-Vancouver Temporal decorrelation analysis at P-band over tropical forest Sandrine Daniel, Pascale Dubois-Fernandez, Aurélien Arnaubec, Sébastien.
Time Dependent Mining- Induced Subsidence Measured by DInSAR Jessica M. Wempen 7/31/2014 Michael K. McCarter 1.
Detection of Wind Speed and Sea Ice Motion in the Marginal Ice Zone from RADARSAT-2 Images Alexander S. Komarov 1, Vladimir Zabeline 2, and David G. Barber.
Date of download: 6/22/2016 Copyright © 2016 SPIE. All rights reserved. Localization of the study area and monitored fields superimposed with a colored.
Using SAR Intensity and Coherence to Detect A Moorland Wildfire Scar.
2003 Tyrrhenian International Workshop on Remote Sensing INGV Digital Elevation Model of the Alban Hills (Central Italy) from ERS1-ERS2 SAR data Andrea.
(Srm) model application: SRM was developed by Martinec (1975) in small European basins. With the progress of satellite remote sensing of snow cover, SRM.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Layover Layover occurs when the incidence angle (  ) is smaller than the foreslope (  + ) i.e.,  <  +. i.e.,  <  +. This distortion cannot be corrected!
1 Thursday 04-Jun ASAR WORKSHOP 2011 Canadian Government Calibration Operations within the RADARSAT Program Assessment of Distributed Target Sites.
HSAF Soil Moisture Training
Active Microwave Remote Sensing
Estimationg rice growth parameters using X-band scatterometer data
Hiroshi Kimura Gifu University, Japan IGARSS 2011 Vancouver, Canada
(L, C and X) and Full-polarization
M. L. Williams1 and T. L. Ainsworth2
M. L. Williams1 and T. L. Ainsworth2
Hiroshi Kimura Gifu University, Japan IGARSS 2011 Vancouver, Canada
POLARIMETRIC OBSERVATION FOR RICE FIELD BY RADARSAT-2 AND ALOS/PALSAR
Forest / Non-forest (FNF) mapping for Viet Nam using PALSAR-2 time series images 2019/01/22 Truong Van Thinh – Master’s Program in Environmental.
Presentation transcript:

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 1 Nicolas Ackermann Supervisor: Prof. Christiane Schmullius Co-supervisors: Dr. Christian Thiel, Dr. Maurice Borgeaud Prague, the 3rd June 2011 Investigations of ALOS PALSAR backscatter intensity and interferometric coherence over Germany’s low mountain range forested areas EARSeL SIG Forestry workshop ENVILAND2 is sponsored by the Space Agency of the German Aerospace Center with federal funds of the German Federal Ministry of Economics and Technology on the basis of legislation by the German Parliament grant no. 50 EE EE 0847

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 2  Introduction  Test site & available data  Pre-processing & preliminary investigations  Analysis of the data  Conclusions Presentation outline

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 3  Context:  The monitoring of forested areas represents a great challenge in the context of the actual Global Warming.  The launch of ALOS PALSAR spaceborne system in January 2006 has been pioneer of new capabilities for the retrieval of forest biophysical parameters.  Objectives:  Investigate the ALOS PALSAR backscattter intensity and interferometric coherence.  Underline the scattering and decorrelation mechanisms occuring in the temperate forest.  Provide some new scientific knowledge for future researches. Introduction

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 4 Test site & available data

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 5 Test site  Thuringian Forest (Germany)  Surface: 110 km x 50 km  Terrain variations  Tree species composition Scots pines Norway Spruce European Beech  Weather conditions cool and rainy frequently clouded  Peculiarities logging for forest exploitation Kyrill storm (February 2007)

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 6 Test site Topography

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 7 Test site Forest understory

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 8 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 ( ) Photos with GPS coord. (2009) Weather data Field work Available Data (state May 2011)

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 9 Sensor Radar- Frequency BeamPolarisation Incident angle # Scenes available PALSARL-BandFBDHH, HV34°60 PALSARL-BandFBSHH34°51 PALSARL-BandPLRHH/HV, VV/VH21°13 ALOS PALSAR data 124 scenes Summer acquisitions Winter acquisitions PLR FBS FBD

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 10 Pre-processing & preliminary investigations

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 11 Topographic normalisation PALSAR PLR 21° R-HH, G-HV, B-HH Asc. 12apr09 Non normalised LiDAR DEM shaded relief High geometrical distorsions by steep incident angle. PALSAR FBD 34° R-HH, G-HV, B-HH Asc. 10jun10 Non normalised

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 12 Local incident angle Ground scattering area Topographic normalisation  Correction main components (Castel et al., 2001)

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 13  Optical crown depth (Castel et al., 2001) Volume scattering: a) Tilted surface facing the radar, b) flat surface, tilted surface opposite to the radar (Castel, 2001) Topographic normalisation

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 14 Sensor orientation : 350° Sensor azimuth angle : +90° 0° PALSAR 34° HV Asc. 06may08 Normalised PALSAR 34° HV Asc. 06may08 Non normalised Gamma nought [dB] Aspect [°] Slopes oriented in Radar flight direction High intensity for steep slopes facing radar. Topographic normalisation

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 15 Topographic normalisation PALSAR 34° HV Asc. 06may08 Normalised Gamma nought [dB] Slopes away from the radar Slopes facing the radar Aspect [°] Overcorrection? Crown optical depth? Other effects?

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 16 Topographic normalisation PALSAR 34° HV Asc. 06may08 Normalised PALSAR 34° HV Asc. 06may08 Normalised + Normalised with n coefficient Gamma nought [dB] Aspect [°]

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 17 Analysis of the data

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 18 Precipitation [mm] Temp [°] Wind [m/s] Series of PALSAR FBD, FBS, 34.6°, HH, Asc. Acquisition date Gamma nought [dB] frozen Snow + high Water equivalent frozen Weather - PALSAR intensity Temperature approaching 0 [°C] implies a decrease of the backscatter intensity

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 19 Weather - PALSAR coherence Precipitation map PALSAR Coherence HH 23jul09_X_ 07sept09 A precipitation event highly affects the degree of coherence

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 20 Urban Layover Precipitation [mm] Coherence Azimuth Weather - PALSAR coherence PALSAR FBD, 34.4°, HH, Asc.

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 21 Stem Volume - PALSAR intensity Investigations 1. Topography 2. Weather 3. Understorey 4. Forest inventory Stem volume [m 3 /ha] Gamma nought [dB] PALSAR 34° HV Asc. 10jun10 r 2 = 0.11 Weak negative correlation between PALSAR HV and stem volume

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 22 r 2 = 0.29 r 2 = 0.31 Stem Volume - PALSAR intensity Stem volume [m 3 /ha] Gamma nought [dB] Stem volume [m 3 /ha] PALSAR 34° HV Asc. 10jun10 Non normalised PALSAR 34° HV Asc. 10jun10 Normalised Daily Precip: 1.9 [mm] Hourly Precip: 0 [mm] Daily Precip: 1.9 [mm] Hourly Precip: 0 [mm] Slopes < 4° !

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 23 Stem Volume - PALSAR intensity Legend Red: Forest stands Blue: Field observations Yellow: Stem volume [m 3 /ha] Orthophoto A. Grass, No forest B. Young, dense C. Young, sparse D. Mature, dense E. Mature, sparse A. B. E. D. Forest typology Low stem volume High stem volume

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 24 Stem Volume - PALSAR intensity Observed values CoherencePALSAR HH PALSAR HVOrthophoto

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 25 CoherencePALSAR HH PALSAR HVOrthophoto Stem Volume - PALSAR intensity

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 26 Stem Volume - PALSAR intensity Stem volume [m 3 /ha] Scattering intensity PALSAR PLR, 21° Asc., 12apr09 r 2 Vol =0.008 r 2 Surf =0.09 r 2 Dbl =0.000 r 2 Vol =0.009 r 2 Surf =0.08 r 2 Dbl =0.005 Stem volume [m 3 /ha] PALSAR PLR, 21° Asc., 12apr09 Freeman DecompositionVan Zyl Decomposition Surface scattering seems to be the dominant scattering mechanism.

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 27  Coherence  The degree of coherence can be related to several factors, each expressing a specific source of decorrelation. Interferometric coherence The temporal decorrelation is related to the stability of the objects between the two acquisitions. The volume decorrelation is related to objects presenting a vertical extension. This factor is spatial baseline dependent.

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 28 ALOS PALSAR coherence A. B. C. PALSAR HH, 7sep oct09 intermediate coherence low coherence high coherence A.Forests: B. Crops: C. Urban:

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 29 ALOS PALSAR coherence Stem Volume [m 3 /ha] Interferometric Coherence R 2 S =0.19 R 2 B =0.038 R 2 P =0.044 R 2 S =0.10 R 2 B =0.009 R 2 P =0.009 R 2 S =0.22 R 2 B =0.012 R 2 P =0.06 R 2 S =0.35 R 2 B =0.12 R 2 P =0.08 R 2 S =0.36 R 2 B =0.15 R 2 P =0.088 R 2 B =0.002 R 2 S =0.29 Spatial baseline Precipitation: 23jul09: 28.6mm 07sept09: 3.9mm Legend Blue: Spruce (S) Yellow: Beech(B) Red: Pine(P) Increase of correlation with higher perpendicular baseline. Stem volume [m 3 /ha]

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 30 SANTORO et al., 2000; SANTORO, et al., 2002b Coherence „Stack“ Select and „fit“ Model Scatterplot + model Inverse model and retrieve Stem volume Estimated Growing stock Volume Compute RMSE and weights Weights Compute Growing Stock volume map Multitemporal GSV map 1., Forest inventory ALOS PALSAR coherence

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 31 -Similarly to spatial averaging, the multitemporal combination act as a filter and decreases the noise. -RMSEi>200 [m 3 /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 Multitemporal coherence biomass map RMSE>200 [m 3 /ha] Preliminary results! ALOS PALSAR coherence

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 32 Conclusions and outlook

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 33 Conclusions and outlook  Different investigations of the ALOS PALSAR backscattter intensity and interferometric coherence have been conducted.  Conclusions and open issues  The topography influence is not fully understood.  Weather conditions and particularly precipitations and temperature affect the backscatter intensity and interferometric coherence.  Interferometric coherence in L-band with 46 day temporal baseline shows a potential for estimating forest biomass.  Future work  Further investigations for the topographic normalisation  Explaination of the weak negative correlation between PALSAR and stem volume  Derivation of indices and ratios between X-band and L-band  Combination/Fusion with optical data

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 34 I would like to thank DLR, ESA, ASI and RESA for the distribution of the data ENVILAND2 is sponsored by the Space Agency of the German Aerospace Center with federal funds of the German Federal Ministry of Economics and Technology on the basis of legislation by the German Parliament grant no. 50 EE EE 0847 Acknowledgements

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 35 Thuringia Forest – July 2010 Thank you for your attention ! Questions ?

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 36  Weather data  DWD: Deutsche Wetter Dienst  Acquisition period:  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 output generation Hourly Regionalised [.txt ] Daily [ raster ] Hourly Input conversion Daily input conversion Hourly Regionalisation Daily Selected [.dat ] Hourly Selected [.dat ] Daily Regionalisation Daily Regionalised [.txt ] Hourly output generation Hourly [ raster ] JAMS Software 90m Station 1 Station 2 Station 5 Station 3 Station 4 Weather data

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 37  Weather parameters outputs  Raster data  90m spatial resolution  Excel table  Describe mean weather values of the overlapping selected forest stands and satellite data Temperature [°C] Precipitations [mm] High: 36 0 Table weather data (simplified version) SensorAcquisition-date Daily (4 days meteorological conditions) Hourly (4 hours meteorological conditions) Beam T°air_mean [°C] T°air_mi n [°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} TSX06/08/ :09:13spot_ TSX31/10/ :25:57spot_ …………………………………… TSX31/08/ :34:23stripNear_ Weather data Daily: 4 daysHourly: 4 hours Example Daily: 31mar apr08 High: 10 0

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 38 K-Nearest Neighbor (K-NN) Non parametric (data-based) Reference data: Forest inventory Assumption: stands with similar forest properties have also similar spectral characteristics Ponderation computed with the Euclidian or Mahalanobis Distance Lehtonen et al K-NN GSV retrieval

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 39 1 Scene 8 Scene RMSE [m 3 /ha] – FBD HV K Distance 50% stands Entire stands PALSAR, FBD, HV 25m -Leave-One Out Cross- Validation (LOOCV) at stand level. -RMSE decreases until reaching a specific K distance. -Best results with “Entire stands” und “8 scenes” (Multitemporal) Tree species Blue: Spruce Green: Beech Red: Pine K-NN GSV retrieval – PALSAR intensity Preliminary results!

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 40 PALSAR HV K-Nearest Neighbor (KNN) K-NN GSV retrieval – PALSAR intensity Preliminary results! Reference Growing Stock Volume (GSV)

N. Ackermann ALOS PALSAR – Backscatter intensity – Interferometric coherence – Forest – Biomass 41 Reference Growing Stock Volume (GSV) PALSAR HV K-Nearest Neighbor (KNN) Preliminary results! K-NN GSV retrieval – PALSAR intensity