Forest Applications. Christiana Schmullius. 2 July 2009

Transcription

1 Forest Applications Christiana Schmullius 2 July 29

2 Contents Motivation Need for Biomass Mapping Biomass Components Physical Background Wavelength Polarisation Coherence Mapping Results Siberia: Coherence Regression Model China: Coherence Interferometric Water Cloud Model Canada: Hyper-temporal Radar Regression Model Polarimetry SRTM/X-SAR

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4 Einführung mit C/X-Äthna-Beispielen MFFU Sommerschule 2

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6 Volume scattering

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8 Scattering Profile (pine): Most scattering comes from upper canopy (X-band) if there are no gaps.

9 AIRSAR (NASA/JPL) polarimetric C-, L- and P-Band with Incidence Angles of 4 and 5 only true for mono-temporal acquisitions! IMHOFF 1995: 514

10 JERS (good contrast forest/open areas) ERS (almost no contrast)

11 IS2 IS7 ASAR AP, Siberia

12 Improvement: Multi-temporal combination ERS Tandem Coherence RMSE: 1 m 3 /ha Relative RMSE: 7 % JERS Backscatter RMSE: 33 m 3 /ha, Relative RMSE: 22 % Santoro et al., RSE, 22

13 Dual-Pass vs. Single-Pass interferometry [MFFU Sommerschule 2]

14 Phase Representation Phase is always ambiguous w.r.t. integer multiples of 2π phase unwrapping required! pictorial representation of phase: grey value color wheel [MFFU Sommerschule 2]

15 Interferometric phase Bachu, China approx. 1 km 8 km [Bamler, R., InSAR Sommerschule 22]

16 Coherence Examples γ =,28 γ =, 5 γ =, 65 γ =, 82 [MFFU Sommerschule 2]

17 Co-registered Forest-GIS Polygons

18 Model definition for coherence γ v Vγ () v γ + ( γ γ ) e = γ γ 75 γ = a + b γ γ γ 75 γ ( v) Vγ ( a + ( b 1 γ ) e = γ 75 + γ γ ) ( ) e γ ( v) = γ γ v= growing stock volume γ = coherence at v = m 3 /ha (non-forest) γ = coherence for asymptotic values of v (corresponding to dense forest) γ 75 = value where the coherence distribution reach 75% of the maximum value (see fig.) 75 V γ = characteristic v value where the exponential function has decreased by e -1 Wagner et al., RSE, 23 v v

19 Classification chain ERS coherence image JERS intensity image Use model to calculate class means Maximum Likelihood Classifier Iterated Contextual Probability Classifier (ICP)

20 Mapping Results Siberia Separability of classes

21 Automatic Adaptive Consistent Validated

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24 Forest DRAGON Project Areas

25 Backscatter model A B C D Water cloud Water cloud with gaps A water cloud with gaps is closer to reality and easy to handle σ o for o βv o = σgre + σveg 1 ( βv e ) Ground backscatter Forest transmissivity Vegetation backscatter Forest transmissivity is related to canopy closure and tree attenuation

26 Interferometric Water Cloud Model Forest coherence is the sum of ground coherence temporal decorrelation canopy coherence temporal and volume decorrelation γ for σ gr σ βv ( V ) = γ gr e + γ veg σ σ for veg for βv ( 1 e )* γ ( h, Bn, α ) vol Ground contribution Vegetation contribution σ o for o βv o = σ gre + σ veg 1 βv ( e ) γ gr and σ gr represent ground temporal coherence and backscatter γ veg and σ veg represent vegetation temporal coherence and backscatter β is related to the forest transmissivity (~ for ERS) Volume decorrelation related to h, Height allometric equation to express it as a function of stem volume B n, perpendicular baseline α, two-way tree attenuation 1 2 db/m depending on season (Askne et al. 1997)

27 Stem volume retrieval procedure 1. Model training 2. Inversion using a test set Retrieval statistics RMSE = 65.6 m 3 /ha Relative RMSE = 29 % R 2 =.59

28 2. Testing Invert the model using backscatter and/or coherence values to estimate stem volume. Error 1. ERS backscatter does not provide any information 2. JERS backscatter provides rather good results Dry-unfrozen conditions Winter-frozen conditions / weather changes 3. ERS tandem coherence provides best results Accuracy depends on 6 % 45 % 3 % Local survey 15 % 1) weather conditions, 2) inventory unit Santoro et al., RSE, 22

29 Temporal and spatial consistency of the coherence How much does the coherence depend upon time and space? Frozen weather conditions Properties of winter coherence: highest ground coherence, highest sensitivity to stem volume Best conditions for retrieval! Why are there different trends? What about the spread? External source: different environmental conditions Intrinsic source: forest stand structure (= homogeneity) Ground data accuracy

30 Forest structure / Quality of inventory data r = r = RS > 5 % Area > 3 ha r = RS > 3 % Area > 3 ha r = Failed update of inventory data (Santoro et al. 27)

31 Stem Density [/ha] Chunsky and Bolshe DBH [cm] Stem Density [/ha] log r=-.993 r=-.989 ln(n)=-1.621*ln(dbh) DBH [cm] log

32 ERS-1/2 Dataset Problem: How to train a semi-empirical model for 223 ERS-1/2 images without Mosaic Ground-truth data R: Coherence G: Sigma nought (ERS-1) B: Sigma nought ratio 223 Coherence images (acquired in all seasons) Baselines: - 4 m Largest area yet mapped with SAR techniques

33 Chinese test sites High RS forests

34 Training of IWCM using VCF What is VCF? The Modis Vegetation Continuous Field product (VCF) provides global sub-pixel estimates of landscape components (tree cover, herbaceous cover and bare cover) at 5 m pixel size (Hanson et al. 22). Why is VCF important in this context? Because coherence and VCF contain similar information

35 Regression vs. VCF Dashed line- regression Solid line - VCF

36 Temporal decorrelation

37 Forest transmissivity β β [ha/m 3 ] Dec Jan Jan 1-2 Jan 9-1 Oct Sep Sep Oct May25-26 Sep May Regression-based estimation of all 5 unknowns.

38 Regression- vs. VCF-based model training Coherence 1.5 Chunsky N 29-3 Dec. 95 Intensity [db] -5-1 Coherence 1.5 Bolshe NE Sep. 97 Intensity [db] -5-1 Dashed line- regression Solid line - VCF Stem volume [m 3 /ha] Chunsky N Jan Stem volume [m 3 /ha] Stem volume [m 3 /ha] Bolshe NE Oct Stem volume [m 3 /ha] Coherence.5 Intensity [db] -5-1 Coherence.5 Intensity [db] Stem volume [m 3 /ha] Chunsky E Jan Stem volume [m 3 /ha] Stem volume [m 3 /ha] Bolshe NE Sep Stem volume [m 3 /ha] Coherence.5 Intensity [db] -5-1 Coherence.5 Intensity [db] Stem volume [m 3 /ha] Bolshe NE 1-2 Jan Stem volume [m 3 /ha] Stem volume [m 3 /ha] Bolshe NW Sep Stem volume [m 3 /ha] Coherence.5 Intensity [db] -5-1 Coherence.5 Intensity [db] Stem volume [m 3 /ha] Primorsky E 9-1 Oct Stem volume [m 3 /ha] Stem volume [m 3 /ha] Bolshe NW May Stem volume [m 3 /ha] Coherence.5 Intensity [db] -5-1 Coherence.5 Intensity [db] Stem volume [m 3 /ha] Stem volume [m 3 /ha] Stem volume [m 3 /ha] Stem volume [m 3 /ha]

39 Variability of coherence within frames Variability of ground coherence Variability of dense canopies Sandy soils, Peat soils

40 Variability of coherence within frames 1 γ gr & γ veg -6 σ gr & σ veg [db] Training for the whole frame VCF Training.5 VCF Training FID Training FID Training 1 γ gr & γ veg -6 σ gr & σ veg Restricted VCF Training.5 VCF Training FID Training FID Training

41 Classification accuracy Classes according to the SIBERIA map: 1-2,2-5,5-8,>8 m^3/ha Test site & image κ All Unfrozen Frozen SD Accuracy [%] >8 [m 3 /ha] SD Overall Acc. [%] kappa Chunsky N 29-3 Dec Chunsky E Jan Bolshe NE Sep Green: VCF-based training Red: Regression-based training Bolshe NW Sep

42 Forest Map of Northeast China ESA DRAGON Project and ERS-coherence

43 ERS-1/2 tandem dataset for South China ~ 5 ERS-1/2 image pairs with baseline < 4 m and high quality One main acquisition phase: November 1995 June 1996 (descending) No further large-scale acquisition after 1996 as for Northeast China Almost full coverage, some multi-temporal tracks (up to 4 coherence images) Lijiang

44 Bn [m] South China baseline and temporal decorrelation pre-analysis South China - ERS-1/2 tandem Perpendicular baseline (B n ) Long (at the limit) in December - January Short (optimal) in February May Temporal decorrelation = coherence contrast High contrast until beginning of May In May different results (rain season starts) 2-3 Mar Jan Mar 96

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46 China Growing Stock Volume Map Courtesy ESA DRAGON Project (Cartus, Santoro)

47 (Frank De Grandi & Ake Rosenqvist)

48 ALOS-PALSAR DATA 1. L band SAR, HH, VV, HV and full polarimetry, multiple incidence Resolution: 25 m, 15 m Swath width: 1 km, 4 km Temporal coverage: 46 days nominal 2. Observation over N. Eurasia: consistent coverage in the frame of the Kyoto and Carbon Initiative Ake Rosenqvist <ake.rosenqvist@jrc.it> 3. Data: available from 4. Products for N. Eurasia from the K&C initiative. Land Use, Biomass Wetland, ARD, Freeze/thaw Contact: Ake.Rosenqvist@jrc.it

49 Chunsky North Regression Analysis Stem volume vs. Coherence (5feb28-22mar28) 12.5 m data Stem volume vs. backscatter (HV) (5aug27) 12.5 m data Very significant correlation between SAR data and stem volume!

50 Chunsky North Biomass map m³/ha 3 m³/ha > 3 m³/ha N N IWC Model Results Good values for RMSE and R² at polygon level! Resolution 12,5 m. Be aware of the RMSE of the ground data

51 Courtesy ESA GSE FM, C.+C.Thiel

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53 Current research: ENVISAT ASAR Global Mode Courtesy A. Bartsch, IPF, TU Wien

54 ENVISAT ASAR Wide Swath dataset During 23 and 24 ENVISAT ASAR data in Wide Swath mode has been acquired over the study area of the SIBERIA-II Project Several hundred ASAR scenes have been acquired, with a high degree of overlap between neighboring tracks The point was imaged 97 times during 23-24

55 Growing stock volume maps WS-based Forest inventory

56 Multitemporal observation using ASAR Wide Swath Santoro, 27) (Courtesy of Maurizio Modeling Inversion A multi-temporal combination of single estimates with weights determined by the backscatter contrast σ veg - σ gr allows obtaining the final estimate

57 Single-image Multi-temporal (29 images) Inventory From a single image it is possible to identify sparse/dense forest patterns at most From multi-temporal combination it is possible to identify biomass levels

58 Chunsky, Siberia Inventory data 1m 15m 2m 25m VCF tree cover [%] VCF=( e+6*Volume).21 VCF tree cover [%] VCF=(3458*Volume).24 VCF tree cover [%] VCF=(677.4*Volume).26 VCF tree cover [%] VCF=( *Volume).25 1 R 2 = R 2 = R 2 = R 2 = Stem volume [m 3 /ha] Stem volume [m 3 /ha] Stem volume [m 3 /ha] Stem volume [m 3 /ha] Courtesy ESA BIOMASAR Project (Cartus, Santoro)

59 Canada Growing Stock Volume Map Landsat IR-Composite ENVISAT WideSwath (hyper-temp.) GSV map

60 Spaceborne SARs Satellite Agency Frequency - Polarisation Resolution - Swath JERS JAXA L-HH 25m 1 km ERS-1 ERS ESA C - VV 25 m 1 km RADARSAT CSA C - HH 1-1 m 45-5 km ENVISAT - ASAR ALOS - PALSAR 22 ESA C - HH/VV/HV 25-1 m 5-5 km 26 JAXA L - Polarimetric 1-1 m 1-35 TerraSAR-X 26 -> DLR X - Polarimetric km 1 16 m 5 1 km Special 35 incidence Interferometry ( ERS-1/2) Multi-incidence Multi-incidence Multi-incidence Multi-incidence and SRTM/X-SAR

61 Polarimetry Pauli Decomposition with ALOS Data: S HH + S VV S HH S VV 2S HV Surface Scattering Double Bounce Volume Scattering

62 Backscatter Mechanisms in a Forest Canopy LE TOAN et al. 21: 4

63 Trennbarkeitsanalyse (Jeffries Matusita) Slide 16/17 Freeman Dekomposition Forest Clear Cut Forest Burn Scar Clear Cut Burn Scar HV,747,571,27 HV tn,767,559,31 Entropy,789,57,431 Alpha,794,51,464 Freeman P VOL,953,812,457 Freeman P DBL,53,39,257 Freeman P ODD,511,318,284 2, 1,8 1,6 1,4 1,2 1,,8,6,4,2, 1 Forest 2 Clear Cut 3 Burn Scar Freeman P VOL

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65 ERS-1/2 tandem coherence

66 Coherence ASAR-IMS HH April/Mai Thüringen April/August Thüringen

67 Weather effects Strong decorrelation occurs with rainfall

68 Temporal Decorrelation ERS tandem (1 day) ERS long-term (35 days) [Strozzi, InSAR Sommerschule 22]

69 Major Conclusions The new VCF-based classification approach is a fast and easy method to map forest stem volume. Algorithm was successfully applied to boreal, temperate and subtropical environments. Open issues due to unavailability of ground truth: 1) Assumed low accuracy of intermediate classes (2-5,5-8 m 3 /ha) based on Siberian field data. 2) Siberian boreal forest Chinese cold-temperate forests: Are there differences in coherence?