Forest Applications. Chris Schmullius, Oliver Cartus, Maurizio Santoro. 5 September 2007, D3PB

Transcription

1 Forest Applications Chris Schmullius, Oliver Cartus, Maurizio Santoro 5 September 2007, D3PB

2 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 2

3 Einführung mit C/X-Äthna-Beispielen MFFU Sommerschule September 2007 D3PB-2 Forest practicals Christiane Schmullius 3

4 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 4

5 Volume scattering 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 5

6 Spaceborne SARs Satellite Agency Frequency - Polarisation Resolution - Swath JERS JAXA L-HH 25m 100 km ERS-1 ERS ESA C - VV 25 m 100 km RADARSAT CSA C - HH m km ENVISAT - ASAR ALOS - PALSAR 2002 ESA C - HH/VV/HV m km 2006 JAXA L - Polarimetric m km Special 35 incidence Interferometry ( ERS-1/2) Multi-incidence Multi-incidence Multi-incidence 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 6

7 ENVISAT ASAR ENVISAT ENVISAT ASAR ASAR Operation Operation Modes Modes Wave VV or HH < 10m resolution 5 x 5 km to 10 x 5 km vignettes Global Monitoring VV or HH 1000m resolution 405 km swath width Wide Swath VV or HH 150m resolution 405 km swath width Image VV or HH < 30m resolution up to 100 km swath Alternating Polarisation VV/HH or VV/VH or HH/HV 30m resolution up to 100 km swath 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 7

8 Repeat-Pass MFFU Sommerschule September 2007 D3PB-2 Forest practicals Christiane Schmullius 8

9 Phases of the single measurements Phase C-VV Phase C-VV 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 9

10 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 10

11 ERS-1/2 tandem coherence 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 11

12 Coherence ASAR-IMS HH April/Mai Thüringen April/August Thüringen 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 12

13 Weather effects Strong decorrelation occurs with rainfall 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 13

14 Temporal Decorrelation ERS tandem (1 day) ERS long-term (35 days) [Strozzi, InSAR Sommerschule 2002] 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 14

15 JERS-1 repeat pass interferometry 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 15

16 ALOS Palsar Repeat-pass: 01 Jan Feb. 2007, Bn ~1,5 km 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 16

17 Classification Source Data ERS intensity JERS intensity ERS Tandem Coherence Small dynamic range Variable response to water Variable response to open areas Can be used as indicator of environmental effects effecting the coherence Medium dynamic range Stable response to water Possible to identify agricultural fields Higher frame to frame variations Higher contrast between forest/non forest Higher sensitivity to forest volume Confusion between water and dense forest Frame to frame variations 4 (L. September Eriksson) 2007 D3PB-2 Forest practicals Christiane Schmullius 17

18 Model definition for coherence γ v Vγ () v γ + ( γ γ ) e = 0 γ γ 75 γ = a + b 0 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 0 = coherence at v = 0 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, 2003 v v 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 18

19 Classification chain ERS coherence image JERS intensity image Use model to calculate class means Maximum Likelihood Classifier Iterated Contextual Probability Classifier (ICP) 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 19

20 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 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 20

21 Coherence modelling Interferometric Water Cloud Model (IWCM) γ for = γ gr σ σ o gr o for e βv + γ veg σ σ o veg o for 1 e 1 e βv αh α α jω ( jωh αh e e ) The total forest coherence is a sum of 2 contributions: Ground coherence, Γ gr Vegetation coherence, Γ veg Model considers tree attenuation (α), gaps (β), InSAR geometry (ω) Empirical relationship γ for βv = γ gr e + γ veg 1 ( βv e ) No dependence upon InSAR geometry, forest backscatter and canopy structure 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 21

22 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 = September 2007 D3PB-2 Forest practicals Christiane Schmullius 22

23 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 60 % 45 % 30 % Local survey 15 % 1) weather conditions, 2) inventory unit Santoro et al., RSE, September 2007 D3PB-2 Forest practicals Christiane Schmullius 23

24 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 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 24

25 Forest structure / Quality of inventory data r = RS > 50 % r = Area > 3 ha r = RS > 30 % Area > 3 ha r = Failed update of inventory data (Santoro et al. 2007) 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 25

26 Stem Density [/ha] Chunsky and Bolshe DBH [cm] Stem Density [/ha] log r= r= ln(n)=-1.621*ln(dbh) DBH [cm] log 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 26

27 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: m Largest area yet mapped with SAR techniques 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 27

28 Chinese test sites High RS forests 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 28

29 What is VCF? Training of IWCM using VCF The Modis Vegetation Continuous Field product (VCF) provides global sub-pixel estimates of landscape components (tree cover, herbaceous cover and bare cover) at 500 m pixel size (Hanson et al. 2002). Why is VCF important in this context? Because coherence and VCF contain similar information 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 29

30 Regression vs. VCF Dashed line- regression Solid line - VCF 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 30

31 Classes according to SIBERIA map: 0-20,20-50,50-80,>80 m^3/ha 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 31

32 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 32

33 Overall acuracy: 92.1% Overall acuracy: 89.7% 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 33

34 Multitemporal observation using ASAR Wide Swath (Courtesy of Maurizio Santoro, 2007) Modeling Inversion A multi-temporal combination of single estimates with weights determined by the backscatter contrast σ 0 veg - σ0 gr allows obtaining the final estimate 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 34

35 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 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 35

36 WS-based Forest inventory LPJ simulation 4 September 2007 D3PB-2 Forest practicals Christiane Schmullius 36