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Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Upscaling carbon model predictions using very.

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Presentation on theme: "Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Upscaling carbon model predictions using very."— Presentation transcript:

1 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Upscaling carbon model predictions using very high resolution satellite imagery: evaluation of contextual approaches to land cover classification and crop identification P.C.S. Traoré, W.M. Bostick, A. Yoroté, J.W. Jones, M.D. Doumbia in partnership with :with funding from :

2 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Overview  The problem  A (slow) process  need for (flawed) time models  need for adjustments using (inaccurate) measurements  need for (problematic?) spatial surrogates  The options  Remote sensing data assimilation  Very high resolution: potential, challenges  Contextual classification & texture analysis  Methods  Quickbird imagery  Study site: Oumarbougou  Ground truthing  Digital image processing  Results (preliminary)  Contour tillage, biophysical parameters  Land cover classification, crop identification  Next steps…

3 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 The problem

4 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Challenges of quantifying C sequestration  Long integration time – requires models (e.g. DSSAT-CENTURY), which are not perfect and need both internal improvement and external adjustment to correct deviations over time  Uncertainty of point estimates - High model and measurement errors relative to annual changes in soil C (standard deviation of 0.058 to 0.21% on a mass basis)  potential of data assimilation techniques (e.g., Kalman Filtering)  Scale - On the order of 10,000 ha (100 km 2 ) of cropland may be needed to sequester a tradable amount of C  potential of remote sensing techniques (e.g., QuickBird)

5 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Specificity of sudano-sahelian agricultural systems  Local ecotypes are different (photoperiod sensitivity, biomass partitioning, …)  Agricultural intensification  lower harvest indices, cash crops (cotton), … Dr. Hoogenboom (2m) x 2 x 3

6 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 The options

7 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Ensemble Kalman Filter (EnKF)  Data assimilation (or optimal control): deals with the inclusion of measured data into numerical models for the forecasting or analysis of the behavior of a system  EnKF: combination of model estimates and measurements from multiple sources (e. g. remote sensing or direct measurements) to estimate system states and parameters in an optimal way  takes into account uncertainty of model estimates and measurements and provides estimates of uncertainty of filtered results  can be used for both spatial and temporal modeling applications

8 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 time X prediction step t update step EnKF Z t+1,  z t+1 EnKF for one state variable X with std. dev.  Monte Carlo methods used to initialize ensemble of equally- likely initial conditions. initialization step Model is propagated forward in time with each realization of the ensemble. When state variable measurement occurs EnKF updates model state variables and parameters and measures of uncertainty thereof.

9 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 RS data assimilation in process based models  Process scales vs. spatial resolutions : from weather / climate to hydrological to crop modeling  Different information available from optical, thermal, microwave observations but…  … always a need to resolve ambiguities in the remote sensing signal : empirical vs. mechanistic approaches (VIs, SAIL/DART, SVATS…)  Different approaches to data assimilation (Moulin & al., 1998)

10 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Model forcing (reproduced from Moulin et al., 1998)

11 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Model re-initialization / re-parameterization (a) (reproduced from Moulin et al., 1998)

12 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Model re-initialization / re-parameterization (b) (reproduced from Moulin et al., 1998)

13 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Very high resolution imagery  Potential: sub-meter pixel sizes open unprecedented perspectives for land cover studies in patchy, heterogenous landscapes, such as the sudano-sahelian zone…  … but: [more spatial detail = lower spectral dimensionality]  1. decrease in performance of pixel-by-pixel classifiers  2. more emphasis should be put on higher level image primitives (segments, regions as opposed to pixels) = contextual operators The spatial context  familiar process for photo-interpreters (intuitive)  Feature extraction (e.g. geology, glaciology,…), pattern recognition (e.g., forestry)  Again, applications function of sensor resolution  Increasingly employed for agricultural applications

14 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004  1 st, 2 d, 3 d order measures – diversity, variance, etc.  Grey Level Co-occurrence Matrix (GLCM) = probability of occurrence of couple of values in 2 neighboring pixels  Measures of contrast: contrast, (dis)similarity, homogeneity (IDM)  Measures of orderliness: energy (ASM), entropy  Descriptive statistics: mean, variance, correlation Measures of texture

15 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 The methods

16 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 QuickBird imagery: specifications (excerpts from www.digitalglobe.com)

17 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 RS data assimilation methodology D D ATA Measurement Soil Sampling Biomass Soil C Weather Management Soil Properties Parameters Biomass Measured Soil C Measured Soil C Simulated Optimized Soil-C Estimation Optimized Biomass Estimation Optimized Parameter Estimation M M ODEL D D ATA A A SSIMILATION ENSEMBLE KALMAN FILTER Biomass Simulated DSSAT -CENTURY Crop/Soil C Model

18 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Study site: Oumarbougou

19 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Processing chain 3. Ground truthing for land cover classification, crop & ridge tillage identification 1. GCPs for improved locational accuracy 2. LAI & biomass sampling for inversion of remote sensing signal 4. Digital image processing (LC, texture) + inversion of remote sensing signal 5. Assimilation of remote sensing for model re- initialization / re-calibration

20 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Results (preliminary)

21 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Resolution: the proof (panchromatic)

22 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 False color composite (R4G2B1)

23 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 ‘Natural’ color composite (R3G2B1)

24 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Ridge tillage detection – preliminary results ridges (‘ados’)  87% of proposed ridge tillage fields confirmed by survey  7% of total actual ridge tillage fields missed  Real potential for simple operational detection method

25 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 (raw) NDVI=  NIR -  VIS /(  NIR +  VIS )

26 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 LAI=f(NDVI) – preliminary results millety = 7.4x - 1.6 R 2 = 0.78 0 1 2 3 4 0.30.40.50.60.7 NDVI LAI 0 1 2 3 4 5 0.30.40.50.60.7 NDVI cottony = 9.7x - 2.9 R 2 = 0.65 LAI 0 0.5 1 1.5 2 2.5 0.40.450.50.550.6 LAI NDVI maizey = 7.8x - 2.1 R 2 = 0.71 0 1 2 3 0.20.30.40.50.6 LAI sorghumy = 5.2x - 0.5 R 2 = 0.54 NDVI

27 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Land cover classification (spectral only)  Spectral information taken alone in pixel-by-pixel classifiers cannot separate agricultural fields from wild vegetation when crop stands are established, and cannot discriminate amongst crops

28 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Land cover classification (spectral only)

29 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 NDVI sub-plots (~34x34m) Cotton Maize Millet Sorghum Wild vegetation

30 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Panchromatic sub-plots (~34x34m) Cotton Maize Millet Sorghum Wild vegetation

31 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 7x7 variance sub-plots (~34x34m) Cotton Maize Millet Sorghum Wild vegetation

32 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 7x7 variance image (1 st order)

33 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Textural analysis on stratified sample

34 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Textural analysis on stratified sample

35 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Panchromatic sub-plots (~34x34m) Cotton Maize Millet Sorghum Wild vegetation

36 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Next steps…

37 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Next steps  Explore the potential of directional operators and variogram modeling for enhancing separability between cotton and cereals  Increase the sample size for texture subsets to improve statistical significance of calculated separability signatures  Overcome file size limitations to generate texture images over the entire study area as inputs to Bayesian / maximum likelihood classifiers

38 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-SANREM/SM-CRSP, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Conclusions  QuickBird can detect ridge tillage management practices with high accuracy (panchromatic)  QuickBird can estimate within-field LAI with high accuracy, but this can be improved by quantifying texture (a surrogate of canopy structure)  A single QuickBird scene on any date can separate natural vegetation from cropland with high accuracy (contextual)  A single QuickBird scene during the growing season can discriminate cotton and cereals with reasonable accuracy (contextual) – additional work is needed though  A single QuickBird scene cannot distinguish cereals from one another : multitemporal imagery would be required  All remote sensing estimates need to be characterized in error terms for data assimilation purposes (LAI in particular : temporal uncertainty)

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