1. 1 Has EO found its customers? GLC 2000 Workshop ‘Methods’ Objectives F. Achard Global Vegetation Monitoring Unit

2. 2 Has EO found its customers? Contents of the presentation Background  Specifications of the GLC-2000 exercise  Strategy for the analysis methodology Methods Requirements  Categories of methods  Review of existing methods  Priorities for methodological development using S1 products Specific objectives of the Workshop Global Vegetation Monitoring Unit

3. 3 Has EO found its customers? Specifications of the GLC 2000 exercise ! Geographical extent: World by sub-windows (around 30 regions) ! Data: S1 daily global SPOT VGT composites at 1 km resolution in Plate Carree projection from 1 st Nov. 1999 to 31 st Dec. 2000 ! The target completion date for the GLC product is early 2002 ! Classification scheme (legend to be used) has been selected: derived from LCCS with a minimum set of classifiers ! Minimum mapping unit --> digital classification at single pixel level ! Open issues: ! assembling the sub-window classification together ! validation (a combined IGBP / TREES approach ?) Global Vegetation Monitoring Unit

4. 4 Has EO found its customers? Strategy for the analysis methodology Premises  the initiative does not require a prescribed data processing methodology  It must however avoid inconsistencies in the resulting global map Proposed strategy Each participant will be free to develop the methodology which best suits the (ecological) conditions of his region, under the following conditions:  the methodology must take into account the GLC 2000 specifications  the method used must be fully documented  the performance of the method will have to be quantified

5. 5 Has EO found its customers? Introduction to the use of VGT S1 data Main question How to use VEGETATION S-1 products for mapping land cover at global level with a distributed approach at continental or regional/national level ? Source of information ! Spectral signatures ! Temporal spectral signatures ! Temporal spectral - angular signatures Global Vegetation Monitoring Unit

6. 6 Has EO found its customers? Characteristics of S1 products Pixels are re-sampled onto a 1 km resolution grid : absolute location < 0.8 km The daily synthesis (S1) is computed from the different passes (P products) of one day on each location. Criteria for synthesis: ð Not a blind or interpolated pixel ð Not flagged as cloudy in the status map ð Highest value of Top of Atmosphere NDVI For each pixel is computed: ð Ground surface reflectances with atmospheric correction performed from P data and using the SMAC procedure and NDVI ð Geometric viewing conditions ð Date and time of selected measurement References of all corrections applied for calibration, atmospheric correction and geometric processing are produced Global Vegetation Monitoring Unit

7. 7 Has EO found its customers? Categories of analysis methods Pre-processing procedures  Geometric corrections  Radiometric corrections :  Atmospheric  Residual contamination  Bi-directional corrections  Use of BRDF models to retrieve complementary parameters  Temporal compositing  Derivation of dedicated spectral indices Classification procedures  Supervised  Unsupervised

8. 8 Has EO found its customers? Priorities for methodological development using VGT S1 products Pre-processing procedures Temporal compositing  Automatic removal of drifted images  Further work on compositing to produce optimized ‘seasonal’ mosaics  Use of dedicated spectral indices Radiometric corrections:  Atmospheric :  already implemented in S1 (SMAC)  more to do for pixel-specific atmospheric contamination ?  Residual contamination  Use of BRDF models for:  Retrieving inversion or complementary parameters  Bi-directional normalization

9. 9 Has EO found its customers? Priorities for methodological development using VGT S1 products Use of BRDF model for radiometric ‘corrections’ Premise: combining spectral and angular dimensions through a BRDF model should allow to improve the land cover classification Two possible options to use BRDF models: 1. To retrieve BRDF model parameters by inversion of the model using multi- angular observations ð Can multi-angular observations be obtained from multi temporal data during ‘stable’ vegetative periods ? ð what is feasible from S1 products ? 2. To normalize the data to a standard viewing geometry ð Is a simple land cover map available everywhere ? IGBP LC map ? ð Deriving the coefficients of the functions from the data itself ? Global Vegetation Monitoring Unit

10. 10 Has EO found its customers? Requirements for classification algorithms Accuracy Reproductibility by others Robustness (not sensitive to small changes in input data) Ability to fully exploit the information content of the data Applicability uniformly over the whole domain Objectiveness (not dependent of the analyst’s decisions)

11. 11 Has EO found its customers? Priorities for methodological development using VGT S1 products Classification procedures Adding ancillary type of data to the spectral values (ecological stratification, land cover or topographic maps) Use of spatial measures such as texture, patterns, shape and context Minimize the role of the analyst/interpreter by preparing specific biophysical products: permanence of green biomass, LAI, leaf longevity Post-classification procedure: assembling the results together (sticking the eco- regions or windows)

12. 12 Has EO found its customers? Specific objectives of the Workshop Main purpose of the presentations:  to review existing methods applicable to VEGETATION S1 products.  to explain the actual availability of the developed procedures for GLC 2000  to indicate your willingness to support the methodological developments through the WG discussions Discussions should focus on:  Optimal methodology (ies) or set of procedures for each main region  A minimum set of guidelines How to organise a forum for discussion after the meeting ?

13. 13 Has EO found its customers? Review of pre-processing procedures using AVHRR Temporal compositing  The objective should be to produce a cloud-free composite image which has radiometric properties of a single-date, fixed geometry image  Often based on Maximum NDVI : de facto standard but main drawback is to select pixels with forward-scattering geometry  Need of further radiometric correction methods to remove ‘noise’ in composites Radiometric corrections  Atmospheric: nominal/climatic parameters are used  Residual contamination: use of temporal dimension such as NDVI temporal trajectory  Bi-directional correction is a complex issue :  a) Inversion is practically impossible because requires # viewing geometries  b) correct the data to a standard viewing geometry: requires knowledge of model to apply, ie land-cover is a pre-requisite

14. 14 Has EO found its customers? Review of classification procedures using coarse resolution data Supervised  Preferable when one knows where desired classes occur  condition: a priori knowledge of all cover types is requested  Variants: decision trees, neural networks, fuzzy classification, mixture modeling Unsupervised  Preferable over large areas where distribution of classes is not known a priori  Advantage: comprehensive information on the spectrally pure clusters  Disadvantages:  Effect of controlled parameters (number of clusters, dispersion around mean)  Potential mismatch between spectral clusters and thematic classes  Use of a large number of initial clusters (100-400) to mitigate these problems  Independent ground information is also required but representativeness is less crucial because clusters are homogeneous  Variants: progressive generalization, enhancement, post-processing adjustments