Landcover Dynamics in the Niger Inland Delta (Mali)

Transcription

1 Fakultät Forst-, Geo- Hydrowissenschaften Institut f. Photogrammetrie & Fernerkundung, Professur Fernerkundung Landcover Dynamics in the Niger Inland Delta (Mali) A Classification Approach for MERIS Data Ralf Seiler, Elmar Csaplovics Frascati, 29 th September 2005

2 Project Frame study long-time changes in land cover & land use for wetlands in semi-arid environment analysed period : from the late 60 s s til present used EO Data: optical Sensors and Imaging Radar objectives using MERIS Data test the capability of MERIS full resolution Data to analyse sparse, mostly non-green vegetation cover at regional scale Classify the main object types (soils, vegetation type, water) use the high repetition rate for change detection purposes

3 Niger Inland Delta region of interest N 4 40 W N 3 26 W central part of the Inland Delta & Plateau du Bandiagara

4 Biome - Characteristics periodical changes seasonal: rainy / dry seasonal: flooding / drainage longtime variation in precipitation red indicated months acq. dates

5 Vegetation Layers landscape, central Delta mid dry season

6 Data Preprocessing for multi-temporal temporal analysis a unique reference is needed done with the provided raster of GCP s & orthorectification-tool of BEAM TOA radiances have to be converted to reflectance values => BOA reflectance values to minimise atmospheric effects (SMAC-processor of BEAM) to reduce amount of data, new uncorrelated bands have been calculated with a PCA

7 Geometric Reference absolute MERIS Level 1B Data have been orthorectified with auxiliary GCP s gtopo30 BEAM s s orthorectification tool Topographic Maps 1: from IGN, France serve as Reference

8 Geometric Reference relative comparison of two scenes june 2003 (left) april 2003 (rigth)

9 workflow eigenvalues - mean ( 5 datasets aug june 2003 ) pc % of total cumulative variance 88,73% 88,73% 8,22% 96,95% 2,52% 99,47% 0,32% 99,79% 0,12% 99,91% 0,05% 99,96% 0,02% 99,98% 0,01% 99,99% 0,00% 99,99% 0,00% 99,99% 0,00% 100,00% 0,00% 100,00% 0,00% 100,00% 0,00% 100,00%

10 Extracting Endmember for Classification mixture tuned matched filtering for each endmember: mf-score and infeasability value => endmember spectra

11 Separating the main object classes using MGVI MGVI provides information about fapar and flags for water surface bright surface clouds / snow / ice bad data fapar, MERIS full resolution

12 bright surface flag of MGVI was used to mask out every pixel that is not non-vegetated Classification of soil types ISODATA classification with 4, 8, 12 a priori classes soil classification MERIS Data

13 Classification of Vegetation on it s temporal behaviour Concept: -MGVI provides estimation of fapar -during one growing season fapar follows specific vegetation cycles

14 Dynamics of Open Water Areas Applying the MGVI water bodies according to MGVI flag data from aug, oct, dec 2002

15 outlock and future prospects MGVI can be used to discriminate vegetation types along their specific temporal behaviour during one growing season also for Biomes with sparse vegetation cover with MGVI flag coding it s possible to separate water bodies and bare soil from vegetated surfaces the reliability of this codings have to be further validated it would be nice to have... a better cloud detection algorithm a fapar product from MERIS full resolution Data on a weekly or 10 day basis (not only for Europe)

16 fin acknowledgments ESA for providing the Data BEAM team for assistance around their software N. Gobron for infos around MGVI K. Adenauer foundation for fundings