1. Has EO found its customers? Global Vegetation Monitoring Unit Mapping of arid regions in N. Africa, middle East and Southeast Asia using VGT S10 Michael Cherlet

2. Has EO found its customers? Global Vegetation Monitoring Unit Mapping of arid regions in N. Africa, middle East and Southeast Asia using VGT S10

3. Has EO found its customers? Global Vegetation Monitoring Unit Mapping of arid regions in N. Africa, middle East and Southeast Asia using VGT S10 Photo from 300 m height

4. Has EO found its customers? Global Vegetation Monitoring Unit Specific Problematic for Mapping Land Cover in Arid Areas Low cover vegetation >> 3% - 40% (LCCS: sparse to open) mixed with background soil S10 NDVI products>>high variability of NDVI not explained only by vegetation

5. Has EO found its customers? Global Vegetation Monitoring Unit IGBP

6. Has EO found its customers? Global Vegetation Monitoring Unit timing of seasonal variability related to vegetation is difficult to determine: -erratic character of rainfall in space and time -influence of two climatic zones N > Mediterranean influence S > ‘tropical’ ITCZ influence not possible to ‘choose’ best period for vegetation development throughout year >> difficult to use S1 Specific Problematic for Mapping Land Cover in Arid Areas

7. Has EO found its customers? Global Vegetation Monitoring Unit Using SPOT VGT S10 or longer composites based on MVC: atmospheric, aerosol or clouds contamination is limited in S10 over arid areas (no persistence) BRDF effect which is probably ‘enhanced’ in relation to topography Spectral behaviour related to lithology and geology (colour) confusion between low cover vegetation and sandy soils/sand-stones Specific Problematic for Mapping Land Cover in Arid Areas

8. Has EO found its customers? Global Vegetation Monitoring Unit Oct dek 1: Unsure: 0.505 % of image Cloud:0.059 % of image Nov dek 1: Unsure: 0.715 % of image Cloud:0.029 % of image Nov dek 2: Unsure: 0.915 % of image Cloud:0.009 % of image Threshold on ratio MIR/BO improves classification of unsure class Contamination on S10

9. Has EO found its customers? Global Vegetation Monitoring Unit Using SPOT VGT S10 or longer composites based on MVC: atmospheric, aerosol or clouds contamination is limited over arid areas (no persistence) BRDF effect which is probably ‘enhanced’ in relation to topography Spectral behaviour related to lithology and geology (colour) confusion between low cover vegetation and sandy soils/sand-stones Specific Problematic for Mapping Land Cover in Arid Areas

10. Has EO found its customers? Global Vegetation Monitoring Unit 1 21 16 26 31 5 10 15 2020 25 30 4 9 1414 24 29 3 8 13 23 2 7 12 Backward Foreward NDVI In general, but locally of importance increases confusion of e.g. sandstone outcrops and vegetation

11. Has EO found its customers? Global Vegetation Monitoring Unit Using SPOT VGT S10 or longer composites based on MVC: atmospheric, aerosol or clouds contamination is limited over arid areas (no persistence) BRDF effect which is probably ‘enhanced’ in relation to topography Spectral behaviour related to lithology and geology (colour) confusion between low cover vegetation and sandy soils/sand-stones Specific Problematic for Mapping Land Cover in Arid Areas

12. Has EO found its customers? Global Vegetation Monitoring Unit 1. producing yearly composites :-NDVI image Max, Min, amplitude + statistics (st. dev….) (cloudmask)-NDWI image Max, Mean, Min, amplitude + statistics (#methods tested)-Minimum B0, B2, B3, Mir differentiation of different zones/masks using Max NDVI thresholds (~ cover) Final Approach still open Three methods tried: 1. NDVI 0.786 = 100% 0.36 =~ 40% Sensor sensitivity: 0.01

13. Has EO found its customers? Global Vegetation Monitoring Unit - non-supervised classification (isoclass) within masks using yearly derived products - grouping of ‘non-vegetation’ vs ‘vegetation’ classes and re-iterate isoclass and regrouping (min 3) subjective interpretation of all available data and field knowledge based on subjective interpretation of all available data and field knowledge - final grouping of all ‘non-vegetation’ and ‘vegetation’ masks - differentiation ofa. physical features using isoclass on bands and regrouping within ‘non-vegetation’ b. different ‘life forms’ within ‘vegetation’ part using NDVI time series statistics and ancillary data

14. Has EO found its customers? Global Vegetation Monitoring Unit Orange: 3 - 6 % cover (GP length?) > LCCS: sparse herbaceous Aquam: 6 - 10 % cover (GP length?) > LCCS: herbaceous green1: 10 - 20 % cover (GP length?) > LCCS: green2: 20 - 40 % cover (GP length?) > LCCS: IGBP

15. Has EO found its customers? Global Vegetation Monitoring Unit 2. 2.- producing yearly composites:-NDVI image Max, Min, amplitude + statistics (st.dev….) -NDWI image Max, Mean, Min, amplitude + statistics -Min B0, B2, B3, Mir stratification of land-units based on classification of bands - stratification of land-units based on classification of bands (isoclass and re-grouping) - non-supervised classification (isoclass) within landunits using yearly derived products - grouping of ‘non-vegetation’ vs ‘vegetation’ classes and re-iterate isoclass and regrouping (min 3) based on subjective interpretation of all available data and field knowledge - final grouping of all ‘non-vegetation’ and ‘vegetation’ masks - differentiation ofa.physical features using isoclass on bands and regrouping within ‘non-vegetation’, = optimizing first stratification b.different ‘life forms’ within ‘vegetation’ part using NDVI time series statistics and ancillary data Used to attach further info to vegetation classes:

16. Has EO found its customers? Global Vegetation Monitoring Unit 3. Determination of ‘vegetation’ character of individual pixels based on detection of significant NDVI change during year 2000 by separation of ‘background noise’ from ‘signal’ using long term time series to establish ‘noise’ level per pixel (*): 3. 1st average - weight=1 Difference less than1% the process stops Δ Decreasing weight with increasing NDVI value above the mean Same weight (1) for the NDVI values under the mean (Using Gaussian density probability function) 2nd average - weight=GF (*) in cooperation with Univ. UCL, Belgium

17. Has EO found its customers? Global Vegetation Monitoring Unit Image of MEAN of ‘dry’ season Image of STANDARD DEVIATION of dry season Pixel FLAGGED NDVI > Mean + nSTD Result of the iterative process Reflects a status of CHANGE in ‘probable’ vegetation cover related to its “dry season” status (whatever that is.... Soil or vegetation....) (*) in cooperation with Univ. UCL, Belgium

18. Has EO found its customers? Global Vegetation Monitoring Unit Avg + 2*STdev

19. Has EO found its customers? Global Vegetation Monitoring Unit Needs refining to be used as base “probable vegetation” - non vegetation Temporal mask …… and spatial mask

20. Has EO found its customers? Global Vegetation Monitoring Unit Conclusions: methods 1 & 2 - straightforward techniques - need for ‘ground’ knowledge - subjective - not very repeatable method 3- still to be validated technique - fine tuning required - objective - repeatable - ‘ground’ knowledge only required in final stage