has eo found its customers? global vegetation monitoring unit mapping of arid regions in n. africa,...

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Global Vegetation Monitoring Unit

Mapping of arid regions in N. Africa, middle East and Southeast Asia using VGT S10

Michael Cherlet

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Photo from 300 m height

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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

In-Salah Oasis

50

55

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Decade

ND

VI

Tademait Plateau (in Salah)

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55

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Decades

ND

VI

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IGBP

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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


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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)

confusionconfusion between low cover vegetation and sandy soils/sand-stones between low cover vegetation and sandy soils/sand-stones


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Oct dek 1:

Unsure: 0.505 % of imageCloud :0.059 % of image

Nov dek 1:


Nov dek 2:


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cloud

less d

ense

dens

e haze

light

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v, lig

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light

soil

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dark

soil/r

ocks

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n ar

ea (m

ali)

MIR

/BO

MIR/BO cloud/haze

MIR/BO soils

2.48 thres

Linear (2.48 thres)

Threshold on ratio MIR/BO improves classification of unsure class

Contamination on S10

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atmospheric, aerosol or clouds contamination is limited over arid areas (no persistence)





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BRDF - Zone 4

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-60 -55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 55 60

VZN

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nir red NDVI Linéaire (NDVI) Linéaire (nir) Linéaire (red)

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2429 3

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Backward Foreward

NDVINDVI

In general, but locally of importance increases confusion of e.g. sandstone outcrops and vegetation

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atmospheric, aerosol or clouds contamination is limited over arid areas (no persistence)





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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 openThree methods tried: 1.

Statistics of y2K Max NDVI green areas

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0.57 0.6

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0.88 0.9

NDVI

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0.78 - 0.792

NDVI 0.786 = 100%

NDVI vs Cover

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% cover

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VI

0.36 =~ 40%

Sensor sensitivity: 0.01

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- 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)based on subjective interpretation of all available data and field knowledgesubjective interpretation of all available data and field knowledge

- final grouping of all ‘non-vegetation’ and ‘vegetation’ masks

- differentiation of a. 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

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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:

IGBPIGBP

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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 bandsstratification 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 of a. 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:

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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 termtime series to establish ‘noise’ level per pixel (*):

3.

0

0.05

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0.25

1st average - weight=1

Difference lessthan1%

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 densityprobability function)

2nd average - weight=GF

(*) in cooperation with Univ. UCL, Belgium

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Image of MEANof ‘dry’ season

Image of STANDARDDEVIATION

of dry season

Pixel FLAGGEDPixel FLAGGEDNDVI > Mean + nSTDNDVI > 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....)

Site2-Point7-Méthod2

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Décades

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NDVI>=0,14 NDVI>=2*s NDVI>=2,5*s NDVI>=3*s NDVI

(*) in cooperation with Univ. UCL, Belgium

Site5-Point6-Méthod2

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Décades

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NDVI>=0,14 NDVI>=2*s NDVI>=2,5*s NDVI>=3*s NDVI

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GPDF Threshold (2*StDev) and Max VI

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0.50%

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1.50%

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2.50%

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4.50%

-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

Ndvi

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Threshold

Max VI

Avg + 2*STdev

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Needs refiningto be used as base “probable vegetation” - non vegetation

Temporal mask …… and spatial mask

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Conclusions:

methods 1 & 2 - straightforward techniques- need for ‘ground’ knowledgesubjective- not very repeatable

method 3 - still to be validated technique- fine tuning required- objective- repeatable- ‘ground’ knowledge only required in final stage

has eo found its customers? global vegetation monitoring unit mapping of arid regions in n. africa,...

Documents

vegetation slide

vegetation development

vgt s10 slide

mapping land cover

spot vgt s10

height slide

vgt s10 michael cherlet

vgt s10 photo