PROBA-V Quality Working Group #6 Lessons learnt from PROBA-V 100m time series exploitation for Locust Habitat Monitoring on the PROBA-V MEP

Inès Moreau and Pierre Defourny UCLouvain, Belgium

Redu, 2017-11-09 UCL PROBA-V MEP Third-Party Services (TPS) • A Mission Exploitation Platform to improve the exploitation of PROBA-V EO

• Five research activities implemented

and demonstrated on the MEP: - Land cover monitoring

- Snow mapping - Detection of disturbances in natural vegetation

- Detection of fires and burned areas

- Desert Locust habitat monitoring

Locust monitoring: a matter of food security • Food security of 60 countries in Northern Africa, Middle East and Asia is intermittently threatened by Desert Locust

• Swarms of billions of individuals damage crops and pastures

Desert locusts (Schistocerca gregaria) swarm, Mauritania Locust habitat monitoring from satellite TS • Desert Locust requires green vegetation for breeding providing food and shelter, and prefers annual vegetation that is becoming green

 Monitor areas where vegetation has become green in the past 1 to 2 decades

Decade  Dynamic greenness maps: Mauritania (16°N, 2°E) No Vgt 1 A time meter to detect from 2 satellite time series the 3 temporary habitat greening 4 5 of the Desert Locusts 6 7 Decade since vegetation 8 onset 9 (Pekel et al., 2010) 10 > 11

UCL Dynamic Greenness Maps • Operational application for FAO (Locust Watch System) since 2009 from Senegal to Pakistan

• MODIS 250m, updated every 10 days Decade No Vgt 1 2 3 4 5 6 7 8 9 10 > 11 Early warning systems to prevent Locust expansion • Discriminating vegetated areas in near real time: a preventive strategy recommended by FAO to fight Desert Locust

• Prevent locust upsurges and the formation of large swarms Greenness Maps: MODIS 250m PROBA-V 100m

Adjust and implement the Greenness timer algorithm to PROBA-V 100 m time series on the PROBA-V MEP 100 m 10-d composites

(Pekel et al., 2010)

RGB to HSV colorimetric Discrimination of vegetated and Time meter application: transformation non-vegetated pixels based on 100m Dynamic (Red, NIR and SWIR bands) Hue index and NDVI greenness maps (specific thresholds) Mauritania (16°N, 13°W) A much finer scale 1-10/08/2016

PROBA-V MODIS Mauritania (16°N, 13°W) A much finer scale 1-10/08/2016 • Better delineation of vegetation patterns PROBA-V

Landsat 11/07 – 12/08 2016

A much finer scale Mali (16°N, 3°W) 21-31/10/2016 • Better delineation of vegetation patterns PROBA-V MODIS Niger (17°N, 7°3) A much finer scale June - September 2016 • Good consistency with the MODIS product PROBA-V MODIS Earlier detection of vegetation North Mali 1-10/07/2016

PROBA-V MODIS Earlier vegetation detection North Mali 1-10/08/2016

PROBA-V MODIS Earlier vegetation detection North Mali 1-10/09/2016

PROBA-V MODIS Use of PROBA-V 300m images • Temporal resolution of 3 to 5 days at 100m spatial resolution: lot of unvalid areas (cloud, shadow, no data) • Grenness maps time series at 300m for gap filling S10 - 100 m S10 - 300 m

DGM 100m (+300m where no data) DGM 100m DGM 300m +

Unvalid data Use of PROBA-V 300m images • Use of 300m time series for unvalid areas (cloud, shadow, no data) • 100m Greenness Maps completed with 300m data Quality of the status map Cloud Shadow • Very good detection of large clouds

Quality of the status map Cloud Shadow • Good detection of small clouds, no remaining borders • Some omissions

Quality of the status map Cloud Shadow • Very few cloud commissions • Here on sand dunes

Quality of the status map Cloud Shadow • Remaining haze and thin clouds

• No large impact on the greenness maps

Quality of the status map Cloud Shadow • Lot of cloud shadow omission

Quality of the status map Cloud Shadow • Lot of cloud shadow omission

• Missing borders of shadow

Consequences on the Grenness Maps Cloud Shadow • Omission of shadow leads sometimes to false vegetation detection

S10 100m With cloud mask Dynamic greenness map

Consequences on the Grenness Maps Cloud Shadow • Omission of shadow leads sometimes to false vegetation detection

S10 100m With cloud mask Dynamic greenness map Consequences on the Grenness Maps Cloud Shadow • Omission of shadow leads sometimes to false vegetation detection

S10 100m With cloud mask Dynamic greenness map Quality of 100m S10 Composites Cloud Shadow • 3 to 5 days revisit: - stripes - cloud mask of S1 images visible on the composites

Quality of 100m S10 Composites Cloud Shadow • Impact on NDVI : false patterns of vegetation

Quality of 100m S10 Composites • 3 to 5 days revisit: - cloud mask of S1 images visible

Consequences on the Grenness Maps

S10 100m S10 100m with cloud mask

Hue values saturated Greenness maps artefact Conclusions • Great improvement of the Greenness maps with the use PROBA-V 100m time series: - Very good consistency with 250m MODIS product - Dynamic Greenness Maps at a much finer scale - Earlier detection of vegetation

• Improvement of the cloud screening • But still some artefacts in the products mainly due to: - cloud shadow omissions First decade - the quality of S10 composites (S1 mask visible)

• Feedbacks from the FAO Locust Team: - 100m maps represent a significant improvement over the current MODIS product - Any chance for reducing artefacts in the first decade ? - Possibility to provide these greenness maps in near-real time every 10 days?

 Importance of cloud and shadow mask performance for land application  Importance of daily acquisition Compositing – S10 PROBA-V 333m

• The maximum value compositing degrades the

quality from the S10 product Date 20131127 Yemen Compositing – Alternatives

• Example: 10-day mean composite

Date 20131127 Yemen