1. Radiative Transfer Modelling for the characterisation of natural burnt surfaces AO/1-5526/07/NL/HE Recommendations P. LEWIS 1, T. QUAIFE 5, J. GOMEZ-DANS 1,2, M. DISNEY 1, M. WOOSTER 2, D. ROY 3, B. PINTY 4 1. NCEO/DEPT. GEOGRAPHY, UNIVERSITY COLLEGE LONDON, GOWER ST., LONDON WC1E 6BT, UK 2. NCEO/DEPT. GEOGRAPHY, KING'S COLLEGE LONDON, STRAND, LONDON WC2R 2LS, UK 3. GEOGRAPHIC INFORMATION SCIENCE CENTER OF EXCELLENCE, SOUTH DAKOTA STATE UNIVERSITY, WECOTA HALL, BOX 506B, BROOKINGS, SD 57007-3510, USA 4. INSTITUTE FOR ENVIRONMENT AND SUSTAINABILITY (IES), EC JOINT RESEARCH CENTRE, VIA E. FERMI 1, TP 440, 21020 ISPRA (VA), ITALY 5. NCEO/DEPT. GEOGRAPHY, EXETER UNIVERSITY,

2. Overview  EO technology overview (talk 1) – Wildfire detection and quantification – Brief summary of relevant results – ESA and related missions  Modelling fire impacts (talk 2) – Semi-analytical – 3D – Thermal – Linear modelling

3. EO technology overview: wildfire  Technologies: – Surface:  Optical (main focus here)  Thermal (secondary focus)  Microwave – Atmosphere  Not considered here  All technologies rely on spatial and temporal localisation of fire

4. Thermal: detection  Detect anomalous high T – Polar orbiting  Don’t view all fires –Orbital convergence –Some methods night only (lower fire activity)  Some methods rely on T saturation – Geostationary  Lower spatial, higher temporal resolution  Low resolution at high latitudes  All impacted by cloud

5. Optical: detection  Sometimes feasible from single image: classification  Mostly use time series  Mostly use SWIR (also NIR) – NBR/NDVI  Compounded by BRDF effects – Mostly considered noise, but can be treated  Worse cloud/smoke problems that thermal – Esp. if shorter wavelengths used  Moderate resolution: global – Polar orbiting (mainly), also geostationary  Higher resolution: – Low revisit  used for specific study areas  Or use longer time between

6. Active microwave: detection  Essentially classification mostly – Time series, generally  Issue of attribution of signal change to fire  Complexity from moisture variations – Not such an issue if materials dry?  Complexity from dry materials  Huge advantage in areas of high cloud cover – Tropics

7. Fire impact  optical fire severity measures  fire radiative energy – integral over time of fire radiative power  ‘direct’ measurements of pre-post biomass – active microwave – lidar measurements – vegetation indices  area affected by fire from spectral unmixing  atmospheric measurement of gasses and particles released by fire

8. Fire detection and impact  Best strategy, combine information – Multiple moderate resolution optical – Constellations of higher resolution – Combined optical and thermal – Combine all sources  Need appropriate theoretical background, models, and algorithms

9. Contributions of this study  Model to estimate fcc – bottom-up approach to C release estimate  Needs fuel load – Compare with FRE  Fcc model generic to all optical sensors – Should be able to combine information – Burn signal results suggest use as constraint – Measure is linear  Simple spatial scaling

10. The relevance of the algorithm to the exploitation of data from ESA and related sensors and missions  ENVISAT  Earth Explorers  Sentinels  Meteorological missions  Others

11. ENVISAT: MERIS  300m, VIS/NIR, many channels  Issues: – No SWIR sampling – Geolocation (?) – BRDF effects not too great  Main route to exploitation: – Detect fire from other sensors – Apply fcc algorithm  Noting issues wrt burn materials/dead vegetation confusion – Would be aided by easy to use gridded surface reflectance product

12. ENVISAT: AATSR  Thermal – Detection (night time saturation or near saturation) (WFA) – Can’t use for FRE  Optical – Relevant wavelengths – Only 4 wavebands  Issues with 3 parameter model  Unless use burn signal constraint – Dual view possibly interesting for fcc directional effects  Same argument for MISR, CHRIS-PROBA

13. Earth Explorers  Earthcare: atmsophere (out of scope) – MSI worth considering?  Wind from ADM-Aeolus relevant (out of scope)  SMOS soil moisture relevant (fire risk) (out of scope)  PREMIER: atmosphere (out of scope)  BIOMASS

14. BIOMASS  P band SAR  Real potential for pre-post fire woody biomass estimates – Would need to demonstrate acceptable precision in change signal  Unlikely viable for dry grass fires – But distinguishing these of interest

15. Sentinels  Sentinel 1 – C band SAR – Arguments above, re detection – but saturation at higher biomass  So biomass change issues if pre-fire biomass high  Sentinels 4,5 – Atmosphere (out of scope)

16. Sentinel 2  13 bands across SW  Varying spatial resolution 60m+  Satellite pair – Increased viewing opportunity – 5 day (cloud free)  ‘extended viewing capability’ – BRDF issues would need treatment  Fcc product at 60m, or implement multi-scale for further localisation – Need detection algorithms – Very intersting platform to develop fcc-based detection algorithm

17. Sentinel 3  OLCI and SLSTR similar to MERIS/AATSR  SLSTR: dedicated low-dynamic range ‘fire’ channels – So FRP & day/night detections  Optical, similar to MERIS/AATSR uses and issues – BUT very interesting combination of platforms (Sentinel-2,-3) for multi-scale strategy (possibly also then Sentinel- 1/BIOMASS)

18. Meteo  MetOp – AVHRR instrument  Optical (includes SWIR) –BRDF effects (can be treated: MODIS algorithm prototyped with AVHRR) –direct value if constrained burn signal used for fcc –Indirect, of value to moderate resolution constellation approach  thermal (fire saturation)  MSG/MTG – Operational FRE – Optical burned area/fcc difficult  Low signal/noise for any by largest fires  BUT tracking fcc post fire would be of interest –Need constraint to burn signal, but probably can get from active fire detections

19. Others  Medium resolution (sub 1m to 10s m) – VIS/NIR – SPOT Pléiades, DMC, SEOSAT-INGENIO, RapidEye, EROS  Revisit period for individual may not be high enough, but have constellation missions  Also CEOS LSI-concept – all as virtual constellation  Probably most useful for localisation of information if fire known (e.g. thermal) –Fcc could be applied if constrained burn signal –And atmospheric correction  EnMAP – Very relevant for characterisation of fcc – BRDF effects for off-nadir pointing – Don’t need full hyperspectral for this though  So maybe target more subtle information

20. Discussion  3 parameter fcc model – Best used with >> 3 bands – Or strong constraint to burn signal  2 single most interesting sensors – Sentinel-2 / EnMAP – Both aim at high repeat coverage high resolution

21. Discussion  A major limitation to monitoring / characterisation is often viewing opportunity – So should develop multi-sensor concepts – Including multi-resolution – Issues:  different spectral sampling  different spatial resolutions  potentially different viewing and illumination angles – Fcc approach can at least partially deal with all of these (BRDF modelling for latter) – Longer term, may consider full DA system (e.g. ESA EOLDAS)  But technology needs development

22. Key Recommendations  Fcc should be developed into operational algorithms to quantify fire impact  Method should be generic – needs testing ESA sensors and Sentinel prototypes  Investigate constraint for application to sensors with not >>3 wavebands  Issues in study in comparison of fcc-FRE – Need further investigation  Most application here to S. Africa – Wider application / testing  Develop method for multiple data streams – Including multiple resolutions