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Radiative Transfer Modelling for the characterisation of natural burnt surfaces AO/1-5526/07/NL/HE Recommendations #2c P. LEWIS 1, T. QUAIFE 5, J. GOMEZ-DANS.

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Presentation on theme: "Radiative Transfer Modelling for the characterisation of natural burnt surfaces AO/1-5526/07/NL/HE Recommendations #2c P. LEWIS 1, T. QUAIFE 5, J. GOMEZ-DANS."— Presentation transcript:

1 Radiative Transfer Modelling for the characterisation of natural burnt surfaces AO/1-5526/07/NL/HE Recommendations #2c 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 , USA 4. INSTITUTE FOR ENVIRONMENT AND SUSTAINABILITY (IES), EC JOINT RESEARCH CENTRE, VIA E. FERMI 1, TP 440, 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 Fcc in detection algorithms  Optical detection algorithms: – ‘sensitive measure’ e.g. NDVI/NBR – And set of rules (thresholds etc.)  If view fcc as the sensitive measure, should be able to integrate into detection algorithms in same way  Can use fcc in probabilistic framework – Then obviate need for ‘rules’ beyond probability threshold – Particularly hopeful of this given stability in burn signals observed

4 Probability of step change

5 The add spatial probability constraints

6 And segment into ‘fires’ at same time as detection

7 Particularly attractive for C/climate models

8 Post fire analysis  NIR/MIR signal increases over week/weeks in savannas – If model operates as intended, expect:  Fcc remain constant over time  a0/a1 parameters vary (char dissipation)

9 Post fire analysis  Such analysis quite complex – Need to do careful BRDF modelling post fire  But would suggest that fcc measure should have some tolerance to post-fire observation timing – Since a0/a1 quite stable, expect to observe some consistent dynamics in feature space as fn of time after fire

10 Post fire analysis  Also, vegetation regrowth post fire should have poor fit to spectral model – so should spot this

11 recommendations  fcc approach should be investigated for detecting fire- affected areas from optical data  extend to spatio-temporal modeling of fires as part of detection algorithms and concepts for multi-sensor merging. – Target variables should move beyond estimation of a pixel count of fire-affected areas, to measures including fcc, fire size, and rates of spread.  temporal dynamics of the fcc signal should receive some attention.


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