Radiative Transfer Modelling for the characterisation of natural burnt surfaces

AO/1-5526/07/NL/HE

Recommendations #2a

P. LEWIS1, T. QUAIFE5, J. GOMEZ-DANS1,2, M. DISNEY1, M. WOOSTER2 , D. ROY3, B. PINTY4

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,

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

Semi-analytical

modelling

Models need to have at least 3 layers– Soil, grass/understorey, tree/overstorey– Number of state variables then quite high

Semi-analytical models model average radiation fields– Not directly relevant to high spatial resolution– can’t easily model some fire effects

E.g. lower (or in some cases higher) density of grass below trees

– Difficult to include anything but simplest fire impact model e.g. assume linear mixture of grass/char here BUT this is same as linear model being tested, so not a full test

BUT can learn about overall behaviour of algorithm AND simpler to set up/run than 3D models

Recommendations: semi-analytical models

rather simplistic and at times circular for testing algorithms: more complex mechanisms for the description of fire impacts might be considered if such models are to play a significant role in algorithm testing.

– Probably best to learn about these from 3D modelling and apply understanding gained to these models

3D Numerical modelling

3 components:– Collection and generation of 3D structure– Simulating fire impacts in 3D models– Testing and validation 3D models

Collection

and

generation

of

3D

structure

3D information from:– ‘models’ (e.g. L-systems etc.)– Measurement (e.g. lidar)

Here, have partially calibrated library models (simple grass model) against field obs.

– Practical approach in absence of other information to enable scene generation

Collection and generation of 3D structure

Library approach proved useful– Each tree not ‘grown’ as such– But can represent density effects etc.

Rapidly advancing measurement method: lidar– Particularly attractive waveform lidar

E.g. CSIRO Echidna Esp. multi-spectral lidars under development

– NIR + SWIR– Possibility of fuel moisture mapping

Incidently, librat s/w delivered capable of simulating these

Validating/testing models

Here, compared with range of field measurements– Manual/photographic … quite time consuming– Airborne/ground lidar would be advantageous for

scene development and validation of structure Compared here with limited range of EO measures

– Limited by cloud cover during campaign …– Wider range necessary

E.g. BRDF … CHRIS-PROBA of value, but …

Si

mulating fire i

mpacts in

3D

models

Simple but effective model of fire impact here– Replace grass by patchy char around plants

and ‘burned grass’ model Can conceptualize how to extend to tree impact

– But not done here (time)

Recommendations

Build 3D model database of pre-post fire vegetation– Need to consider validation data

range of EO airborne/spaceborne

– Develop coordinated field campaigns to achieve this

Develop measurement technologies/algorithms– Lidar

Develop more sophisticated fire impact models Build linkages to 3D thermal models

– Fire impact on 3D model