Remote sensing of burned areas

Click to edit Master title style

Earth Observation of Burned Areas

Emilio Chuvieco Department of Geography, University of Alcalá (Spain)

[email protected]


Main University Building: 1502


Our city

Click to edit Master title style Our “kangaroos”

Click to edit Master title style Activities of UAH research group

• Fire risk estimation:

– Generation of input variables: Fuel type classification, Lidar, FMC, Socio-economic variables.

– Integration & validation methods.

• Fire effects assessment:

– Mapping burned areas at global and regional scales.

– Burn severity estimation.

– Input of Burned Area into Global Vegetation Models.

Click to edit Master title style Outline of the seminar

• Global mapping of burned areas:

– Background

– ESA fire_cci project

• Approaches to map burn severity from RS data.

Click to edit Master title style ESA-CCI programme

Aerosol cci Cloud cci

Fire cci

GHG cci

Glaciers cci

Land Cover cci

Ocean Colour cci

Ozone cci

Sea-level cci

Sea Ice cci

Sea Surface Temperature cci

Ice Sheets

CMUG

Soil moisture

Click to edit Master title style Fire_cci science context

• Fire affects:

– GHG and aerosol emissions.

– Carbon budgets and vegetation cycles.

– Land cover change (defforestation)

• Fire is affected by:

– Temperature-rainfall trends, particularly heat waves and “El Niño” episodes (climate prediction)

– Socio-economic changes (land use policy).

Click to edit Master title style Science questions

• What are the recent trends in fire activity?

• What factors are behind fire occurrence?

• What is the actual magnitude of fire impacts?

– How much area is burned annually?

– How much biomass is actually consumed?

– What is the combustion efficiency (CO/CO2)?

– What is the role of fire in carbon accounting? Is biomass burning “carbon neutral”?

Click to edit Master title style ¿How much area is burned every year?

• Inconsistencies between RS products and official forest fire statistics.

• Inconsistencies between RS products.

• Internal uncertainty of each RS product.


FRA2010 GVED v3

FAO (FRA2010): 0.6 Mkm². Only 78 countries are covered.

Average 4 Mkm²

Click to edit Master title style Different EO BA estimations

% of BA from different satellite products

Red: over estimation

Blue: under estimation

(Giglio et al., 2010).

-L3JRC: 3.5 - 4.5 Mkm²

(2000-07)

-MCD45 c5: 3.3 - 3.6 Mkm²

(2000–2006)

-GFED v3: 3.39 - 4.31 Mkm²

(1997-2009).

Click to edit Master title style Inconsistency in derived products

SEVIRI GFEDv2

2004 From FREEVAL final report. Courtesy of Martin Schultz

Comparison between SEVIRI FRP and GFED estimates of combusted biomass

Click to edit Master title style Uncertainty within a product (GFED v3)

Burned area proportion Uncertainty Giglio, L., J. T. Randerson, G. R. van der Werf, P. S. Kasibhatla, G. J. Collatz, D. C. Morton y R. S. DeFries (2010): Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences Discuss., 7: 1171-1186, doi:10.5194/bg-7-1171-2010

This is very relevant for climate-carbon modelers!

Click to edit Master title style Scientific goals of fire_CCI

1. Refine definition of user requirements (GCOS are unrealistic).

2. Improve current estimations of global burned area (based on European sensors: VGT-ATSR-MERIS).

3. Validate and intercompare existing BA global products.

4. Test improvements of climate-vegetation-carbon models with new BA data.

Click to edit Master title style Consortium composition

Climate Modeling

User Group (CMUG)

International Science

Working Group:

•UMD – MODIS team

•FAO REDD

•JRC EFFIS

•NGO, CI

Science

Coordinator

Project

Manager

EO Science

Team

Algorithm Development

& Intercomparison

Validation

Data pre-

processing

Climate Modelers

System Engineering

Click to edit Master title style fire_cci production targets

• Temporal series of BA over 10 selected study sites (500x500 km) (1995-2009):

– Assure spatial accuracy and stability.

– Consistency across multiple satellites

– Demonstrate full-time series available.

• Global coverage for five years (1999, 2000, 2003, 2005 and 2008):

– Demonstrate the semi-operational processing.

– Ensemble chain, bulk processing of data.

Click to edit Master title style Study sites

Click to edit Master title style Target products

• Burned pixels (mixing all three sensors whenever possible): – Monthly files with date of detection.

– Minimum Mapping Unit (MMU) is under discussion.

– GeoTiff format

• Grid product: – 0.5 x 0.5 degree (CGM) / improvements

to 0.25 or 0.1 degrees are foreseen.

– NetCDF format.

Click to edit Master title style Tiles for the pixel product

In addition to standard tiles, the user will have a web tool to interactively select his/her target site and apply for personal downloads

Click to edit Master title style Project status

User requirements

External BA

algorithms

Product

specifications Geometric correction

Calibrated reflectances

Validation

Topographic shadow

correction

Atmospheric correction

Global BA

production

Raw Data:

ATSR, VGT, MERIS

Water-snow-

cloud masking

Round robin

Development of BA

algorithms

DEM

Testing of models

BA reference data

Merging algorithm

Done

In process

BA

reference

data

Click to edit Master title style Major deliverables

• User Requirement Document (URD).

• Product Specification Document (PSD).

• Product Validation Plan (PVP).

• Comprehensive Error Characterisation Report (CERC).

• ATBDs (Pre-processing, BA algorithms, Merging).

• System Requirement Document (SRD)

• System Specification Document (SSD).

Click to edit Master title style Pre-processing

• Geometric correction.

• Masking (cloud, haze, snow, water).

• Atmospheric correction (ATCOR).

• 10 sites x 3 sensors x (12-9-5) years: more than 70,000 corrected reflectance images + masks have been processed.

• Global processor is being implemented.

Click to edit Master title style BRDF: TOA/BOA time series

• Time series RGB (Meris bands 7, 5, 3):

Meris-FRS, Australia, Pixel 844,555 after atmospheric correction (BOA)

Meris-FRS, Australia, Pixel 844,555 before atmospheric correction (TOA)

Meris-FRS: Blue 1-4, Green 5, Red 6-6, Red-Edge 8, NIR 9-15, O2-Absorption 11, Water vapour 15

Click to edit Master title style BA VGT Algorithm (ISA, Portugal) Pereira and Mota, 2012

Click to edit Master title style BA Algorithm VGT Results

VGT vs MODIS VGT detection dates

AUSTRALIA

Click to edit Master title style MERIS algorithm (UAH, Spain)


6 layers for a monthly product: example from June 2005 Australian study site: BA, CL, days between burn date and last valid bservation before that date, valid obs, all obs, cloud obs

Auxiliary layers

Click to edit Master title style MERIS results: Australian site

2005 2006

Click to edit Master title style MERIS results: Canadian site

2005 2006

Click to edit Master title style MERIS results: Kazakhstan site

2005 2006

Click to edit Master title style Sensor Combinations (PL1) ATSR Algo1 (1km)

VGT Algo1 (1km)

MERIS Algo2 (300m)

Sensor combo (1km)

Uncertainty reduction: Multiple observation of same burn

500km2 Area stats

ATSR – 3691km2

– core burns

– overlap 89.9%

VEGETATION – 4211km2

– core burns

– overlap 86.7%

MERIS – 6977km2

– cores, detail, other events

– overlap 56.9%

Click to edit Master title style Date of detection PL2 ATSR Algo1 (JD)

VGT Algo1 (JD)

MERIS Algo2 (JD)

Earliest detection (JD) • Dates brought back

• Patch progression

Julian Day (August)

Uncertainty reduction: Reduced time lapse of observation

Click to edit Master title style Grid output (1)

Burned pixels (1km) Sum burned m2(0.5 degree) Unsuitable obs %. (0.5 deg)

Jan Feb Mar

Apr May Jun

Jul Aug Sep

Oct Nov Dec


Burned pixels (1km)

Jan Feb Mar

Apr May Jun

Jul Aug Sep

Oct Nov Dec

Confidence (0.5 degree) Conf sd (0.5 degree)

Apr May Jun

Jul Aug Sep

Grid output (2)


Burned pixels (1km)

Jan Feb Mar

Apr May Jun

Jul Aug Sep

Oct Nov Dec

Homog. ind (0.5 degree) Land cover (0.5 degree)

Grid output (3)

Click to edit Master title style Validation (UAH – GAF)

• Standard CEOS Validation protocol.

• 250 Landsat-TM/ETM+ multitemporal pairs are being processed:

– Temporal validation: study sites.

– Spatial validation: stratified random sampling.

• Validation metrics:

– Accuracy (agreement global-reference data).

– Error balance (over-under estimation).

– Temporal consistency.

Click to edit Master title style Temporal validation

Click to edit Master title style Temporal validation

Canada Colombia Brazil Portugal Angola South Africa Kazachstan Russia Australia Borneo

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Light green, SLC-OFF

Click to edit Master title style Spatial validation

Click to edit Master title style Generation of reference perimeters

• ABAMS: (Bastarika et al. 2011). Based on a two-phase algorithm:

– Seed detection.

– Region-growing algorithm.

– Includes multitemporal images.

• Results are visually reviewed and cross-check with another interpreter.

• Standard documentation protocol (CEOS).

Click to edit Master title style Examples of fire reference data

•

Canada

Click to edit Master title style Examples

• Brasil

Click to edit Master title style Examples

pre post Angola

Click to edit Master title style Fuzzy error matrix

Reference data

Global

product Burned Unburned

Global

total

Burned p11 p12 p1+

Unburned p21 p22 p2+

Reference Total

p+1 p+2 p=1

Error matrix

commission

true burned

omission

true unburned

Click to edit Master title style Round Robin results

• BA algorithms/products tend to underestimate (red areas), with exceptions (green areas)

Click to edit Master title style Modeling exercises with BA data

• Monthly C emissions from biomass burning for the period of the ESA fire product

• Carbon budgets and Vegetation dynamics (Orchidee).

• Update Mouillot & Field 2005 historical database.

• Estimating errors in existing historical C emissions reconstructions

• Comparing regional and global estimations.

Click to edit Master title style International scope

• Critical phases are monitored by Key Science bodies.

• Close connection with GOFC-GOLD Fire IT and CEOS Cal-Val.

• Openness: Round Robin exercise.

• Regional validation workshops:

– Tropical: Brazil.

– Boreal: ¿Russia?

Click to edit Master title style Stresa R-R workshop (17-18 October, 2011)

Click to edit Master title style Main challenges of fire_CCI

• GCOS requirements very demanding.

• Input data for BA mapping:

– None of the input sensors (ATSR, VGT, MERIS) was designed for BA mapping.

– Little experience with ESA sensors.

• None for MERIS

• Limited for VGT and ATSR (Globcarbon and L3JRC)

– Existing MODIS BA products (2000-2011).

• Time constrains, particularly for BA algorithms.

Click to edit Master title style Fortitudes of fire_CCI

• Output product will combine three input sensors.

• Validation, temporal and spatial datasets.

• Uncertainty and error characterization.

• Strong connections with climate and international science community (GOFC-GOLD Fire IT).

Click to edit Master title style http://www.esa-fire-cci.org/

http://www.esa-fire-cci.org/





Click to edit Master title style Burn severity

• Degree of post-fire disturbance.

• Factors:

– Previous biomass loads.

– Fire behaviour (intensity and duration).

• Importance:

– BS is critical for post-fire regeneration and soil degradation.

– BS is an key factor in estimating gas emissions.


ECOLOGICAL CONSECUENCES ON BIOPHYSICAL PRE-FIRE

COMPONENTS IMMEDIATELY AFTER THE FIRE

DESCRIBE THE RECOVERY OF THE ECOSYSTEM FROM FIRE IMPACTS

From Key (2006)

Temporal scales

Click to edit Master title style Fire behaviour variations

Click to edit Master title style Field methods

• Quantitative measurements:

– Depth of charcoal layer.

– Ash/charcoal proportion.

– Amount of dead species.

– Depth of soil organic layer affected.

– Thickness of the minimum branch left.

• Qualitative observations:

– Visual estimations: ordinal ranks: low, medium, high.

– Quantitative ranges, visually estimated: CBI (Key and Benson, 2005).

Click to edit Master title style Remote Sensing methods

• Empirical models: – Collection of field samples.

– Extraction of satellite information (after calibration).

– Generation of statistical fittings.

• Simulation models: – Find a good model.

– Provide sound input parameters (realistic scenarios).

– Find a good inversion method.

Click to edit Master title style • Empirical models

• Simulation models

N

Cab

Cw

Cm

DIRECT

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

B3 B4 B1 B2 B5 B6 B7

INVERSE

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

B3 B4 B1 B2 B5 B6 B7

SdNDVICBI 574.983.2679.1


Study case:

Fire started on July 16,

2005 and it was caused by

careslessness

Click to edit Master title style U

nd

ers

tory

C

an

op

y

Plo

t CBI method: strata


B+C

1-5 m

A

Substrate

D+E

5-30 m

Click to edit Master title style Models selected

• Leaf level: PROSPECT.

• Canopy level: Kuusk.

– Includes two vegetation layer + background.

– Vegetation is assumed to be distributed homogeneously.

• Reference: Kuusk, Journal of Quantitative Spectroscopy & Radiative Transfer 71 (2001): 1 –9


LAI

s()

l

sl

a

s

v

v

Direct mode

R(,s,v,v) PROSPECT Kuusk 2 Layer RTM

N

Cab

Cw

Cm

LAI

s()

l

sl

a

s

v

v

()

()

Model overview

Target CBI

Inverse mode

Fixed parameters

Variables

Model outputs

PFA PCC

SB


Variables Strata

Substrate

Lineal Mixture of soil

and char + ash

spectra (SB)

Canopy

Understory Percentage in Cover

Change (PCC). Linear

change of LAI

Percentage of Foliage

Altered (PFA). Linear

change from green to

brown leaves

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

longitud de onda (nm)

Refl

ecti

vid

ad

0

20

40

65

80

Variables in the simulation

Click to edit Master title style Model implementation: LUT

Substrate

Canopy

Understory

Look up

table

Simulated Spectra

Final

Spectra

CBI Filters

CBI B+C > CBI D+E: No canopy fires are more severe than understory fires.

If CBI A = 3 then CBI B+C>2 regardless CBI D+E (Severe fires in the soil imply medium to heavy understory fires)

If CBI A < 3 then CBI B+C>CBI A (most commonly severity is higher in the understory than substrate)


LUT 201 spectral bands Convolution to fit target

sensor (Landsat-TM)

LUT to Spectral Library

Spectral signatures for different CBI values


BAND 1

BA

ND

2

Spectral

angle Reference spectrum

Inversion criterion: SAM

• Selects the LUT spectrum with the minimum angle to the target and assigns it the correspondent CBI value.

• It is less sensitive to albedo variations than minimum distance.

Click to edit Master title style Comparison with empirical model results (De Santis and Chuvieco, 2007)

R2= 0.66 Variables included: dNDVI+Sat Tendency to smooth CBI values

R2= 0.63 Supervised simulation

Dr. Viegas


0.0

0.5

1.0

1.5

2.0

2.5

3.0

P-N7 P59 P46 P12 P86 P5 P11 P73 P45 P16 P32 P71 P49 P17 P96 P68

CBI plot Supervised Empirical

Empirical - Simulation model

Empirical fitting tends to smooth CBI range


CBI

DOES NOT

TAKE INTO

ACCOUNT

% OF DEAD LEAVES LITTER

FCOV OF EACH VEGETATION STRATUM

The results of RTM inversion suggest that both variables and their mixing effects are key factors of burn severity estimation

from remotely sensed data.

Problems with CBI


2 NEW VARIABLES PER STRATUM :

n

n

m

m

m

m

m

mm

FCOV

FCOVCBI

GeoCBI

1

1

)*(

FIELD EXPERIENCE SIMULATION ANALYSIS RESULTS

The new version of CBI proposed, called GeoCBI (which is short for Geometrically structured

Composite Burn Index), was computed as follows:

% OF CHANGES IN THE LAI

FCOV OF VEGETATION STRATA

from 0 to 3 (for strata C, D and E ), as the original variables

0 to 1 (for strata B, C, D and E )

WEIGHTING FACTOR

where m is the identification of each stratum and n is the number of strata.

GeoCBI

Click to edit Master title style FIELD PLOTS CBI GeoCBI SPECTRAL SIGNATURES(TM)

P95

2.58

2.7

P86 2.85

P23 2.8

0

0.05

0.1

0.15

0.2

0.25

400 900 1400 1900 2400R

EFLE

CTA

NC

E

WAVELENGTH (nm)

P95

P86

P23


Previous model at canopy level: Kuusk

New model at canopy level: GeoSail

SUBSTRATUM

UNDERSTORY

OVERSTORY Model at leaf

level: PROSPECT

EASY TO COMPUTE (few inputs)

SUCCESSFULLY APPLIED TO CONIFERS (Cheng et al., 2006; Kötz et al, 2003 y 2004; Zarco-Tejada et al., 2004) EASY TO COMPUTE(few inputs)

IT CAN SIMULATE SEVERAL VEG. LAYERS FITS WELL INTO THE STRUCTURE OF GeoCBI

New RTM simulation tools

Click to edit Master title style Advantages of simulation models

• Interpretation is based on physical roots.

• They are applicable everywhere (properly parametrized).

• They can simulate a wide range of conditions (difficult to find in a single fire).

Click to edit Master title style Other sites

De Santis and Chuvieco, 2009, RSE

Click to edit Master title style From Burn Severity to Burning efficiency Seiler and Crutzen [1980] model Mk,i = (BLi * BEi * BSi * AEk)*10-15 Mk,I = Emissions of gas k (Tg) BLi= Biomass loads (gr/m2) BEi = Burning efficiency (combustion completness) (0/1) BSi= Burned surface(m2) AEk= Emission factors (gr gas / Kg biomass)

BE is estimated from: • Standard coeffcients. • Fuel moisture content. • Remote sensing.

Click to edit Master title style BE standard values

ID Land Cover Category BE

1 Evergreen Needleleaf Woods 0.250

2 Evergreen Broadleaf Forest 0.250

3 Deciduous Needleleaf Wood 0.250

4 Deciduous Broadleaf Woods 0.250

5 Mixed Forest 0.250

6 Woody Savanna (30-60% >2m) 0.350

7 Savanna Trees (10-30%) 0.400

8 Closed Shrubland (scrub) 0.500

9 Open Shrubland (semidesert) 0.950

10 Grassland 0.950

11 Cropland Herbaceous & villages 0.800

12 Barren deserts volcanos 0.200

13 Urban/ suburban built-up 0.100

14 Water +/- coastal 0.100

15 Permanent wetlands 0.500

16 Cropland/ grass-woods(Field-woods) 0.400

17 Snow& Ice 0.000

18 Tundra/ Paramo 0.300

19 Woodlands trees (40-60%> 5)m 0.400

20 Forest-Field Mix (40-60% woods) 0.300

21 Mediterranean scrub 0.700

Click to edit Master title style Emission estimate formula’s

• Emission= Aburned x C x Eeff x Fload

– Aburned is the area burned (retrieved)

– C is the combustion completeness (guessed)

– Eeff Emission Efficiency (guessed)

– Fload is the fuel load (computed with Biomass model)

• Emission = Efactor x

dtEnergyRadiativeFire ).__(


Martin J. Wooster’s results (in press)

Click to edit Master title style BE from RS methods

• Post-fire reflectance analysis:

– BE from Burn Severity.

– BE from simulation models.

• Energy released by the fire (FRP):

– Instantaneous to total FRP.

– Relation of FRP to biomass consumption.

Click to edit Master title style BE from BS

Local estimation of burned severity (RTM

– CBI)

Regional estimation

Burning efficiency

Validation with Landsat TM

BA map

Vegetation cover Max/Min values

of BE Burning Severity

BE

Low Med Severe

Grass 0.85 0.9 0.98

Shrub 0.7 0.85 0.95

Conifer 0.25 0.42 0.57

Deciduous 0.25 0.4 0.56 Oliva and Chuvieco, 2012

Click to edit Master title style Results

Oliva and Chuvieco, 2012

Click to edit Master title style Thank you!!

Remote sensing of burned areas

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