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Citation for published version (APA):Mota, B., Benali, A., & Pereira, J. M. (2014). MODIS 250M burnt area detection algorithm: A case study applied,optimized and evaluated over continental Portugal. In European Geosciences Union : April 29th 2014 (pp.11781)

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Download date: 20. May. 2018

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European Geosciences Union – Wildfires Session NH 7.1 - April, 29th 2014 M

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Bernardo MotaEarth and Environmental Dynamics Research Group,

Department of Geography, King's College London, UK

Akli Benali & Jose Miguel PereiraForest Research Centre, University of Lisbon, Portugal

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mBackground | Overview | Data | Algorithm | Optimization | Results | Conclusions

I – Background

II – Overview

III – Data

IV – Algorithm

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IV – Algorithm

V – Optimization

VI – Results

VII – Discussion

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•Burned area (BA) analysis is based on detecting the char/ash/scar signals, if

preceded by vegetation, the change highlights a fire event

•Compared with the VISIBLE (0.4-0.7 µm) and the SWIR (2.0-2.5 µm) the NIR

is, unquestionably, the best spectral region to discriminate between burned

areas and other surfaces, such as vegetation and not-too-dark soils. NIR

reflectance can only decrease.

Background

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Visible

NIRSWIR

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Band 2 used for BA detection

•The algorithm presented is

a spin-off of one of

classification algorithms

developed for VGT and

Band

Wavelength

(nm)

Resolution

(m) Primary Use

1 620–670 250Land/Cloud/Aerosols

Boundaries2 841–876 250

3 459–479 500

Land/Cloud/Aerosols

Properties

4 545–565 500

5 1230–1250 500

6 1628–1652 500

7 2105–2155 500

8 405–420 1000

9 438–448 1000

MODIS sensor spectral bands (1-19)

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developed for VGT and

AATSR under the FIRE-CCI

project.

•The algorithm was already

applied to the South

American tropical forest

(EGU 2013 poster)

Ocean Color/

Phytoplankton/

Biogeochemistry

10 483–493 1000

11 526–536 1000

12 546–556 1000

13 662–672 1000

14 673–683 1000

15 743–753 1000

16 862–877 1000

17 890–920 1000

Atmospheric

Water Vapor

18 931–941 1000

19 915–965 1000

... and more 16.

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Aim

Develop a BA detection algorithm for NIR imagery captured by

satellites with high frequency of observation.

Objective

Background | Overview | Data | Algorithm | Optimization | Results | ConclusionsM

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Objective

Adjust and optimize the BA algorithm to MODIS 250 m2

imagery for continental Portugal for the period (2001-2013).

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I – Background

II – Overview

III – Data

IV – Algorithm


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IV – Algorithm

V – Optimization

VI – Results

VII – Discussion

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Overview1. Did a significant and persistent drop in reflectance occur

at a given pixel ?

2. Did it occur in the right time of the year?

Score changes when and how.

Change detection algorithm

Spectral

analysis

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2. Did it occur in the right time of the year?

3. Is it coherent with its surroundings?

Compare with known fire seasonality to

separate other from source of change.

Prob. LUT of Scores vs. Fire seasonality

Look for evidence for similar changes in

the nearby pixels.

MRF segmentation dates & probabilities

Temporal

revision

Spatial

revision

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I – Background

II – Overview

III – Data

IV – Algorithm

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IV – Algorithm

V – Optimization

VI – Results

VII – Discussion

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•MODIS 250 m2 (MOD09DQ) daily NIR band (2) images for tiles

covering continental Portugal (h17v04 and h17v05) for the

period 2001-2013 – input to the algorithm

•MOD09DA cloud mask layer information available from the

500 m2 reflectance daily images (2001-2013) – cloud screening.

Data

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500 m reflectance daily images (2001-2013) – cloud screening.

•Fire Activity Distribution (0.5o) based on adjustments made

with von Mises distributions to active fire counts(Benali et al.

2012) – penalize off-season events.

•Landsat derived BA binary maps at 30 m2 (2001-2009) –

parameter optimization and accuracy assessment.

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I – Background

II – Overview

III – Data

IV – Algorithm


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IV – Algorithm

V – Optimization

VI – Results

VII – Discussion

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Time-series analysis

High daily variability (~15%)


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Time-series of the NIR reflectance for a pixel located in the centre of

Portugal over forest for year 2005.

Low density

of obs.

Low density of obs.

Major disturbance

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Time-series analysis - Construct a series of ρρρρNIR minima

High

contamination


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Determine the upper and lower envelopes of time series. Oscillation

maxima are “noisier”, affected by residual atmospheric contamination.

Lower contamination

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Time-series analysis – Robust filtering


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•Robust filtering of minima time series, to remove cloud shadow

spikes in ρNIR.

•Determine the absolute minimum refectance of the time-series.

ρNIR minimum

Filtered time series

niiy :1}min{=

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Time-series analysis – Change detection


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Change points are those points in time which divide a data set into distinct

homogeneous segments.

Pruned Exact Linear Time (PELT): changes in mean ρρρρNIR, assuming it follows

Normal distribution with constant variance and changing mean (Killick et al,.

2012).

Change points

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CP scores represent the “likelihood” of corresponding to a perturbation

event. The score magnitude at a given CP is determined by the ratio

between ρNIR drop and its potential drop.

where

is the mean ρNIR of the post-CP time segment

ρ

KS

SS

i

ii

−

− +1

1+iS

Time-series analysis – Change point scoring


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is the mean ρNIR of the pre-CP time segment

M is

K is

iS

2.0*8.0 <⇒ MM

niiy :1}min{=

2.016.02.0*8.0 ≥⇒= M

factor to allow better

discrimination post-change

ρNIR = minimum ρ value.

increases the spectral difference between

post-change reflectance and the time

series minimum

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Time-series analysis – Select CP


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Form all change points that are associated with a decrease select the one

with the highest score. It represents the biggest distrunbance leading to

lower reflectance.

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Scores

Time-series analysis – Outputs

Date


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NIR pertubations due to vegetation removal may also result from other

causes: harvesting, grazing, windthrow, or defoliation by plant pests and

diseases.

CP score and associated date will serve as imputs to the temporal revison

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Graphic of DOY and CP

P1

Se

aso

na

lity

LUT

Pro

ba

bil

ity

Temporal revision

B

Se

aso

na

lity

Se

aso

na

lity

Seasonal fire distribution example

Centre PTNorth PT

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P2

P3Score

Se

aso

na

lity

The conversion of pertubations into “probabilities” takes into account

the date of the year and the magnitude of the scores.

LUT probability surface is defined assuming 2 types of score vs.

Seasonality relations: Off fire-season (A) and during fire-season (B) and

is generated from 3 parameters (P1, P2, P3). Rate of change is defined by

a logit function (P4, P5).

A

Se

aso

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• The last processing step is to classify the pixels taking into

account the spatial and temporal relations between them.

• The solution is one that solves the maximum a posteriori –

Markov random field (MAP-MRF) problem by determining the

max. flow/min. source-sink cut in a graph, where the source is

Spatial revision

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max. flow/min. source-sink cut in a graph, where the source is

the vertex “unburned” and the sink is the vertex “burned”.

•This partition gives the segmentation of the image into

unburned and burned pixels.

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Spatial revision

Vertex are the pixels that have

edges:

1. connecting to “unburned” or

“burned” with length defined by

the burnt probability. Decision to

10 x 10 pixel representation


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the burnt probability. Decision to

which made by a threshold (Tr).

2. connecting surrounding pixels

with length defined by the day

difference over a maximum

allowed (Dmax).

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I – Background

II – Overview

III – Data

IV – Algorithm

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IV – Algorithm

V – Optimization

VI – Results

VII – Discussion

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Optimization Scheme

Input

Variables

Bootstrap

Resampling

Calibration Data Set Validation Data Set

N = 50

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Calibration Data Set

(70%)

OptimizationSearch for the Parameters set

that Maximize Coen’s Kappa

Validation Data Set

(30%)

Evaluation

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I – Background

II – Overview

III – Data

IV – Algorithm

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IV – Algorithm

V – Optimization

VI – Results

VII – Discussion

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Algorithm Performance

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Initial Empirical Parameters

Optimized Parameters

Optimized Parameters with Spatial Revision

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2001 2002 2003 2004

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2005 2006 2007 2008

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2009 2010 2011 2012

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2013

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I – Background

II – Overview

III – Data

IV – Algorithm

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IV – Algorithm

V – Optimization

VI – Results

VII – Conclusions

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•The flexibility of the algorithm to bad data and atmospheric

contamination.

•Easily adapted to other regions limited only by the

availability and consistency of a time series of observations.

Conclusions

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•Explore the use of Object based classifications to the

resulted date of the year maps

•Improve the optimization procedure and evaluate the cost

function of the spatial revision

Future work

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•Adjust parameters and apply to Mediterranean regions (not

only in Europe but also in US, Australia, South America and

Australia)

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Benali A., Mota B., Pereira J.M.C., Oom D., Carvalhais N. "Global patterns of vegetation fire

seasonality" In proceeding of European Geophysical Union General Assembly (2013)

Killick R., Paul F., I. A. Eckley I. A. "Optimal detection of changepoints with a linear

computational cost." Journal of the American Statistical Association 107.500 (2012): 1590-

1598.

Mota B., Pereira J.M.C., Campagnolo M., Killick R. "MODIS 250m burned area mapping based

on an algorithm using change" (2013) In proceeding of European Geophysical Union General

References

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on an algorithm using change" (2013) In proceeding of European Geophysical Union General

Assembly (2013)

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