Comparison of the two methods

COMPARISON OF TWO REMOTE SENSING TIME SERIES ANALYSIS METHODS FOR MONITORING FOREST DECLINE J. Lambert, A. Jacquin, J-P. Denux, V. Chéret

Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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INTRODUCTION

Context: - During the last decade in the South-West of France : Forest health decline was observed. - Consequence of the 2003 summer drought. - In the coniferous stands in the South of the Massif Central

Mountains: Several clear-cuts (40 km²) due to climateinducted forest decline. - Needs to evaluate and map the forest decline phenomena.

Healthy tree (left) and declining tree (right) Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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INTRODUCTION

A previous study (Chéret et al., 2008) evaluated the forest decline with time series of NDVI MODIS images (2000 – 2007) using a phenological approach Other time series analysis methods exist. BFAST (Break For Additive and Trends) (Verbesselt et al., 2010) is a tool for time series analysis based on the decomposition of time series

Objectives of this study:

1.

To compare a trend analysis method based on the extraction of trends by the decomposition of time series with BFAST to the phenological method used in the previous study

2.

To evaluate the ability of BFAST to detect abrupt phenological changes Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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MATERIAL

900 km² of coniferous forests Spruce fir / Vancouver fir

In two provinces of France : Tarn and Aveyron

NDVI MODIS images time series :

- MOD13Q1 v5 product - 16 days synthesis

- spatial resolution : 250 meters - period : 2000-2007


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METHODS and RESULTS


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METHODS – Comparison of trend analysis methods

SG (Spring Greenness) is the sum of NDVI values from the onset of spring greenness to the end of spring greenness (maxNDVI)

From 2000 to 2007: Linear trends were fitted to the SG time series

250 1

Valeur de NDVI NDVI

Phenological indicator approach

Vegetation season Spring greenness

240

Senescence

End of spring greenness

230 220 210 0,85 200

Onset of spring greenness

190 180

SG

0,70 170

Annual NDVI profile and phenologic metrics figure adapted Mois from B.C. Reed (1994)

Extraction of slope of linear trend (pixel by pixel) Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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Phenological indicator approach Significance of trends : t-test with a null slope as reference Classification of trends : Classification of SG trends values on 4 classes, using field data Class 0 No significant decrease – increase Healthy stands

Class 1

Class 2

Class 3 Very high decrease – declining stands and clear cuts

29 % of forest area caracterized by a very high decrease of activity

Map of classified SG trends, from Chéret et al., 2008 Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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BFAST trend approach - Based on a temporal decomposition method in three components (trend, seasonal and remainder)

Extraction of the slope of the linear trend (pixel by pixel) Significance test Classification of slopes

BFAST decomposition of NDVI time series over 2000-2007 for a single pixel of Douglas fir. Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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Comparison of the two methods SG indicator trends and BFAST trends (with the whole time series)

SG indicator trends and BFAST trends (spring season)

- Standardization of slopes divided by the standard error - Scatterplots and coefficients of determination - Post-classification comparison : error matrices Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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RESULTS – Comparison of trend analysis methods

Comparison of trends processed with the two methods Scatterplots n = 3309

n = 3309

- Significant coefficient of determination in both cases - Better correlation (r² = 0,97) when taking into account only spring dates


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Comparison of trends processed with the two methods Post-classification comparison

BFAST trend classes SG trend classes

0

1

2

3

0

93.7%

5.2%

1.1%

0%

1

72.1%

21.5%

6.4%

0%

2

32%

31.8%

34.6%

1.6%

3

0.6%

3.2%

36.5%

59.6%

whole time series

Class 0 No significant decrease – increase Healthy stands

Class 1

Class 2

Class 3 Very high decrease – declining stands and clear cuts


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Comparison of trends processed with the two methods Post-classification comparison BFAST trend classes (spring dates)

BFAST trend classes SG trend classes

0

1

2

3

SG trend classes

0

1

2

3

0

93.7%

5.2%

1.1%

0%

0

93.1%

6.7%

0.2%

0%

1

72.1%

21.5%

6.4%

0%

1

8.4%

67.8%

23.7%

0%

2

32%

31.8%

34.6%

1.6%

2

0%

7.4%

86.1%

6.5%

3

0.6%

3.2%

36.5%

59.6%

3

0%

0%

1.9%

98.1%

whole time series

spring dates

- Strong correspondence for class 0 - Strong correspondence for all and for class 3 the classes - Weak correspondence for class 1 and for class 2, due to inadapted threshold values Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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Comparison of trends processed with the two methods Post-classification comparison

- Same localization of areas with a very high decrease in activity, in both cases (class 3)

- Weak corresponding localization of areas with an intermediary decrease in activity

SG indicator trend

BFAST trend (whole time series)


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METHODS – Detection of abrupt changes in activity

Breakpoints detection Detection of the most important breakpoint

Extraction of the date (number of the synthesis) and the magnitude of change BFAST decomposition of NDVI time series over 2000-2007 for a single pixel. The date of the most important breakpoint is indicated (- - -).

BFAST parameters: h = 0.15 (at least one year between detected breakpoints) model: “harmonic” (adapted for vegetation dynamics)


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RESULTS – Detection of abrupt changes in activity

Breakpoints detection 35%

- 84,2 % with breakpoint detected - 84,7 % of these breakpoints during or after 2003

Percentage of breakpoint

Pixels with significant negative trend :

30% 25% 20% 15% 10% 5% 0%

NA

2001

2002

2003

2004

NA : No breakpoint detected

- Impact of the 2003 drought

2006

84,7%

0,04

breakpoint magnitude

- Annual mean of breakpoints magnitude highly negative from 2003 to 2006

2005

0,03 0,02 0,01 0 -0,01

NA

2001

2002

2003

2004

2005

2006

-0,02 -0,03 -0,04


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CONCLUSION: Interpretation of results

Comparison of the two trend analysis methods The two trend analysis methods give similar results. When taking into account the same period, trends calculated are identical (r²=0,97).

Taking into account the whole series with BFAST, the difference of the two methods, come from differents phenological periods studied. In that case, studied period is more decisive than the method employed to express the forest decline. - With BFAST : no need to define a period to characterize the vegetation activity (determination of the start and the end of spring greenness season, to calculate SG) - But integration of the variability of activity during summer (highly influenced by annual climatic conditions) Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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CONCLUSION: Interpretation of results

Detection of abrupt phenolocial changes

BFAST allow to detect abrupt decrease of activity, on declining forest In that case, that allow to highlight the consequence of the 2003 summer drought on the declining forest behavior

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CONCLUSION: Future works

Process trend analysis with longer time series (2000 – 2011)

- trends evolution several years after the drought of 2003: to study a regrowth phenomena or the accentuation of the forest health decline To determine a trend threshold

- with localization of clear-cuts and decline field measurements - to discriminate areas caraterized by clear-cuts and high decrease of vegetation activity Use these methods in other territories and on different species (fir and oak forests)

- To determine the potentialities and limits of trend analysis for monitoring forest decline Comparison of two remote sensing time series analysis methods for monitoring forest decline – Multitemp 2011 – 13 July 2011

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Thank you for your attention


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