PhD defence - Steven Vanonckelen

Detection and analysis of forest cover dynamics with Landsat satellite imagery

Application in the Romanian Carpathian Ecoregion

Steven Vanonckelen

Public PhD defense – 5 March 2014

Supervisor: Prof. A. Van Rompaey

ARENBERG DOCTORAL SCHOOL

Faculty of Science

Detection and analysis of forest cover dynamics with Landsat satellite imagery 1/38

• Large dynamics

Introduction Conclusions 4 Research Questions

Forests

• Large dynamics

• Worldwide: deforestation and afforestation

• Provision of ecosystem services


Need for reliable data: – Definition of forest

– Up and downward trends

– Patchy structures

– Validation data

– Remote areas


Forests

Need for reliable data: – Definition of forest

– Up and downward trends

– Patchy structures

– Validation data

– Remote areas

Remote sensing data: + Recurrence time, costs, accessibility, …

- Distortions, calibration, mosaicking, …

(USGS, 2013)


Forest cover dynamics



Vergelijking bosbestand in centraal Europa: 900 AD en. 1900 AD (Schlüter, 1952)

Jaarlijkse snelheid van verandering in landgebruik in de Karpatan tussen 1760 -2010 (Munteanu et al., 2014)

.

Conceptual forest transition curve.


Forest transition

“the process in which the forested area in a given region or country changes from decreasing to expanding” (Mather and Needle, 1998)


Estimated deforestation, by type of forest and time period.

(Williams, 2002; FAO, 2010).

Forest transition

(FAO, 2010)



Forest transition

Recent forest transitions (Meyfroidt and Lambin, 2011).



Scientific challenges?

Reliable forest assessments

• standardized procedures at regional scale, • link with ecosystem services, • allow policies and financial compensations.

Detection of forest cover dynamics

• fluctuating forest cover trends and patchy landscapes, • in remote and inaccessible areas, • inclusion of translocation processes.

Use of satellite data (remote sensing tools)

• different remote sensors, • improvement of mapping procedures, • allows mapping of large areas, • trade-off: complexity automation.

(FAO, 2010)

(FAO, 2010)



4 Research questions

1. Do atmospheric and topographic corrections improve Landsat satellite imagery in mountain areas?

2. Does image preprocessing (corrections) lead to more accurate land cover classification?

Introduction 4 Research Questions Conclusions

(Riaño et al., 2003)

(ASTRIUM, 2013)


4 Research questions

3. Does topographic correction and pixel-based image compositing improve large area change mapping? 4. What are the controlling factors of forest cover dynamics in the Romanian Carpathians?

Introduction 4 Research Questions Conclusions



Problems with satellite imagery:

• Sensor calibration,

• Orbital drift,

• Atmospheric and topographic distortions correction

decomposition and evaluation

Atmospheric correction:

Introduction Research Question 1 Conclusions

Process of solar (ir)radiance entering a sensor: (a) path radiance, (b) solar direct irradiance, (c) sky diffuse irradiance, and (d) adjacent terrain reflected irradiance.


Selection of 2 atmospheric correction (AC) methods (Vanonckelen et al., 2013).

complex


Topographic correction:

15 combined corrections



Selection of 4 topographic correction (TC) methods (Vanonckelen et al., 2013).

complex

AC/TC No TC Band Ratio Cosine PBM PBC

No AC

DOS

TF

Study area

One Landsat footprint in the Romanian Carpathians (185 x 185 km, 2009).

(a) Mixed and broadleaved forests, (b) coniferous forests, (c) small vegetation, and (d) grasses above the tree line.




Elevation range (SRTM, 2010).

Methodology

Overview of the methodology (Vanonckelen et al., accepted).




Evaluation methods

a. Differences in reflectance values between shaded

and illuminated slope groups,

b. Coefficient of variation,

c. Correlation between reflectance and cos β.

Average reflectance (%) calculated in the forest class as a function of spectral band (Vanonckelen et al., accepted).




True color composite images (RGB: band 3, 2 and 1) of the study area (Vanonckelen et al., accepted).

(a) no AC or TC (b) TF with cosine (c) TF with PBC

Evaluation methods

a. Differences in reflectance values between shaded and illuminated slope groups,

b. Coefficient of variation of reflectance values,

c. Correlation between reflectance values and cos β.

Yes!


(a) no AC or TC (b) DOS without TC (c) DOS with band ratio

(d) TF with cosine (e) TF with PBM (f) TF with PBC

True color composite images (RGB: band 3, 2 and 1) of the study area (Vanonckelen et al., accepted).



2. Does image preprocessing lead to more accurate land cover classification?

Methodology

2009 and 2010

Overview of the methodology (Vanonckelen et al., 2013).




Evaluation methods

a. Separability of reflectance values,

b. Overall accuracy,

c. Class accuracy,

d. Accuracy in 3 illumination conditions.


TF-P

BM

ref

lect

ance

(%

)

Wavelength (µm)

0

5

10

15

20

25

30

35

40

0.49 0.56 0.66 0.82 1.65 2.25

0

5

10

15

20

25

30

35

40

0.49 0.56 0.66 0.82 1.65 2.25

Un

corr

. ref

lect

ance

(%)

(a) (b)

Wavelength (µm)


Average reflectance values per wavelength and land cover type : uncorrected and corrected ( Vanonckelen et al., 2013).


Evaluation methods

a. Separability of reflectances,

b. Overall accuracy (average kappa),

c. Class accuracy,



0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

No

TC

Ban

d ra

tio

Co

sin

e

PB

C

PB

M

No

TC

Ban

d ra

tio

Co

sin

e

PB

C

PB

M

No

TC

Ban

d ra

tio

Co

sin

e

PB

C

PB

M

No AC DOS TF

Ave

rage

kap

pa


Average kappa coefficients of the 2009 and 2010 images (Vanonckelen et al., 2013).


Evaluation methods



c. Class accuracy (δkappa values),


OVERALL

Average 2009-2010 δkappa per class between uncorrected and corrected images (Vanonckelen et al., 2013).




Evaluation methods



c. Class accuracy,

d. Accuracy in 3 illumination conditions (average kappa).

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

No

TC

Ban

d ra

tio

Co

sin

e

PB

C

PB

M

No

TC

Ban

d ra

tio

Co

sin

e

PB

C

PB

M

No

TC

Ban

d ra

tio

Co

sin

e

PB

C

PB

MNo AC DOS TF

Ave

rage

Kap

pa



Average kappa coefficients of the 2009 and 2010 images for three different illumination characteristics (Vanonckelen et al., 2013).


Evaluation methods



c. Class accuracy,


Yes!



True color composite (RGB: band 3, 2 and 1) and ML classification of 2009 (Vanonckelen et al., 2013).

(c) TF with PBC

(a) no AC or TC

(b) TF with cosine

3. Does topographic correction and pixel-based image compositing improve large area change mapping?

Study area: Romanian Carpathian Ecoregion



Elevation range (Vanonckelen et al., submitted)

Pixel-based compositing:

- Selection of the most suitable pixels from available imagery,

- For three years: 1985, 1995 and 2010,

Topographic correction: PBM,

Two classification methods: ML and SVM.

(Vanonckelen et al., submitted)




(Vanonckelen et al., submitted)


Methodology



Overview of the methodology.

a. Overall accuracy for different scenarios:

- uncorrected and corrected,

- 2 classifiers: ML and SVM,

- number of classes: 8 or 4.

Yes!

b. Land cover maps,

c. Land cover change maps.




Overall accuracies between uncorrected (uncorr.) and corrected (corr.) data.

1985




2010 1995 b.

1985-1995 1995-2010



(foto: Deliever, 2011)


c.

1995-2010



2000-2010

(Hansen et al., 2013)

vs.


c.

a. Accessibility, distance to • primary roads, • secondary roads, • nearby settlement.

b. Demography

• demographic evolution.

c. Land use policy

• protection level (SCI or SPA).

d. Biophysical environment

• slope gradient, • elevation, • soil type (7 types).

4. What are the controlling factors of forest cover dynamics in the Romanian Carpathians?


(foto’s: Deliever, 2011) Afforestation (AFFOR)


Deforestation (DEFOR)

1985-1995 1995-2010

a. Accessibility, distance to • primary roads + corr. for AFFOR, - corr. for DEFOR • secondary roads - corr. for AFFOR and DEFOR • nearby settlement + corr. for AFFOR, - corr. for DEFOR

b. Demography


c. Land use policy

• protection level (SCI or SCA).





Primary and secondary roads (European Commission, 2013b).



b. Demography

• demographic evolution + corr. for AFFOR and DEFOR

(2nd per.)

c. Land use policy • protection level (SCI or SPA).





(foto’s: Deliever, 2011)


Change in inhabitants per km2 between 1986-2000 (NIS, 2013).


b. Demography


c. Land use policy

• protection level (SCI or SPA) + corr. for DEFOR (2nd per.)





Protected area s (European Commission, 2013b).



b. Demography


c. Land use policy

• protection level (SCI or SPA).


• slope gradient + corr. for AFFOR and DEFOR • elevation + corr. for AFFOR and DEFOR • soil type (7 types) Podzols + corr. for DEFOR




Elevation range (SRTM, 2010).

Main soil types (FAO/UNESCO, 1998).

Conclusions


Yes, homogeneity of pixels increased and dependency of reflectance values on terrain illumination was reduced after correction.

2. Does image preprocessing lead to more accurate land cover classification? Yes, corrected images resulted in higher overall classification accuracies than uncorrected images, especially on weakly illuminated slopes.



Conclusions

3. Does topographic correction and pixel-based image compositing improve large area change mapping? Yes, highest classification accuracies for corrected scenario with 4 classes and observation of a steady greening between 1985 and 2010. 4. What are the controlling factors of forest cover dynamics in the Romanian Carpathians? Mainly positively related to the biophysical environment (elevation and slope gradient) for both periods.



Further research

• Improvement of forest maps and monitoring of ecosystem services,

• Basis for policy instruments and conservation measures,

• Comparative analyses of corrections,

• Evaluation of trade-offs between correction complexity and automation,

• Topographic correction before pixel-based compositing,

• Addition of extra parameters in the pixel-based compositing technique.



ARENBERG DOCTORAL SCHOOL

Faculty of Science

Thanks for your attention!

PhD defence - Steven Vanonckelen

Environment

forest cover trends

definition of forest

type of forest

forest transition fao

landsat footprint

introduction research

combined corrections

topographic corrections