Combining SAR and optical time series for

monitoring tropical forest change (Group 4)

Johannes Reiche, Jan Verbesselt, Dirk Hoekman, Eliakim Hamunyela, Sytze de

Bruin, Arun Pratihast, Jan Pokorn, Christos Sotiropoulos, Martin Herold*

Wolf Forstreuter **

* Laboratory of Geo-Information Science and Remote Sensing, Wageningen University &

Research, The Netherlands

** SOPAC, Fiji

Content

1. Introduction

2. “Bayesian approach" to combine multi-sensor time series for

NRT deforestation detection

3. Work progress (Bolivia, Fiji, Ethiopia)

1

Introduction: NRT monitoring of tropical forest change

Near-real time (NRT) = capacity to detect changes in new satellite

images once they are available

Optical time series approaches: mainly for detecting historical

changes; limited in cloud covered regions

SAR time series approaches: few exist, but limited observations in

the past & commercial distribution

Multi-sensor SAR-optical: “pioneer” approaches exist, but limited

to combining Landsat & ALOS PALSAR (e.g. Lehmann et al., 2012,

Reiche et al. 2015a/b)

2

-> Need & potential for SAR-optical multi-sensor combination!

3

Introduction: Medium resolution optical and SAR sensors

Reiche et al., 2016 (Nature Climate Change, 6, 120-122): Combining satellite data for better tropical forest monitoring.

Introduction: Correlation and/or fusion of optical and SAR

time series not directly feasible!

90% missing data

Lan

dsa

t N

DV

I

PALS

AR

HV

HH

i. they are discrete

ii. their individual observations are non-equidistant in time

iii. their observation times are not identical

iv. their scales/unit are not directly compatible (e.g. NDVI vs. SAR backscatter)

4

Bayesian approach to combine multi-sensor

time series for NRT deforestation detection

Reiche et al., 2015, (Remote Sensing, 7, 4973-4996): A Bayesian Approach to Combine Landsat and ALOS PALSAR Time

Series for Near Real-Time Deforestation Detection.

5

Input: Multi-sensor time series observations in NRT environment

s1t-2

Past observations

s2t-1 s1t s1t+1 s2t+2 s2t+n

Current observation

Future observations

s2t+2

sensor 2 at time = t+2

(2nd future observation)

sensor 2 =

ALOS PALSAR HV

backscatter

Bayesian approach

s1t

sensor 1 at time = t

(current observation)

sensor 1 =

Landsat NDVI

6

Step 1: Deriving and combining TS of conditional non-forest (NF) probabilities

s1t-2

Past observations

s2t-1 s1t s1t+1 s2t+2 s2t+n

Current observation

Future observations

Sensor specific forest (F) and non-forest (NF) pdfs

sNFt-2 sNF

t-1 sNFt sNF

t+1 sNFt+2 sNF

t+i Conditional non-forest probabilities

Multi-sensor time series observations

Bayesian approach

7

Step 1: Deriving and combining TS of conditional non-forest (NF) probabilities

s1t-2

sNFt-2 NF

F

Sensor specific forest (F) and non-forest (NF) pdfs

Sensor specific forest (F) and non-forest (NF) pdfs for sensor 1 (Landsat NDVI)

1

( 1 | )( | 1 )

( 1 | ) ( 1 | )

tt s

t t

p s NFP NF s for t T

p s NF p s F

P(NF|s1) = conditional NF probability

p(s1|F) = conditional probability of s1 given the presence of F

p(s1|NF) = conditional probability of s1 given the presence of NF

1( | 1 ) :

( | ) :

t sNF

t

t sn

P NF s t T

s

P NF sn t T

sNF = combined time series of conditional NF probabilities

s1t-2 (NDVI ) = 0.7 = 0.1

= 0.18

= 0.64

Bayesian approach

8

Step 1: Deriving and combining TS of conditional non-forest (NF) probabilities

s1t-2

Past observations

s2t-1 s1t s1t+1 s2t+2 s2t+n

Current observation

Future observations

Sensor specific forest (F) and non-forest (NF) pdfs

sNFt-2 sNF

t-1 sNFt sNF

t+1 sNFt+2 sNF

t+i Conditional non-forest probabilities

Multi-sensor time series observations

Bayesian approach

9

s1t-2

Past observations

s2t-1 s1t s1t+1 s2t+2 s2t+n

Current observation

Future observations

sNFt-2 sNF

t-1 sNFt sNF

t+1 sNFt+2 sNF

t+i Conditional non-forest probabilities

Multi-sensor time series observations

Conditional probability of deforestation at t

( | )NFt t iP D s

Step 2: Iterative Bayesian updating of the probability of deforestation

Bayesian approach

10

s1t-2

Past observations

s2t-1 s1t

Current observation

sNFt-2 sNF

t-1 sNFt

Conditional probability of deforestation at t

( | )NFt t iP D s

• If conditional NF probability (sNF ) > 0.5 --> Flag potential deforestation --> Calculate conditional probability of deforestation, P(Dt|sNF

t+i)

Bayesian probability updating

1( | ) ( | )( | )

( )

NF NFNF t i t t i

t t i NFt i

P s D P D sP D s

P s

Step 2a: Flag potential changes and calculate probability of deforestation

Bayesian approach

13

s1t-2

Past observations

s2t-1 s1t

Current observation

sNFt-2 sNF

t-1 sNFt

Conditional probability of deforestation at t

( | )NFt t iP D s

• Future observations used as new evidence to update P(Dt|sNF

t+i) and to confirm or reject the deforestation event by exceeding a threshold

s1t+1

sNFt+1

Step 2b: Iterative Bayesian updating using upcoming observations

Bayesian approach

• If conditional NF probability (sNF ) > 0.5 --> Flag potential deforestation --> Calculate conditional probability of deforestation, P(Dt|sNF

t+i)

Bayesian probability updating

1( | ) ( | )( | )

( )

NF NFNF t i t t i

t t i NFt i

P s D P D sP D s

P s

1.0

0.8

0.6

0.4

-4

-5

-6

-7

ALO

S PA

LSA

R

H

VH

Hm

t [d

B]

Lan

dsa

t N

DV

I

2005.0 2006.0 2007.0 2008.0 2009.0 2010.0

remaining cloud

11

Original time series (top)

Bayesian approach: single-pixel example

1.0

0.8

0.6

0.4

1.0

0.8

0.6

0.4

0.2

0

-4

-5

-6

-7

ALO

S PA

LSA

R

H

VH

Hm

t [d

B]

Lan

dsa

t N

DV

I sN

F

2005.0 2006.0 2007.0 2008.0 2009.0 2010.0

old flagged change

TF (DOY: 2008.219)

T (DOY: 2008.266)

Reference: 2008.3 (DOY: 2008.182 – 2008.273)

remaining cloud

Original time series (top) Conditional NF probabilities (sNF) (bottom)

11

-4

-5

-6

-7

ALO

S PA

LSA

R

H

VH

Hm

t [d

B]

remaining cloud

old flagged change

TF (DOY: 2008.219)

T (DOY: 2008.266)

old flagged change

Change flagged (DOY: 2008.219)

Change confirmed (DOY: 2008.266)

Bayesian approach: single-pixel example

Work progress

1. Bolivia: NRT deforestation detection in dry forest

(Sentinel-1 + PALSAR-2 + Landsat)

2. Fiji (FIJ-1): Post-cyclone forest disturbance detection

(Sentinel-1 + PALSAR-2 + Landsat)

[MSC thesis of Jan Pokorn; started 09/16)

3. Ethiopia (ETH-1): Combining remote sensing and community-based

data streams

[MSC thesis Christos Sotiropoulos; started 10/16)

12

Size: 100 x 100 km

Tropical dry broadleaf

forests

Industrial logging

Frequent cloud cover

13

Combining Sentinel-1, PALSAR-2 & Landsat for NRT

deforestation detection in dry forest (Bolivia)

Sentinel-1 VV

Reiche et al., (in prep): Near real-time deforestation monitoring in dry tropical forest by combining Sentinel-1, PALSAR-2 and Landsat time series imagery.

14

Valid observation (10/2014 – 10/2016)

Time series processing

Sentinel-1 VV & ALSO-2 PALSAR-2 HVHH-ratio: standard processing,

quality control, multi-temp filtering (Gamma software)

Landsat NDVI: standard processing, quality control (Ledaps, Fmask)

Co-registration of Sentinel-1, PALSAR-2 to Landsat (Gamma software)

Spatial normalisation to reduce seasonality in time series (Eliakim

Hamunyela et al., 2016, RSE)

15

Spatial normalisation

Apply forest mask

Image-wise spatial normalisation

Normalised pixel = pixel – 95th percentile of surrounding area

Landsat

Sentinel-1

16

Spatial normalisation

Landsat

16

PALSAR-2

Apply forest mask

Image-wise spatial normalisation

Normalised pixel = pixel – 95th percentile of surrounding area

Detected deforestation 10/2015 – 10/2016

17 0 10 km

2016/10

2015/10

Detected deforestation 10/2015 – 10/2016

17 0 10 km

Change flagged (DOY):2016.364 Change confirmed (DOY): 2016.5

2016/10

2015/10

Detecting post-cyclone forest disturbance (FIJ-1)

Cyclone Winston (20-02-2016)

● 40,000 homes were damaged or destroyed

● Fiji Pine lost 2 million dollars

Close cooperation with Fiji Forestry Department (FFD) & SOPAC

Reference data: VHR (Airbus), Ground data (FFD)

MSC thesis Jan Pokorn; started in 09/2016

18

Detecting post-cyclone forest disturbance (FIJ-1)

19

Conclusion

Thanks GFOI, ESA & K&C!

Very, very exciting!

Combining SAR and optical time series makes a lot of sense

Work plan for next year:

Finish Bolivia work (validation & publishing)

Continue work in Fiji (FIJ-1) & Ethiopia (ETH-1)

Further develop “Bayesian approach” beyond “pioneer level” (e.g.

multi-modal and multi-spectral class descriptions)

R-package on “Bayesian approach” with reproducible examples

24

“More observations make everything better!”

Curtis Woodcock, ESA LPS 2016

Reiche et al., 2015, (Remote Sensing, 7, 4973-4996): A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series

for Near Real-Time Deforestation Detection.

Reiche et al., 2016 (Nature Climate Change, 6, 120-122): Combining satellite data for better tropical forest monitoring.

Reiche et al., (in prep): Near real-time deforestation monitoring in dry

tropical forest by combining Sentinel-1, PALSAR-2 and Landsat time series imagery.