radar remote sensing for forest parameter...

Radar remote sensing for forest parameter estimation

Radar remote sensing for forest

parameter estimation

Stefan Erasmi, Daniel Baron

Georg-August-Universität Göttingen

Institute of Geography

Cartography, GIS & Remote Sensing Dept.


Contents

1. Basics of SAR data

2. Concepts of SAR data analysis

for forest mapping and monitoring

3. Example: BoDEM project

15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen2


1. Basics: Pros and Cons of SAR data


Advantages of SAR data(compared to optical remote sensing data)

• Microwaves enable a weather- and

illumination-independent imaging process

• Higher temporal resolution

(repeat cycle e.g. 6 days for Sentinel-1)

potential to fill spatial and temporal gaps in forest inventory data

© SAR-Edu (2014), FAO (2009), Balzter (2001)

TerraSAR-X

Disadvantages of SAR data

• Backscatter saturation, especially

in mature forests with complex stand structure

• Topography effect in rugged or

mountainous regions

can affect / eliminate vegetation

backscatter

• experimental / case-study stages


Fig. and Tab.: Main scatterers at different frequencies (LE TOAN ET AL., 2001).

The main scatterers in a canopy are the elements having dimension of the order of the wavelength.


1. Basics:

Different wavelengths in forest parameter estimation


1. Basics:

Different polarizations in forest parameter estimation


http://www.eorc.jaxa.jp/ALOS/en/img_up/pal_polarization.htm

• Single polarization

– HH (horizontal -horizontal)

– VV (vertikal -vertikal)

• Scattering depends on the polarization properties of the target

• Thus, the different scattering patterns among polarizations can be used to observe forest

parameters, e.g.:

• Volume scattering leads to inversion of polarization HV / VH for vertical structure

assessment

Cross polarization:

HV (horizontal -vertikal)

VH (vertikal -horizontal)


1. Basics:

SAR Interferometry (InSAR)


Coherence and InSAR phase contain information on forest parameters

Interferometric Coherence – correlation of two complex SAR images

Fig.: Concept InSAR (RIBBES et al., 1997).

**

*

2211

12

ssss

ss

ie

12 , ss

degree of coherence

interferometric phase

ensemble average

co-registered compleximage values

Complex interferogram:

Fig.: Coherence image of simultaneous C-band acquisition


Physical modellingClassification / regression

2. Concepts:

Estimation methods for forest variables

Forestparameter

Backscatter InSAR PolInSAR

Relating the backscatter

values to plot parameters

(e.g. type, biomass, stem

density) using regression

analysis or classification

algorithms

Examining the coherence

of two SAR images

collected from similar

viewing positions with a

short time-lag

direct estimation of e.g.

forest height from single

frequency polarimetric-

interferometric SAR data

Conversion to stand

variable, e.g. biomass

through allometric relations

[modified after GHASEMI, 2011]



3. Example:

Processing of digital terrain models from X- and C-band

SAR data for the derivation of high resolution surface

layers for soil and ecosystem mapping (BoDEM)

sub-project:

Modelling of structural parameters in forests

from multi-frequency, multi-polarized SAR

satellite data

8 15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen


Background: the TanDEM-X Mission

Overall aim:

– Generation of global DEM at 12 m resolution

Concept:

– Twin satellites flying in close formation

forming a single-pass SAR interferometer

– Standard image mode:

one transmitter, two receiving satellites (bistatic)

9

Abb.: 1.TanDEM-X-und TerraSAR-X. Source: DLR

Figure 5: Illustration of the Helix orbit

configuration of both spacecraft (image

credit: DLR)

15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen

Figure 2: Concept of TanDEM-X InSAR observations in bistatic (left) and

monostatic (right) modes (image credit: DLR)


Background: the TanDEM-X Mission

10

Figure 1: DEM-level versus coverage

indicating the uniqueness of the global

TanDEM-X HRTI-3 product (image

credit: DLR)


© DLR


Problem / Challenge of TanDEM-X DEM data

(and all other satellite based DEMs)

11

Represents top-of-canopy / digital surface model (DSM)

Limited usability for quantitative modeling in ecology, hydrology,

soil science, …

Aim of project BoDEM:

Development of a workflow for reduction / elimination of object

height (e.g. forest) from TanDEM-X DEM (DSM DTM)

Additional benefit:

Evaluation of canopy height estimation from multi-frequency,

polarimetric and interferometric SAR satellite data.

Digital Surface ModelDigital Terrain Model

Canopy Height Model



Forest MaskForest

stratificationForest

structure

Forest(InSAR) height

Requirements of workflow for TanDEM-X DEM correction

(or forest height mapping resp.)

12

Reproducibility, transferibility

based on satellite SAR missions (specification of AO by DLR)

No information about ground height (DTM) needed!

Milestones / generalized workflow of forest parameterization:



BoDEM: Test sites

13Test sites in Germany

(temperate forests)


Test sites in Canada (boreal forests)

Hainich,

Germany


Satellite data(AP2100)

TanDEM-X

DEM

TanDEM-X

CoSSC

Sentinel-1

RADARSAT-

2

Sensor parameters(AP3100)

TanDEM-X

Metadata:• HoA / baseline

• Incidence angle

• Viewing direction

• Data of acquisition

• No. of acquisitions

• Height Error

• Inconsistencies

TanDEM-X

AuxFiles

TanDEM-X

Production

Database

DHM Height

Stand parameters(AP4000)

Amplitude

Amplitude / Pol.

Decomposition

Layers

Coherence/

Amplitude

Forest mask/

gap detection

Stratification

Structural

attributes

InSAR-Height

Valid

ation

(AP

61

00

)

Indirect forest parameter estimation from SAR

General concept:

• Look-Up Table (LuT) with all possible combinations of sensor and

stand parameters

• Multi-criteria-analysis



First results: Forest Mask from Sentinel-1 / TanDEM-x


Forest/non-forest maps from interferometric TanDEM-X and multitemporal

Sentinel-1 SAR data – an example from the Hainich Region, Germany

Flowchart of the steps performed for Sentiel-1 and TanDEM-X for a multisensor unsupervised classification.

Forest MaskForest

stratificationForest structure

Forest (InSAR)

height



• Input data for Hainich test site:

- 49 Sentinel-1A IW SLC backscatter intensity scenes from March to

November 2015 in ascending and descending orbit and VV and

VH polarisation

- 40 grey-level co-occurrence matrix texture measures compressed

with PC transformation from Sentinel-1A

- 4 coefficients of variation. One per polarisation and orbit

- 9 coherence scenes from TanDEM-X CoSSC bistatic stripmap

mode data

- Total: 102 scenes as input

• Classification unsupervised with random forest and kmeans

Forest MaskForest


Forest (InSAR)

height




Forest MaskForest


Forest (InSAR)

height


Backscatter(Sentinel-1) Texture

(Sentinel-1)

CoV(Sentinel-1)

Coherence(TanDEM-X)



• Importance of textural features demonstrated

• Descending orbit seems to be more suitable

• VH polarisation more sensitive to vegetation (volume

scattering)

• For final classification only the 18 Most important

variables plus coherence were used

Boxplot of the variable importance calculated by the

random forest classifier. Illustrated are the 30 most

important features from five runs of random forest.


Preliminary forest mask from

TanDEM-X / Sentinel-1 data,

test site Hainich, Germany



Comparison of the

different forest/non-

forest masks and

their overall

accuracies (test site

Hainich, Germany).



Outlook: Direct PolInSAR forest height estimation

• Forest Height Inversion Modeling

• Polarimetric InterferometricSAR data (PolInSAR)

• Adaptation of RVoG modelto TanDEM-X X-band dual pol data


Fig.: Concept InSAR (RIBBES et al., 1997).

InSAR

height

© adapted from Deutscher,

J. et al. Remote Sens. 2013, 5, 648-663.

Forest MaskForest


Forest (InSAR)

height


Outlook: Direct PolInSAR forest height estimation

LIDAR first return

from forest canopy

LIDAR last return

from forest floor

P-band return from

forest floor

Fig.: WOODHOUSE; data from SASSAN SAATCHI, JPL.

Problem:

Measurement relies on height of phase center for different polarizations.

In all other than P-band, phase center is not the ground and depends on

forest parameters (e.g. density).



Summary and outlook

• Tremenduous potential of SAR satellite remote sensing for forest monitoring

and mapping issues at regional to global level due to frequent and reliable

observations

• SAR signal processing is complex compared to optical satellite data but

yields physical quantitative measures that describe the vertical structure of

vegetation layers more directly

• Established workflows for forest mapping based on backscatter analysis

(single- / dual-polarisation; X-,C-,L-band) and coherence information (e.g.

TanDEM-X) available

• Polarimetry / polarimetric interferometry: still need for research, lack of

operational sensors / availability of data



Thank you for your attention!

Gracias por su atención!



Credits

References:Ghasemi, N., Sahebi, M. R., & Mohammadzadeh, A. (2011). A review on biomass estimation methodsusing synthetic aperture radar data. International Journal of Geomatics and Geosciences, 1(4), 776-788.

Ribbes, F., Le Toan, T., Bruniquel, J., Floury, N., Stussi, N., Liew, S. C. & Wasrin, U. R. (1997). Forestmapping in tropical region using multitemporal and interferometric ERS-1/2 data. Proceedings ofCongrès. Space at the service of our environment, 3rd ERS Symposium, March 14-21, 1997, Florence,Italy.

Le Toan, T. (2001). On the relationships between Radar measurements and forest structure andbiomass. Proceedings of the Third International Symposium on Retrieval of Bio- and GeophysicalParameters from SAR Data for Land Applications, September 11.-14., Sheffield, UK.

Woodhouse (not specified): Forest biomass from active remote sensing? University of Edinburgh.Edinburgh Earth Observatory. Retrieved on August, 10, 2012 from <http://www.geos.ed.ac.uk/conferences/measuring-carbon-in-practice/presentation_IW.pdf>

© SAR-Edu (2014): content of slides 4, 6, 7, 20 and 21 is partly taken from online resources of the SarEDUinitiative (https://saredu.dlr.de/) and is licensed under a Attribution-ShareAlike 4.0 International License(CC BY-SA 4.0)


https://saredu.dlr.de/

http://creativecommons.org/licenses/by-sa/4.0/

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