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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.
Radar remote sensing for forest parameter estimation
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
Radar remote sensing for forest parameter estimation
1. Basics: Pros and Cons of SAR data
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen3
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
Radar remote sensing for forest parameter estimation
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.
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen4
1. Basics:
Different wavelengths in forest parameter estimation
Radar remote sensing for forest parameter estimation
1. Basics:
Different polarizations in forest parameter estimation
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen5
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)
Radar remote sensing for forest parameter estimation
1. Basics:
SAR Interferometry (InSAR)
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen6
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
Radar remote sensing for forest parameter estimation
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]
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen7
Radar remote sensing for forest parameter estimation
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
Radar remote sensing for forest parameter estimation
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)
Radar remote sensing for forest parameter estimation
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)
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen
© DLR
Radar remote sensing for forest parameter estimation
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
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen
Radar remote sensing for forest parameter estimation
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:
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen
Radar remote sensing for forest parameter estimation
BoDEM: Test sites
13Test sites in Germany
(temperate forests)
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen
Test sites in Canada (boreal forests)
Hainich,
Germany
Radar remote sensing for forest parameter estimation
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
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen14
Radar remote sensing for forest parameter estimation
First results: Forest Mask from Sentinel-1 / TanDEM-x
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen15
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
Radar remote sensing for forest parameter estimation
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen16
• 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
stratificationForest structure
Forest (InSAR)
height
First results: Forest Mask from Sentinel-1 / TanDEM-x
Radar remote sensing for forest parameter estimation
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen17
Forest MaskForest
stratificationForest structure
Forest (InSAR)
height
First results: Forest Mask from Sentinel-1 / TanDEM-x
Backscatter(Sentinel-1) Texture
(Sentinel-1)
CoV(Sentinel-1)
Coherence(TanDEM-X)
Radar remote sensing for forest parameter estimation
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen18
• 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.
First results: Forest Mask from Sentinel-1 / TanDEM-x
Preliminary forest mask from
TanDEM-X / Sentinel-1 data,
test site Hainich, Germany
Radar remote sensing for forest parameter estimation
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen19
Comparison of the
different forest/non-
forest masks and
their overall
accuracies (test site
Hainich, Germany).
First results: Forest Mask from Sentinel-1 / TanDEM-x
Radar remote sensing for forest parameter estimation
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
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen20
Fig.: Concept InSAR (RIBBES et al., 1997).
InSAR
height
© adapted from Deutscher,
J. et al. Remote Sens. 2013, 5, 648-663.
Forest MaskForest
stratificationForest structure
Forest (InSAR)
height
Radar remote sensing for forest parameter estimation
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).
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen21
Radar remote sensing for forest parameter estimation
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
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen22
Radar remote sensing for forest parameter estimation
Thank you for your attention!
Gracias por su atención!
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen23
Radar remote sensing for forest parameter estimation
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)
15. November 2016DAAD Alumni and Student Workshop, Santiago de Chile, Stefan Erasmi, Georg-August-University Göttingen24
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