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Forest Parameters Extraction Methods from Airborne X-band InSAR and P-band PolSAR Data
Erxue Chen1, Zengyuan Li1, Xin Tian1, Qi Feng1, Lan Li1, Lei Zhao1, Wen Hong2, Eric Pottier3
1 Institute of Forest Resources Information Technique, CAF
2Institute of Electronic , CAS 3I.E.T.R - Univ Rennes 1
• Background • Airborne campaigns & data processing • Forest parameters extraction • Summary and future plan • Other activities
Outlines
Background
Project team Prof. Eric Pottier IETR CNRS UMR 6164, University of Rennes 1 Campus de Beaulieu,-Bat 11D, 263 Avenue Gal Leclerc, 35042 Rennes Cedex, France Tel/Fax : +33 223 235763 / 236963 e-mail: [email protected]
Prof. Erxue Chen Institute of Forest Resources Information technique (IFRIT), CAF. Beijing 100091, P.R. China Tel/Fax: +86 10 6288 99163 / 98315 e-mail: [email protected]
Dr. Konstantinos P. Papathanassiou, DLR Prof. Dr. Irena Hajnsek, DLR Prof. Juan M. Lopez-Sanchez , IUII, University of Alicante, Spain
Prof. Zengyuan Li, IFRIT, CAF. Prof. Wen Hong, Prof. Maosheng Xiang, Mr. Yang Li, IECAS Dr. Xinwu Li, Dr. Lu Zhang, RADI, CAS
DRAGON 3 PolInSAR Project
Dr. Thuy Le Toan, CESBIO, France
https://dragon3.esa.int/web/dragon-3/home
Objectives • Experiments on the airborne PolSAR, InSAR, Pol-InSAR
system with ground truth campaigns over forested areas • Development and test of the methodologies using the
airborne (ESAR- BioSAR 2007 and BioSAR 2008, AgriSAR, China airborne SAR system) PolSAR, Pol-InSAR and Pol-TomSAR data
DRAGON 3 PolInSAR Project
Airborne campaigns (2012, 2013)
Campaign test site • Cold temperate climate zone
Major tree species: Birch, Larch
Airborne Campaign (2012): LiDAR and CCD
Time：Aug. 16th 2012-Sept.25th 2012
Airplane: Yun-5
Coverage area: 360km2
LiDAR system: Leica ALS60+Leica WDM65
CCD: Leica RCD105
Ground true data collection
Yigen Genhe
LiDAR: Riegl LMS-Q680i
Wavelength 1550 nm Laser beam divergence
0.3 mrad
Laser pulse length
3 ns Scan angle range ±30°
Maximum laser pulse
400 KHz Maximum scanning speed
200 lines/s
Waveform sampling 1 ns Vertical accuracy 0.15 m
CCD: DigiCAM-60
Pixel resolution 20cm
Imaging sensor size 43.30mm*53.78mm
Pixel size 6 um
Radiometric resolution
16 bits
Imaging focal length 50 mm
Daxinganling: Yigen & Genhe as intensive test sites
Parameters Operating configure X-band InSAR P-band PolSAR
Operating frequency (GHz) 9.6 600 Available bandwidth (MHz) 400 200 PRF (Hz) 390.623 800 Pulse peak power (kW) 4.0 1.0 Interferometric baseline (m) 2.198 N/A Polarization mode HH HH,HV,VH,VV Ground resolution (m) 0.5 1.0 Swath width (km) 4.864 5.625 Incidence angle (°) 47.1 55.1 Onboard electronics 2 cabinets, 2 antennas 1 cabinet, 1 antenna
Airborne Campaign (2013): X-InSAR and P-PolSAR Airborne SAR System of CASM (Chinese Academy of Surveying & Mapping)
Airplane: Citation II(B-7025) In operation from 2010.
Time： Sept.13th 2013-Sept.16th 2013
Flight height:5670 ~ 5810m
Flight lines:32
Look direction: Right look
Coverage area: more than 5619 km2
7 repeat flights (from west to east)
from east to west
from west to east
7 repeat flights
Daxinganling: Yigen & Genhe as intensive test sites
Yigen Genhe
Airborne Campaign (2013): X-InSAR and P-PolSAR
(m)
• Plot in total.: 39 • Plot size: 314m2
• Plot shape: circle
North
South
East West 314m2
• Forest plots
Length 800m×width 10m
• Forest sampling line
80 plots of size 10m ×10m
Ground true data collected-Yigen test site
Plots distribution over the test site
7
1
4
8 9
5 6
2 3
45m
15m
15m
45m
Ground true data collected-Genhe test site
19 forest plots of size 45m*45m One forest plot
Ground based LiDAR data
Airborne data processing
LiDAR and CCD products-----Yigen test site
DSM DEM
CHM CCD
LiDAR data processing and information extraction
LiDAR and CCD products ----- Genhe test site
DSM DEM CHM CCD
1163
0
(m) 40
0
(m) 1342
0
(m)
LiDAR data processing and information extraction
Extract percentile of 5% h5, 10% h10, 15% h15…95% h95 and the maximum height hmax in each sample plot as a set of independent variables to estimate forest height.
LiDAR estimated forest height----Genhe test site
LiDAR data processing and information extraction
Modeling Accuracy validation
•Take cloud points percentiles and density as independent variables •Take AGB computed from forest plot data as dependent variables •Apply multiple linear stepwise regression model
LiDAR estimated forest above ground biomass----Genhe test site
LiDAR data processing and information extraction
Airborne SAR data processing• X-band Intensity radiometric normalization
• Geometric processing to produce:
Geocoded terrain correction SAR (GTC) image mosaic
• InSAR processing, to produce:
Coherence image
Interferometric Land Use (ILU) image
Digital surface model (DSM)
Whole imaging area(~60km*50km)
P-PolSAR GTC mosaic image
P-PolSAR Pauli-RGB
P-PolSAR Pauli-RGB
P-PolSAR image——forest area
P-PolSAR image——Urban area
P-PolSAR Pauli-RGB
ILU image • Interferometric land use image (ILU)---Yigen
R: coherence; G: mean intensity; B: master to slave intensity ratio
Forest Parameters extraction: method & results
1. Forest AGB estimation from P-PolSAR
intensity
2. Forest height inversion from single track X-InSAR coherence
3. Forest AGB estimation from multi-tracks X-InSAR data
1. Forest AGB estimation from P-PolSAR intensity
P-band PolSAR Pauli-image Local incidence angle image The empirical model for AGB estimation (Sassan Saatchi, 2007)
•Data used •Model applied
160.4
1.1
ton/ha
LiDAR derived AGB
• Derive average AGB with
grid size of 50m*50m,
N=1950
• Model training: N/2
PolSAR estimated AGB
1. Forest AGB estimation from P-PolSAR intensity
Accuracy validation in forest sub-compartment level
N=118
2. Forest height inversion from single track X-InSAR coherence
Model: SINC inversion
LiDAR CHM Coherence Estimated forest height
247 LiDAR derived heights used for validation
kπγπ
kγ
hzz
v v)/)(asin21(2)(2sinc 8.0-1 −
== )(sin2
θλπ
RBk z
⊥=
• 7 repeat observation of forest using X-InSAR
Ground range
Forest AGB can be estimated from combing the DSMs derived from the 7 tracks.
3. Forest AGB estimation from multi-tracks X-InSAR data
Independently sampling of the forest structure: seeing different forest structure -Viewing angle has difference; -There is time gap between the 7 tracks: ~30 minutes to hours.
• Step 1: extract 7 DSM in 2m*2m pixel size
3. Forest AGB estimation from multi-tracks X-InSAR data
627.2
764.8 (m)
632.8
763.8 (m)
630.6
762.7 (m)
630.9
765.7 (m)
631.2
764.2 (m)
630.9
765.0 (m)
630.0
761.8 (m)
Track 1
Track 7 Track 6 Track5 Track 4
Track 3 Track 2
• Step 2: CHM=X-InSAR DSM – LiDAR DTM • Step 3: height distribution histogram with gird size of N*N CHM
3. Forest AGB estimation from multi-tracks X-InSAR data
Track 1 CHM Grid size=15*15
0.0 m
15.0 m
Count/total pixels
Histogram of CHM
7 tracks 7 histograms
• Step 4: mean histogramhmax
Mean histogram
hmax
Step 5: AGB=a+b*hmax Model fitting with some LiDAR AGB
3. Forest AGB estimation from multi-tracks X-InSAR data
• Estimation result and accuracy validation
33.7 ton/ha
0
45.3 ton/ha
0
X-InSAR AGB LiDAR AGB Grid size: 30*30m
Total accuracy=89.45%
3. Forest AGB estimation from multi-tracks X-InSAR data
Summary: 1. P-PolSAR polarimetric intensity can be used for forest AGB estimation
taking the effect of topography into model; 2. The airborne X-InSAR coherence is very useful for forest height
mapping, no temporal baseline InSAR is the key point; 3. Developed one AGB estimation method using multi-tracks X-InSAR
data, with good mapping results but with low map resolution.
Future plan: 1. How to extend the P-PolSAR AGB method to the whole test site?
lacking of high resolution DEM; 2. Physical understanding of X-InSAR coherence’s sensitivity to forest
height.
Other activities
Prof. Eric Pottier Prof. Erxue Chen Prof. Wen Hong All participated as lecturers on this advanced training course.
Advanced Training Course on Land and Water Remote Sensing
Prof. Laurent Ferro-Famil
Dr. Stefano Tebaldini
They gave both lectures and practices on SAR tomography and applications for one whole week at IECAS.
Advanced Training Course in SAR Tomography and Applications
Other activities
Young Scientist Exchange
Yin Qiang, Ph.D candidate, Visited ESRIN as Research Fellow from 2014.2-2015.1
Project: Soil Moisture Estimation Based on Interferometric Phase of Multi-Temporal SAR Data ESA Data: Envisat-ASAR & AgriSAR
EUSAR 2014
Trainees & Fellows Meeting 2014
Co-author paper @PolInSAR 2015
Other activities
Book Translation
S.R. Cloude Published soon in 2015
The new Chinese language edition provides a coherent summary of the original book, taking the form of notations.
Notations in Chinese
Other activities
