Forest mapping using multi-temporal polarimetric SAR data in southwest China
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Transcript of Forest mapping using multi-temporal polarimetric SAR data in southwest China
Forest mapping using multi-temporal polarimetric SAR data
in southwest China
Yun Shao*, Fengli Zhang*, Maosong Xu#, Zhongsheng#
Xia, Chou Xie*, Kun Li*, Zi Wan*, Ridha Touzi
•*Institute of Remote Sensing Applications, Chinese Academy of sciences, China
•#State Foresty Adaministration
•Canada Center for Remote Sensing
E-mail: [email protected]
IGARSS 2011
Outline
2
Introduction
Forest mapping and protection has remained an important task in China.
Dense forest distributes in southwest China where the weather condition is usually poor, with the annual clear days less than 50-100.
Introduction
SPOT 5 has ever
been widely used for
forestry inventory,
but severely
hindered by the
cloudy and rainy
weather.
SAR can be used
for forest
monitoring
because of its all
weather
capabilities.
Introduction
In this paper, six Fine Quad-polarization RADARSAT-2
images were obtained with the support of the Science and
Operational Applications Research for RADARSAT-2
Program (SOAR), and used for forest mapping capability
assessment.
Methods for forest mapping based on polarimetric
decomposition and multi-temporal polarimetric SAR data
fusion were proposed.
Test site and data sources
Zhazuo area, Guizhou
province, southwest of China,
area of 218 km2.
Mid-subtropical rainy and
humid climate, four distinct
seasons, warm, humid and
long frost-free period.
Covered with dense
coniferous forests with long
growth cycle.The four dominant forest species
Test site and data sources In early 2008, a severe snow swept south China and with long lasting
cold, frozen weather, not happened for last 50 years. It caused great
damage to forest ecosystems in 18 provinces.
Test site and data sources
Field investigations were
carried out in the test site.
12 sample plots, with the
average size of 1024m2,
covering dominant species
and different ages.
Ground measurements
Investigating land cover types and
their distribution in the test site. All
sample plots were geo-referenced
using a global positioning system
(GPS).
Leaf area index, tree height,
diameter at breast height, tree
canopy parameters, such as crown
thickness; branch length/diameter
and other parameters.
Date of eight times of ground test:
1 : 4/8/2008 - 22/8/2008
2 : 28/9/2008 - 11/10/2008
3 : 27/10/2008 - 8/11/2008
4 : 4/12/2008 - 9/12/2008
5 : 5/2/2009 - 23/2/2009
6 : 8/4/2009 - 16/4/2009
7 : 16/6/2009 - 23/6/2009
8 : 18/8/2009 - 23/8/2009
10
Polarimetric SAR data gathered
SatelliteDate
(UTC)Pass
IncidenceAngle
(degree)Mode
Resolution (m)
RADARSAT-2 4/11/2008 Descending 34.64 FQ 7.9
RADARSAT-2 8/2/2009 Descending 34.64 FQ 7.9
RADARSAT-2 26/7/2009 Descending 34.64 FQ 7.9
RADARSAT-2 19/8/2009 Descending 34.64 FQ 7.9
RADARSAT-2 12/9/2009 Descending 34.64 FQ 7.9
RADARSAT-2 6/10/2009 Descending 34.64 FQ 7.9
FQ=Fine
The six RADARSAT-2 images were all in descending orbit, owned repeat orbit and nearly the same incidence angle, with maximum baseline about 386 meters.
Polarimetric signatures
All types of forests, including coniferous forest, deciduous forest, and mixed forest, own similar polarimetric signatures and have apparent volume scattering.
In co-polarized signature , there are low values from orientation angle from ψ=45 to 135, probably because the dihedral scattering formed by the sparse trees and the ground is more strong for deforestation area, while for normal forest less C-band microwave can penetrate the canopy and then form dihedral scattering.
Forest is dominated by dipole scattering, showing in green.
Buildings in urban areas tend to form dihedral scattering, or even scattering,
so urban presents in pink.
Farmland is mainly surface scattering and appears in blue.
Red=even scattering, Green=π/4 even
scattering, Blue=odd scattering
Forest identification based on polarimetric decomposition
Identification deforestation caused by snow storm
For deforestation area, Freeman decomposition seems better than Pauli decomposition.
Forest damaged by snow is still dominated by volume scattering, yet surface scattering is much more than normal forest, showing obvious light blue in Freeman decomposition image.
Pauli decomposition Freeman decomposition
Pauli decomposition for multi-temporal SAR data
Forest information changes not much with time, because most is coniferous. Farmland represents obvious seasonal variations due to the specific
phenological calendar. In winter, farmland represents blue because rough surface scattering is dominant. In summer, paddy fields represent red, yet dry-land crops are difficult to distinguish from forests because the weak penetrating capability of C-band microwave.
Fusion of multi-temporal polarimetric SAR data and forest identification
Multi-temporal polarimetric SAR data fusion
Image on February 8, 2009
was used as reference and the
other images were registered
with accuracy about 1/8 pixel.
The image quality is well
improved with speckle noises
reduced significantly.
Boundary of different land
type is clear and well
distinguished.
Forest takes on green with
obvious three-dimensional
characteristics, in good
accordance with terrain.
Deforestation area caused by snow storm takes on dark spot distributed among
normal forest, yet not very obvious mainly due to the regeneration of forest and
the restriction of spatial resolution of SAR data.
Multi-temporal polarimetric SAR data fusion
Conclusions
Polarimetric signatures of forest were significantly
different with other targets. Forest can be well
distinguished using polarimetric decomposition.
Fusion of multi-temporal polarimetric RADARSAT-2
images can effectively improve image quality and enhance
forest and deforestation information, jointly utilizing the
temporal and polarimetric information.
Polarimetric SAR data with higher spatial resolution
seems to be more promising for forest species identification
and deforestation mapping.
Yun shaoYun shaoInstitute of Remote Sensing Institute of Remote Sensing
Applications, Applications, Chinese Academy of SciencesChinese Academy of Sciences
Tel: 86-10-64876313Tel: 86-10-64876313E-mail: [email protected]: [email protected]