Forest mapping using multi-temporal polarimetric SAR data in southwest China

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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 IGARSS 2011

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IGARSS 2011. 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 - PowerPoint PPT Presentation

Transcript of Forest mapping using multi-temporal polarimetric SAR data in southwest China

Page 1: 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

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Outline

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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.

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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.

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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.

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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

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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.

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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.

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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

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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.

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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.

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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

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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

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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.

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Fusion of multi-temporal polarimetric SAR data and forest identification

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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.

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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

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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.

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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]