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

1. IGARSS2011 Forest mapping using multi-temporal polarimetric SAR data in southwest China Yun Shao*,FengliZhang*,MaosongXu#,Zhongsheng#Xia,ChouXie*,KunLi*,ZiWan*,RidhaTouzi *Institute of Remote Sensing Applications, Chinese Academy of sciences, China #State Foresty Adaministration Canada Center for Remote Sensing E-mail: yunshao@irsa.ac.cn

2. Outline

3. 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.

4. 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.

5. 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.

6. Testsiteanddatasources • 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. Thefourdominant forest species

7. Testsiteanddatasources 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.

8. Testsiteanddatasources • Field investigations were carried out in the test site. • 12 sample plots, with the average size of 1024m2, covering dominant species and different ages.

9. Ground measurements 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 • 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.

11. PolarimetricSARdata gathered • The six RADARSAT-2 images were allin descending orbit, owned repeat orbit and nearly the same incidence angle, with maximum baseline about 386 meters. FQ=Fine

12. Polarimetric signatures Inco-polarized signature ,there are low values from orientation angle from ψ=45to 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. All types of forests, including coniferous forest, deciduous forest, and mixed forest, own similar polarimetric signatures and have apparent volume scattering.

13. Forest identification based on polarimetricdecomposition Pauli decompositionforRADARSAT-2imageonFebruary 8, 2009 • Forest is dominated by dipole scattering, showing in green. • Buildings in urban areas tend toform 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

14. Identification deforestation caused by snow storm Pauli decomposition Freemandecomposition • Fordeforestationarea,Freeman decompositionseemsbetterthanPauli decomposition. • Forest damaged by snow is still dominated by volume scattering,yetsurface scattering is much more than normal forest,showing obvious light blueinFreeman decomposition image.

15. Paulidecompositionformulti-temporal SAR data Pauli decomposition image of six-temporal RADARSAT-2 data • Forest information changesnotmuchwithtime,becausemostisconiferous. • 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 theweak penetrating capabilityofC-band microwave.

16. Fusion of multi-temporal polarimetric SAR data and forest identification

17. Multi-temporal polarimetric SAR datafusion • 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 clearandwell distinguished. • Forest takes on green with obvious three-dimensional characteristics, in good accordance with terrain. Multi-temporal fusion image

18. Multi-temporal polarimetric SAR datafusion • 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 resolutionofSARdata.

19. Conclusions Polarimetric signatures of forest were significantly different with other targets. Forestcanbewelldistinguishedusingpolarimetric decomposition. Fusion of multi-temporal polarimetricRADARSAT-2 images can effectively improve image quality and enhance forest and deforestation information,jointlyutilizingthetemporalandpolarimetricinformation. PolarimetricSARdatawithhigherspatialresolutionseemstobemorepromisingforforestspeciesidentificationanddeforestationmapping.

20. Thanks! Yunshao Institute of Remote Sensing Applications, Chinese Academy of Sciences Tel: 86-10-64876313 E-mail: yunshao@irsa.ac.cn