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

  • Forest mapping using multi-temporal polarimetric SAR data in southwest ChinaYun 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 AdaministrationCanada Center for Remote SensingE-mail: [email protected] 2011

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  • IntroductionForest 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.

  • IntroductionSPOT 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.

  • 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.

  • 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

  • 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.

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

  • 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:14/8/200822/8/2008 228/9/200811/10/2008327/10/20088/11/200844/12/20089/12/200855/2/200923/2/200968/4/200916/4/2009716/6/200923/6/2009818/8/200923/8/2009

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  • FQ=FineThe six RADARSAT-2 images were all in descending orbit, owned repeat orbit and nearly the same incidence angle, with maximum baseline about 386 meters.

    SatelliteDate (UTC)PassIncidenceAngle(degree)ModeResolution (m)RADARSAT-24/11/2008Descending34.64FQ7.9RADARSAT-28/2/2009Descending34.64FQ7.9RADARSAT-226/7/2009Descending34.64FQ7.9RADARSAT-219/8/2009Descending34.64FQ7.9RADARSAT-212/9/2009Descending34.64FQ7.9RADARSAT-26/10/2009Descending34.64FQ7.9

  • 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=/4 even scattering, Blue=odd scattering

  • 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 decompositionFreeman decomposition

  • 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.

  • 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.

  • 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 shaoInstitute of Remote Sensing Applications, Chinese Academy of SciencesTel: 86-10-64876313E-mail: [email protected]

    ** Forest inventory in these areas usually can not be accomplished timely due to lack of remote sensing data.

    ****Urban areas scattering mechanism is relative stable, almost no change over time.In November and February there are mainly short crops or bare soils in the field, and in July, August, September and the early October, there are mainly rice and some dry-land crops in the field. Joint use of multi-temporal SAR images will help for the discrimination of forest and farmland, and thus a scheme for multi-temporal SAR data fusion was proposed.

    Farmland in the fusion image takes on purple due to the regualr seasonal variations of cultivated crops in this area.