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    2011_0728_IGARSS2011_Motohka.ppt 2011_0728_IGARSS2011_Motohka.ppt Presentation Transcript

    • Relationships between PALSAR backscattering data and forest above ground biomass in Japan ○ Takeshi Motohka (Japan Aerospace Exploration Agency) 2011/07/28: IGARSS 2011, Vancouver, Canada. Masanobu Shimada (Japan Aerospace Exploration Agency) Osamu Isoguchi (Remote Sensing Technology Center of Japan) Masae I. Ishihara (Japan Wildlife Research Center) Satoshi N. Suzuki (Japan Wildlife Research Center)
    • Outline
      • Background
      • Data
        • In situ biomass data
        • PALSAR yearly mosaic data
      • Results
        • Relationship between biomass and PALSAR data
        • Mapping forest biomass
      • Summary and future works
    • Background
      • Forest biomass is a key parameter to assess
        • Emission of greenhouse gasses (CO 2 , CH 4 , etc.)
        • Accumulated carbon in forests
        • Biodiversity
      A large-scale, time-series, globally consistent biomass monitoring is important for various projects such as REDD+.
    • Biomass monitoring by PALSAR
        • Microwave backscatter shows high correlation with forest tree biomass especially for longer wavelength (i.e. L-band , P-band, …).
        • Spatial (10 – 100 m) and temporal (46 days) resolution meet the REDD+ or FCT methodologies.
        • Well calibrated global datasets for 5 years (2006 ~ )
        • ALOS mission was ended in 2011, but next ALOS-2 (PALSAR-2) will be launched in 2013.
      Phased Array type L-band Synthetic Aperture Radar PALSAR PALSAR-2 ALOS ALOS-2
    • Lucas et al ., 2011 Mitchard et al ., 2009 Englhart et al ., 2011
        • Many studies have revealed the relationships between forest biomass and PALSAR data at various regions and forest types.
      Africa Australia Indonesia
    • Purpose of the study
      • Target  =  Japan
      • Investigating the relationships between PALSAR backscattering data and above ground biomass of Japanese forests
      • Testing the retrieval of forest biomass using the obtained empirical relationships and PALSAR yearly mosaic data
    • In situ Biomass Data Website:   http://www.biodic.go.jp/moni1000/ “ Monitoring Site 1000” project   by the Ministry of Environment of Japan (since 2003)
      • Network of long-term research sites for biodiversity assessment
      • 49 tree census sites
      • Located at various forest types
      • Only natural forests were selected in the study (not including artificial forests)
    • Deciduous broadleaf forest (Tomakomai, Hokkaido) Evergreen coniferous forest (Otanomousu-daira, Nagano) Deciduous broadleaf forest (Chichibu, Saitama) Evergreen broadleaf forest (Yona, Okinawa)
    •  
    • Tree diameter of breast height [cm] (DBH) Dry weight   [kg] measured all trees in about 1 ha plot except for DBH < about 5 cm. Allometric equations for each specie Ʃ (dry weight) / stand-size Biomass [t/ha] Processing of tree census data
    • Statistics of the forest stands (n=44) DBH: Diameter at Breast Height
    • PALSAR yearly mosaic R: HH G: HV B: HH/HV Year: 2007, 2009 Mode :   Fine beam dual (HH, HV) Mosaicking period :   Jun. - Sep. Pixel sampling :   10 m Orbit: Ascending
      • Long-strip processing
      • Ortho-rectification
      • Slope-correction
      • Mosaicking
      γ 0 [dB] = 10 log 〈 DN 2 〉 - 83 15 x 15 pixels averaging Shimada & Otaki (2011); Shimada (2011) in “ IEEE JSTAR special issue on Kyoto and Carbon Initiative” Generation of PALSAR yearly mosaics Converting DN to gamma naught
    • Saturation level : - dy/dx = 0.01 … 91 [t/ha] - dy/dx = 0.005 … 182 [t/ha] HV RMSE : 0.703 [dB] PALSAR γ 0 vs. biomass
    • Saturation level : - dy/dx = 0.01 … 68 [t/ha] - dy/dx = 0.005 … 136 [t/ha] PALSAR γ 0 vs. biomass RMSE : 1.053 [dB] HH
    • Red ●: After correction    Green +: Before correction RMSE : 1.053 [dB] RMSE 0.703 [dB] RMSE : 2.312 [dB] RMSE 2.073 [dB] Effect of slope-correction
    • HV HH Precipitation vs. PALSAR gamma naught rainfall (over 10mm during 3 days before obs.) RMSE: HH : 0.670 dB HV : 0.402 dB Mean bias between rainy and non-rainy data: HH : +0.177 dB HV : +0.044 dB
    • Inversion of forest biomass   RMSE:   106.23 t/ha   %RMSE:   39.3 %    (=RMSE/mean ) PALSAR HV γ 0 (15 x 15 pix average) Water, Lay-over, Shadowing mask Urban area mask Biomass map
    • 400 ~ 0 100 200 300 Above ground biomass (t/ha)
    • Cropland Cropland Wetland 400 ~ 0 100 200 300 Above ground biomass (t/ha)
    • 1 km (c) Google Earth 400 ~ 0 100 200 300 Above ground biomass (t/ha)
    • Summary
      • We examined the relationships between PALSAR backscattering data and forest biomass in Japan.
        • HV polarization was better to use (low RMSE and high saturation level).
        • Slope correction was very important to reduce the error especially in mountainous regions.
        • More data points were needed to investigate the difference among vegetation types. Airborne LiDAR can be good solution of this.
    • Summary
      • Simple inversion of forest biomass
        • Spatial pattern seems to be good.
        • Problems: accuracy and saturation
        • Possible solution:
            • Additional SAR analysis (multi-temporal data, full-polarimetric data, etc…)
            • Data fusion with ALOS/PRISM DSM and ALOS/AVNIR-2 (10-m res. VIS&NIR) data
            • Data fusion with ICESAT/GLAS data
            • More in-situ data points and more evaluation