• Title of article

    Monitoring spruce volume and biomass with InSAR data from TanDEM-X

  • Author/Authors

    Solberg، نويسنده , , Svein and Astrup، نويسنده , , Rasmus and Breidenbach، نويسنده , , Johannes and Nilsen، نويسنده , , Barbi and Weydahl، نويسنده , , Dan، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2013
  • Pages
    8
  • From page
    60
  • To page
    67
  • Abstract
    There is a need for monitoring methods for forest volume, biomass and carbon based on satellite remote sensing. In the present study we tested interferometric X-band SAR (InSAR) from the Tandem-X mission. The aim of the study was to describe how accurate volume and biomass could be estimated from InSAR height and test whether the relationships were curvilinear or not. The study area was a spruce dominated forest in southeast Norway. We selected 28 stands in which we established 192 circular sample plots of 250 m2, accurately positioned by a Differential Global Positioning System (dGPS). Plot level data on stem volume and aboveground biomass were derived from field inventory. Stem volume ranged from zero to 596 m3/ha, and aboveground biomass up to 338 t/ha. We generated 2 Digital Surface Models (DSMs) from InSAR processing of two co-registered, HH-polarized TanDEM-X image pairs – one ascending and one descending pair. We used a Digital Terrain Model (DTM) from airborne laser scanning (ALS) as a reference and derived a 10 m × 10 m Canopy Height Model (CHM), or InSAR height model. We assigned each plot to the nearest 10 m × 10 m InSAR height pixel. We applied a nonlinear, mixed model for the volume and biomass modeling, and from a full model we removed effects with a backward stepwise approach. InSAR height was proportional to volume and aboveground biomass, where a 1 m increase in InSAR height corresponded to a volume increase of 23 m3/ha and a biomass increase of 14 t/ha. Root Mean Square Error (RMSE) values were 43–44% at the plot level and 19–20% at the stand level.
  • Keywords
    Nonlinear mixed models , INSAR , Forest inventory , Volume , BIOMASS
  • Journal title
    Remote Sensing of Environment
  • Serial Year
    2013
  • Journal title
    Remote Sensing of Environment
  • Record number

    1633730