Carbon Stock Estimation of Tropical Forests on Borneo, Indonesia, for REDD+

  • Sandra EnglhartEmail author
  • Jonas Franke
  • Vanessa Keuck
  • Florian Siegert
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 18)


Tropical peat swamp forests are among the most carbon rich ecosystems and are therefore in the focus of the program Reducing Emissions from Deforestation and Forest Degradation plus (REDD+), which requires accurate aboveground biomass (AGB) estimations for emission assessments. The present study evaluates different SAR frequencies and polarizations as well as spectral mixture analysis (SMA) of optical satellite data for estimating AGB on the basis of forest inventory data. Furthermore, two different approaches, continuous and discrete AGB estimation, were compared for their performance to retrieve large scale AGB estimations on the basis of multispectral data. The continuous approach relates satellite signals to field derived AGB values, while the discrete approach is based on a land cover classification by linking AGB values to each land cover class (stratify & multiply). Using the continuous approach, a correlation between AGB and SAR backscatter was found with TerraSAR-X HH and ALOS PALSAR HV data while no significant correlation was found with RADARSAT-2 HH, HV and ALOS PALSAR HH imagery. RapidEye matched filtering (MF) fractions derived from reference spectra of green vegetation (GV), soil and non-photosynthetic vegetation (NPV) were determined and all three MF fractions showed a correlation to AGB. The combined TerraSAR-X HH and ALOS PALSAR HV polarized AGB model was more accurate (r2 = 0.68) than the single-frequency models. Similarly, the combined multispectral MF fractions model was also more accurate (r2 = 0.92) than the single MF fractions models, while a synergistic use of SAR and multispectral data produced no improvements in AGB estimation. The comparison of the continuous and discrete approach based on multispectral imagery showed that the continuous AGB estimation approach depicted the spatial variability within one land cover class in contrast to the discrete approach, while it suffered from saturation either of the SAR backscatter or of the MF fractions in the higher biomass ranges. Such evaluation of AGB estimations using different satellite data and approaches considering the accuracy is considered as a relevant step for recommendations on defining MRV (Monitoring, Reporting and Verification) methods with regard to REDD+.


Carbon Stock Land Cover Class Land Cover Classification Green Vegetation Field Inventory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the German Space Agency (DLR) and Canadian Space Agency (CSA) for providing TerraSAR-X and RADARSAT-2 data within the SOAR-DLR initiative (ID 5035), as well as the Japan Aerospace Agency (JAXA) for supplying ALOS PALSAR data (PI project No. 211). RapidEye data were provided by the German Aerospace Center (DLR) via the RESA – RapidEye Science Archive with funds from the German Federal Ministry of Economics and Technology (proposal no 267). Special thanks to Suwido Limin and his team from the Centre for International Co-operation in Management of Tropical Peatland (CIMTROP) in Palangka Raya for the logistical support during the field surveys. Furthermore, we acknowledge FAUNA & FLORA International (FFI) for providing aboveground biomass data of riparian forests.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Sandra Englhart
    • 1
    Email author
  • Jonas Franke
    • 1
  • Vanessa Keuck
    • 1
  • Florian Siegert
    • 1
    • 2
  1. 1.Remote Sensing Solutions GmbHBaierbrunnGermany
  2. 2.Biology Department II, GeoBio CenterLudwig-Maximilians-UniversityPlanegg-MartinsriedGermany

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