International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-5/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W3, 7–12, 2019
https://doi.org/10.5194/isprs-archives-XLII-5-W3-7-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W3, 7–12, 2019
https://doi.org/10.5194/isprs-archives-XLII-5-W3-7-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Dec 2019

05 Dec 2019

ESTIMATION METHODOLOGY FOR FOREST BIOMASS IN MONGOLIA USING REMOTE SENSING

T. Altanchimeg1,3, T. Renchin1, P. De Maeyer2, E. Natsagdorj1, B. Tseveen3, and B. Norov4 T. Altanchimeg et al.
  • 1NUM-ITC-UNESCO Laboratory for Space Science and Remote Sensing, National University of Mongolia, Ulaanbaatar, Mongolia
  • 2Department of Geography, Faculty of Sciences, Ghent University, Belgium
  • 3Department of Environmental and Forest Engineering, National University of Mongolia, Ulaanbaatar, Mongolia
  • 4Laboratory for Geo mineralization, Mongolian National University (MNU), Mongolia

Keywords: Forest biomass, Allometric equation, Soil moisture, Satellite data

Abstract. The forest biomass is one of the most important parameters for the global carbon stock. Information on the forest volume, coverage and biomass are important to develop the global perspective on the CO2 concentration changes. Objective of this research is to estimate forest biomass in the study area. The study area is Hangal sum, Bulgan province, Mongolia. Backscatter coefficients for vertical transmit and vertical receive (VV), for vertical transmit and horizontal receive (VH) from Sentinel data and Leaf Area Index (LAI) from Landsat data were used in the study area. We developed biomass estimation approach using ground truth data which is DBH, height and soil moisture. The coefficient α, β, δ, γ were found from the approach. The output map from the approach was compared with VV and VH, LAI data. The relationship between output map and VH data shows a positive result R2 = 0.61. This study suggests that the biomass estimation using Remote sensing data can be applied in forest region in the North.