Volume IV-5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 121-128, 2018
https://doi.org/10.5194/isprs-annals-IV-5-121-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 121-128, 2018
https://doi.org/10.5194/isprs-annals-IV-5-121-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Nov 2018

15 Nov 2018

ESTIMATION OF BIOMASS AND CARBON POOL IN BARKOT FOREST RANGE, UK USING GEOSPATIAL TOOLS

P. Attri1 and S. P. S. Kushwaha2 P. Attri and S. P. S. Kushwaha
  • 1Haryana Space Applications Centre - HARSAC Node, Department of Science & Technology, Haryana, India
  • 2Indian Institute of Remote Sensing, Indian Space Research Organization, Dehradun, India

Keywords: Biomass, Growing stock, Carbon, NDVI, Shorea robusta, Tectona grandis

Abstract. The forest ecosystem is an important carbon sink and source containing majority of the aboveground terrestrial organic carbon. Carbon management in forests is the global concern to mitigate the increased concentration of green house gases in the atmosphere. The present study estimated vegetation carbon pool and biophysical spectral modelling to correlate biomass with reflectance/ derivatives in Barkot Forest Range, Uttarakhand. The study was carried out using Cartosat-1, IRS-P6 LISS-IV MX, IRS LISS-III, Landsat 7 ETM satellite data and ground data collected from stratified random sampling. Forest type and forest crown density was mapped using resolution merged Cartosat-1 and LISS-IV imagery. Growing stock, biomass and carbon was calculated for the individual sample plots using inventory-based biomass assessment technique. Field-inventoried data was correlated with the surface reflectance and derivatives of it. Among the four vegetation types, viz. Shorea robusta, S. robusta mixed, S. robusta Tectona grandis mixed, T. grandis plantation, mixed plantation, Grassland and Agriculture/orchard, the S.robusta was found to be the dominant vegetation in the area, covering 55.86km2 of the total area. The study revealed that the S.robusta with high density had the highest aboveground biomass (AGB) (t/ha) was found in S.robusta >70% (530tha−1), followed by S.robusta 40–70% (486tha−1) and minimum was found in mixed plantation <10% (101ha−1). The general trend showed the decrease in AGB with decrease of forest density in each forest type category. The average AGB of S. robusta T. grandis forest was found (308tha−1–458tha−1) due to the dominancy of S. robusta trees. The study highlighted the invaluable role of geospatial technology and field inventory for growing stock, biomass and carbon assessment.