Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 877-882, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-877-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 877-882, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-877-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Nov 2018

27 Nov 2018

MAPPING AND MODELLING ABOVEGROUND WOODY BIOMASS AND CARBON STOCK IN SAL (SHOREA ROBUSTA GAERTN. F.) FORESTS OF DOON VALLEY USING GEOSPATIAL TECHNIQUES

S. Purohit1,2, S. P. Aggarwal2, and N. R. Patel2 S. Purohit et al.
  • 1Forest Research Institute Deemed University, Dehradun, India
  • 2Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun, India

Keywords: Aboveground Woody Biomass, Shorea robusta, Doon Valley, Landsat 8 OLI, NDVI, Cokriging

Abstract. Information on the quantitative and qualitative distribution of forest biomass is helpful for effective forest management. Besides its quantitative use, Biomass plays a twin role by acting as a carbon source and sinks but its long-term carbon-storing ability is of considerable importance which is helpful in lessening global warming and climate change impacts. The present study was done for mapping aboveground woody biomass (Bole) (AGWB) of Shorea robusta (Gaertn.f.) forests in Doon valley by establishing relationships between field measured data, satellite data derived variables and geostatistical techniques. Landsat 8 Operational Land Imager (OLI) data was used in preparing the forest homogeneity map (forest type and density). 55 sampling plots of 0.1ha were laid across the Doon Valley using stratified random sampling. Correlations were established between Landsat 8 OLI derived variables and field measured data and were evaluated. Field measured biomass has got the maximum correlation with NDVI (0.7553) and it was further used for carrying out multivariate kriging (Cok) for biomass prediction map. Prediction errors for the AGWB were lowest for exponential model with RMSE=66.445Mg/ha, Average Standard Error=71.07694Mg/ha and RMSS=0.95097. Carbon is calculated as 47% of the biomass value.AGWB was ranged from 163.381 to 750.025 Mg/ha and Carbon from 76.789 to 352.512Mg/ha. Cokriging was found as a better alternative as compared to direct radiometric relationships for the spatial distribution of the AGWB of Shorea robusta (Gaertn.f.) forests and this study would be helpful in better forest management planning and research purposes.