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Estimation of Change in Forest Aboveground Carbon in Bhimbandh Wildlife Sanctuary, Bihar, India Between 2007 and 2016


Affiliations
1 Department of Civil Engineering, Haldia Institute of Technology, Hatiberia, Haldia 721 657, India
 

This study analyses the status and temporal dynamics of the tropical forest aboveground carbon (AGC) stocks. We used an integrated geospatial approach incorporating satellite synthetic aperture radar (SAR) data with a continuous forest inventory over a tenyear period utilizing statistical up-scaling procedure over a tropical deciduous forest of India as a case study. Logarithmic regression relationship was observed as the best fit model to derive the aboveground biomass from SAR backscatter coefficients with an absolute model accuracy of 80.61%. This was further employed to model the change in forest AGC stock from 2007 to 2016. Results show a significant decrease in carbon stock and the release of 918.5 Gg of carbon in the atmosphere from deforestation and forest degradation in the study area within the ten-year period.

Keywords

Carbon, Forest Aboveground Biomass, Regression, Synthetic Aperture Radar.
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  • Estimation of Change in Forest Aboveground Carbon in Bhimbandh Wildlife Sanctuary, Bihar, India Between 2007 and 2016

Abstract Views: 273  |  PDF Views: 98

Authors

Suman Sinha
Department of Civil Engineering, Haldia Institute of Technology, Hatiberia, Haldia 721 657, India
Abhisek Santra
Department of Civil Engineering, Haldia Institute of Technology, Hatiberia, Haldia 721 657, India

Abstract


This study analyses the status and temporal dynamics of the tropical forest aboveground carbon (AGC) stocks. We used an integrated geospatial approach incorporating satellite synthetic aperture radar (SAR) data with a continuous forest inventory over a tenyear period utilizing statistical up-scaling procedure over a tropical deciduous forest of India as a case study. Logarithmic regression relationship was observed as the best fit model to derive the aboveground biomass from SAR backscatter coefficients with an absolute model accuracy of 80.61%. This was further employed to model the change in forest AGC stock from 2007 to 2016. Results show a significant decrease in carbon stock and the release of 918.5 Gg of carbon in the atmosphere from deforestation and forest degradation in the study area within the ten-year period.

Keywords


Carbon, Forest Aboveground Biomass, Regression, Synthetic Aperture Radar.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi6%2F1090-1094