Above ground carbon stock mapping over Coimbatore and Nilgiris Biosphere: a key source to the C sink

Abstract

Mapping and quantifying above ground carbon (AGC) Stocks reflect significant dynamics in the terrestrial carbon cycle and cascade climate change. Estimation of such key driver was performed for the dominant species (Bamboo, Eucalyptus and Teak) over Coimbatore and Nilgiris Biosphere (2006 − 2018 quadruple interval) of Tamilnadu, India with the developed global stepwise multiple linear regression (SMLR) and local geographically weighted regression (GWR) models using multi-dynamic variables. Evapotranspiration (ET) developed using Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model for the region was analysed with the best-fitted AGC estimation model to understand AGC—ET synergistic pertinence dynamics. The study compared and validated the estimation by the models and indicated that AGC estimation using SMLR exhibiting a high degree of accuracy ( ) with nominal negative bias in the estimation ranging from with amplification of GWR prediction indicted positive bias with comparatively least mean accuracy ( ). The ET—AGC reciprocity for the dominant species resulted that, bamboo with lower AGC correlated with higher ET tailed by teak with higher AGC and ET and eucalyptus with relatively higher AGC and lower ET and respectively. The analysis resulted in minimal biasness in AGC mapping using SMLR, and both the model signifies that the region can potentially be considered a long-term carbon sink.

Keywords

• carbon stocks
• biomass estimation
• evapotranspiration
• geographically weighted regression
• multiple linear regression
• remote sensing modelling