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

Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems

“…Long-term analysis of an enviro-climate response demands the estimation of terrestrial ecosystem carbon, for which a method that is non-destructive to the biota in an ecosystem should be employed. Although these methods of estimation lead to multiple inevitable uncertainties, 92,111 they are broadly stimulated due to their proximity to higher temporal and spatial steps. The various important non-destructive estimations employed in the estimation of terrestrial carbon are discussed in the following sections.…”

“…They are generic to the global scale for species-based models under similar traits (like plantations). Statistically significant multispecies allometric models are developed for estimating multi-variate biotic species in an ecosystem 111 . Conversely, geographical specific species allometric models provide higher accuracy in carbon estimation.…”

“…Optical RS 15,93 , which exhibit significant properties towards biota, are considered to be a sensitive estimator of AGC to date. Like the carbon flux estimation, carbon stocks are estimated with various VIs to understand the biotic attributes in estimating AGC and BGC 111 . The most widely used VIs in the estimation includes DVI, EVI, NDVI, SAVI, SRI etc., by optimizing multispectral bands 52,134 .…”

“…The most widely used VIs in the estimation includes DVI, EVI, NDVI, SAVI, SRI etc., by optimizing multispectral bands 52,134 . They are, in turn, supported with the texture analysis (using GLCM or FOTO method) to understand the biotic canopy attributes to differentiate the multi-variate structure in an ecosystem, which is being an effective carbon stock predictor 111 . By integrating the RS with ground-based estimators like allometric models and inventory datasets: the VIs, LAI, canopy structure, spectral mixture analysis etc., are considered to be the most important biotic variables in estimating the available carbon stocks using empiricalregression-and integrated-models 113 .…”

“…Pervasively, the regression (OLS, SMLR, GWR etc.) models are widely employed because of their local precision with minimal uncertainty, which is optimal for multivariable independent predictors in carbon stock estimations 111,147 . Such models are improved further with various biophysical, spatial and enviro-climatic variables for higher accuracy with minimal uncertainty 94 .…”