Abstract
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 0.1 ha 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 = 66.445 Mg/ha, Average Standard Error = 71.07694 Mg/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 352.512 Mg/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.
Highlights
Terrestrial ecosystems are one of the major pools for long-term carbon storage with forests in the forefront of it. (Zhao and Zhou 2005; Tan, et al 2007)
Many studies have shown that satellite data derived spectral information has a good statistical correlation with the aboveground forest biomass collected in the field (Viana et al 2012; Lu et al 2012; Manna et al 2014; Kushwaha et al 2014).Use of parametric and semi-parametric techniques like Cokriging is still limited in forestry (Corona et al.,2014) as compared to the nonparametric techniques like k-NN, ANN etc. which are more popular in estimating the Above Ground Biomass (AGB)
Present work is an attempt to use the approach of geostatistical prediction and mapping by combining satellite-derived variables and field data for aboveground woody biomass distribution of Sal Forests in Doon Valley
Summary
Terrestrial ecosystems are one of the major pools for long-term carbon storage with forests in the forefront of it. (Zhao and Zhou 2005; Tan, et al 2007). Aboveground Biomass can be estimated by conventional field-based methods such as forest inventories and destructive sampling. These are considered as the most reliable and accurate (Huang et al, 2013) as they are direct measurements. Many studies have shown that satellite data derived spectral information has a good statistical correlation with the aboveground forest biomass collected in the field (Viana et al 2012; Lu et al 2012; Manna et al 2014; Kushwaha et al 2014).Use of parametric and semi-parametric techniques like Cokriging is still limited in forestry (Corona et al.,2014) as compared to the nonparametric techniques like k-NN, ANN etc. Many studies have shown that satellite data derived spectral information has a good statistical correlation with the aboveground forest biomass collected in the field (Viana et al 2012; Lu et al 2012; Manna et al 2014; Kushwaha et al 2014).Use of parametric and semi-parametric techniques like Cokriging is still limited in forestry (Corona et al.,2014) as compared to the nonparametric techniques like k-NN, ANN etc. which are more popular in estimating the AGB
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