Forest ecosystem stores carbon by sequestering a substantial amount of carbon dioxide from the atmosphere, globally accounting around 92% of all terrestrial biomass storing approximately 400Gt of carbon (Dixon et al. 1994; IPCC 2014; MacDicken 1997; Maraseni and Pandey 2014). Different literature indicates that the amount of carbon stored in forest differs according to the spatial and temporal factors (Karki et al. 2016; Pandey and Bhusal 2016; Wei et al. 2013) such as forest types (Jina et al. 2009), size, age (Wei et al. 2013), stand structure, associated vegetation (Jina et al. 2009), ecological zonation (Wei et al. 2013), silvicultural treatments (Pandey et al. 2014), forest management intensities and modalities
(Pandey et al. 2019; Parrotta, Wildburger, and Mansourian 2012; Le Quéré et al. 2009; Scott et al. 2004; Sharma and Kakchapati 2018; Tashi et al. 2017) etc. Yohannes and Soromessa (2015) estimated 324.79 ton/ha above ground carbon in lower slope compared to 187.49 ton/ha in higher slope and also concluded that variation in total carbon stock by aspect for Gedo forest of Ethiopia. An experiment conducted in mixed deciduous and evergreen broadleaf forest by International Center for Integrated Mountain Development (ICIMOD) knowledge park Nepal shows that the carbon stock density of dense forest strata was higher than that of the sparse strata, whereas soil organic carbon was lower (164.02 ton C/ha) in dense forest strata compared to in sparse strata (180.93 ton C/ha) in which mean soil organic carbon (fine fractions) percentage was 5.27% and 6.01% for sparse and dense forest strata, respectively (Karki et al. 2016). Similarly, Sharma and Kakchapati (2018) also concluded that the stem density has significant association with the total biomass carbon content, where plots with less than 20 trees per plot showed higher carbon stock.
The carbon stock estimation equation for forest ecosystem has shown significant statistical dependence with the factors like DBH, stem volume, stand density, crown dimensions and management modalities (Ni-Meister et al. 2010; Pragasan 2015; Scott et al. 2004; Wei et al. 2013). However, even standard allometric equations which are reasonably accurate to predict the biomass mostly incorporate easily measured parameter like DBH and height considering forest cover as uniform (Baral, Malla, and Ranabhat 2010; Cole and Ewel 2006; Karki et al. 2016; Ni-Meister et al. 2010; Pandey and Bhusal 2016; Parrotta, Wildburger, and Mansourian 2012; Ribeiro et al. 2015; Shah and Acharya 2010; Sharma and Kakchapati 2018). The advancement in the technology and tools vegetation parameters like crown coverage cab ne mixed in carbon estimation especially in tropical forest where cover condition differs with site quality (Urbazaev et al. 2018). A study by Pandey, Cockfield, and Maraseni (2014) for REDD + piloting project that included crown cover strata (sparse < 70% crown cover and dense ≥ 70% crown cover) in tropical Sal forest shows that there is a significant difference in stand-level carbon stock estimation by canopy coverage. Moreover, results-based payment in REDD + program also requires accurate estimation of carbon stock by forest types, composition, management modalities, crown cover and geographic locations (Pandey, Cockfield, and Maraseni 2014; Sharma and Kakchapati 2018). In such case, incorporating crown cover for stratification with remote sensing techniques while conducting forest measurement or developing allometric equation not only reduces the cost of forest inventory or estimate more accurate values but also improves the reliability of the equation.
Various studies have been conducted to develop biomass equation for specific species and groups of species with or without stratification in tropical area. Several biomass-prediction equations have been developed for tropical plant species like: (1) Cole and Ewel (2006) for Cedrela odorata, Cordia alliodora, Hyeronima alchorneoides, and Euterpe oleracea; (2) Ounban et al. (2016) for Tectona grandis and Ecaluptus camaldulenis; (3) Mohd Zaki et al. (2016) for Dipterocarp species of Malaysia; and (4) Chave et al. (2005) for tropical forest. Currently, in Nepal, estimate of biomass depends on biomass prediction and allocation system developed by Sharma and Pukkala (1990) or Chave et al. (2005). Even though Joshi et al. (2015) developed general allometric equation for Paulownia tementosa, Shrestha et al. (2018) developed local volume table for Terai species and Bhandari and Neupane (2014) for Castanopsis indica, there is no specific allometric equation for predicting carbon stock by variable like crown cover even for species popular species Sal (Shorea robusta). According to Department of Forest Research and Survey (DFRS) (2015) Sal forest alone accounts for 15.27% of total forest area. In addition, the study also found that another 24.61% of forest area exists in the form of tropical mixed hardwood forest resulting in Sal representing total of 26–28% of stem volume in Nepal whose carbon stock distribution varies spatially by species composition, cover condition site quality and edaphological factor. So, developing regression equation for estimation of carbon stock for natural Sal forest by crown cover will not only estimate the accurate carbon stock for REDD + benefits but also helps to remove uncertainty in estimation of biomass and carbon due to site quality resulting in variation in crown coverage. Therefore, this study aimed to estimate carbon stock by crown cover class and develop regression models to estimate carbon stock for various crown cover measuring DBH and total height of tree at field level and classifying crown cover with remote sensing for cost effective carbon estimation in tropical Sal forest.