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Carbon Status and Regression Model for Tree Carbon by Crown Cover for Sal Forest of Nepal


 Background: Volume, biomass and carbon of forest ecosystem are generally estimated using lookup tables or allometric equations known as models. These general equation-based models are usually exclusively based on dimensional measurement such as diameter at breast height (DBH) and/or height, which sometimes makes it difficult to judge applicability of equation to given forest condition or types. It is therefore important to estimate carbon stock and develop models to predict biomass or carbon stock with stratification by categorical variables like crown cover, slope, forest types, etc. Stratification of forest by remote sensing approach while designing forest inventory not only improves the reliability of the estimation but also reduces the cost of measurement and uncertainty in estimation. Taking crown coverage (<25%, 25-50%, 50-75% and >75%) and slope (0-8.5%, 8.5-19%, 19-31% and >31%) as a categorical variable, this study assessed the status of carbon stock and develop a regression model to predict carbon stock for each canopy class of Sal (Shorea robusta) forest in Nepal. DBH and height were measured for trees with more than 7 cm DBH in 82 sample plots (18, 22, 22 and 20 for <25%, 25-50%, 50-75% and >75 % respectively).Results: On average 297 stands per hectare were recorded with 94.80 m3/ha growing stock. Carbon stock was highest for >75% crown cover class (89.83 ton C/ha) and lowest for <25% crown cover class (27.47 ton C/ha) with average 60.41 ton C/ha, where per tree carbon stock was lowest in crown cover class 25-50% (0.16 ton C/tree). TukeyHSD shows that four pairs of crown cover classes have significant difference in carbon stock at 95% confidence interval. However, with increase in slope carbon stock per hectare was decreasing. Regression model with natural logarithm of DBH2 and total tree height (i.e. log transformed polynomial equation) was best fitted for estimation of carbon stock per tree in different crown cover class with adjusted R2 >0.99 and residuals were normally distributed. Conclusions: Adjustment of model (natural logarithm of DBH2 and height) with high accuracy (R2 >0.99) shows the importance of stratification especially by crown cover for accurate estimation of carbon stock for optimization of carbon benefits.