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Pandey, Rajiv
- Regression Equations for Estimating Tree Volume and Biomass of Important Timber Species in Meghalaya, India
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PDF Views:87
Authors
Affiliations
1 Rain Forest Research Institute, Jorhat - 785 001, IN
2 Forest Research Institute, Dehradun - 248 001, IN
3 Forest and Environment Department, Government of Meghalaya, Shillong - 793 001, IN
1 Rain Forest Research Institute, Jorhat - 785 001, IN
2 Forest Research Institute, Dehradun - 248 001, IN
3 Forest and Environment Department, Government of Meghalaya, Shillong - 793 001, IN
Source
Current Science, Vol 116, No 1 (2019), Pagination: 75-81Abstract
Linear regression models were developed for four ecologically and economically important tree species of Meghalaya, India, viz. Betula alnoides, Duabanga grandiflora, Magnolia champaca and Toona ciliata. In the present study a non-destructive approach has been used for measurement of required variables, i.e. diameter at breast height (DBH), basal diameter, tree height, end-diameters and length of frustum. Comparison of various models of relationship on the basis of adj. R2 values showed that the value for linear function (V = f (d2 h)) was more than 0.90 for all the four tree species, except lowest diameter class of T. ciliata (10–30 cm diameter class). Hence this linear regression equation was selected for development of diameter class-wise volume equations. Volume of the stem was taken as the dependent variable, while DBH and tree height were used as independent variables, transformed in the form of d2 h to develop regression equation. Similarly, linear regression equations for each tree species were also developed using linear function [(V = f (d2 ))], considering tree volume as an dependent variable and DBH as an independent variable, transformed in the form of V = d2 . The present study is among a few attempts to develop regression models without the felling of trees since 1977 and an initial attempt using advanced measurement equipment in North East (NE) India, under the current regime of ban on tree felling. The regression equations developed in this study can be used for estimation of timber yield and carbon content of the selected tree species found in the Meghalaya forests.Keywords
Biomass, Regression Equations, Tree Volume, Timber Species.Full Text
References
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Abstract Views :253 |
PDF Views:68
Authors
Affiliations
1 Department of Forestry, HNB Garhwal University, Srinagar-Garhwal 249 161, IN
2 Department of Informatics, Forest Research Institute, Dehradun 248 003, IN
1 Department of Forestry, HNB Garhwal University, Srinagar-Garhwal 249 161, IN
2 Department of Informatics, Forest Research Institute, Dehradun 248 003, IN
Source
Current Science, Vol 116, No 9 (2019), Pagination: 1506-1514Abstract
Chir pine (Pinus roxburghii Sargent) stands were selected across their distributional range from Himalaya, i.e. from lower altitude to upper altitude to understand distribution of chir tree density, basal cover and biomass with altitudes. Tree density was highest >1800 m (405 ind ha–1) and lowest (171.67 ind ha–1) between 1401 and 1800 m. Tree height was highest (23.69 m) between 1001 and 1400 m and lowest (17.71 m) >1800 m. Basal area was highest (30.51 m2 ha–1) between 1001 and 1400 m and lowest (17.16 m2 ha–1) between 1401 and 1800 m. The highest volume was observed between 1001 and 1400 m altitude and lowest between 1401 and 1800 m. Bole biomass was highest (145.51 t ha–1) between 1001 and 1400 m and lowest (80.78 t ha–1) between 1401 and 1800 m. The mean leaf litter biomass production was highest in summer and showed decreasing trend in winter to rainy seasons, except in Rudraprayag where the highest biomass was observed in summer and regressed from rainy to winter seasons. The study concluded that, the density, height, basal area and volume of Pinus roxburgii trees varied with altitude in the Himalaya, but it is not directional. Density of trees plays an important role which changes biomass accordingly. Litter production had inverse relation with altitude, however increase in biomass of litter at >1801 m was observed due to new plantations.Keywords
Carbon, Conifers, Greenhouse Gas, Pure Forest, REDD+.Full Text
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