The forest inventory surveys in the bird area were initiated in 1981 and transects were made permanent in 1991 by Tom Siccama who created and designed this tree survey. The inventory is representative of approximately 2.5 km2 of mid elevation northern hardwood forest. The data set is particularly geared toward producing accurate mortality and recruitment estimates. It consists of a total inventory of all trees greater than or equal to 10 cm dbh within each of four 10 m wide belt transects. The parallel transects are placed approximately 200 m apart and 290° bearing in an east-west direction for 2200 to 2900 m. In 1991, each live stem greater than or equal to 10 cm dbh was tagged with a unique number. Tree vigor is assessed every two years and diameter is remeasured every ten years. Every two years, new tags are placed on stems that have grown into the 10 cm diameter class. A survey of smaller trees (greater than or equal to 2 to less than 10 cm dbh) was first taken in 1991 and is resurveyed every ten years. This dataset includes 1991 and subsequent samplings. Data from an earlier sampling in 1981 can be found in: Sherry, T., D. Holmes, and T. Siccama. 2019. Forest Inventory of a Northern Hardwood Forest: Bird Area at the Hubbard Brook Experimental Forest, 1981 ver 7. Environmental Data Initiative. https://doi.org/10.6073/pasta/206b98f6553f1ff95cf584dd2185554e (Accessed 2021-09-16). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. These data have been used in the following publication: Siccama, T.G., Fahey, T.J., Johnson, C.E., Sherry, T.W., Denny, E.G., Girdler, E.B., Likens, G.E., and Schwarz, P.A. 2007. Population and biomass dynamics of trees in a northern hardwood forest at Hubbard Brook. Can. J. For. Res. 37(4): 737–749. doi:10.1139/X06-261.
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Simulation of the effects of forest harvesting under changing climate to inform long-term sustainable forest management using a biogeochemical model
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null (Ed.)ABSTRACT We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Ly α forest. We train a Neural Network using redshift z = 3 outputs from cosmological hydrodynamic simulations and mock data sets constructed from them. We evaluate how well the trained network is able to reconstruct the optical depth for Ly α forest absorption from noisy and often saturated transmitted flux data. The Neural Network outperforms an alternative reconstruction method involving log inversion and spline interpolation by approximately a factor of 2 in the optical depth root mean square error. We find no significant dependence in the improvement on input data signal to noise, although the gain is greatest in high optical depth regions. The Ly α forest optical depth studied here serves as a simple, one dimensional, example but the use of Deep Learning and simulations to approach the inverse problem in cosmology could be extended to other physical quantities and higher dimensional data.more » « less
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- https://doi.org/10.1016/j.scitotenv.2020.144881
