Determining allometric relationships within tree species for a quantitative understanding of forest-atmosphere water fluxes coupled with remote-sensing-based methods for determining forest structure at an individual-tree scale
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Date
2011-06
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The Ohio State University
Abstract
Chapter 1 Abstract
Transpiration from forests contributes to both regional and global hydrological cycles. Rates of transpiration depend on a tree’s physiology and its surrounding environmental factors. Many of these factors control water stress levels that in turn affect transpiration rates. By studying tree architecture and allometric relationships within that architecture, we can begin to quantify the ‘pipe network’ of a tree (i.e. its branching network). These relationships can produce parameters that define the hydrodynamic framework of a specific tree species. The framework can help further define the water stresses a tree experiences within its ‘pipe network’. This information can be incorporated into hydrological models to enhance their predictive capabilities of forest-level transpiration. The models can consider changes in forest composition because each tree will have a different representative hydrodynamic framework. As forests change, the corresponding change in transpiration will be better incorporated into the models. Our results support that such hydrodynamic models can be developed. Virtual trees can be created that accurately depict a specific tree species. This is possible because many allometric relationships exist within a single tree, between trees of the same species, and between trees of different species.
Chapter 2 Abstract
Atmosphere-biosphere interactions involve exchanges of water, heat and carbon dioxide between the atmosphere and forest ecosystems. This exchange is important in contributing to weather and climate change. Current land-surface models do not resolve the effects of canopy structure change or incorporate ‘real’ forest heterogeneity as input parameters. The primary goal of this work is to provide a remote-sensing-based method to determine forest structure at an individual-tree scale. This will be done by: (1) parameterization of empirical allometric relationships that govern the scaling of tree-crown hydraulic structure with tree size, species, and relative location in the canopy, and (2) tree-type classification of high-resolution airborne images combining visible and LIDAR. To determine allometric relationships, measurements of structural-hydraulic characteristics of trees, such as stem diameter and taper, branch diameter, splitting patterns, and leaf distribution were taken at existing experimental plots. 2010 census data containing tree species, location, and diameter at breast height (DBH) was obtained to provide additional forest composition information. Remote sensing data was provided by the National Agricultural Imagery Program. Aerial LIDAR data supplemented these images. Species-specific allometric equations were generated relating tree height and crown diameter to DBH, which will be input parameters to existing hydrological models. Using ENVI 4.5, an image analysis program, we will visually attempt to classify the heterogeneity of a forest. This will be coupled with the LIDAR data to provide additional classification schemes. The validity of our ENVI results will be compared to the 2010 census. Combining remote-sensing-determined composition with allometric relationships will allow simulations using new generation hydraulic models, which simulate water flow through the forest at the individual-tree scale and strengthen the predictive capabilities of existing hydrological models, and thus producing better predictions of atmosphere-biosphere interactions.
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Keywords
Remote-sensing, Tree architecture, Allometric Relationships, Regional classification