Year of Award
2013
Document Type
Dissertation
Degree Type
Doctor of Philosophy (PhD)
Degree Name
Forestry
Department or School/College
College of Forestry and Conservation
Committee Chair
Steven Running
Commitee Members
Cory Cleveland, Anna Sala, Jon Graham, John Bardsley
Keywords
carbon cycle, forest carbon offsets, forest sampling, growth and yield and process modeling, LiDAR remote sensing, stratification and stand delineation
Publisher
University of Montana
Abstract
The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time. This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling.
Recommended Citation
Golinkoff, Jordan Seth, "ESTIMATION AND MODELING OF FOREST ATTRIBUTES ACROSS LARGE SPATIAL SCALES USING BIOMEBGC, HIGH-RESOLUTION IMAGERY, LIDAR DATA, AND INVENTORY DATA" (2013). Graduate Student Theses, Dissertations, & Professional Papers. 384.
https://scholarworks.umt.edu/etd/384
© Copyright 2013 Jordan Seth Golinkoff