Nima Madani

Year of Award


Document Type

Dissertation - Campus Access Only

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Systems Ecology

Department or School/College

College of Forestry and Conservation

Committee Chair

John S. Kimball

Commitee Members

Steven W. Running, Anna Sala, Ashley Ballantyne, David Affleck, Marco Maneta


University of Montana


Improvements in understanding carbon sources and sinks allows insight into the dimensions of the human footprint on the earth system and the natural capacity for mitigation. Global satellite remote sensing of vegetation activity has a critical role in monitoring the near real-time effect of climate on global ecosystems. Remote sensing based productivity models, known as the Light Use Efficiency models (LUE), are able to provide estimation of vegetation carbon uptake from photosynthesis at large scales with high spatial and temporal fidelity. These models use spectral vegetation indices along with surface meteorology and land cover classification information to estimate plant carbon uptake from regional to global scales. Based on these models, plants in a biome matrix operate at their maximum photosynthetic capacity in up-taking atmospheric CO2 and converting it into vegetation biomass when the climatic conditions are optimal for growth. However, the biome predefined photochemical efficiency parameter, known as the maximum LUE, is a major source of uncertainty in these models. This research addresses the current uncertainties in remote sensing productivity models using the LUE logic by conducting a spatial analysis of the ecosystem LUE and bioclimatic factors that constrain plant growth between and among different global land cover types. For this goal, plant key trait information and the satellite observation of solar induced chlorophyll fluorescence were used to cover the biome homogeneity assumptions in the global ecosystem LUE model. Results showed the new optimum LUE data can significantly improve the GPP model estimates when compared with the GPP model using fixed parameters per plant functional types. Findings of this study provide a pathway for improving satellite based assessments and monitoring of global vegetation productivity.

This record is only available
to users affiliated with
the University of Montana.

Request Access



© Copyright 2017 Nima Madani