|Title||Using Light-Use and Production Efficiency Models to Predict Photosynthesis and Net Carbon Exchange During Forest Canopy Disturbance|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Cook, Bruce D., Bolstad Paul V., MARTIN JONATHAN G., Heinsch Faith Ann, Davis Kenneth J., Wang Weiguo, Desai Ankur R., and Teclaw Ron M.|
|Pagination||26 - 44|
|Keywords||carbon utilization efficiency, ecosystem respiration, Malacosoma disstria Hübner, MODIS, primary production, quantum efficiency|
Vegetation growth models are used with remotely sensed and meteorological data to monitor terrestrial carbon dynamics at a range of spatial and temporal scales. Many of these models are based on a light-use efficiency equation and two-component model of whole-plant growth and maintenance respiration that have been parameterized for distinct vegetation types and biomes. This study was designed to assess the robustness of these parameters for predicting interannual plant growth and carbon exchange, and more specifically to address inconsistencies that may arise during forest disturbances and the loss of canopy foliage. A model based on the MODIS MOD17 algorithm was parameterized for a mature upland hardwood forest by inverting CO2 flux tower observations during years when the canopy was not disturbed. This model was used to make predictions during a year when the canopy was 37% defoliated by forest tent caterpillars. Predictions improved after algorithms were modified to scale for the effects of diffuse radiation and loss of leaf area. Photosynthesis and respiration model parameters were found to be robust at daily and annual time scales regardless of canopy disturbance, and differences between modeled net ecosystem production and tower net ecosystem exchange were only approximately 2 g C m-2 d-1 and less than 23 g C m-2 y-1. Canopy disturbance events such as insect defoliations are common in temperate forests of North America, and failure to account for cyclical outbreaks of forest tent caterpillars in this stand could add an uncertainty of approximately 4–13% in long-term predictions of carbon sequestration.