Improved LAI estimation and simulation

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Leaf area index (LAI) is a fundamental parameter for any work related to vegetation, but especially for modeling of natural phenomena. Specifically, it is a key element in climate models, hydrology models, and land surface processing models. The LAI is important because it is strongly related to the exchange of carbon dioxide, water, and energy between vegetation and atmosphere. Unfortunately, the vast majority of current models are based on low accuracy LAI estimates. This greatly limits the model's simulation and prediction accuracy. This alone demonstrates the strong need to improve LAI estimation and to provide a better approach for LAI parameterization. To respond to these concerns, the long term objectives of this program are threefold: 1) to improve LAI estimation accuracy with considerations of scale, heterogeneity, and land cover type; 2) to develop a better LAI parameterization approach for models in hydrology, climatology, and ecosystem; and 3) to investigate the effects of changes in land cover and land use on LAI dynamics. For the proposed five year period, my short term objectives are 1) to investigate spatial variation of LAI under different land cover types in Canadian prairies; 2) to develop a more accurate LAI derivation model from satellite imagery with considerations of topography, land cover, and vegetation condition; 3) to monitor temporal trajectory of LAI over the entire growing season; and 4) to assess the results of improved LAI on two well-established models. To achieve these goals, this program will study the target area at different spatial scales. Data will be collected at the field level and space level, covering different land cover types and spanning the entire growing season. Satellite imagery will range from high (10m), medium (30m), to low (1000m) resolutions. Output from this program will provide a better approach to LAI estimation and simulation for the Canadian prairies and will be applied to studies of the interaction between vegetation, atmosphere, and hydrology. The results of this research will eventually allow for a great improvement in the accuracy of any models relating to vegetation. Finally, this research program will provide a great opportunity for highly qualified personnel training.