| Summary: | The productivity of autotrophic organisms affects all life on Earth; hence, gaining insight in the variability of autotrophic productivity has received significant research interest. At cell to organism level, much knowledge has been gained under controlled conditions through laboratory analysis. At the stand level and beyond, control over the driving variables is limited, and hence experiments have relied on extensive time series, and geospatial analysis to observe changes in productivity across a wide range of environmental conditions. Significant technologies at these scales are eddy covariance that provides point sample estimates of productivity by measuring CO₂ fluxes between land and atmosphere, and remote sensing that provides for extrapolating eddy-covariance measurements across the landscape using canopy-reflectance data. Challenges in fusing eddy covariance with remote sensing relate to the limited capacity of airborne and spaceborne instruments to observe changes in the biophysical state of deep canopy strata; hence, eddy-covariance estimates that capture the productivity of an arbitrarily dense canopy volume are extrapolated based on top-of-canopy reflectance data. Proximal-sensing technology extends the acquisition of reflectance data to arbitrary locations within the canopy; however, these data are affected by the immediate canopy structure surrounding the sensor that introduces a sensor-location bias, and the direct use of these data in stand-level models is therefore challenging. This thesis explores the simulation of photosynthetic down-regulation using geometrically explicit forest models and meteorological records. The geometrically explicit models are constructed by combining laser-scanning data with tree-regeneration models, and are used to simulate a time series of leaf-level incident radiation. The parameters of a leaf-level photosynthesis model are then optimized against eddy-covariance productivity estimates. Finally, the potential of geometrically explicit models for the fusion of remote sensing and proximal sensing data is discussed. === Forestry, Faculty of === Graduate
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