Spatially Explicit Forest Modeling and Connection with Remote Sensing

Individual Based Models are forest succession models with explicit structure attributes. Forest structure is considered in the model as emergence, growth and mortality of individual trees' species within a plot of given area. Explicit individual structure attributes include species, diameter breast height (DBH), and geographical location (x, y coordinates). Typically, other structure metrics (height, above ground biomass(AGB), crown dimensions) can be derived from this set. Plot level structure attributes are also important and tree location, AGB, Height distribution, density and cover. Our past work used a non-spatially explicit IBM ( Zelig) and a remote sensing model and field data to estimate forest biomass through time for a northern forest in Maine, USA. Simulated stands were used to augment ground data in the analysis to extend the inference space. In this work we use the modeled explicit spatial structure from simulated forest stands to parameterize 3D remote sensing models that calculate Lidar and Radar energy interactions within forest canopies. This paper will discuss the use of a recently updated IBM model for boreal forests ( Sibbork) to inform two spatially explicit remote sensing models developed by the authors. The remote sensing response will be compared with our early work to evaluate differences. The preliminary results on biomass estimation from LUT inversion, and the potential of SAR/lidar data fusion for forest parameters estimation at high spatial resolution will be discussed.