F3: Simulating spatiotemporal forest change from field inventory, remote sensing, growth modeling, and management actions

Abstract

Abstract Contemporary forest management requires highly-detailed, spatially-contiguous, multi-temporal, and scenarios-comparable forest conditions. Field inventories and individual-tree models often contain highly-detailed data and allow for long-term complex scenarios comparison, but the information is only at sampled locations and lacks complete spatial coverage. Forest landscape models (FLMs) provide landscape-level spatiotemporal data, but the details that are important to land managers are often lost in the generalized outputs. We developed a modeling framework, F3, to integrate FIA (Forest Inventory and Analysis) plots, the Forest Vegetation Simulator (FVS), and FastEmap (Field And SatelliTe for Ecosystem MAPping) to simulate spatiotemporal forest change under natural succession and vegetation management. F3 extrapolates the details of forest inventory plots and individual-tree model outputs to a spatially-contiguous landscape by fusing tree-list field measurements, individual tree growth and yield models, remote sensing and environmental geospatial datasets. F3 allows for area-specific management action simulations. F3 compares FVS results with field measurements for temporal accuracy assessment and uses a leave-one-out cross-validation for spatial accuracy assessment. F3 adopts parallel computation techniques to implement the modeling in an automatic and efficient manner. The proof of concept of F3 was demonstrated in Tahoe National Forest (TNF) showing spatiotemporal changes on six forest structural metrics (quadratic mean diameter, basal area, biomass, habitat suitability index, canopy cover, and coarse woody debris) under natural succession, regeneration-cut, and thinning scenarios for the years 2014–2114 at a 30 m resolution. F3 can be used for initializing FLMs and for analyzing a wide range of ecosystem services; however, the under-representation of certain forest types in the FIA plot data set, the modeling bias from FVS, and choice of FastEmap covariates contribute to major uncertainties in the framework.