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Current technological advances mean that we are on the verge of a fusion of ecological modeling and remote sensing that should improve our prediction of ecological responses to global change for forests over continental scales. The need for such a capability could not be greater: the planet is warming (IPCC 2014) and past changes in climate have resulted in extinctions, major shifts in species distributions, and ecosystems with novel species combinations (Shugart and Woodward 2011). Predicting the consequences of warming involves extrapolation, which is always a tricky scientific proposition. The changes that human actions are causing thus highlight the need for better model prediction capabilities.Two technological innovations have the potential to bring about improved prediction of forest-ecosystem dynamics at large scales: first, innovations in airborne and satellite remote-sensing instruments are providing increased potential for large-scale measurement of forest structure, notably forest height and biomass; second, increased computing power is permitting continentalscale implementation of forest individual-based models (IBMs), which simulate the dynamic changes originating from interactions between individual trees ( Figure 1) over very large areas. Shugart and Woodward (2011) provided several examples of individual tests of model predictions against independent data for different forest IBMs. At the continental scale, however, independent data are still needed that can test IBM predictions; these data could be produced by new remote-sensing technology. If successful at these large scales, such tests will assess the accuracy of large-area predictions; if not, they can be used to improve the models. This predict-and-test cycle is the hypothetico-deductive paradigm -the scientific method often used by high-school physics teachers -applied to forest dynamics over large regions and under novel conditions. Critical to this evaluation is the statistical independence of the test data from the calibration data. Successful tests against spatially extensive data would provide confidence in direct scaling up of forest IBMs.n New infrastructure and technology: an opportunity for a new level of model-data synthesisThe scaled-up cousins of traditional ecosystem process models -both land-surface models (LSMs), which are components of the general circulation models that are Global environmental change necessitates increased predictive capacity; for forests, recent advances in technology provide the response to this challenge. "Next-generation" remote-sensing instruments can measure forest biogeochemistry and structural change, and individual-based models can predict the fates of vast numbers of simulated trees, all growing and competing according to their ecological attributes in altered environments across large areas. Application of these models at continental scales is now feasible using current computing power. The results obtained from individual-based models are testable against remotely sensed data, and so can be u...

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Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.

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Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (∆H) and the actual changes in aboveground biomass (∆AGB). Different approaches can be used to accomplish this task: direct approaches link ∆H directly to ∆AGB, while indirect approaches are based on deriving AGB stock estimates for two points in time and calculating the difference. In some studies, direct approaches led to more accurate estimations, while, in others, indirect approaches led to more accurate estimations. It is unknown how each approach performs under different conditions and over the full range of possible changes. Here, we used a forest model (FORMIND) to generate a large dataset (>28,000 ha) of natural and disturbed forest stands over time. Remote sensing of forest height was simulated on these stands to derive canopy height models for each time step. Three approaches for estimating ∆AGB were compared: (i) the direct approach; (ii) the indirect approach and (iii) an enhanced direct approach (dir+tex), using ∆H in combination with canopy texture. Total prediction accuracies of the three approaches measured as root mean squared errors (RMSE) were RMSE direct = 18.7 t ha −1 , RMSE indirect = 12.6 t ha −1 and RMSE dir+tex = 12.4 t ha −1 . Further analyses revealed height-dependent biases in the ∆AGB estimates of the direct approach, which did not occur with the other approaches. Finally, the three approaches were applied on radar-derived (TanDEM-X) canopy height changes on Barro Colorado Island (Panama). The study demonstrates the potential of forest modeling for improving the interpretation of changes observed in remote sensing data and for comparing different methodologies.