Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.

Making predictions from ecological models—and comparing them to data—offers a coherent approach to evaluate model quality, regardless of model complexity or modelling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies, and the public has been hampered by disparate perspectives on prediction and inadequately integrated approaches. We present an updated foundation for Predictive Ecology based on seven principles applied to ecological modelling: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows that are routinely Tested (PERFICT). We outline some benefits of working with these principles: accelerating science; linking with data science; and improving science‐policy integration.

Modelling is widely used in ecology and its utility continues to increase as scientists, managers and policy‐makers face pressure to effectively manage ecosystems and meet conservation goals with limited resources. As the urgency to forecast ecosystem responses to global change grows, so do the number and complexity of predictive ecological models and the value of iterative prediction, both of which demand validation and cross‐model comparisons. This challenges ecologists to provide predictive models that are reusable, interoperable, transparent and able to accommodate updates to both data and algorithms. We propose a practical solution to this challenge based on the PERFICT principles (frequent Predictions and Evaluations of Reusable, Freely accessible, Interoperable models, built within Continuous workflows that are routinely Tested), using a modular and integrated framework. We present its general implementation across seven common components of ecological model applications—(i) the modelling toolkit; (ii) data acquisition and treatment; (iii) model parameterisation and calibration; (iv) obtaining predictions; (v) model validation; (vi) analysing and presenting model outputs; and (vii) testing model code—and apply it to two approaches used to predict species distributions: (1) a static statistical model, and (2) a complex spatiotemporally dynamic model. Adopting a continuous workflow enabled us to reuse our models in new study areas, update predictions with new data, and re‐parameterise with different interoperable modules using freely accessible data sources, all with minimal user input. This allowed repeating predictions and automatically evaluating their quality, while centralised inputs, parameters and outputs, facilitated ensemble forecasting and tracking uncertainty. Importantly, the integrated model validation promotes a continuous evaluation of the quality of more‐ or less‐parsimonious models, which is valuable in predictive ecological modelling. By linking all stages of an ecological modelling exercise, it is possible to overcome common challenges faced by ecological modellers, such as changing study areas, choosing between different modelling approaches, and evaluating the appropriateness of the model. This ultimately creates a more equitable and robust playing field for both modellers and end users (e.g. managers), and contributes to position predictive ecology as a central contributor to global change forecasting.

AIM-North is a proposed satellite mission that would provide observations of unprecedented frequency and density for monitoring northern greenhouse gases (GHGs), air quality (AQ) and vegetation. AIM-North would consist of two satellites in a highly elliptical orbit formation, observing over land from ∼40°N to 80°N multiple times per day. Each satellite would carry a near-infrared to shortwave infrared imaging spectrometer for CO₂, CH₄, and CO, and an ultraviolet-visible imaging spectrometer for air quality. Both instruments would measure solar-induced fluorescence from vegetation. A cloud imager would make near-real-time observations, which could inform the pointing of the other instruments to focus only on the clearest regions. Multiple geostationary (GEO) AQ and GHG satellites are planned for the 2020s, but they will lack coverage of northern regions like the Arctic. AIM-North would address this gap with quasi-geostationary observations of the North and overlap with GEO coverage to facilitate intercomparison and fusion of these datasets. The resulting data would improve our ability to forecast northern air quality and quantify fluxes of GHG and AQ species from forests, permafrost, biomass burning and anthropogenic activity, furthering our scientific understanding of these processes and supporting environmental policy.

Understanding and quantifying carbon (C) exchanges between the biosphere and the atmosphere—specifically the process of C removal from the atmosphere, and how this process is changing—is the basis for developing appropriate adaptation and mitigation strategies for climate change. Monitoring forest systems and reporting on greenhouse gas (GHG) emissions and removals are now required components of international efforts aimed at mitigating rising atmospheric GHG. Spatially-explicit information about forests can improve the estimates of GHG emissions and removals. However, at present, remotely-sensed information on forest change is not commonly integrated into GHG reporting systems. New, detailed (30-m spatial resolution) forest change products derived from satellite time series informing on location, magnitude, and type of change, at an annual time step, have recently become available. Here we estimate the forest GHG balance using these new Landsat-based change data, a spatial forest inventory, and develop yield curves as inputs to the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to estimate GHG emissions and removals at a 30 m resolution for a 13 Mha pilot area in Saskatchewan, Canada. Our results depict the forests as cumulative C sink (17.98 Tg C or 0.64 Tg C year−1) between 1984 and 2012 with an average C density of 206.5 (±0.6) Mg C ha−1. Comparisons between our estimates and estimates from Canada’s National Forest Carbon Monitoring, Accounting and Reporting System (NFCMARS) were possible only on a subset of our study area. In our simulations the area was a C sink, while the official reporting simulations, it was a C source. Forest area and overall C stock estimates also differ between the two simulated estimates. Both estimates have similar uncertainties, but the spatially-explicit results we present here better quantify the potential improvement brought on by spatially-explicit modelling. We discuss the source of the differences between these estimates. This study represents an important first step towards the integration of spatially-explicit information into Canada’s NFCMARS.

Widespread impacts of a warming planet are fuelling climate change mitigation efforts world-wide. Decision makers are turning to forests, the largest terrestrial primary producer, as a nature-based contribution to mitigation efforts. Resource-based economies, however, have yet to include carbon (C) in their resource planning, slowing the implementation of these important measures for atmospheric greenhouse gas reduction. The realisation of forest mitigation potential depends greatly on our ability to integrate C-sequestration practices in our forest management applications. This requires robust C-estimates, an understanding of the natural potential for a specific landscape to sequester C, the current state of the landscape relative to this potential, and the evaluation of management practices as a tool to sequester forest C in the midst of all the other values forests offer humans. Discrepancies between models used in management decisions and C estimation are the first hurdle impeding the application of forest-based mitigation strategies. Here, we combine forest disturbance and management models with a well-established C model on an open-source simulation platform. We then use the modelling system to produce C estimates of the natural C-holding capacity (potential) and two management scenarios for a study area in BC, Canada. Our simulations provide an essential metric if forests are to be managed for C-sequestration: the natural landscape C-holding capacity. Our simulations also point to a decreasing trend in simulated C on the study area over time and to a bias of the current C-levels compared to the landscape C-holding capacity (477 vs 405.5 MtC). Our explanations for this bias may provide an avenue for improved current C-state estimates. We provide a framework and the information needed for the implementation of nature-based solutions using forests for climate change mitigation. This study is a step towards modelling systems that can unify scientifically based forest management and informed C-management.