Gregory Paradis

Operational forest machine scheduling remains dominated by site-specific tools that are difficult to transfer, audit, and compare across studies. The recent systematic review by Jaffray et al. highlights this gap and calls for reusable, transparent planning workflows. FHOPS addresses that requirement by providing an open-source operational planning platform built around a common scenario contract, a solver stack (mixed-integer programming plus SA/ILS/Tabu heuristics), and a unified evaluation layer for schedule quality, robustness, and cost interpretation. We demonstrate the platform on a reproducible British Columbia (BC) ground-based dataset ladder (tiny7, small21, med42) plus synthetic scaling tiers, with manuscript tables and figures generated directly from versioned benchmark assets. This paper documents the reusable software platform and baseline evidence; detailed rolling-horizon design analysis and broader deployment studies are treated as companion modelling work.

Sustainable forest management (SFM) is critical for ensuring that forests provide social, economic, and environmental benefits for generations to come. Forest management planning is often presented...

Stem diameter distributions underpin growth projections, harvest scheduling, and carbon accounting, yet permanent sample plot inventories are routinely truncated by merchantability limits and maximum expected diameters. The accepted remedy is to fit truncated versions of the desired density, but those forms are seldom documented or supported in common software, so practitioners often default to biased complete-form fits. We revisit the truncated-diameter problem and introduce a two-stage weighted least-squares workflow that retains the familiar complete-form implementation while recovering the truncated solution. Stage one estimates a scaling factor alongside the density parameters; stage two freezes that normaliser to deliver unbiased shape and scale estimates. Applied to fixed-area plots from Quebec, Canada, the approach matches truncated Weibull and gamma fits across 11 species-group / cover-type combinations, limiting the absolute AICc gap to 0.006. The companion repository provides the tallies, scripts, and notebooks required to regenerate every figure, table, and LaTeX artefact, supporting operational replication.

Horizontal point sampling (HPS) produces size-biased tallies that cannot be fit directly with standard probability distributions without distorting diameter distribution estimates. Previous work resolves this by deriving bespoke size-biased probability density functions (PDFs) for each assumed distribution. We revisit the problem and formalise a weighted non-linear least squares approach that fits standard-form PDFs to expanded HPS stand tables while preserving the statistical equivalence with the size-biased formulation. The new pipeline leverages contemporary open-source software, is fully reproducible, and includes accompanying code that regenerates all figures and tables. Computational experiments on permanent sample plot data from Quebec demonstrate that the weighted method matches the reference approach to machine precision across Weibull and Gamma distributions. The manuscript and companion software provide a turnkey solution for practitioners who require stable, transparent, and replicable HPS diameter distribution fitting.

Transparent decision support for forest landscapes demands integrated scheduling, carbon account-ing, and spatial reporting. WS3 is an open-source Python framework that unifies modular simu-lation, solver-backed Model I optimization, and raster allocation in a single workflow. The systemingests Woodstock-style inventories, actions, and scenarios; exposes an explicit data model; andautomates spatial/aspatial conversions. Native linkage to the Canadian Forest Service CarbonBudget Model (libCBM) provides carbon stock and flux estimation for Canada and other libCBM-calibrated jurisdictions. We document the architecture, mathematical formulation, and reproduciblecase studies that pair optimization-driven harvest scheduling with libCBM and spatial allocationto illustrate policy trade-offs in harvest flows, carbon dynamics, and disturbance footprints. WS3ships with an open reproduction package, documentation, and Zenodo-archived releases that ensuredeterministic builds of figures, tables, and parity tests. The framework lowers barriers to auditable,climate-aware forest planning for researchers, agencies, and practitioners.

Sustainable forest management relies on effective operational planning to ensure that harvesting practices support long-term objectives. Operations research methods have largely been used to support operational decision-making in forest harvest planning but the broader strengths, limitations and barriers to adoption remain unclear. This review addresses this gap by synthesizing existing research on operational planning tools for forest harvesting. Using PRISMA protocols, we conducted systematic searches in Scopus and Web of Science and identified 23 peer-reviewed studies published between 2005 and 2024. The included studies employed diverse approaches across geographic regions, most commonly being mixed-integer programming and geographic information systems (GIS). Results show that while these models provide valuable insights and demonstrate technical expertise, they are often hard-coded to specific sites, lack reproducibility and are rarely open-source. Developing modular, transparent and user-centric tools could strengthen the existing connection between research and practice, enabling forest planners to manage uncertainty and improve efficiency while aligning with broader sustainability goals. Our findings highlight the importance of designing adaptable frameworks that embed site-specificity as a structural element rather than a limitation. We synthesize findings into a practitioner checklist, covering inputs, constraints, solution approach, validation, user experience and openness to guide tool design and evaluation.

This study, realized in partnership with a leading global gold producer, explores how forest management nature-based solutions (NbS) can be integrated into decarbonization strategies to achieve carbon neutrality by 2050. NbS, including carbon sequestration in forests, are expected to play an important role in meeting decarbonization commitments in the mining sector. Our primary contribution is a prototype open-source decision support system (DSS) framework for modeling forest management scenarios using publicly available data and reproducible workflows. We demonstrate the framework on three distinct mining sites in British Columbia (BC), Canada, using geospatial data, forest inventory, and landscape-level models. The prototype compares a business-as-usual scenario with alternative harvesting scenarios to examine objectives such as maximizing harvest volume, minimizing harvested area, maximizing ecosystem carbon stocks, and minimizing net emissions. Key performance indicators (KPIs) are tracked to evaluate environmental, economic, and social aspects. The case study serves to illustrate that both the method and code implementation adapt readily to sites with different forest dynamics. While the current notebook-based interface is limited in usability, it supports a fully open, transparent, and reproducible analysis pipeline. We argue that further development of this approach is warranted to improve usability while retaining transparency and auditability. The study does not prescribe specific harvesting strategies; rather, the findings illustrate how forest management objectives influence outcomes across KPIs. Scenarios prioritizing higher harvest volumes yield greater economic and social benefits but often coincide with trade-offs in environmental indicators. Conversely, restricting harvesting can improve environmental indicators. While not production-ready, the prototype demonstrates promising attributes for nature-based decarbonization analyses and suggests a direction for further development and evaluation.