Olha Nahorna finished her PhD in SmartForest last year, and the last study of her work, evaluating the value of raster cell-level information in multi-objective forest planning was just recently published in the European Journal of Forest Research by Springer Nature.
the article 👉 Evaluating the value of raster cell-level information in multi-objective forest planning
the code 👉 Vol_cell_level
Accurate forest inventory is key for good management, but aggregating remote-sensing cell-level predictions into large stands can lose important spatial detail. Olha Nahorna, Terje Gobakken and Kyle Eyvindson used a value-of-information (VoI) analysis with a multi-objective optimization model to test whether grouping cells into alternative management units (segments) — while keeping cell-level data — improves decisions versus traditional stand-level aggregation.
They found that using cell-level data with either custom segments or existing stand boundaries consistently produced better management outcomes, with optimized segmentation giving the largest gains. Even keeping conventional stands but preserving cell information yielded meaningful improvements. The approach lets decision-makers tailor segmentation to their objectives and improve planning without sacrificing operational feasibility.
https://link.springer.com/article/10.1007/s10342-026-01897-3