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Tervetuloa käyttämään Jukuria, Luonnonvarakeskuksen (Luke) avointa julkaisuarkistoa. Jukurissa on tiedot Luken julkaisutuotannosta. Osa julkaisuista on vapaasti ladattavissa. Luken muodostaneiden tutkimuslaitosten aikaisemmasta julkaisutuotannosta osan tiedot ovat järjestelmässä jo nyt ja kattavuus paranee jatkuvasti.

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Viimeksi tallennetut

An Integrated GIS-MILP Framework for Cost-Optimal Forest Biomass-to-Bioenergy Supply Chains: A Case Study in Queensland, Australia

Van Holsbeeck, Sam; Acuna, Mauricio; Ezzati, Sättar
Forests : 9 (MDPI, 2025)

Renewable energy expansion requires cost-effective strategies to integrate underutilized biomass resources into energy systems. In Australia, forest residues represent a significant but largely untapped feedstock that could contribute to a more diversified energy portfolio. This study presents an integrated geospatial and optimization decision-support model designed to minimize the total cost of forest biomass-to-bioenergy supply chains through optimal facility selection and network design. The model combined geographic information systems with mixed-integer linear programming to identify the optimal candidate facility sites based on spatial constraints, biomass availability and infrastructure proximity. These inputs then informed an optimization framework that determined the number, size, and geographical distribution of bioenergy plants. The model was applied to a case study in Queensland, Australia, evaluating two strategic scenarios: (i) a biomass-driven approach that maximizes the use of forest residues; (ii) an energydriven approach that aligns facilities with regional energy consumption patterns. Results indicated that increasing the minimum facility size reduced overall costs by capitalizing on economies of scale. Biomass collection accounted for 81%–83% of total supply chain costs (excluding capital installation), emphasizing the need for logistically efficient sourcing strategies. Furthermore, the system exhibited high sensitivity to transportation distance and biomass availability; energy demands exceeding 400 MW resulted in sharply escalating transport expenses. This study provides a scalable, data-driven framework for the strategic planning of forest-based bioenergy systems. It offers actionable insights for policymakers and industry stakeholders to support the development of robust, cost-effective, and sustainable bioenergy supply chains in Australia and other regions with similar biomass resources.

Integrating Pre-Harvest UAV Scans to Enhance Harvester Tree Localization Accuracy

Lopatin, Evgeny; Väätäinen, Kari; Kaartinen, Harri; Hyyti, Heikki; Sikanen, Lauri; Nuutinen, Yrjö; Acuna, Mauricio
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS, 2025)

Accurate geolocation of individual trees during forest harvesting operations is crucial for effective decision-making, yet traditional cut-to-length (CTL) harvesters often experience significant positional errors (0.5–10 m) due to unreliable GNSS performance under dense forest canopies. This uncertainty hampers the precise integration of harvester-generated data into operational forest management systems. To address this problem, we investigated the integration of high-resolution pre-harvest UAV LiDAR data with harvester-collected positional information. UAV laser scanning (DJI Matrice equipped with Zenmuse L2 LiDAR) was conducted over a dense, mixed-species boreal forest stand scheduled for its first thinning operation. Following harvesting, stump positions were precisely recorded using centimeter-grade GNSS as ground truth. Harvester-recorded tree positions were matched to tree crowns delineated from UAV LiDAR point clouds using Canopy Height Model (CHM) segmentation. For each crown, structural (height, crown size) and spectral (RGB statistics) features were extracted, and tree species (spruce, pine, birch) were classified using Random Forest (RF) and XGBoost models. Comparative positional error analysis revealed that mean harvester GNSS errors were 1.52 m, whereas UAV-derived tree positions showed significantly lower mean errors of 0.63 m. Integrating UAV data with harvester positions successfully reduced the mean positional error to 0.76 m. Species classification accuracy exceeded 91% overall for both RF and XGBoost models, with coniferous species (pine, spruce) classified at approximately 94% accuracy and deciduous birch slightly lower at around 71%. These results highlight the potential of integrating pre-harvest UAV scans to substantially enhance tree-level geolocation accuracy, enabling precise digital twins and improved real-time operational decision-making during harvesting. The study addresses a critical research gap by developing a practical workflow for combining UAV and harvester data, thereby facilitating precision forestry applications such as targeted tree selection, automated navigation, and enforcing environmental safeguards.

Tietokortti: Maatalousmaiden typpioksiduulipäästöt

Semberg, Sanni; Manninen, Petra (Luonnonvarakeskus, 2025)

Suuri osa Suomen typpioksiduulipäästöistä (N₂O) on peräisin maataloudesta. Osa lannoitteiden typestä päätyy mikrobien toiminnan seurauksena ilmakehään sen sijaan, että päätyisi viljelykasvien käyttöön. Maaperän N₂O-päästöihin voi vaikuttaa suhteellisen pienilläkin toimilla, esimerkiksi kasvivalinnoilla ja optimoimalla lannoituksen määrän ja ajankohdan.

Catchment-based approach for water table management with irrigation for cultivated peatlands

Läpikivi, Miika; Liimatainen, Maarit; Kløve, Bjørn; Marttila, Hannu
Agricultural water management (Elsevier, 2025)

Controlled drainage and subsurface irrigation have been proposed to enable shallow-drained agriculture in organic soils and mitigate greenhouse gas (GHG) emissions from intensively cultivated peatlands. However, the effects of current drainage practices on peatland water table depth (WTD) and the potential of using runoff from upstream catchment areas to adjust WTD in northern conditions are still poorly understood. To address these issues, WTD monitoring was initiated on 13 cultivated peatlands with different drainage systems in the flat western coastal region of Finland. Monitoring locations with old subsurface drainage, new subsurface drainage, and open ditch drainage had average WTD of 0.51 m, 0.82 m, and 0.95 m, respectively, during the two monitoring years (11/2022 – 10/2024). For each field, we estimated the size of the upper catchment, median summer total runoff, and mean 7-day summer low flow rate. The water required to reach a 0.3 m target WTD was estimated from peat specific yield. Each 0.1 m decrease in mean WTD was estimated to require of 13.2 mm of additional water. Median summer total runoff from the upper catchment was sufficient to reach any target WTD, but the summer low-flow rate did not fulfil the daily water demand. Most runoff is available during early summer, thus creating a timing challenge with water availability even in region with excess annual precipitation. This highlights the importance of catchment-scale management for GHG mitigation. In this study, we propose a generally applicable framework to link peatland GHG mitigation with water resources and catchment-scale management.

Fosforilannoituksen sääntelystä

Luostarinen, Sari
Käytännön maamies : 09/2022 (Terramedia oy, 2022)