Articles containing the keyword 'remote sensing'

The purpose of this study was to test the benefits of a forest site quality map, when applying satellite image-based forest inventory. By combining field sample plot data from national forest inventories with satellite imagery and forest site quality data, it is possible to estimate forest stand characteristics with higher accuracy for smaller areas. The reliability of the estimates was evaluated using the data from a stand-wise survey for area sizes ranging from 0.06 ha to 300 ha. When the mean volume was estimated, a relative error of 14 per cent was obtained for areas of 50 ha; for areas of 30 ha the corresponding figure was below 20 per cent. The relative gain in interpretation accuracy, when including the forest site quality information, ranged between 1 and 6 per cent. The advantage increased according to the size of the target area. The forest site quality map had the effect of decreasing the relative error in Norway spruce (Picea abies) volume estimations, but it did not contribute to Scots pine (Pinus sylvestris) volume estimation procedure.

• Tokola, E-mail: tt@mm.unknown
• Heikkilä, E-mail: jh@mm.unknown

A method for using satellite data in forest inventories and updating is described and tested. The stand characteristics estimated by the method showed high correlation with the same characteristics measured in the field. The correlation coefficients for volume, age and mean height were about 0.85. It seems that the method is applicable to practical forestry. Extensive work in programming, however, is required.

The PDF includes an abstract in Finnish.

• Poso, E-mail: sp@mm.unknown
• Paananen, E-mail: rp@mm.unknown
• Similä, E-mail: ms@mm.unknown

The paper presents a method based on two phase sampling and applicable to forest inventories. The first phase estimates are obtained from satellite imagery and, if required, from extra material such as maps. Second phase estimates are measured in the field. The method is flexible and also applicable to compartmentwise forest inventories. The experiments were based on six study areas with 439 relascope plots. The correlation coefficients between first and second stage estimates varied largely according to the study area.

The PDF includes a summary in Finnish.

• Poso, E-mail: sp@mm.unknown
• Häme, E-mail: th@mm.unknown
• Paananen, E-mail: rp@mm.unknown

After the Second World War Finnish Forest Service was faced with large e-mapping and timber surveying project in Northern Finland. The funds for mapping were very limited. In order to re-map the large areas, the only way was to look for alternative methods for the ground methods. The photogrammetric equipment of Finnish Army was made available to the civil service. Consequently, since 1947 several forest mapping projects were carried out in co-operation between the Forest Service and the Army Topographic Service.

When more funds were coming available for the project, new instruments were acquired. The article describes the present mapping procedure and suggests alternative ways in procedure and utilization of new equipment. It concludes that if the forest area under modern timber management plans is several million acres, the ideal implemental framework for mapping and timber surveying unit in Finland should be the following: Radial Secator RS I and slotted templates for the radial line plot, Stereotope Plotter for drafting general maps, the old Delft Scanning Stereoscope for photo interpretation, and Aero-Sketchmaster for transport of the photo details.

• Kuusela, E-mail: kk@mm.unknown

Silva Fennica Issue 69 includes presentations held in 1948-1950 in the fourth professional development courses, arranged for foresters working in the Forest Service. The presentations focus on practical issues in forest management and administration, especially in regional level. The education was arranged by Forest Service.

Forest Service begun the aerial mapping of the state forests in northern Finland in 1948. This presentation describes the state of the work, practices and methods of the work.

• Olenius, E-mail: lo@mm.unknown

Silva Fennica Issue 69 includes presentations held in 1948-1950 in the fourth professional development courses, arranged for foresters working in the Forest Service. The presentations focus on practical issues in forest management and administration, especially in regional level. The education was arranged by Forest Service.

This presentation describes the development of aerial mapping, its principles and methods. The use of aerial photographs and the costs of the method is discussed.

• Linnamies, E-mail: ol@mm.unknown

The presence/absence data of 27 forest insect taxa (Retinia resinella, Formica spp., Pissodes spp., several scolytids) and recorded environmental variation were used to investigate the applicability of modelling insect occurrence based on satellite imagery. The sampling was based on 1,800 sample plots (25 m by 25 m) placed along the sides of 30 equilateral triangles (side 1 km) in a fragmented forest area (approximately 100 km²) in Evo, Southern Finland. The triangles were overlaid on land use maps interpreted from satellite images (Landsat TM 30 m multispectral scanner imagery 1991) and digitized geological maps. Insect occurrence was explained using either environmental variables measured in the field or those interpreted from the land use and geological maps. The fit of logistic regression models carried between species, possibly because some species may be associated with characteristics of single trees while other species with stand characteristics. The occurrence of certain insect species at least, especially those associated with Scots pine, could be relatively accurately assessed indirectly on the basis of satellite imagery and geological maps. Models based on both remotely sensed and geological data better predicted the distribution of forest insects except in the case of Xylechinus pilosus, Dryocetes sp. and Trypodendron lineatum, where the differences were relatively small in favour of the models based on field measurements. The number of species was related to habitat compartment size and distance from the habitat edge calculated from the land use maps, but logistic regressions suggested that other environmental variables in general masked the effect of these variables in species occurrence at the present scale.

• Väisänen, E-mail: rv@mm.unknown
• Heliövaara, E-mail: kh@mm.unknown

Spectral characteristics of rapid changes in forest and the spectral separability of change categories were studied through the analysis of satellite scanner images. A computational model of the spectral reflectance of a Scots pine (Pinus sylvestris L.) stand as a function of time was constructed and compared with empirical data. The study area, centred at 61°51’ N, 24°22’ E, was located in boreal forest in Southern Finland. Ground truth data consisted of forest stands and sample plots. Spectral data comprised multitemporal Landsat Thematic Mapper and Spot images as well as spectroradiometer measurements. The separability of the changes was tested with statistical tests and classifications. The separability varied according to the change category. A scheme for fully automated change monitoring system was presented.

The PDF includes a summary in Finnish.

• Häme, E-mail: th@mm.unknown

• Kuusinen, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland https://orcid.org/0000-0002-8063-1739 E-mail: nea.kuusinen@aalto.fi
• Hovi, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland https://orcid.org/0000-0002-4384-5279 E-mail: aarne.hovi@aalto.fi
• Rautiainen, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi

Pathogenic wood decay fungi such as species of Heterobasidion are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of Picea and Abies, these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce (Picea abies L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.

• Allen, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: benjamin.allen@nmbu.no
• Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38098 San Michele all’Adige (TN), Italy E-mail: michele.dalponte@fmach.it
• Hietala, Norwegian Institute of Bioeconomy Research, Innocamp Steinkjer, Skolegata 22, NO-7713 Steinkjer, Norway E-mail: Ari.Hietala@nibio.no
• Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: hans-ole.orka@nmbu.no
• Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no

Accurate mapping of the spatial distribution of understory species from spectral images requires ground reference data which represent the prevailing phenological stage at the time of image acquisition. We measured the spectral bidirectional reflectance factors (BRFs, 350–2500 nm) at varying view angles for lingonberry (Vaccinium vitis-idaea L.) and blueberry (Vaccinium myrtillus L.) throughout the growing season of 2017 using Finnish Geospatial Research Institute’s FIGIFIGO field goniometer. Additionally, we measured spectra of leaves and berries of both species, and flowers of lingonberry. Both lingonberry and blueberry showed seasonality in visible and near-infrared spectral regions which was linked to occurrences of leaf growth, flowering, berrying, and leaf senescence. The seasonality of spectra differed between species due to different phenologies (evergreen vs. deciduous). Vegetation indices, normalized difference vegetation index (NDVI), moisture stress index (MSI), plant senescence reflectance index (PSRI), and red-edge inflection point (REIP2), showed characteristic seasonal trends. NDVI and PSRI were sensitive to the presence of flowers and berries of lingonberry, while with blueberry the effects were less evident. Off-nadir observations supported differentiating the dwarf shrub species from each other but showed little improvement for detection of flowers and berries. Lingonberry and blueberry can be identified by their spectral signatures if ground reference data are available over the entire growing season. The spectral data measured in this study are reposited in the publicly open SPECCHIO Spectral Information System.

• Forsström, Aalto University, School of Engineering, Department of Built Environment, FI-00076 Aalto, Finland https://orcid.org/0000-0002-2357-2517 E-mail: petri.forsstrom@aalto.fi
• Peltoniemi, Finnish Geospatial Research Institute (FGI), Department of Geodesy and Geodynamics, Geodeetinrinne 2, FI-02430 Masala, Finland https://orcid.org/0000-0002-4701-128X E-mail: jouni.peltoniemi@nls.fi
• Rautiainen, Aalto University, School of Engineering, Department of Built Environment, FI-00076 Aalto, Finland; Aalto University, Department of Electronics and Nanoengineering, FI-00076 Aalto, Finland https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi

Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy of stand-based estimates has been quite close to that of ALS while the costs are markedly smaller. Thus, it is important to know if the better accuracy of ALS is worth the higher costs for forest owners. In many recent studies, the value of forest inventory information in the harvest scheduling has been examined, for instance through cost-plus-loss analysis. Cost-plus-loss means that the quality of the data is accounted for in monetary terms through calculating the losses due to errors in the data in the forest management planning context. These costs are added to the inventory costs. In the current study, we compared the losses of ALS and DAP at plot level. According to the results, the data produced using DAP are as good as data produced using ALS from a decision making point of view, even though ALS is slightly more accurate. ALS is better than DAP only if the data will be used for more than 15 years before acquiring new data, and even then the difference is quite small. Thus, the increased errors in DAP do not significantly affect the results from a decision making point of view, and ALS and DAP data can be equally well recommended to the forest owners for management planning. The decision of which data to acquire, can thus be made based on the availability of the data on first hand and the costs of acquiring it on the second hand.

• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80170 Joensuu, Finland E-mail: annika.kangas@luke.fi
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no
• Puliti, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: stefano.puliti@nibio.no
• Hauglin, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: marius.hauglin@nmbu.no
• Naesset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km². Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
• Pitkänen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: timo.p.pitkanen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: Annika.Kangas@luke.fi

The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] –13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD –2.0% to 2.8%, with the exception of the Tornio region – RMSE 11.4%, MD –9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration.

• Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi

We investigated if coarse-resolution satellite data from the MODIS sensor can be used for regional monitoring of insect disturbances in Fennoscandia. A damage detection method based on z-scores of seasonal maximums of the 2-band Enhanced Vegetation Index (EVI2) was developed. Time-series smoothing was applied and Receiver Operating Characteristics graphs were used for optimisation. The method was developed in fragmented and heavily managed forests in eastern Finland dominated by Scots pine (Pinus sylvestris L.) (pinaceae) and with defoliation of European pine sawfly (Neodiprion sertifer Geoffr.) (Hymenoptera: Diprionidae) and common pine sawfly (Diprion pini L.) (Hymenoptera: Diprionidae). The method was also applied to subalpine mountain birch (Betula pubescens ssp. Czerepanovii N.I. Orlova) forests in northern Sweden, infested by autumnal moth (Epirrita autumnata Borkhausen) and winter moth (Operophtera brumata L.). In Finland, detection accuracies were fairly low with 50% of the damaged stands detected, and a misclassification of healthy stands of 22%. In areas with long outbreak histories the method resulted in extensive misclassification. In northern Sweden accuracies were higher, with 75% of the damage detected and a misclassification of healthy samples of 19%. Our results indicate that MODIS data may fail to detect damage in fragmented forests, particularly when the damage history is long. Therefore, regional studies based on these data may underestimate defoliation. However, the method yielded accurate results in homogeneous forest ecosystems and when long-enough periods without damage could be identified. Furthermore, the method is likely to be useful for insect disturbance detection using future medium-resolution data, e.g. from Sentinel‑2.

• Olsson, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: per-ola.olsson@nateko.lu.se
• Kantola, Texas A & M University, Knowledge Engineering Laboratory, Department of Entomology, College Station, TX, USA E-mail: tuula.kantola@helsinki.fi
• Lyytikäinen-Saarenmaa, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: paivi.lyytikainen-saarenmaa@helsinki.fi
• Jönsson, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: anna_maria.jonsson@nateko.lu.se
• Eklundh, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: lars.eklundh@nateko.lu.se

To promote the growth and survival of regenerated forests, site preparation prior to tree planting on clearcuts is necessary. This is often performed with scarifiers, either through trenching or mounding. Mounding is generally considered better in a plant survival perspective but is inefficient on obstacle-rich clearcuts. By utilising machine vision through e.g. remote sensing methods, new strategies can enable efficient mound positioning. In this paper, three realistic strategies utilizing ideal clearcut object identification through machine vision have been developed that can be used for more efficient mounding. The results show that mounding efficiency can be significantly improved with a new mound positioning strategy that employs ideal object identification, especially on obstacle-rich clearcuts.

• Lideskog, Luleå University of Technology, Department of Engineering Sciences and Mathematics, Division of Product and Production Development, SE-971 87 Luleå, Sweden E-mail: hakan.lideskog@ltu.se
• Karlberg, Luleå University of Technology, Department of Engineering Sciences and Mathematics, Division of Product and Production Development, SE-971 87 Luleå, Sweden E-mail: magnus.karlberg@ltu.se

• Niemi, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland E-mail: mikko.t.niemi@helsinki.fi
• Vastaranta, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland E-mail: mikko.vastaranta@helsinki.fi
• Peuhkurinen, Arbonaut Oy Ltd., Latokartanontie 7 A, FI-00700, Finland E-mail: jussi.peuhkurinen@arbonaut.com
• Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland E-mail: markus.holopainen@helsinki.fi

• Muinonen, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: eero.muinonen@metla.fi
• Anttila, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: perttu.anttila@metla.fi
• Heinonen, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: jaakko.heinonen@metla.fi
• Mustonen, Stora Enso, Talvikkitie 40 C, FI-01300 Vantaa, Finland E-mail: jukka.mustonen@storaenso.com

• Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
• Eerikäinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ke@nn.fi
• Schibalski, University of Potsdam, Karl-Liebknecht-Strasse 24–25, 14476 Potsdam, Germany E-mail: as@nn.de
• Haakana, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mh@nn.fi
• Lehtonen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: al@nn.fi

• Korpela, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland E-mail: ilkka.korpela@helsinki.fi
• Tuomola, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland E-mail: tt@nn.fi
• Tokola, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland E-mail: tt@nn.fi
• Dahlin, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland E-mail: bd@nn.fi

• Rautiainen, Tartu Observatory, 61602 Tõravere, Estonia, and Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: miina.rautiainen@helsinki.fi
• Mõttus, Tartu Observatory, 61602 Tõravere, Estonia E-mail: mm@nn.ee
• Stenberg, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ps@nn.fi
• Ervasti, City of Vantaa, Land Use and Environment / Green Area Unit, Kielotie 13, FI-01300 Vantaa, Finland E-mail: se@nn.fi

• Vohland, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: mv@nn.de
• Stoffels, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: js@nn.de
• Hau, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: ch@nn.de
• Schüler, Research Institution for Forest Ecology and Forestry (FAWF), Department of Forest Growth and Silviculture, Trippstadt, Germany E-mail: gs@nn.de

• Korpela, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ilkka.korpela@helsinki.fi
• Tuomola, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: tt@nn.fi
• Välimäki, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ev@nn.fi

• Tuominen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: sakari.tuominen@metla.fi
• Poso, Department of Forest Resource Management, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: sp@nn.fi

• Poso, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: simo.poso@helsinki.fi
• Wang, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: gw@nn.fi
• Tuominen, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: st@nn.fi

• Mattila, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: eero.mattila@metla.fi

• Fridman, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden E-mail: jonas.fridman@slu.se
• Holm, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden E-mail: soren.holm@slu.se
• Nilsson, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden E-mail: mats.nilsson@slu.se
• Nilsson, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden E-mail: per.nilsson@slu.se
• Ringvall, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden E-mail: Anna.Ringvall@slu.se
• Ståhl, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden E-mail: goran.stahl@slu.se

• Zawadzki, Environmental Engineering Department, Warsaw Technical University, Ul. Nowowiejska 20, 00-653 Warsaw, Poland E-mail: jaroslaw.zawadzki@is.pw.edu.pl
• Cieszewski, D. B. Warnell School of Forest Resources, University of Georgia, Athens, GA 30602, USA E-mail: cjc@nn.us
• Zasada, Department of Forest Productivity, Faculty of Forestry, Warsaw Agricultural University, Poland E-mail: mz@nn.pl
• Lowe, D. B. Warnell School of Forest Resources, University of Georgia, Athens, GA 30602, USA E-mail: rcl@nn.us

Foliage spectra form an important input to physically-based forest reflectance models. However, little is known about geographical variability of coniferous needle spectra. In this research note, we present an assessment of the geographical variability of Norway spruce (Picea abies (L.) H. Karst.) needle albedo, reflectance, and transmittance spectra across three study sites covering latitudes of 49–62°N in Europe. All spectra were measured and processed using exactly the same methodology and parameters, which guarantees reliable conclusions about geographical variability. Small geographical variability in Norway spruce needle spectra was observed, when compared to variability observed between previous measurement campaigns (employing slightly varying measurement and processing parameters), or to variability between plant functional types (broadleaved vs. coniferous). Our results suggest that variability of needle spectra is not a major factor introducing geographical variability to forest reflectance. The results also highlight the importance of harmonizing measurement protocols when collecting needle spectral libraries. Furthermore, the data collected for this study can be useful in studies where accurate information on spectral differences between broadleaved and coniferous tree foliage is needed.

• Hovi, Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00760 Aalto, Finland https://orcid.org/0000-0002-4384-5279 E-mail: aarne.hovi@aalto.fi
• Lukeš, Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech Republic https://orcid.org/0000-0002-3707-6557 E-mail: lukes.p@czechglobe.cz
• Homolová, Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech Republic https://orcid.org/0000-0001-7455-2834 E-mail: homolova.l@czechglobe.cz
• Juola, Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00760 Aalto, Finland https://orcid.org/0000-0002-6050-7247 E-mail: jussi.juola@aalto.fi
• Rautiainen, Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00760 Aalto, Finland; Aalto University, School of Electrical Engineering, Department of Electronics and Nanoengineering, P.O. Box 15500, FI-00760 Aalto, Finland https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi

The pulse density of airborne Light Detection and Ranging (LiDAR) is increasing due to technical developments. The trade-offs between pulse density, inventory costs, and forest attribute measurement accuracy are extensively studied, but the possibilities of high-density airborne LiDAR in stream extraction and soil wetness mapping are unknown. This study aimed to refine the best practices for generating a hydrologically conditioned digital elevation model (DEM) from an airborne LiDAR -derived 3D point cloud. Depressionless DEMs were processed using a stepwise breaching-filling method, and the performance of overland flow routing was studied in relation to a pulse density, an interpolation method, and a raster cell size. The study area was situated on a densely ditched forestry site in Parkano municipality, for which LiDAR data with a pulse density of 5 m^–2 were available. Stream networks and a topographic wetness index (TWI) were derived from altogether 12 DEM versions. The topological database of Finland was used as a ground reference in comparison, in addition to 40 selected main flow routes within the catchment. The results show improved performance of overland flow modeling due to increased data density. In addition, commonly used triangulated irregular networks were clearly outperformed by universal kriging and inverse-distance weighting in DEM interpolation. However, the TWI proved to be more sensitive to pulse density than an interpolation method. Improved overland flow routing contributes to enhanced forest resource planning at detailed spatial scales.

• Niemi, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland E-mail: mikko.t.niemi@helsinki.fi

Information on forest trafficability (i.e. carrying capacity of the forest floor) is required before harvesting operations in Southern Boreal forest conditions. It describes the seasons when harvesting operations may take place without causing substantial damage to the forest soil using standard logging machinery. The available trafficability information have been based on subjective observations made during the wood procurement planning. For supporting forest operations, an open access map product has been developed to provide information on trafficability of forests. The forest stands are distributed into classes that characterize different harvesting seasons based on topographic wetness index, amount of vegetation, ground water height and ditch depth. The main goal of this case study was to evaluate the information of the static forest trafficability map in relation to the detected rutting within logging tracks measured in the field. The analysis concentrated on thinning stands since the effect of rutting is significant on the growth of the remaining trees. The results showed that the static trafficability map provided reliable and slightly conservative estimation of the forest trafficability. The majority (91.7%) of the evaluated stands were harvested without causing significant damage if harvesting was timed correctly compared to the trafficability information. However, it should be pointed out that the weather history at small scale, the skills of a driver, and effects of used machinery are not considered in the map product although they can have a considerable impact on the rutting.

• Kankare, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland; Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland https://orcid.org/0000-0001-6038-1579 E-mail: ville.kankare@uef.fi
• Luoma, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland E-mail: ville.luoma@helsinki.fi
• Saarinen, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland; School of Forest Sciences, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland E-mail: ninni.saarinen@helsinki.fi
• Peuhkurinen, Arbonaut Oy, Malminkaari 13–19, FI-00700 Helsinki, Finland E-mail: jussi.peuhkurinen@arbonaut.com
• Holopainen, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi
• Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland E-mail: mikko.vastaranta@uef.fi

• Krooks, Finnish Geodetic Institute, Geodeetinrinne 2, FI–02431 Masala, Finland E-mail: Anssi.Krooks@fgi.fi
• Kaasalainen, Finnish Geodetic Institute, Geodeetinrinne 2, FI–02431 Masala, Finland E-mail: Sanna.Kaasalainen@fgi.fi
• Kankare, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland E-mail: ville.kankare@helsinki.fi
• Joensuu, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland E-mail: marianna.joensuu@alumni.helsinki.fi
• Raumonen, Tampere University of Technology, Department of Mathematics, P.O. Box 553, Tampere, FI-33101, Finland E-mail: Pasi.Raumonen@tut.fi
• Kaasalainen, Tampere University of Technology, Department of Mathematics, P.O. Box 553, Tampere, FI-33101, Finland E-mail: Mikko.Kaasalainen@tut.fi

• Päivinen, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: risto.paivinen@efi.fi
• Anttila, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: pa@nn.fi