Articles containing the keyword 'spectra'

The light reflected from the crowns of Scots pine (Pinus sylvestris L.) clones was measured spectroradiometrically during and after growing season. Standard deviations of the spectra of pine clones showing differences in moose browsing intensity were compared. A new algorithm was developed for predicting the browsing intensity of moose (Alces alces).

The PDF includes an abstract in Finnish.

• Silvennoinen, E-mail: rs@mm.unknown
• Hämäläinen, E-mail: rh@mm.unknown
• Nygrén, E-mail: kn@mm.unknown
• Weissenberg, E-mail: kw@mm.unknown

• Halme, VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT Espoo, Finland https://orcid.org/0000-0002-7517-6663 E-mail: eelis.halme@vtt.fi
• Mõttus, VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT Espoo, Finland https://orcid.org/0000-0002-2745-1966 E-mail: matti.mottus@vtt.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

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m² collected over 350 km² of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.

• Ørka, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
• Hansen, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway; Norwegian Forest Extension Institute, Honnevegen 60, NO-2836 Biri, Norway https://orcid.org/0000-0001-5174-4497 E-mail: eh@skogkurs.no
• Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige, TN, Italy https://orcid.org/0000-0001-9850-8985 E-mail: michele.dalponte@fmach.it
• Gobakken, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
• Næsset, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

Despite the importance of spectral properties of woody tree structures, they are seldom represented in research related to forests, remote sensing, and reflectance modeling. This study presents a novel imaging multiangular measurement set-up that utilizes a mobile handheld hyperspectral camera (Specim IQ, 400–1000 nm), and can measure stem bark spectra in a controlled laboratory setting. We measured multiangular reflectance spectra of silver birch (Betula pendula Roth), Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) stem bark, and demonstrated the potential of using bark spectra in identifying tree species using a Support Vector Machine (SVM) based approach. Intraspecific reflectance variability was the lowest in visible (400–700 nm), and the highest in near-infrared (700–1000 nm) wavelength regions. Interspecific variation was the largest in the red, red-edge and near-infrared spectral bands. Spatial variation of reflectance along the tree height and different sides of the stem (north and south) were found. Both birch and pine had increased reflectance in the forward-scattering directions for visible to near-infrared wavelength regions, whilst spruce displayed the same only for the visible wavelength region. In addition, spruce had increased reflectance in the backward-scattering directions. In spite of the intraspecific variations, SVM could identify tree species with 88.8% overall accuracy when using pixel-specific spectra, and with 97.2% overall accuracy when using mean spectra per image. Based on our results it is possible to identify common boreal tree species based on their stem bark spectra using images from mobile hyperspectral cameras.

• 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
• 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
• 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

Leaf reflectance and transmittance spectra are essential information in many applications such as developing remote sensing methods, computing shortwave energy balance (albedo) of forest canopies, and monitoring health or stress of trees. Measurement of coniferous needle spectra has usually been carried out with single integrating spheres, which has involved a lot of tedious manual work. A small double integrating sphere would make the measurements considerably faster, because of its ease of operation and small sample sizes required. Here we applied a compact double integrating sphere setup, used previously for measurement of broad leaves, for measurement of coniferous needles. Test measurements with the double integrating sphere showed relative underestimation of needle albedo by 5–39% compared to a well-established single integrating sphere setup. A small part of the bias can be explained by the bias of the single sphere. Yet the observed bias is quite significant if absolute accuracy of measurements is required. For relative measurements, e.g. for monitoring development of needle spectra over time, the double sphere system provides notable improvement. Furthermore, it might be possible to reduce the bias by building an optimized measurement setup that minimizes absorption losses in the sample port. Our study indicates that double spheres, after some technical improvement, may provide a new and fast way to collect extensive spectral libraries of tree species.

• 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
• Mõttus, VTT Technical Research Centre Finland, P.O. Box 1000, FI-02044 VTT, Finland https://orcid.org/0000-0002-2745-1966 E-mail: matti.mottus@gmail.com
• Juola, Aalto University, School of Engineering, Department of Built Environment, P.O.Box 14100, FI-00760 Aalto, Finland E-mail: jussi.juola@aalto.fi
• Manoocheri, Aalto University, School of Electrical Engineering, Metrology Research Institute, Maarintie 8, FI-02150 Espoo, Finland https://orcid.org/0000-0003-3935-3930 E-mail: farshid.manoocheri@aalto.fi
• Ikonen, VTT Technical Research Centre Finland, P.O. Box 1000, FI-02044 VTT, Finland; Aalto University, School of Electrical Engineering, Metrology Research Institute, Maarintie 8, FI-02150 Espoo, Finland https://orcid.org/0000-0001-6444-5330 E-mail: erkki.ikonen@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

This study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectral–temporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time.

• Grigorieva, A.F. Mozhaysky’s Military-Space Academy, Krasnogo Kursanta Street 19a, 197198, Saint Petersburg, Russia E-mail: alenka12003@gmail.com
• Brovkina, Global Change Research Institute CAS, Bělidla 986/4a, 603 00, Brno, Czech Republic E-mail: brovkina.o@czechglobe.cz
• Saidov, A.F. Mozhaysky’s Military-Space Academy, Krasnogo Kursanta Street 19a, 197198, Saint Petersburg, Russia E-mail: celestial.azura@gmail.com

Remote sensing using unmanned aerial vehicle (UAV) -borne sensors is currently a highly interesting approach for the estimation of forest characteristics. 3D remote sensing data from airborne laser scanning or digital stereo photogrammetry enable highly accurate estimation of forest variables related to the volume of growing stock and dimension of the trees, whereas recognition of tree species dominance and proportion of different tree species has been a major complication in remote sensing-based estimation of stand variables. In this study the use of UAV-borne hyperspectral imagery was examined in combination with a high-resolution photogrammetric canopy height model in estimating forest variables of 298 sample plots. Data were captured from eleven separate test sites under weather conditions varying from sunny to cloudy and partially cloudy. Both calibrated hyperspectral reflectance images and uncalibrated imagery were tested in combination with a canopy height model based on RGB camera imagery using the k-nearest neighbour estimation method. The results indicate that this data combination allows accurate estimation of stand volume, mean height and diameter: the best relative RMSE values for those variables were 22.7%, 7.4% and 14.7%, respectively. In estimating volume and dimension-related variables, the use of a calibrated image mosaic did not bring significant improvement in the results. In estimating the volumes of individual tree species, the use of calibrated hyperspectral imagery generally brought marked improvement in the estimation accuracy; the best relative RMSE values for the volumes for pine, spruce, larch and broadleaved trees were 34.5%, 57.2%, 45.7% and 42.0%, respectively.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 2, FI-00791 Helsinki, Finland http://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@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
• Honkavaara, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: eija.honkavaara@nls.fi
• Pölönen, University of Jyväskylä, Faculty of Information Technology, P.O. Box 35, FI-40014 Jyväskylä, Finland E-mail: ilkka.polonen@jyu.fi
• Saari, VTT Microelectronics, P.O. Box 1000, FI-02044 VTT, Finland E-mail: heikki.saari@vtt.fi
• Hakala, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: teemu.hakala@nls.fi
• Viljanen, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: niko.viljanen@nls.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

• Stenberg, Department of Forest Ecology, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: pauline.stenberg@helsinki.fi
• Rautiainen, Department of Forest Ecology, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: mr@nn.fi
• Manninen, Finnish Meteorological Institute, Meteorological research, Ozone and UV radiation research, P.O. Box 503, FIN-00101 Helsinki, Finland E-mail: tm@nn.fi
• Voipio, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: pv@nn.fi
• Smolander, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: hs@nn.fi

• Lindholm, Saima Centre for Environmental Sciences, University of Joensuu, Linnankatu 11, FIN-57130 Savonlinna, Finland E-mail: ml@nn.fi
• Lehtonen, Finnish Forest Research Institute, Joensuu Research Station, Box 68, FIN-80101 Joensuu, Finland E-mail: hl@nn.fi
• Kolström, Finnish Forest Research Institute, Joensuu Research Station, Box 68, FIN-80101 Joensuu, Finland E-mail: tk@nn.fi
• Meriläinen, Saima Centre for Environmental Sciences, University of Joensuu, Linnankatu 11, FIN-57130 Savonlinna, Finland E-mail: jm@nn.fi
• Eronen, Department of Geology, Division of Geology and Palaeontology, Box 11, FIN-00014 University of Helsinki, Finland E-mail: me@nn.fi
• Timonen, Finnish Forest Research Institute, Rovaniemi Research Station, Box 16, FIN-96301 Rovaniemi, Finland E-mail: mt@nn.fi

Spectral mixture analysis was used to estimate the contribution of woody elements to tree level reflectance from airborne hyperspectral data in boreal forest stands in Finland. Knowledge of the contribution of woody elements to tree or forest reflectance is important in the context of lea area index (LAI) estimation and, e.g., in the estimation of defoliation due to insect outbreaks, from remote sensing data. Field measurements from four Scots pine (Pinus sylvestris L.), five Norway spruce (Picea abies (L.) Karst.) and four birch (Betula pendula Roth and Betula pubescens Ehrh.) dominated plots, spectral measurements of needles, leaves, bark, and forest floor, airborne hyperspectral as well as airborne laser scanning data were used together with a physically-based forest reflectance model. We compared the results based on simple linear combinations of measured bark and needle/leaf spectra to those obtained by accounting for multiple scattering of radiation within the canopy using a physically-based forest reflectance model. The contribution of forest floor to reflectance was additionally considered. The resulted mean woody element contribution estimates varied from 0.140 to 0.186 for Scots pine, from 0.116 to 0.196 for birches and from 0.090 to 0.095 for Norway spruce, depending on the model used. The contribution of woody elements to tree reflectance had a weak connection to plot level forest variables.

• Kuusinen, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland E-mail: nea.kuusinen@aalto.fi
• Hovi, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland E-mail: aarne.hovi@aalto.fi
• Rautiainen, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland; Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 15500, FI-00076 Aalto, Finland https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi