Current issue: 58(1)

Under compilation: 58(2)

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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles containing the keyword 'spectra'

Category : Article

article id 5443, category Article
Raimo Silvennoinen, Rauno Hämäläinen, Kaarlo Nygrén, Kim von Weissenberg. (1991). Spectroradiometric characteristics of Scots pine and intensity of moose browsing. Silva Fennica vol. 25 no. 2 article id 5443. https://doi.org/10.14214/sf.a15597
Keywords: Pinus sylvestris; Scots pine; Alces alces; aerial photography; moose; spectral analysis; reflectance; multispectral photography; browsing
Abstract | View details | Full text in PDF | Author Info

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 (email)
  • Hämäläinen, E-mail: rh@mm.unknown
  • Nygrén, E-mail: kn@mm.unknown
  • Weissenberg, E-mail: kw@mm.unknown

Category : Research article

article id 22028, category Research article
Eelis Halme, Matti Mõttus. (2023). Improved parametrisation of a physically-based forest reflectance model for retrieval of boreal forest structural properties. Silva Fennica vol. 57 no. 2 article id 22028. https://doi.org/10.14214/sf.22028
Keywords: forest structure; Sentinel-2; reflectance; hyperspectral; tree distribution
Highlights: Spatial distribution of trees is a key driver for forest reflectance; Knowledge of the ratio of branch to leaf area improves forest reflectance simulation substantially; Different optical properties of the two leaf sides have a notable effect on forest reflectance.
Abstract | Full text in HTML | Full text in PDF | Author Info
Physically-based reflectance models offer a robust and transferable method to assess biophysical characteristics of vegetation in remote sensing. Forests exhibit explicit structure at many scales, from shoots and branches to landscape patches, and hence present a specific challenge to vegetation reflectance modellers. To relate forest reflectance with its structure, the complexity must be parametrised leading to an increase in the number of reflectance model inputs. The parametrisations link reflectance simulations to measurable forest variables, but at the same time rely on abstractions (e.g. a geometric surface forming a tree crown) and physically-based simplifications that are difficult to quantify robustly. As high-quality data on basic forest structure (e.g. tree height and stand density) and optical properties (e.g. leaf and forest floor reflectance) are becoming increasingly available, we used the well-validated forest reflectance and transmittance model FRT to investigate the effect of the values of the “uncertain” input parameters on the accuracy of modelled forest reflectance. With the state-of-the-art structural and spectral forest information, and Sentinel-2 Multispectral Instrument imagery, we identified that the input parameters influencing the most the modelled reflectance, given that the basic forestry variables are set to their true values and leaf mass is determined from reliable allometric models, are the regularity of the tree distribution and the amount of woody elements. When these parameters were set to their new adjusted values, the model performance improved considerably, reaching in the near infrared spectral region (740–950 nm) nearly zero bias, a relative RMSE of 13% and a correlation coefficient of 0.81. In the visible part of the spectrum, the model performance was not as consistent indicating room for improvement.
article id 10606, category Research article
Benjamin Allen, Michele Dalponte, Ari M. Hietala, Hans Ole Ørka, Erik Næsset, Terje Gobakken. (2022). Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery. Silva Fennica vol. 56 no. 2 article id 10606. https://doi.org/10.14214/sf.10606
Keywords: Picea abies; Heterobasidion; remote sensing; root rot; hyperspectral imagery; forest pathology
Highlights: Hyperspectral imagery can be used to detect Root, Butt, and Stem Rot in Picea abies with moderate accuracy; Spectral derivatives improved classification accuracy; Bands around 540, 700, and 1650 nm tended to be the most important for classification models.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 (email)
  • 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
article id 10244, category Research article
Hans Ole Ørka, Endre H. Hansen, Michele Dalponte, Terje Gobakken, Erik Næsset. (2021). Large-area inventory of species composition using airborne laser scanning and hyperspectral data. Silva Fennica vol. 55 no. 4 article id 10244. https://doi.org/10.14214/sf.10244
Keywords: airborne laser scanning; Dirichlet regression; hyperspectral; species proportions; species-specific forest inventory
Highlights: A methodology for using hyperspectral data in the area-based approach is presented; Hyperspectral data produced satisfactory results for species composition in 90% of the cases; Parametric Dirichlet regression is an applicable method to predicting species proportions; Normalization and a tree-based selection of pixels provided the overall best results; Both visible to near-infrared and shortwave-infrared sensors gave acceptable results.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 ORCID https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no (email)
  • 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 ORCID 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 ORCID 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 ORCID 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
article id 10331, category Research article
Jussi Juola, Aarne Hovi, Miina Rautiainen. (2020). Multiangular spectra of tree bark for common boreal tree species in Europe. Silva Fennica vol. 54 no. 4 article id 10331. https://doi.org/10.14214/sf.10331
Keywords: classification; reflectance; hyperspectral; imaging spectrometer; near-infrared; SVM; visible
Highlights: Novel multiangular measurement set-up for hyperspectral imaging; Multiangular spectra of silver birch (Betula pendula), Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) stem bark samples were collected; Intra- and interspecific variations in reflectance were analyzed; Demonstration of tree species identification based on stem bark spectra; Collected spectra openly available in SPECCHIO Spectral Information System.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 ORCID https://orcid.org/0000-0002-6050-7247 E-mail: jussi.juola@aalto.fi (email)
  • Hovi, Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00760 Aalto, Finland ORCID 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 ORCID https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi
article id 10270, category Research article
Aarne Hovi, Matti Mõttus, Jussi Juola, Farshid Manoocheri, Erkki Ikonen, Miina Rautiainen. (2020). Evaluating the performance of a double integrating sphere in measurement of reflectance, transmittance, and albedo of coniferous needles. Silva Fennica vol. 54 no. 2 article id 10270. https://doi.org/10.14214/sf.10270
Keywords: vegetation; albedo; reflectance; transmittance; needle carrier; spectra
Highlights: Adaptation of a compact double integrating sphere for spectral measurements of coniferous needles; Double integrating sphere is fast to operate and suitable for monitoring purposes and collection of large spectral databases; Measured spectra showed negative bias, which could potentially be reduced by building an optimized measurement setup.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 ORCID https://orcid.org/0000-0002-4384-5279 E-mail: aarne.hovi@aalto.fi (email)
  • Mõttus, VTT Technical Research Centre Finland, P.O. Box 1000, FI-02044 VTT, Finland ORCID 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 ORCID 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 ORCID 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 ORCID https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi
article id 10143, category Research article
Olga Grigorieva, Olga Brovkina, Alisher Saidov. (2020). An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data. Silva Fennica vol. 54 no. 2 article id 10143. https://doi.org/10.14214/sf.10143
Keywords: boreal forest; phenological period; space spectroscopy; spectral signature
Highlights: Differences between spectral reflectance of tree species are statistically significant in the sub-seasons of spring, first half of summer, and main autumn; Classification using multitemporal multispectral data is more productive than is classification using a single hyperspectral image; the method improves recent forest mapping in the study regions.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 (email)
  • Saidov, A.F. Mozhaysky’s Military-Space Academy, Krasnogo Kursanta Street 19a, 197198, Saint Petersburg, Russia E-mail: celestial.azura@gmail.com
article id 7721, category Research article
Sakari Tuominen, Andras Balazs, Eija Honkavaara, Ilkka Pölönen, Heikki Saari, Teemu Hakala, Niko Viljanen. (2017). Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fennica vol. 51 no. 5 article id 7721. https://doi.org/10.14214/sf.7721
Keywords: forest inventory; digital photogrammetry; aerial imagery; hyperspectral imaging; radiometric calibration; UAVs; stereo-photogrammetric canopy modelling
Highlights: Hyperspectral imagery and photogrammetric 3D point cloud based on RGB imagery were acquired under weather conditions changing from cloudy to sunny; Calibration of hyperspectral imagery was required for compensating the effect of varying weather conditions; The combination of hyperspectral imagery and photogrammetric point cloud data resulted in accurate forest estimates, especially for volumes per tree species.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 ORCID http://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi (email)
  • 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
article id 471, category Research article
Michael Vohland, Johannes Stoffels, Christina Hau, Gebhard Schüler. (2007). Remote sensing techniques for forest parameter assessment: multispectral classification and linear spectral mixture analysis. Silva Fennica vol. 41 no. 3 article id 471. https://doi.org/10.14214/sf.471
Keywords: Picea abies; remote sensing; stand variables; stem number; multispectral classification; Linear Spectral Mixture Analysis
Abstract | View details | Full text in PDF | Author Info
One of the most common applications of remote sensing in forestry is the production of thematic maps, depicting e.g. tree species or stand age, by means of image classification. Nevertheless, the absolute quantification of stand variables is even more essential for forest inventories. For both issues, satellite data are attractive for their large-area and up-to-date mapping capacities. This study followed two steps, and at first a supervised parametric classification was performed for a German test site based on a radiometrically corrected Landsat-5 TM scene. There, eight forest classes were identified with an overall accuracy of 87.5%. In the following, the study focused on the estimation of one key stand variable, the stem number per hectare (SN), which was carried out for a number of Norway spruce stands that had been clearly identified in the multispectral classification. For the estimation of SN, the approach of Linear Spectral Mixture Analysis (LSMA) was found to be clearly more effective than spectral indices. LSMA is based on the premise that measured reflectances can be linearly modelled from a set of so-called endmember spectra. In this study, the endmember sets were held variable to decompose pixel values to abundances of a vegetation, a background (soil, litter, bark) and a shade fraction. Forest structure determines the visible portions of these fractions, and therefore, a multiple regression using them as predictor variables provided the best SN estimates. LSMA allows a pixel-by-pixel quantification of SN for complete satellite images. This opens the view to exploit these data for an improved calibration of large-scale multi-parameter assessment strategies (e.g. statistical modelling or the kNN method for satellite data interpretation).
  • Vohland, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: mv@nn.de (email)
  • 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
article id 431, category Research article
Pauline Stenberg, Miina Rautiainen, Terhikki Manninen, Pekka Voipio, Heikki Smolander. (2004). Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica vol. 38 no. 1 article id 431. https://doi.org/10.14214/sf.431
Keywords: Landsat ETM ; Leaf Area Index; spectral vegetation indices; boreal coniferous forests
Abstract | View details | Full text in PDF | Author Info
Estimation of leaf area index (LAI) using spectral vegetation indices (SVIs) was studied based on data from 683 plots on two Scots pine and Norway spruce dominated sites in Finland. The SVIs studied included the normalised difference vegetation index (NDVI), the simple ratio (SR), and the reduced simple ratio (RSR), and were calculated from Landsat ETM images of the two sites. Regular grids of size 1 km2 with gridpoints placed at 50 m intervals were established at the sites and measurements of LAI using the LAI-2000 instrument were taken at the gridpoints. SVI-LAI relationships were examined at plot scale, where the plots were defined as circular areas of radius 70 m around each gridpoint. Plotwise mean LAI was computed as a weighted average of LAI readings taken around the gridpoints belonging to the plot. Mean LAI for the plots ranged from 0.36 to 3.72 (hemisurface area). All of the studied SVIs showed fair positive correlation with LAI but RSR responded more dynamically to LAI than did SR or NDVI. Especially NDVI showed poor sensitivity to changes in LAI. RSR explained 63% of the variation in LAI when all plots were included (n = 683) and the coefficient of determination rose to 75% when data was restricted to homogeneous plots (n = 381). Maps of estimated LAI using RSR showed good agreement with maps of measured LAI for the two sites.
  • Stenberg, Department of Forest Ecology, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: pauline.stenberg@helsinki.fi (email)
  • 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
article id 616, category Research article
Markus Lindholm, Hannu Lehtonen, Taneli Kolström, Jouko Meriläinen, Matti Eronen, Mauri Timonen. (2000). Climatic signals extracted from ring-width chronologies of Scots pines from the northern, middle and southern parts of the boreal forest belt in Finland. Silva Fennica vol. 34 no. 4 article id 616. https://doi.org/10.14214/sf.616
Keywords: boreal forest; Scots pine; tree-rings; ring-width chronologies; growth variability; growth responses; spectral analysis
Abstract | View details | Full text in PDF | Author Info
Climatic signals were extracted from ring-width chronologies of Scots pines (Pinus sylvestris L.) from natural stands of the northern, middle, and southern parts of the boreal forest belt in Finland. The strength of the common growth signals (forcing factors) were quantified as a function of time. This was achieved by mean inter-series correlations, calculated over a moving 30-year window, both within and between the regional chronologies. Strong regional signals and also evidence for common forcings were found, especially between northern and central, central and eastern, as well as central/eastern and southern chronologies. Response function analyses revealed that growing season temperatures govern the growth rates of northern pines, while towards south, pine growth becomes less affected by temperatures, and more affected by e.g. precipitation. During some periods, growing conditions seem to have been favorable in the south, while they have been unfavorable in the north (growth inversions). Going from the north to the south, the variability of radial growth clearly decreases, and the variance of ring-width series becomes smaller. Growth variability in the four regions was compared during the common interval of the chronologies, from 1806 to 1991. The spectral densities of the northern, central, eastern and southern chronologies were also compared as functions of frequency, viz. cycles per year. The variance is much greater and there is more periodic behavior in the north than in the south in high, medium, as well as lower frequencies.
  • Lindholm, Saima Centre for Environmental Sciences, University of Joensuu, Linnankatu 11, FIN-57130 Savonlinna, Finland E-mail: ml@nn.fi (email)
  • 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

Category : Research note

article id 10600, category Research note
Nea Kuusinen, Aarne Hovi, Miina Rautiainen. (2021). Contribution of woody elements to tree level reflectance in boreal forests. Silva Fennica vol. 55 no. 5 article id 10600. https://doi.org/10.14214/sf.10600
Keywords: reflectance model; bark; hyperspectral; spectral mixture analysis
Highlights: Contribution of woody elements to reflectance of boreal tree species was estimated using spectral mixture analysis and airborne hyperspectral data; Mean woody element contribution varied between 0.14–0.19 (Scots pine), 0.12–0.20 (birches) and 0.09–0.10 (Norway spruce).
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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 (email)
  • 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 ORCID https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi

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