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Articles containing the keyword 'spectroscopy'

Category : Research article

article id 22014, category Research article
Nea Kuusinen, Aarne Hovi, Miina Rautiainen. (2023). Estimation of boreal forest floor lichen cover using hyperspectral airborne and field data. Silva Fennica vol. 57 no. 1 article id 22014. https://doi.org/10.14214/sf.22014
Keywords: remote sensing; Cladonia; spectroscopy
Highlights: A pilot study on estimating forest floor lichen cover from hyperspectral data; Multiple endmember spectral mixture analysis applied to field and airborne data; Accuracy of lichen cover estimates was good; Tree cover and presence of dwarf shrubs may influence lichen cover estimation.
Abstract | Full text in HTML | Full text in PDF | Author Info
Lichens are sensitive to competition from vascular plants, intensive silviculture, pollution and reindeer and caribou grazing, and can therefore serve as indicators of environmental changes. Hyperspectral remote sensing data has been proved promising for estimation of plant diversity, but its potential for forest floor lichen cover estimation has not yet been studied. In this study, we investigated the use of hyperspectral data in estimating ground lichen cover in boreal forest stands in Finland. We acquired airborne and in situ hyperspectral data of lichen-covered forest plots, and applied multiple endmember spectral mixture analysis to estimate the fractional cover of ground lichens in these plots. Estimation of lichen cover based on in situ spectral data was very accurate (coefficient of determination (r2) 0.95, root mean square error (RMSE) 6.2). Estimation of lichen cover based on airborne data, on the other hand, was fairly good (r2 0.77, RMSE 11.7), but depended on the choice of spectral bands. When the hyperspectral data were resampled to the spectral resolution of Sentinel-2, slightly weaker results were obtained. Tree canopy cover near the flight plots was weakly related to the difference between estimated and measured lichen cover. The results also implied that the presence of dwarf shrubs could influence the lichen cover estimates.
  • Kuusinen, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland ORCID https://orcid.org/0000-0002-8063-1739 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 ORCID 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 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 1334, category Research article
Abolfazl Daneshvar, Mulualem Tigabu, Asaddollah Karimidoost, Per Christer Oden. (2015). Single seed Near Infrared Spectroscopy discriminates viable and non-viable seeds of Juniperus polycarpos. Silva Fennica vol. 49 no. 5 article id 1334. https://doi.org/10.14214/sf.1334
Keywords: NIRS; OPLS; seed sorting; Iran; juniper; near infrared spectroscopy
Highlights: Near Infrared (NIR) Spectroscopy discriminates viable and non-viable (empty, insect-attacked and shriveled) seeds of J. polycarpos with 98% and 100% accuracy, respectively; The origins of spectral differences between non-viable and viable seeds were attributed to differences in seed coat chemical composition and storage reserves; The results demonstrate that NIR spectroscopy has great potential as seed sorting technology to ensure precision sowing.
Abstract | Full text in HTML | Full text in PDF | Author Info

A large quantity of non-viable (empty, insect-attacked and shriveled) seeds of Juniperus polycarpos (K. Koch) is often encountered during seed collection, which should be removed from the seed lots to ensure precision sowing in the nursery or out in the field. The aims of this study were to evaluate different modelling approaches and to examine the sensitivity of the change in detection system (Silicon-detector in the shorter vis-a-vis InGsAs-detector in the longer NIR regions) for discriminating non-viable seeds from viable seeds by Near Infrared (NIR) spectroscopy. NIR reflectance spectra were collected from single seeds, and discriminant models were developed by Partial Least Squares – Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures – Discriminant Analysis (OPLS-DA) using the entire or selected NIR regions. Both modelling approaches resulted in 98% and 100% classification accuracy for viable and non-viable seeds in the test set, respectively. However, OPLS-DA models were superb in terms of model parsimony and information quality. Modelling in the shorter and longer wavelength region also resulted in similar classification accuracy, suggesting that prediction of class membership is insensitive to change in the detection system. The origins of spectral differences between non-viable and viable seeds were attributed to differences in seed coat chemical composition, mainly terpenoids that were dominant in non-viable seeds and storage reserves in viable seeds. In conclusion, the results demonstrate that NIR spectroscopy has great potential as seed sorting technology to upgrade seed lot quality that ensures precision sowing.

  • Daneshvar, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53, Alnarp, Sweden; (permanent address) Department of Natural Resources, Gonbad Kavous University, Shahid Fallahi Street, P.O. Box 163, Gonbad, Iran E-mail: abolfazl.daneshvar@slu.se
  • Tigabu, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53, Alnarp, Sweden E-mail: mulualem.tigabu@slu.se (email)
  • Karimidoost, Agriculture and Natural Resources Research Center of Golestan Province, Beheshti Ave. P.O. Box 4915677555, Gorgan, Iran E-mail: karimidoost@yahoo.com
  • Oden, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53, Alnarp, Sweden E-mail: per.oden@slu.se

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