Eurasian Journal of Soil Science

Volume 12, Issue 1, Jan 2023, Pages 37-62
DOI: 10.18393/ejss.1183524
Stable URL: http://ejss.fess.org/10.18393/ejss.1183524
Copyright © 2023 The authors and Federation of Eurasian Soil Science Societies



Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data

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Dmitriev,P., Kozlovsky,B., Dmitrieva,A., Lysenko,V., Chokheli,V., Minkina,T., Mandzhieva,S., Sushkova,S., Varduni,T., 2023. Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data. Eurasian J Soil Sci 12(1):37-62. DOI : 10.18393/ejss.1183524
Dmitriev,P.Kozlovsky,B.Dmitrieva,A.Lysenko,V.Chokheli,V.Minkina,T.Mandzhieva,S.Sushkova,S.,,& Varduni,T. Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data Eurasian Journal of Soil Science, 12(1):37-62. DOI : 10.18393/ejss.1183524
Dmitriev,P.Kozlovsky,B.Dmitrieva,A.Lysenko,V.Chokheli,V.Minkina,T.Mandzhieva,S.Sushkova,S.,, and ,Varduni,T."Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data" Eurasian Journal of Soil Science, 12.1 (2023):37-62. DOI : 10.18393/ejss.1183524
Dmitriev,P.Kozlovsky,B.Dmitrieva,A.Lysenko,V.Chokheli,V.Minkina,T.Mandzhieva,S.Sushkova,S.,, and ,Varduni,T. "Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data" Eurasian Journal of Soil Science,12(Jan 2023):37-62 DOI : 10.18393/ejss.1183524
P,Dmitriev.B,Kozlovsky.A,Dmitrieva.V,Lysenko.V,Chokheli.T,Minkina.S,Mandzhieva.S,Sushkova.T,Varduni "Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data" Eurasian J. Soil Sci, vol.12, no.1, pp.37-62 (Jan 2023), DOI : 10.18393/ejss.1183524
Dmitriev,Pavel ;Kozlovsky,Boris ;Dmitrieva,Anastasiya ;Lysenko,Vladimir ;Chokheli,Vasily ;Minkina,Tatiana ;Mandzhieva,Saglara ;Sushkova,Svetlana ;Varduni,Tatyana Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data. Eurasian Journal of Soil Science, (2023),12.1:37-62. DOI : 10.18393/ejss.1183524

How to cite

Dmitriev, P., Kozlovsky, B., Dmitrieva, A., Lysenko, V., Chokheli, V., Minkina, T., Mandzhieva, S., Sushkova, S., Varduni, T., 2023. Identification of species of the genus Quercus L. with different responses to soil and climatic conditions according to hyperspectral survey data. Eurasian J. Soil Sci. 12(1): 37-62. DOI : 10.18393/ejss.1183524

Author information

Pavel Dmitriev , Southern Federal University, Rostov-on-Don, Russia
Boris Kozlovsky , Southern Federal University, Rostov-on-Don, Russia
Anastasiya Dmitrieva , Southern Federal University, Rostov-on-Don, Russia
Vladimir Lysenko , Southern Federal University, Rostov-on-Don, Russia
Vasily Chokheli , Southern Federal University, Rostov-on-Don, Russia
Tatiana Minkina , Southern Federal University, Rostov-on-Don, Russia
Saglara Mandzhieva , Southern Federal University, Rostov-on-Don, Russia
Svetlana Sushkova , Southern Federal University, Rostov-on-Don, Russia
Tatyana Varduni , Southern Federal University, Rostov-on-Don, Russia

Publication information

Article first published online : 03 Oct 2022
Manuscript Accepted : 01 Oct 2022
Manuscript Received: 15 May 2022
DOI: 10.18393/ejss.1183524
Stable URL: http://ejss.fesss.org/10.18393/ejss.1183524

Abstract

Soil standing may be studied indirectly using remote sensing through an assessment of state of the plants growing on it. The ability to evaluate the physiological state of plants using the hyperspectral survey data also provides a tool to characterize vegetation cover and individual samples of woody plants. In the present work the hyperspectral imaging was applied to identify the species of the woody plants evaluating the differences in their physiological state. Samples of Quercus macrocarpa Michx., Q. robur L. and Q. rubra L. were studied using Cubert UHD-185 hyperspectral camera over five periods with an interval of 7-10 days. In total, 80 vegetation indices (VIs) were calculated. Sample sets of values of VIs were analyzed using analysis of variance (ANOVA), principal component analysis (PCA), decision tree (DT), random forest (RF) methods. It was shown using the ANOVA, that the following VIs are the most dependent on the species affiliation of the samples: Carter2, Carter3, Carter4, CI, CI2, CRI4, Datt, Datt2, GMI2, Maccioni, mSR2, MTCI, NDVI2, OSAVI2, PRI, REP_Li, SR1, SR2, SR6, Vogelmann, Vogelmann2, Vogelmann4. VIs that are effective for the separation of oak species, were also revealed using the DT method – these are Boochs, Boochs2, CARI, CRI1, CRI3, D1, D2, Datt, Datt3; Datt4, Datt5, DD, DDn, EGFN, Gitelson, MCARI2, MTCI, MTVI, NDVI3, PRI, PSND, PSRI, RDVI, REP_Li, SPVI, SR4, Vogelmann, Vogelmann2, Vogelmann3. PCA and RF methods reliably differentiated Q. rubra from Q. robur and Q. macrocarpa. Q. rubra, unlike other species, was under stress from the impact of soil pH against the background of drought. This was manifested in leaf chlorosis. Influence of the environmental stress factors on the reliability and efficiency of species identification was demonstrated. Q. robur and Q. macrocarpawere were poorly separated by PCA and RF methods all over the five periods of the experiment.

Keywords

Hyperspectral imaging, vegetation indices, Quercus macrocarpa, Quercus robur, Quercus rubra, environmental stress, drought stress, reflection spectra.

Corresponding author

References

Aasen, H., Burkart, A., Bolten, A., Bareth, G., 2015. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing 108: 245–259.

Bareth, G., Aasen, H., Bendig, J., Gnyp, M.L., Bolten, A., Jung, A., Michels, R., Soukkamäki, J., 2015. Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements. Photogrammetrie - Fernerkundung - Geoinformation 1: 69–79.

Blackburn, G.A., 1998. Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches. Remote Sensing of Environment 66: 273–285.

Bolca, M., Kurucu, Y., Altınbaş, Ü., Esetlili, M.T., Özen, F., 2012. A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique. Eurasian Journal of Soil Science 1(2): 116 - 126.

Cao, J., Leng, W., Liu, K., Liu, L., He, Z., Zhu, Y., 2018. Object-Based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sensing 10(1): 89.

Dainelli, R., Toscano, P., Di Gennaro, S.F., Matese, A., 2021. Recent advances in unmanned aerial vehicles forest remote sensing - A systematic review. Part II: Research applications. Forests 12(4): 397.

Datt, B., 1999. Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. Remote Sensing. 20: 2741–2759.

Dmitriev, P., Kozlovsky, B., Minkina, T., Rajput, V.D., Dudnikova, T., Barbashev, A., Ignatova, M.A., Kapralova, O.A., Varduni, T.V., Tokhtar, V.K., Tarik, E.P., Akça, İ., Sushkova, S., 2022a. Hyperspectral imaging for small-scale analysis of Hordeum vulgare L. leaves under the benzo[a]pyrene effect. Environmental Science and Pollution Research

Dmitriev, P.A., Kozlovsky, B.L., Kupriushkin, D.P., Lysenko, V.S., Rajput, V.D., Ignatova, M.A., Tarik, E.P., Kapralova, O.A., Tokhtar, V.K., Singh, A.K., Minkina, T.М., Varduni, T.V., Sharma, M., Taloor, A.K., Thapliyal, A., 2022b. Identification of species of the genus Acer L. using vegetation indices calculated from the hyperspectral images of leaves. Remote Sensing Applications: Society and Environment 25, 100679.

Fassnacht, F.E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L.T., Straub, C., Ghosh, A., 2016. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment 186: 64–87.

Kozlovskiy, B.L., Kuropyatnikov, M.V., Fedorinova, O.I., 2016. Results of introduction testing of species of the genus Quercus L. In the botanical garden of the Southern Federal. Bulletin of the Udmurt University. Series Biology. Earth Sciences 26(2):53–58.

Kozlovsky, B.L., Ogorodnikova, T.K., Kuropyatnikov, M.V., Fedorinova, O.I., 2009. An assortment of woody plants for green building in the Rostov region. Rostov-on-D.: Publishing House of Southern Federal University. 416p.

Lehnert, L.W., Meyer, H., Obermeier, W.A., Silva, B., Regeling, B., Thies, B., Bendix, J., 2019. Hyperspectral Data Analysis in R: The hsdar Package. Journal of Statistical Software 89 (12): 1–23.

Lemaire, C., Kohn, S.C., Brooker, R.A., 2004. The effect of silica activity on the incorporation mechanisms of water in synthetic forsterite: a polarised infrared spectroscopic study. Contributions to Mineralogy and Petrology 147: 48–57.

Miyoshi, G.T., Arruda, Md.S., Osco, L.P., Junior, J.M., Gonçalves, D.N., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., Gonçalves, W.N., 2020b. A novel deep learning method to identify single tree species in UAV-based hyperspectral images. Remote Sensing 12(8): 1294.

Miyoshi, G.T., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., 2020a. Spectral differences of tree species belonging to atlantic forest obtained from UAV hyperspectral images. 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) pp. 60-65.

Nezami, S., Khoramshahi, E., Nevalainen, O., Pölönen, I., Honkavaara, E., 2020. Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sensing 12(7): 1070.

Oppelt, N., Mauser, W., 2004. Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing 25(1): 145–159.

Ronay, I., Ephrath, J.E., Eizenberg, H., Blumberg, D.G., Maman, S., 2021. Hyperspectral reflectance and indices for characterizing the dynamics of crop–weed competition for water. Remote Sensing 13(3): 513.

Saarinen, J., Rogerson, C.M., Hall, C.M., 2017. Geographies of tourism development and planning. Tourism Geographies 19(3): 307–3017.

Sothe, C., Dalponte, M., Almeida, C.M., Schimalski, M.B., Lima, C.L., Liesenberg, V., Miyoshi, G.T., Tommaselli, A.M.G., 2019. Tree species classification in a highly diverse subtropical forest integrating UAV-based photogrammetric point cloud and hyperspectral data. Remote Sensing 11(11): 1338.

Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127–150.

Tuominen, S., Balazs, A., Honkavaara, E., Pölönen, I., Saari, H., Hakala, T., Viljanen, N., 2017. Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fennica 51(5): 7721.

Vogelmann, J.E., Rock, B.N., Moss, D.M., 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14: 1563–1575.

Zarco-Tejada, P.J., Pushnik, J.C., Dobrowski, S., Ustin, S.L., 2003. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment 84: 283–294.

Zozulin, G.M., 1992. Forests of the Lower Don. Rostov-on-Don: Publishing of Rostov University. 200p.

Abstract

Soil standing may be studied indirectly using remote sensing through an assessment of state of the plants growing on it. The ability to evaluate the physiological state of plants using the hyperspectral survey data also provides a tool to characterize vegetation cover and individual samples of woody plants. In the present work the hyperspectral imaging was applied to identify the species of the woody plants evaluating the differences in their physiological state. Samples of Quercus macrocarpa Michx., Q. robur L. and Q. rubra L. were studied using Cubert UHD-185 hyperspectral camera over five periods with an interval of 7-10 days. In total, 80 vegetation indices (VIs) were calculated. Sample sets of values of VIs were analyzed using analysis of variance (ANOVA), principal component analysis (PCA), decision tree (DT), random forest (RF) methods. It was shown using the ANOVA, that the following VIs are the most dependent on the species affiliation of the samples: Carter2, Carter3, Carter4, CI, CI2, CRI4, Datt, Datt2, GMI2, Maccioni, mSR2, MTCI, NDVI2, OSAVI2, PRI, REP_Li, SR1, SR2, SR6, Vogelmann, Vogelmann2, Vogelmann4. VIs that are effective for the separation of oak species, were also revealed using the DT method – these are Boochs, Boochs2, CARI, CRI1, CRI3, D1, D2, Datt, Datt3; Datt4, Datt5, DD, DDn, EGFN, Gitelson, MCARI2, MTCI, MTVI, NDVI3, PRI, PSND, PSRI, RDVI, REP_Li, SPVI, SR4, Vogelmann, Vogelmann2, Vogelmann3. PCA and RF methods reliably differentiated Q. rubra from Q. robur and Q. macrocarpa. Q. rubra, unlike other species, was under stress from the impact of soil pH against the background of drought. This was manifested in leaf chlorosis. Influence of the environmental stress factors on the reliability and efficiency of species identification was demonstrated. Q. robur and Q. macrocarpawere were poorly separated by PCA and RF methods all over the five periods of the experiment.

Keywords: Hyperspectral imaging, vegetation indices, Quercus macrocarpa, Quercus robur, Quercus rubra, environmental stress, drought stress, reflection spectra.

References

Aasen, H., Burkart, A., Bolten, A., Bareth, G., 2015. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing 108: 245–259.

Bareth, G., Aasen, H., Bendig, J., Gnyp, M.L., Bolten, A., Jung, A., Michels, R., Soukkamäki, J., 2015. Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements. Photogrammetrie - Fernerkundung - Geoinformation 1: 69–79.

Blackburn, G.A., 1998. Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches. Remote Sensing of Environment 66: 273–285.

Bolca, M., Kurucu, Y., Altınbaş, Ü., Esetlili, M.T., Özen, F., 2012. A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique. Eurasian Journal of Soil Science 1(2): 116 - 126.

Cao, J., Leng, W., Liu, K., Liu, L., He, Z., Zhu, Y., 2018. Object-Based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sensing 10(1): 89.

Dainelli, R., Toscano, P., Di Gennaro, S.F., Matese, A., 2021. Recent advances in unmanned aerial vehicles forest remote sensing - A systematic review. Part II: Research applications. Forests 12(4): 397.

Datt, B., 1999. Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. Remote Sensing. 20: 2741–2759.

Dmitriev, P., Kozlovsky, B., Minkina, T., Rajput, V.D., Dudnikova, T., Barbashev, A., Ignatova, M.A., Kapralova, O.A., Varduni, T.V., Tokhtar, V.K., Tarik, E.P., Akça, İ., Sushkova, S., 2022a. Hyperspectral imaging for small-scale analysis of Hordeum vulgare L. leaves under the benzo[a]pyrene effect. Environmental Science and Pollution Research

Dmitriev, P.A., Kozlovsky, B.L., Kupriushkin, D.P., Lysenko, V.S., Rajput, V.D., Ignatova, M.A., Tarik, E.P., Kapralova, O.A., Tokhtar, V.K., Singh, A.K., Minkina, T.М., Varduni, T.V., Sharma, M., Taloor, A.K., Thapliyal, A., 2022b. Identification of species of the genus Acer L. using vegetation indices calculated from the hyperspectral images of leaves. Remote Sensing Applications: Society and Environment 25, 100679.

Fassnacht, F.E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L.T., Straub, C., Ghosh, A., 2016. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment 186: 64–87.

Kozlovskiy, B.L., Kuropyatnikov, M.V., Fedorinova, O.I., 2016. Results of introduction testing of species of the genus Quercus L. In the botanical garden of the Southern Federal. Bulletin of the Udmurt University. Series Biology. Earth Sciences 26(2):53–58.

Kozlovsky, B.L., Ogorodnikova, T.K., Kuropyatnikov, M.V., Fedorinova, O.I., 2009. An assortment of woody plants for green building in the Rostov region. Rostov-on-D.: Publishing House of Southern Federal University. 416p.

Lehnert, L.W., Meyer, H., Obermeier, W.A., Silva, B., Regeling, B., Thies, B., Bendix, J., 2019. Hyperspectral Data Analysis in R: The hsdar Package. Journal of Statistical Software 89 (12): 1–23.

Lemaire, C., Kohn, S.C., Brooker, R.A., 2004. The effect of silica activity on the incorporation mechanisms of water in synthetic forsterite: a polarised infrared spectroscopic study. Contributions to Mineralogy and Petrology 147: 48–57.

Miyoshi, G.T., Arruda, Md.S., Osco, L.P., Junior, J.M., Gonçalves, D.N., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., Gonçalves, W.N., 2020b. A novel deep learning method to identify single tree species in UAV-based hyperspectral images. Remote Sensing 12(8): 1294.

Miyoshi, G.T., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., 2020a. Spectral differences of tree species belonging to atlantic forest obtained from UAV hyperspectral images. 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) pp. 60-65.

Nezami, S., Khoramshahi, E., Nevalainen, O., Pölönen, I., Honkavaara, E., 2020. Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sensing 12(7): 1070.

Oppelt, N., Mauser, W., 2004. Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing 25(1): 145–159.

Ronay, I., Ephrath, J.E., Eizenberg, H., Blumberg, D.G., Maman, S., 2021. Hyperspectral reflectance and indices for characterizing the dynamics of crop–weed competition for water. Remote Sensing 13(3): 513.

Saarinen, J., Rogerson, C.M., Hall, C.M., 2017. Geographies of tourism development and planning. Tourism Geographies 19(3): 307–3017.

Sothe, C., Dalponte, M., Almeida, C.M., Schimalski, M.B., Lima, C.L., Liesenberg, V., Miyoshi, G.T., Tommaselli, A.M.G., 2019. Tree species classification in a highly diverse subtropical forest integrating UAV-based photogrammetric point cloud and hyperspectral data. Remote Sensing 11(11): 1338.

Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127–150.

Tuominen, S., Balazs, A., Honkavaara, E., Pölönen, I., Saari, H., Hakala, T., Viljanen, N., 2017. Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fennica 51(5): 7721.

Vogelmann, J.E., Rock, B.N., Moss, D.M., 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14: 1563–1575.

Zarco-Tejada, P.J., Pushnik, J.C., Dobrowski, S., Ustin, S.L., 2003. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment 84: 283–294.

Zozulin, G.M., 1992. Forests of the Lower Don. Rostov-on-Don: Publishing of Rostov University. 200p.



Eurasian Journal of Soil Science