Response of microscopical hyperspectral data to past climatic variable
Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendrocl...
Ausführliche Beschreibung
Autor*in: |
Wang, Lingjun [verfasserIn] |
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Sprache: |
Englisch |
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2022 |
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© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: Theoretical and applied climatology - Wien [u.a.] : Springer, 1948, 150(2022), 3-4 vom: 01. Okt., Seite 1145-1155 |
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Übergeordnetes Werk: |
volume:150 ; year:2022 ; number:3-4 ; day:01 ; month:10 ; pages:1145-1155 |
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DOI / URN: |
10.1007/s00704-022-04219-w |
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Katalog-ID: |
SPR048577626 |
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520 | |a Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. | ||
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650 | 4 | |a Tree rings; Paleoclimate |7 (dpeaa)DE-He213 | |
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700 | 1 | |a Zheng, Yonghong |4 aut | |
700 | 1 | |a Zhu, Haifeng |4 aut | |
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10.1007/s00704-022-04219-w doi (DE-627)SPR048577626 (SPR)s00704-022-04219-w-e DE-627 ger DE-627 rakwb eng Wang, Lingjun verfasserin aut Response of microscopical hyperspectral data to past climatic variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. Microscopic-hyperspectral (dpeaa)DE-He213 Tree rings; Paleoclimate (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Fei, Teng (orcid)0000-0002-3415-1654 aut Bian, Meng aut Xu, Yadan aut Zhang, Haochen aut Zheng, Yonghong aut Zhu, Haifeng aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 150(2022), 3-4 vom: 01. Okt., Seite 1145-1155 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:150 year:2022 number:3-4 day:01 month:10 pages:1145-1155 https://dx.doi.org/10.1007/s00704-022-04219-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 150 2022 3-4 01 10 1145-1155 |
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10.1007/s00704-022-04219-w doi (DE-627)SPR048577626 (SPR)s00704-022-04219-w-e DE-627 ger DE-627 rakwb eng Wang, Lingjun verfasserin aut Response of microscopical hyperspectral data to past climatic variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. Microscopic-hyperspectral (dpeaa)DE-He213 Tree rings; Paleoclimate (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Fei, Teng (orcid)0000-0002-3415-1654 aut Bian, Meng aut Xu, Yadan aut Zhang, Haochen aut Zheng, Yonghong aut Zhu, Haifeng aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 150(2022), 3-4 vom: 01. Okt., Seite 1145-1155 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:150 year:2022 number:3-4 day:01 month:10 pages:1145-1155 https://dx.doi.org/10.1007/s00704-022-04219-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 150 2022 3-4 01 10 1145-1155 |
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10.1007/s00704-022-04219-w doi (DE-627)SPR048577626 (SPR)s00704-022-04219-w-e DE-627 ger DE-627 rakwb eng Wang, Lingjun verfasserin aut Response of microscopical hyperspectral data to past climatic variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. Microscopic-hyperspectral (dpeaa)DE-He213 Tree rings; Paleoclimate (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Fei, Teng (orcid)0000-0002-3415-1654 aut Bian, Meng aut Xu, Yadan aut Zhang, Haochen aut Zheng, Yonghong aut Zhu, Haifeng aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 150(2022), 3-4 vom: 01. Okt., Seite 1145-1155 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:150 year:2022 number:3-4 day:01 month:10 pages:1145-1155 https://dx.doi.org/10.1007/s00704-022-04219-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 150 2022 3-4 01 10 1145-1155 |
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10.1007/s00704-022-04219-w doi (DE-627)SPR048577626 (SPR)s00704-022-04219-w-e DE-627 ger DE-627 rakwb eng Wang, Lingjun verfasserin aut Response of microscopical hyperspectral data to past climatic variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. Microscopic-hyperspectral (dpeaa)DE-He213 Tree rings; Paleoclimate (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Fei, Teng (orcid)0000-0002-3415-1654 aut Bian, Meng aut Xu, Yadan aut Zhang, Haochen aut Zheng, Yonghong aut Zhu, Haifeng aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 150(2022), 3-4 vom: 01. Okt., Seite 1145-1155 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:150 year:2022 number:3-4 day:01 month:10 pages:1145-1155 https://dx.doi.org/10.1007/s00704-022-04219-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 150 2022 3-4 01 10 1145-1155 |
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10.1007/s00704-022-04219-w doi (DE-627)SPR048577626 (SPR)s00704-022-04219-w-e DE-627 ger DE-627 rakwb eng Wang, Lingjun verfasserin aut Response of microscopical hyperspectral data to past climatic variable 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. Microscopic-hyperspectral (dpeaa)DE-He213 Tree rings; Paleoclimate (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Fei, Teng (orcid)0000-0002-3415-1654 aut Bian, Meng aut Xu, Yadan aut Zhang, Haochen aut Zheng, Yonghong aut Zhu, Haifeng aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 150(2022), 3-4 vom: 01. Okt., Seite 1145-1155 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:150 year:2022 number:3-4 day:01 month:10 pages:1145-1155 https://dx.doi.org/10.1007/s00704-022-04219-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 150 2022 3-4 01 10 1145-1155 |
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Wang, Lingjun |
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response of microscopical hyperspectral data to past climatic variable |
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Response of microscopical hyperspectral data to past climatic variable |
abstract |
Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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title_short |
Response of microscopical hyperspectral data to past climatic variable |
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https://dx.doi.org/10.1007/s00704-022-04219-w |
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Fei, Teng Bian, Meng Xu, Yadan Zhang, Haochen Zheng, Yonghong Zhu, Haifeng |
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Fei, Teng Bian, Meng Xu, Yadan Zhang, Haochen Zheng, Yonghong Zhu, Haifeng |
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up_date |
2024-07-03T20:07:35.466Z |
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score |
7.4011316 |