Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS)
Abstract Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Yicong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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: Wetlands ecology and management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1989, 31(2022), 1 vom: 01. Dez., Seite 1-18 |
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| Übergeordnetes Werk: |
volume:31 ; year:2022 ; number:1 ; day:01 ; month:12 ; pages:1-18 |
| Links: |
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| DOI / URN: |
10.1007/s11273-022-09897-x |
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SPR049386743 |
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| 520 | |a Abstract Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. | ||
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| 700 | 1 | |a Yu, Changbin |4 aut | |
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10.1007/s11273-022-09897-x doi (DE-627)SPR049386743 (SPR)s11273-022-09897-x-e DE-627 ger DE-627 rakwb eng Wang, Yicong verfasserin aut Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. Image monitoring (dpeaa)DE-He213 Growth model (dpeaa)DE-He213 Ecological restoration (dpeaa)DE-He213 Liao River Estuary wetland (dpeaa)DE-He213 Liang, Xianmeng aut Yu, Yang aut Yu, Changbin aut Yang, Min aut Wu, Guojun aut Enthalten in Wetlands ecology and management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1989 31(2022), 1 vom: 01. Dez., Seite 1-18 (DE-627)320569985 (DE-600)2016379-4 1572-9834 nnns volume:31 year:2022 number:1 day:01 month:12 pages:1-18 https://dx.doi.org/10.1007/s11273-022-09897-x 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_101 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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 31 2022 1 01 12 1-18 |
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10.1007/s11273-022-09897-x doi (DE-627)SPR049386743 (SPR)s11273-022-09897-x-e DE-627 ger DE-627 rakwb eng Wang, Yicong verfasserin aut Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. Image monitoring (dpeaa)DE-He213 Growth model (dpeaa)DE-He213 Ecological restoration (dpeaa)DE-He213 Liao River Estuary wetland (dpeaa)DE-He213 Liang, Xianmeng aut Yu, Yang aut Yu, Changbin aut Yang, Min aut Wu, Guojun aut Enthalten in Wetlands ecology and management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1989 31(2022), 1 vom: 01. Dez., Seite 1-18 (DE-627)320569985 (DE-600)2016379-4 1572-9834 nnns volume:31 year:2022 number:1 day:01 month:12 pages:1-18 https://dx.doi.org/10.1007/s11273-022-09897-x 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_101 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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 31 2022 1 01 12 1-18 |
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10.1007/s11273-022-09897-x doi (DE-627)SPR049386743 (SPR)s11273-022-09897-x-e DE-627 ger DE-627 rakwb eng Wang, Yicong verfasserin aut Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. Image monitoring (dpeaa)DE-He213 Growth model (dpeaa)DE-He213 Ecological restoration (dpeaa)DE-He213 Liao River Estuary wetland (dpeaa)DE-He213 Liang, Xianmeng aut Yu, Yang aut Yu, Changbin aut Yang, Min aut Wu, Guojun aut Enthalten in Wetlands ecology and management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1989 31(2022), 1 vom: 01. Dez., Seite 1-18 (DE-627)320569985 (DE-600)2016379-4 1572-9834 nnns volume:31 year:2022 number:1 day:01 month:12 pages:1-18 https://dx.doi.org/10.1007/s11273-022-09897-x 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_101 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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 31 2022 1 01 12 1-18 |
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10.1007/s11273-022-09897-x doi (DE-627)SPR049386743 (SPR)s11273-022-09897-x-e DE-627 ger DE-627 rakwb eng Wang, Yicong verfasserin aut Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. Image monitoring (dpeaa)DE-He213 Growth model (dpeaa)DE-He213 Ecological restoration (dpeaa)DE-He213 Liao River Estuary wetland (dpeaa)DE-He213 Liang, Xianmeng aut Yu, Yang aut Yu, Changbin aut Yang, Min aut Wu, Guojun aut Enthalten in Wetlands ecology and management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1989 31(2022), 1 vom: 01. Dez., Seite 1-18 (DE-627)320569985 (DE-600)2016379-4 1572-9834 nnns volume:31 year:2022 number:1 day:01 month:12 pages:1-18 https://dx.doi.org/10.1007/s11273-022-09897-x 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_101 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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 31 2022 1 01 12 1-18 |
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10.1007/s11273-022-09897-x doi (DE-627)SPR049386743 (SPR)s11273-022-09897-x-e DE-627 ger DE-627 rakwb eng Wang, Yicong verfasserin aut Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. Image monitoring (dpeaa)DE-He213 Growth model (dpeaa)DE-He213 Ecological restoration (dpeaa)DE-He213 Liao River Estuary wetland (dpeaa)DE-He213 Liang, Xianmeng aut Yu, Yang aut Yu, Changbin aut Yang, Min aut Wu, Guojun aut Enthalten in Wetlands ecology and management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1989 31(2022), 1 vom: 01. Dez., Seite 1-18 (DE-627)320569985 (DE-600)2016379-4 1572-9834 nnns volume:31 year:2022 number:1 day:01 month:12 pages:1-18 https://dx.doi.org/10.1007/s11273-022-09897-x 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_101 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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 31 2022 1 01 12 1-18 |
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Springer Nature or its licensor (e.g. a society or other partner) 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image monitoring</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Growth model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ecological restoration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Liao River Estuary wetland</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Xianmeng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Yang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Changbin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Min</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Guojun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Wetlands ecology and management</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1989</subfield><subfield code="g">31(2022), 1 vom: 01. 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Wang, Yicong |
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Wang, Yicong misc Image monitoring misc Growth model misc Ecological restoration misc Liao River Estuary wetland Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) |
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Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) Image monitoring (dpeaa)DE-He213 Growth model (dpeaa)DE-He213 Ecological restoration (dpeaa)DE-He213 Liao River Estuary wetland (dpeaa)DE-He213 |
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misc Image monitoring misc Growth model misc Ecological restoration misc Liao River Estuary wetland |
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misc Image monitoring misc Growth model misc Ecological restoration misc Liao River Estuary wetland |
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Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) |
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Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) |
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Wang, Yicong |
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Wang, Yicong Liang, Xianmeng Yu, Yang Yu, Changbin Yang, Min Wu, Guojun |
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Elektronische Aufsätze |
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Wang, Yicong |
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10.1007/s11273-022-09897-x |
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recording and modeling the seasonal growth of salt marsh vegetation at liao river estuary, china, based on the wetland image monitoring system (wims) |
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Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) |
| abstract |
Abstract Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 Wetland ecology monitoring is an essential technical guarantee for the protection and restoration of the fragile ecosystem of wetlands. Due to the degradation of keystone species Suaeda HeteropteraPall. (S. Heteroptera) in the Liao River Estuary wetland, the Wetland Image Monitoring System (WIMS) was established to obtain real-time, continuous, high spatiotemporal resolution data about the coverage and height of S. Heteroptera. Based on the monitoring data, we elicited the best model to describe the variation of the coverage and height for S. Heteroptera. The results showed that the growth of S. Heteroptera could be divided into three stages: rapid growth stage (April–May), slow growth stage (June–August), and stable stage (after September). The Bertalanffy model was the best choice for the coverage simulation of S. Heteroptera. The segment model composed of the linear and Gompertz models was suitable for the height simulation of S. Heteroptera, which could effectively reduce the relative error compared with the single model. In addition, the WIMS could potentially capture other important ecological factors in local regions, including benthic animals, birds, waterlogging conditions, etc. Although WIMS has some application limitations, the high spatiotemporal resolution and relatively low cost make it an effective tool to explore the degradation of typical ecosystems under climate change and human activities. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) 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 |
Recording and modeling the seasonal growth of salt marsh vegetation at Liao river estuary, China, based on the wetland image monitoring system (WIMS) |
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https://dx.doi.org/10.1007/s11273-022-09897-x |
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| author2 |
Liang, Xianmeng Yu, Yang Yu, Changbin Yang, Min Wu, Guojun |
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Liang, Xianmeng Yu, Yang Yu, Changbin Yang, Min Wu, Guojun |
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10.1007/s11273-022-09897-x |
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2024-07-04T00:36:15.945Z |
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| score |
7.401041 |