Intra-annual phenology for detecting understory plant invasion in urban forests
Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Singh, Kunwar K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid - Skiadopoulos, V. ELSEVIER, 2013, official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:142 ; year:2018 ; pages:151-161 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.isprsjprs.2018.05.023 |
|---|
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ELV043686354 |
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| 245 | 1 | 0 | |a Intra-annual phenology for detecting understory plant invasion in urban forests |
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| 520 | |a Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. | ||
| 520 | |a Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. | ||
| 650 | 7 | |a Ligustrum sinense |2 Elsevier | |
| 650 | 7 | |a Biological invasion |2 Elsevier | |
| 650 | 7 | |a Vegetation phenology |2 Elsevier | |
| 650 | 7 | |a Normalized difference vegetation index |2 Elsevier | |
| 650 | 7 | |a Chinese privet |2 Elsevier | |
| 650 | 7 | |a Vegetation indices |2 Elsevier | |
| 650 | 7 | |a Random forest |2 Elsevier | |
| 700 | 1 | |a Chen, Yin-Hsuen |4 oth | |
| 700 | 1 | |a Smart, Lindsey |4 oth | |
| 700 | 1 | |a Gray, Josh |4 oth | |
| 700 | 1 | |a Meentemeyer, Ross K. |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Skiadopoulos, V. ELSEVIER |t In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid |d 2013 |d official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) |g Amsterdam [u.a.] |w (DE-627)ELV016966376 |
| 773 | 1 | 8 | |g volume:142 |g year:2018 |g pages:151-161 |g extent:11 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.isprsjprs.2018.05.023 |3 Volltext |
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10.1016/j.isprsjprs.2018.05.023 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001218.pica (DE-627)ELV043686354 (ELSEVIER)S0924-2716(18)30163-1 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Singh, Kunwar K. verfasserin aut Intra-annual phenology for detecting understory plant invasion in urban forests 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Ligustrum sinense Elsevier Biological invasion Elsevier Vegetation phenology Elsevier Normalized difference vegetation index Elsevier Chinese privet Elsevier Vegetation indices Elsevier Random forest Elsevier Chen, Yin-Hsuen oth Smart, Lindsey oth Gray, Josh oth Meentemeyer, Ross K. oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:142 year:2018 pages:151-161 extent:11 https://doi.org/10.1016/j.isprsjprs.2018.05.023 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 142 2018 151-161 11 |
| spelling |
10.1016/j.isprsjprs.2018.05.023 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001218.pica (DE-627)ELV043686354 (ELSEVIER)S0924-2716(18)30163-1 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Singh, Kunwar K. verfasserin aut Intra-annual phenology for detecting understory plant invasion in urban forests 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Ligustrum sinense Elsevier Biological invasion Elsevier Vegetation phenology Elsevier Normalized difference vegetation index Elsevier Chinese privet Elsevier Vegetation indices Elsevier Random forest Elsevier Chen, Yin-Hsuen oth Smart, Lindsey oth Gray, Josh oth Meentemeyer, Ross K. oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:142 year:2018 pages:151-161 extent:11 https://doi.org/10.1016/j.isprsjprs.2018.05.023 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 142 2018 151-161 11 |
| allfields_unstemmed |
10.1016/j.isprsjprs.2018.05.023 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001218.pica (DE-627)ELV043686354 (ELSEVIER)S0924-2716(18)30163-1 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Singh, Kunwar K. verfasserin aut Intra-annual phenology for detecting understory plant invasion in urban forests 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Ligustrum sinense Elsevier Biological invasion Elsevier Vegetation phenology Elsevier Normalized difference vegetation index Elsevier Chinese privet Elsevier Vegetation indices Elsevier Random forest Elsevier Chen, Yin-Hsuen oth Smart, Lindsey oth Gray, Josh oth Meentemeyer, Ross K. oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:142 year:2018 pages:151-161 extent:11 https://doi.org/10.1016/j.isprsjprs.2018.05.023 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 142 2018 151-161 11 |
| allfieldsGer |
10.1016/j.isprsjprs.2018.05.023 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001218.pica (DE-627)ELV043686354 (ELSEVIER)S0924-2716(18)30163-1 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Singh, Kunwar K. verfasserin aut Intra-annual phenology for detecting understory plant invasion in urban forests 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Ligustrum sinense Elsevier Biological invasion Elsevier Vegetation phenology Elsevier Normalized difference vegetation index Elsevier Chinese privet Elsevier Vegetation indices Elsevier Random forest Elsevier Chen, Yin-Hsuen oth Smart, Lindsey oth Gray, Josh oth Meentemeyer, Ross K. oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:142 year:2018 pages:151-161 extent:11 https://doi.org/10.1016/j.isprsjprs.2018.05.023 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 142 2018 151-161 11 |
| allfieldsSound |
10.1016/j.isprsjprs.2018.05.023 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001218.pica (DE-627)ELV043686354 (ELSEVIER)S0924-2716(18)30163-1 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Singh, Kunwar K. verfasserin aut Intra-annual phenology for detecting understory plant invasion in urban forests 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. 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Intra-annual phenology for detecting understory plant invasion in urban forests |
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Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. |
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Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. |
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Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. |
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We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. 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ELSEVIER</subfield><subfield code="t">In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid</subfield><subfield code="d">2013</subfield><subfield code="d">official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV016966376</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:142</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:151-161</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.isprsjprs.2018.05.023</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.57</subfield><subfield code="j">Energiespeicherung</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.36</subfield><subfield code="j">Energiedirektumwandler</subfield><subfield code="j">elektrische Energiespeicher</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">142</subfield><subfield code="j">2018</subfield><subfield code="h">151-161</subfield><subfield code="g">11</subfield></datafield></record></collection>
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