Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar
Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegeta...
Ausführliche Beschreibung
Autor*in: |
Sumnall, Matthew J [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Rechteinformationen: |
Nutzungsrecht: © 2015 Taylor & Francis 2015 |
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Übergeordnetes Werk: |
Enthalten in: International journal of remote sensing - London [u.a.] : Taylor & Francis, 1980, 37(2016), 1, Seite 78 |
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Übergeordnetes Werk: |
volume:37 ; year:2016 ; number:1 ; pages:78 |
Links: |
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DOI / URN: |
10.1080/01431161.2015.1117683 |
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OLC1970922419 |
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520 | |a Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. | ||
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10.1080/01431161.2015.1117683 doi PQ20160430 (DE-627)OLC1970922419 (DE-599)GBVOLC1970922419 (PRQ)i1165-dfc3de8fe8b81715714d388d67ced3155478e1751c5fdbf4e929f03fe9ab45ef0 (KEY)0100254620160000037000100078estimatingleafareaindexatmultipleheightswithintheu DE-627 ger DE-627 rakwb eng 620 DNB Sumnall, Matthew J verfasserin aut Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. Nutzungsrecht: © 2015 Taylor & Francis 2015 Fox, Thomas R oth Wynne, Randolph H oth Blinn, Christine oth Thomas, Valerie A oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 78 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:78 http://dx.doi.org/10.1080/01431161.2015.1117683 Volltext http://www.tandfonline.com/doi/abs/10.1080/01431161.2015.1117683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 78 |
spelling |
10.1080/01431161.2015.1117683 doi PQ20160430 (DE-627)OLC1970922419 (DE-599)GBVOLC1970922419 (PRQ)i1165-dfc3de8fe8b81715714d388d67ced3155478e1751c5fdbf4e929f03fe9ab45ef0 (KEY)0100254620160000037000100078estimatingleafareaindexatmultipleheightswithintheu DE-627 ger DE-627 rakwb eng 620 DNB Sumnall, Matthew J verfasserin aut Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. Nutzungsrecht: © 2015 Taylor & Francis 2015 Fox, Thomas R oth Wynne, Randolph H oth Blinn, Christine oth Thomas, Valerie A oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 78 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:78 http://dx.doi.org/10.1080/01431161.2015.1117683 Volltext http://www.tandfonline.com/doi/abs/10.1080/01431161.2015.1117683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 78 |
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10.1080/01431161.2015.1117683 doi PQ20160430 (DE-627)OLC1970922419 (DE-599)GBVOLC1970922419 (PRQ)i1165-dfc3de8fe8b81715714d388d67ced3155478e1751c5fdbf4e929f03fe9ab45ef0 (KEY)0100254620160000037000100078estimatingleafareaindexatmultipleheightswithintheu DE-627 ger DE-627 rakwb eng 620 DNB Sumnall, Matthew J verfasserin aut Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. Nutzungsrecht: © 2015 Taylor & Francis 2015 Fox, Thomas R oth Wynne, Randolph H oth Blinn, Christine oth Thomas, Valerie A oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 78 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:78 http://dx.doi.org/10.1080/01431161.2015.1117683 Volltext http://www.tandfonline.com/doi/abs/10.1080/01431161.2015.1117683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 78 |
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10.1080/01431161.2015.1117683 doi PQ20160430 (DE-627)OLC1970922419 (DE-599)GBVOLC1970922419 (PRQ)i1165-dfc3de8fe8b81715714d388d67ced3155478e1751c5fdbf4e929f03fe9ab45ef0 (KEY)0100254620160000037000100078estimatingleafareaindexatmultipleheightswithintheu DE-627 ger DE-627 rakwb eng 620 DNB Sumnall, Matthew J verfasserin aut Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. Nutzungsrecht: © 2015 Taylor & Francis 2015 Fox, Thomas R oth Wynne, Randolph H oth Blinn, Christine oth Thomas, Valerie A oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 78 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:78 http://dx.doi.org/10.1080/01431161.2015.1117683 Volltext http://www.tandfonline.com/doi/abs/10.1080/01431161.2015.1117683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 78 |
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10.1080/01431161.2015.1117683 doi PQ20160430 (DE-627)OLC1970922419 (DE-599)GBVOLC1970922419 (PRQ)i1165-dfc3de8fe8b81715714d388d67ced3155478e1751c5fdbf4e929f03fe9ab45ef0 (KEY)0100254620160000037000100078estimatingleafareaindexatmultipleheightswithintheu DE-627 ger DE-627 rakwb eng 620 DNB Sumnall, Matthew J verfasserin aut Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. Nutzungsrecht: © 2015 Taylor & Francis 2015 Fox, Thomas R oth Wynne, Randolph H oth Blinn, Christine oth Thomas, Valerie A oth Enthalten in International journal of remote sensing London [u.a.] : Taylor & Francis, 1980 37(2016), 1, Seite 78 (DE-627)13048721X (DE-600)754117-X (DE-576)016073037 0143-1161 nnns volume:37 year:2016 number:1 pages:78 http://dx.doi.org/10.1080/01431161.2015.1117683 Volltext http://www.tandfonline.com/doi/abs/10.1080/01431161.2015.1117683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_65 GBV_ILN_70 GBV_ILN_201 GBV_ILN_601 GBV_ILN_2006 AR 37 2016 1 78 |
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estimating leaf area index at multiple heights within the understorey component of loblolly pine forests from airborne discrete-return lidar |
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Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar |
abstract |
Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. |
abstractGer |
Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. |
abstract_unstemmed |
Airborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42-0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions. |
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Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar |
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