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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
18/01/2018 |
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Actualizado : |
16/10/2018 |
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Tipo de producción científica : |
Documentos |
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Autor : |
MONTOSSI, F.; SAN JULIÁN, R.; DE MATTOS, D.; BERRETTA, E.J.; RÍOS, M.; ZAMIT, W.; LEVRATTO, J. |
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Afiliación : |
FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROBERTO SAN JULIAN SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DANIEL DE MATTOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELBIO JOAQUIN BERRETTA CARVALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WILFREDO SHAMIL ZAMIT DUARTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN CARLOS LEVRATTO CORTES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Alimentación y manejo de la oveja de cría durante el último tercio de gestación en la región de basalto. |
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Fecha de publicación : |
1999 |
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Fuente / Imprenta : |
Anuario Sociedad Criadores de Merino Australiano, 1999, p. 38-43. |
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Idioma : |
Español |
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Palabras claves : |
BASALTO; OVEJA DE CRÍA. |
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Thesagro : |
OVINOS. |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
LEADER 00617nam a2200205 a 4500
001 1057968
005 2018-10-16
008 1999 bl uuuu u00u1 u #d
100 1 $aMONTOSSI, F.
245 $aAlimentación y manejo de la oveja de cría durante el último tercio de gestación en la región de basalto.
260 $aAnuario Sociedad Criadores de Merino Australiano, 1999, p. 38-43.$c1999
650 $aOVINOS
653 $aBASALTO
653 $aOVEJA DE CRÍA
700 1 $aSAN JULIÁN, R.
700 1 $aDE MATTOS, D.
700 1 $aBERRETTA, E.J.
700 1 $aRÍOS, M.
700 1 $aZAMIT, W.
700 1 $aLEVRATTO, J.
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Registro original : |
INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
16/03/2022 |
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Actualizado : |
16/03/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. |
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Afiliación : |
ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CRISTINA ACOSTA-MUÑOZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANTONIO JESÚS ARIZA SALAMANCA, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; MARIA ÁNGELES VARO-MARTINEZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANA CECILIA RACHID CASNATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE FRANCO, Faculty of Agronomy, University of the Republic, Paysandú, Uruguay; RAFAEL NAVARRO-CERRILLO, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain. |
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Título : |
A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Annals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073 |
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ISSN : |
1844-8135 |
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DOI : |
10.15287/afr.2021.2073 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021.
Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes. |
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Contenido : |
ABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved. MenosABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied throu... Presentar Todo |
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Palabras claves : |
Intensive silviculture; LAI canopy; Machine learning algorithms. |
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Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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URL : |
https://www.afrjournal.org/index.php/afr/article/viewFile/2073/1177
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Marc : |
LEADER 03380naa a2200265 a 4500
001 1062842
005 2022-03-16
008 2021 bl uuuu u00u1 u #d
022 $a1844-8135
024 7 $a10.15287/afr.2021.2073$2DOI
100 1 $aHIRIGOYEN, A.
245 $aA machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data.$h[electronic resource]
260 $c2021
500 $aArticle history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021. Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes.
520 $aABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved.
653 $aIntensive silviculture
653 $aLAI canopy
653 $aMachine learning algorithms
700 1 $aACOSTA-MUÑOZ, C.
700 1 $aSALAMANCA, A.J.A.
700 1 $aVARO-MARTINEZ, M.Á.
700 1 $aRACHID, C.
700 1 $aFRANCO, J.
700 1 $aNAVARRO-CERRILLO, R.
773 $tAnnals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073
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