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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
16/03/2022 |
Actualizado : |
16/03/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. |
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. |
Título : |
A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. |
Fecha de publicación : |
2021 |
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 |
ISSN : |
1844-8135 |
DOI : |
10.15287/afr.2021.2073 |
Idioma : |
Inglés |
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. |
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 |
Palabras claves : |
Intensive silviculture; LAI canopy; Machine learning algorithms. |
Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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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INIA Las Brujas (LB) |
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| Acceso al texto completo restringido a Biblioteca INIA Tacuarembó. Por información adicional contacte bibliotb@tb.inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha actual : |
09/09/2014 |
Actualizado : |
27/04/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
VIÑOLES, C.; JAURENA, M.; DE BARBIERI, I.; DO CARMO, M.; MONTOSSI, F. |
Afiliación : |
CAROLINA VIÑOLES GIL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN DO CARMO CORUJO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Effect of creep feeding and stocking rate on the productivity of beef cattle grazing grasslands. |
Fecha de publicación : |
2013 |
Fuente / Imprenta : |
New Zealand Journal of Agricultural Research, 22 October 2013, v. 56, no. 4, p. 279-287. http://dx.doi.org/10.1080/00288233.2013.840320 |
DOI : |
10.1080/00288233.2013.840320 |
Idioma : |
Inglés |
Notas : |
History article: Received 17 January 2013; accepted 15 August 2013. Acknowledgements The authors would like to acknowledge the staff of the Research unit Glencoe, especially Pablo Cuadro and the graduate students of the Veterinary Faculty of Uruguay: Andrés Michelena, Andrea Martín, Verónica Echenique, Andrés Betancurt, Ignacio Quagliotti and Hector
Rosano for their excellent work during the development of the experiment. We would also like to thank Mariana Carriquiry and Paul Kenyon for their critical comments on this manuscript. |
Contenido : |
Ninety-six Hereford cow-calf pairs grazing Campo grasslands were used in a 2 × 2 factorial design that evaluated stocking rate (high [H] vs low [L]) and creep feeding (CF; yes or no). Creep-fed calves grazing L had a greater average daily gain (1.07 ± 0.03 kg/d) than CF calves grazing H (0.96 ± 0.03 kg/d; P < 0.05), but L?CF (0.78 ± 0.03 kg/d) and H?CF calves (0.73 ± 0.03 kg/d) had similar average daily gains (P > 0.05). Similarly, L+CF calves were heavier at weaning (172 ± 3 kg) than H+CF
calves (160 ± 3 kg), but weaning weights between L?CF (144 ± 3 kg) and H?CF (138 ± 3 kg; P > 0.05) did not differ. Creep-fed calves grazed less (39 ± 10%) than non-supplemented calves (58 ± 15%; P < 0.05). Creep feeding had no effect on milk production, body condition and live weight of the dams, so it had no impact on their reproductive performance. We conclude that CF promotes greater live weight gains and weaning weights of Hereford calves grazing Campo grasslands. |
Palabras claves : |
CALVES; CAMPO GRASSLANDS; CREEP FEEDING; PRODUCTIVE PERFORMANCE; WEANING WEIGHT. |
Thesagro : |
GANADO VACUNO; PASTURAS; SUPLEMENTACION. |
Asunto categoría : |
L01 Ganadería |
Marc : |
LEADER 02400naa a2200289 a 4500 001 1050047 005 2020-04-27 008 2013 bl uuuu u00u1 u #d 024 7 $a10.1080/00288233.2013.840320$2DOI 100 1 $aVIÑOLES, C. 245 $aEffect of creep feeding and stocking rate on the productivity of beef cattle grazing grasslands. 260 $c2013 500 $aHistory article: Received 17 January 2013; accepted 15 August 2013. Acknowledgements The authors would like to acknowledge the staff of the Research unit Glencoe, especially Pablo Cuadro and the graduate students of the Veterinary Faculty of Uruguay: Andrés Michelena, Andrea Martín, Verónica Echenique, Andrés Betancurt, Ignacio Quagliotti and Hector Rosano for their excellent work during the development of the experiment. We would also like to thank Mariana Carriquiry and Paul Kenyon for their critical comments on this manuscript. 520 $aNinety-six Hereford cow-calf pairs grazing Campo grasslands were used in a 2 × 2 factorial design that evaluated stocking rate (high [H] vs low [L]) and creep feeding (CF; yes or no). Creep-fed calves grazing L had a greater average daily gain (1.07 ± 0.03 kg/d) than CF calves grazing H (0.96 ± 0.03 kg/d; P < 0.05), but L?CF (0.78 ± 0.03 kg/d) and H?CF calves (0.73 ± 0.03 kg/d) had similar average daily gains (P > 0.05). Similarly, L+CF calves were heavier at weaning (172 ± 3 kg) than H+CF calves (160 ± 3 kg), but weaning weights between L?CF (144 ± 3 kg) and H?CF (138 ± 3 kg; P > 0.05) did not differ. Creep-fed calves grazed less (39 ± 10%) than non-supplemented calves (58 ± 15%; P < 0.05). Creep feeding had no effect on milk production, body condition and live weight of the dams, so it had no impact on their reproductive performance. We conclude that CF promotes greater live weight gains and weaning weights of Hereford calves grazing Campo grasslands. 650 $aGANADO VACUNO 650 $aPASTURAS 650 $aSUPLEMENTACION 653 $aCALVES 653 $aCAMPO GRASSLANDS 653 $aCREEP FEEDING 653 $aPRODUCTIVE PERFORMANCE 653 $aWEANING WEIGHT 700 1 $aJAURENA, M. 700 1 $aDE BARBIERI, I. 700 1 $aDO CARMO, M. 700 1 $aMONTOSSI, F. 773 $tNew Zealand Journal of Agricultural Research, 22 October 2013$gv. 56, no. 4, p. 279-287. http://dx.doi.org/10.1080/00288233.2013.840320
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