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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
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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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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| 12. |  | FRAGA, M.; PERELMUTER, K.; VALENCIA, M.J.; MARTÍNEZ, M.; ABIN-CARRIQUIRY, A.; CAJARVILLE, C.; ZUNINO, P. Evaluation of native potential probiotic bacteria using an in vitro ruminal fermentation system. Annals of Microbiology, September 2014, Volume 64, Issue 3, pp 1149-1156. Article history:Received: 11 July 2013 /Accepted: 29 October 2013.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : B - 1 |
| Biblioteca(s): INIA La Estanzuela. |
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| 13. | | LOZANO, J.; FERNÁNDEZ-CIGANDA, S.; GONZÁLEZ REVELLO, A.; HIRIGOYEN, D.; MARTÍNEZ, M.; SCORZA, C.; ZUNINO, P. Probiotic potential of GABA-producing lactobacilli isolated from Uruguayan artisanal cheese starter cultures. Journal of Microbiology, 2022, volume 133, issue 3, pages 1610-1619. doi: https://doi.org/10.1111/jam.15664 Article history: Received 18 March 2022; Revised 5 May 2022; Accepted 6 June 2022; Published online September 2022. -- Corresponding author: Zunino, P.; Department of Microbiology, Instituto de Investigaciones Biológicas Clemente Estable,...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA La Estanzuela. |
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| 14. | | RIZZO-MARTÍN, I.; HIRIGOYEN, A.; ARTHUS-BACOVICH, R.; VARO-MARTÍNEZ, M.A.; NAVARRO-CERRILLO, R. Site index estimation using airborne laser scanner data in Eucalyptus dunnii Maide stands in Uruguay. Forests, 2023, Volume 14, Issue 5, article 933. https://doi.org/10.3390/f14050933 -- OPEN ACCESS. Article history: Received 16 March 2023; Revised 23 April 2023; Accepted 27 April 2023; Published 1 May 2023. -- Correspondence: Rizzo-Martín, I.; Department of Forest Production and Wood Technology, Faculty of Agronomy, University of the...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 15. | | HIRIGOYEN, A.; VARO-MARTINEZ, M.A.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R.M. Stand characterization of eucalyptus spp. Plantations in uruguay using airborne lidar scanner technology. Remote Sensing, 1 December 2020, Volume 12, Issue 23, Article number 3947, Pages 1-19. Open Access. Doi: https://doi.org/10.3390/rs12233947 Article history: Received: 16 October 2020 / Revised: 5 November 2020 / Accepted: 21 November 2020 / Published: 2 December 2020. Acknowledgments: The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Tacuarembó. |
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| 17. | | HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. Annals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073 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...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 18. | | NERVI, E.; SCHINCA, C.; CARBAJALES, A.; DE GUIDA, M.; PIPPOLO, D.; DABEZIES, M.; DEMARÍA, K.; OLIVERA, A.; MARTÍNEZ, M.; BOCCARDI, L.; MIGUEZ, D.; VILLANUEVA, J. P.; PARDO, H.; LAMELA, S.; PECOITS, E.; CABEZAS, A.; MOIZO, P.; CAFARO, N.; CARRASCO-LETELIER, L. Hacia el uso sostenible del agua en la producción. Proyecto Custodia del Agua en la Cuenca del Santa Lucía. Revista INIA Uruguay, 2020, no. 60, p. 75-78. (Revista INIA; 60).| Tipo: Artículos en Revistas Agropecuarias |
| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
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| 19. | | CISNEROS, F.; CARRERAS, R.; GARCÍA, M.N.; VILLALBA, P.V.; AGUIRRE, N.C.; RIVAS, J.G.; MARTÍNEZ, M.C.; ACUÑA, C.V.; LUDUEÑA, A.; DÍAZ, M.; LÓPEZ, J.; LÓPEZ, J.A.; CAPPA, E.P.; PATHAUER, P.S.; PALAZZINI, D.; HARRAND, L.; OBERSCHELP, J.; MARCÓ, M.A.; MOGLIA, J.G.; ARÉVALO, C.; ELIZAUL, J.; HOPP, H.E.; RODRÍGUES, J.C.; BALMELLI, G.; MORAN, M.O.; GRATTAPAGLIA, D.; MARCUCCI POLTRI, S.N. Proyecto MINCYT-UE EuropeAid/136-457: Selección acelerada de Eucalyptus para bioenergía de los programas de mejoramiento forestal del MERCOSUR [Resumen Bahía Blanca]. In: Simposio Nacional de Biotecnología, 2., 11-13 de setiembre, Bahía Blanca, 2017.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Tacuarembó. |
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| 20. | | CISNEROS, F.; CARRERAS, R.; GARCÍA, M.N.; VILLALBA, P.V.; AGUIRRE, N.C.; RIVAS, J.G.; MARTÍNEZ, M.C.; ACUÑA, C.V.; LUDUEÑA, A.; DÍAZ, M.; LÓPEZ, J.; LÓPEZ, J.A.; CAPPA, E.P.; PATHAUER, P.S.; PALAZZINI, D.; HARRAND, L.; OBERSCHELP, J.; MARCÓ, M.A.; MOGLIA, J.G.; ARÉVALO, C.; ELIZAUL, J.; HOPP, H.E.; RODRÍGUES, J.C.; BALMELLI, G.; MORAN, M.O.; GRATTAPAGLIA, D.; MARCUCCI POLTRI, S.N. Proyecto MINCYT-UE EuropeAid/136-457: Selección acelerada de Eucalyptus para bioenergía de los programas de mejoramiento forestal del MERCOSUR [Resumen Concordia]. In: Jornadas Forestales de Entre Ríos, 31., Concordia, 31, 5-6 octubre, 2017.| Biblioteca(s): INIA Tacuarembó. |
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