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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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INIA Las Brujas (LB) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
05/06/2023 |
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Actualizado : |
05/06/2023 |
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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 : |
PORTUGAL, T. B.; DE FACCIO CARVALHO, P. C.; DE CAMPOS, B.M.; SZYMCZAK, L.S.; SAVIAN, J.V.; ZUBIETA, A.S.; DE SOUZA FILHO, W.; ROSSETTO, J.; BREMM, C.; DE OLIVEIRA, L.B.; DE MORAES, A.; BAYER, C.; GOMES MONTEIRO, A.L. |
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Afiliación : |
THALES BAGGIO PORTUGAL, Department of Crop Production and Protection, Federal University of Paraná, Curitiba, 80035-050, Brazil; CONSIPA - Consulting on Integrated Crop-Livestock Systems, Ponta Grossa, 84015-500, Brazil; PAULO CÉSAR DE FACCIO CARVALHO, Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, 91540-000, Brazil; BRENO MENEZES DE CAMPOS, Department of Crop Production and Protection, Federal University of Paraná, Curitiba, 80035-050, Brazil; LEONARDO SILVESTRI SZYMCZAK, Department of Crop Production and Protection, Federal University of Paraná, Curitiba, 80035-050, Brazil; Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, 91540-000, Brazil; JEAN VICTOR SAVIAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANGEL SÁNCHEZ ZUBIETA, Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, 91540-000, Brazil; WILLIAM DE SOUZA FILHO, Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, 91540-000, Brazil; JUSIANE ROSSETTO, Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, 91540-000, Brazil; CAROLINA BREMM, Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, 91540-000, Brazil; LEANDRO BITTENCOURT DE OLIVEIRA, Department of Crop Production and Protection, Federal University of Paraná, Curitiba, 80035-050, Brazil; ANIBAL DE MORAES, Department of Crop Production and Protection, Federal University of Paraná, Curitiba, 80035-050, Brazil; CIMÉLIO BAYER, Department of Soil Science, Faculty of Agronomy, Federal University of Rio Grande Do Sul (UFRGS), Porto Alegre, Brazil; ALDA LUCIA GOMES MONTEIRO, Sheep and Goat Production and Research Center, Federal University of Paraná, Curitiba, 80035-050, Brazil. |
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Título : |
Methane emissions and growth performance of beef cattle grazing multi-species swards in different pesticide-free integrated crop-livestock systems in southern Brazil. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Journal of Cleaner Production, 15 August 2023, Volume 414, Article 137536. https://doi.org/10.1016/j.jclepro.2023.137536 |
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ISSN : |
0959-6526 |
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DOI : |
10.1016/j.jclepro.2023.137536 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 28 December 2022; Received in revised form 16 May 2023; Accepted 19 May 2023; Available online 22 May 2023. -- Correspondence author: Portugal, T.B.; Department of Crop Production and Protection, Federal University of Paraná, Curitiba, Brazil; email:baggio.thales@gmail.com -- Handling Editor: Cecilia Maria Villas Boas de Almeida. -- Funding: This work was supported by the Coordination for the Improvement of Higher Education Personnel ( CAPES ) of the Brazilian Ministry of Education and the National Council for Scientific and Technological Development ( CNPq ) of Brazil for the research grants and experimental protocol funding. |
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Contenido : |
The aim of this study was to assess the growth performance, forage intake and methane (CH4) emissions by beef cattle grazing under different spatiotemporal integrated crop-livestock systems (ICLSs). The experiment was conducted for two years (2017-2018 and 2018-2019) in warm season perennial pastures and cool season annual pastures grazed by beef steers. Three pesticide-free ICLS treatments - livestock-forestry (LF); crop-livestock (CL), and crop-livestock-forestry (CLF) - plus, a livestock control pesticide-free system (L) were conducted in randomized complete block design, with three replicates per treatment. © 2023 Elsevier Ltd |
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Palabras claves : |
Animal production; Enteric methane emissions; Eucalyptus; Greenhouse gases; Mixed swards; Steers. |
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Asunto categoría : |
L01 Ganadería |
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Marc : |
LEADER 02502naa a2200373 a 4500
001 1064179
005 2023-06-05
008 2023 bl uuuu u00u1 u #d
022 $a0959-6526
024 7 $a10.1016/j.jclepro.2023.137536$2DOI
100 1 $aPORTUGAL, T. B.
245 $aMethane emissions and growth performance of beef cattle grazing multi-species swards in different pesticide-free integrated crop-livestock systems in southern Brazil.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 28 December 2022; Received in revised form 16 May 2023; Accepted 19 May 2023; Available online 22 May 2023. -- Correspondence author: Portugal, T.B.; Department of Crop Production and Protection, Federal University of Paraná, Curitiba, Brazil; email:baggio.thales@gmail.com -- Handling Editor: Cecilia Maria Villas Boas de Almeida. -- Funding: This work was supported by the Coordination for the Improvement of Higher Education Personnel ( CAPES ) of the Brazilian Ministry of Education and the National Council for Scientific and Technological Development ( CNPq ) of Brazil for the research grants and experimental protocol funding.
520 $aThe aim of this study was to assess the growth performance, forage intake and methane (CH4) emissions by beef cattle grazing under different spatiotemporal integrated crop-livestock systems (ICLSs). The experiment was conducted for two years (2017-2018 and 2018-2019) in warm season perennial pastures and cool season annual pastures grazed by beef steers. Three pesticide-free ICLS treatments - livestock-forestry (LF); crop-livestock (CL), and crop-livestock-forestry (CLF) - plus, a livestock control pesticide-free system (L) were conducted in randomized complete block design, with three replicates per treatment. © 2023 Elsevier Ltd
653 $aAnimal production
653 $aEnteric methane emissions
653 $aEucalyptus
653 $aGreenhouse gases
653 $aMixed swards
653 $aSteers
700 1 $aDE FACCIO CARVALHO, P. C.
700 1 $aDE CAMPOS, B.M.
700 1 $aSZYMCZAK, L.S.
700 1 $aSAVIAN, J.V.
700 1 $aZUBIETA, A.S.
700 1 $aDE SOUZA FILHO, W.
700 1 $aROSSETTO, J.
700 1 $aBREMM, C.
700 1 $aDE OLIVEIRA, L.B.
700 1 $aDE MORAES, A.
700 1 $aBAYER, C.
700 1 $aGOMES MONTEIRO, A.L.
773 $tJournal of Cleaner Production, 15 August 2023, Volume 414, Article 137536. https://doi.org/10.1016/j.jclepro.2023.137536
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