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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
22/12/2015 |
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Actualizado : |
11/07/2017 |
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Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
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Autor : |
GRAS |
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Afiliación : |
UNIDAD DE AGROCLIMA Y SISTEMAS DE INFORMACIÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Aplicación SIGRAS (Sigras App). |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Revista INIA Uruguay, 2015, No.43, p.60-62. |
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Serie : |
(Revista INIA; 43) |
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ISSN : |
1510-9011 |
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Idioma : |
Español |
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Notas : |
Se incluye link de acceso a la nueva aplicación para teléfonos móviles disponible desde el Portal INIA |
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Contenido : |
SIGRAS App brinda información actual e histórica del estado de la vegetación (NDVI), agua en el suelo, climatología, suelo y cartografía general, entre otras, para el área en donde el usuario se encuentre posicionado u otra ubicación que seleccione. La aplicación dispone además de algunas herramientas y alertas, tales como pronósticos de heladas y lluvias
elaboradas por el CPTEC de Brasil, un sistema para estimación personalizada de agua en el suelo (CuantAgua) y pronósticos de Don en trigo. Esta aplicación fue desarrollada por la Unidad GRAS de INIA en el marco del proyecto denominado ?Contribución al desarrollo del Sistema Nacional de Información Agropecuaria, (SNIA) del MGAP?, con información
elaborada de manera conjunta con la Dirección Nacional de Recursos Naturales Renovables (RENARE) del MGAP, el Instituto Uruguayo de Meteorología (INUMET) y el Instituto Internacional de Investigación en Clima y Sociedad (IRI) de la Universidad de Columbia. |
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Palabras claves : |
GRAS. |
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Thesagro : |
SNIA (SISTEMA NACIONAL DE INFORMACIÓN AGROPECUARIA). |
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Asunto categoría : |
P40 Meteorología y climatología |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5396/1/Rev.INIA-2015-No43-p.60-62.pdf
http://www.inia.uy/gras/lanzamiento-de-sigras-app
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Marc : |
LEADER 01543naa a2200181 a 4500
001 1054136
005 2017-07-11
008 2015 bl uuuu u00u1 u #d
022 $a1510-9011
100 1 $aGRAS
245 $aAplicación SIGRAS (Sigras App).
260 $c2015
490 $a(Revista INIA; 43)
500 $aSe incluye link de acceso a la nueva aplicación para teléfonos móviles disponible desde el Portal INIA
520 $aSIGRAS App brinda información actual e histórica del estado de la vegetación (NDVI), agua en el suelo, climatología, suelo y cartografía general, entre otras, para el área en donde el usuario se encuentre posicionado u otra ubicación que seleccione. La aplicación dispone además de algunas herramientas y alertas, tales como pronósticos de heladas y lluvias elaboradas por el CPTEC de Brasil, un sistema para estimación personalizada de agua en el suelo (CuantAgua) y pronósticos de Don en trigo. Esta aplicación fue desarrollada por la Unidad GRAS de INIA en el marco del proyecto denominado ?Contribución al desarrollo del Sistema Nacional de Información Agropecuaria, (SNIA) del MGAP?, con información elaborada de manera conjunta con la Dirección Nacional de Recursos Naturales Renovables (RENARE) del MGAP, el Instituto Uruguayo de Meteorología (INUMET) y el Instituto Internacional de Investigación en Clima y Sociedad (IRI) de la Universidad de Columbia.
650 $aSNIA (SISTEMA NACIONAL DE INFORMACIÓN AGROPECUARIA)
653 $aGRAS
773 $tRevista INIA Uruguay, 2015, No.43, p.60-62.
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
09/06/2023 |
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Actualizado : |
09/06/2023 |
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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 : |
RIZZO-MARTÍN, I.; HIRIGOYEN, A.; ARTHUS-BACOVICH, R.; VARO-MARTÍNEZ, M.A.; NAVARRO-CERRILLO, R. |
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Afiliación : |
IVÁN RIZZO-MARTÍN, Department of Forest Production and Wood Technology, Faculty of Agronomy, University of the Republic, Montevideo 12900, Uruguay; ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RODRIGO ARTHUS-BACOVICH, Observatory of Global Change of the Mediterranean Forest, Department of Forest Engineering, University of Córdoba, E-14071 Córdoba, Spain; MARÍA ÁNGELES VARO-MARTÍNEZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 Córdoba, Spain; RAFAEL NAVARRO-CERRILLO, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 Córdoba, Spain. |
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Título : |
Site index estimation using airborne laser scanner data in Eucalyptus dunnii Maide stands in Uruguay. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Forests, 2023, Volume 14, Issue 5, article 933. https://doi.org/10.3390/f14050933 -- OPEN ACCESS. |
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ISSN : |
1999-4907 (electronic). |
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DOI : |
10.3390/f14050933 |
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Idioma : |
Inglés |
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Notas : |
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 Republic, Montevideo, Uruguay; email:ivan-rizzo@hotmail.com -- Funding: This research was funded by SILVADAPT.NET (RED2018-102719-T), EVIDENCE (Ref: 2822/2021) and REMEDIO (PID2021-128463OB-I00). -- This article belongs to the Special Issue Application of Laser Scanning and Satellite Image in Forest Mensuration (https://www.mdpi.com/journal/forests/special_issues/Application_Mensuration ). -- License: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
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Contenido : |
Intensive silviculture demands new inventory tools for better forest management and planning. Airborne laser scanning (ALS) was shown to be one of the best alternatives for high-precision inventories applied to productive plantations. The aim of this study was to generate multiple stand-scale maps of the site index (SI) using ALS data in the intensive silviculture of Eucalyptus dunnii Maide plantations in Uruguay. Forty-three plots (314.16 m3) were established in intensive E. dunnii plantations in the departments of Río Negro and Paysandú (Uruguay). ALS data were obtained for an area of 1995 ha. Linear and Random Forest models were fitted to estimate the height and site index, and OrpheoToolBox (OTB) software was used for stand segmentation. Linear models for dominant height (DH) estimation had a better fit (R2 = 0.84, RMSE = 0.94 m, MAPE = 0.04, Bias = 0.002) than the Random Forest (R2 = 0.85, RMSE = 1.27 m, MAPE = 7.20, Bias=-0.173) model when including only the 99th percentile metric. The coefficient between RMSE values of the cross-validation and RMSE of the model had a higher value for the linear model (0.93) than the Random Forest (0.75). The SI was estimated by applying the RF model, which included the ALS metrics corresponding to the 99th height percentile and the 80th height bicentile (R2 = 0.65; RMSE = 1.62 m). OTB segmentation made it possible to define a minimum segment size of 2.03 ha (spatial radius = 30, range radius = 1 and minimum region size = 64). This study provides a new tool for better forest management and promotes the need for further progress in the application of ALS data in the intensive silviculture of Eucalyptus spp. plantations in Uruguay. © 2023 by the authors. Licensee MDPI, Basel, Switzerland. MenosIntensive silviculture demands new inventory tools for better forest management and planning. Airborne laser scanning (ALS) was shown to be one of the best alternatives for high-precision inventories applied to productive plantations. The aim of this study was to generate multiple stand-scale maps of the site index (SI) using ALS data in the intensive silviculture of Eucalyptus dunnii Maide plantations in Uruguay. Forty-three plots (314.16 m3) were established in intensive E. dunnii plantations in the departments of Río Negro and Paysandú (Uruguay). ALS data were obtained for an area of 1995 ha. Linear and Random Forest models were fitted to estimate the height and site index, and OrpheoToolBox (OTB) software was used for stand segmentation. Linear models for dominant height (DH) estimation had a better fit (R2 = 0.84, RMSE = 0.94 m, MAPE = 0.04, Bias = 0.002) than the Random Forest (R2 = 0.85, RMSE = 1.27 m, MAPE = 7.20, Bias=-0.173) model when including only the 99th percentile metric. The coefficient between RMSE values of the cross-validation and RMSE of the model had a higher value for the linear model (0.93) than the Random Forest (0.75). The SI was estimated by applying the RF model, which included the ALS metrics corresponding to the 99th height percentile and the 80th height bicentile (R2 = 0.65; RMSE = 1.62 m). OTB segmentation made it possible to define a minimum segment size of 2.03 ha (spatial radius = 30, range radius = 1 and minimum region size = 64). This stu... Presentar Todo |
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Palabras claves : |
Eucaliptus spp; LiDAR; Orpheo ToolBox (OTB); Precision silvicultural; Random forest; Site Index; Stand segmentation. |
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Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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URL : |
https://www.mdpi.com/1999-4907/14/5/933/pdf
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Marc : |
LEADER 03491naa a2200289 a 4500
001 1064185
005 2023-06-09
008 2023 bl uuuu u00u1 u #d
022 $a1999-4907 (electronic).
024 7 $a10.3390/f14050933$2DOI
100 1 $aRIZZO-MARTÍN, I.
245 $aSite index estimation using airborne laser scanner data in Eucalyptus dunnii Maide stands in Uruguay.$h[electronic resource]
260 $c2023
500 $aArticle 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 Republic, Montevideo, Uruguay; email:ivan-rizzo@hotmail.com -- Funding: This research was funded by SILVADAPT.NET (RED2018-102719-T), EVIDENCE (Ref: 2822/2021) and REMEDIO (PID2021-128463OB-I00). -- This article belongs to the Special Issue Application of Laser Scanning and Satellite Image in Forest Mensuration (https://www.mdpi.com/journal/forests/special_issues/Application_Mensuration ). -- License: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
520 $aIntensive silviculture demands new inventory tools for better forest management and planning. Airborne laser scanning (ALS) was shown to be one of the best alternatives for high-precision inventories applied to productive plantations. The aim of this study was to generate multiple stand-scale maps of the site index (SI) using ALS data in the intensive silviculture of Eucalyptus dunnii Maide plantations in Uruguay. Forty-three plots (314.16 m3) were established in intensive E. dunnii plantations in the departments of Río Negro and Paysandú (Uruguay). ALS data were obtained for an area of 1995 ha. Linear and Random Forest models were fitted to estimate the height and site index, and OrpheoToolBox (OTB) software was used for stand segmentation. Linear models for dominant height (DH) estimation had a better fit (R2 = 0.84, RMSE = 0.94 m, MAPE = 0.04, Bias = 0.002) than the Random Forest (R2 = 0.85, RMSE = 1.27 m, MAPE = 7.20, Bias=-0.173) model when including only the 99th percentile metric. The coefficient between RMSE values of the cross-validation and RMSE of the model had a higher value for the linear model (0.93) than the Random Forest (0.75). The SI was estimated by applying the RF model, which included the ALS metrics corresponding to the 99th height percentile and the 80th height bicentile (R2 = 0.65; RMSE = 1.62 m). OTB segmentation made it possible to define a minimum segment size of 2.03 ha (spatial radius = 30, range radius = 1 and minimum region size = 64). This study provides a new tool for better forest management and promotes the need for further progress in the application of ALS data in the intensive silviculture of Eucalyptus spp. plantations in Uruguay. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
653 $aEucaliptus spp
653 $aLiDAR
653 $aOrpheo ToolBox (OTB)
653 $aPrecision silvicultural
653 $aRandom forest
653 $aSite Index
653 $aStand segmentation
700 1 $aHIRIGOYEN, A.
700 1 $aARTHUS-BACOVICH, R.
700 1 $aVARO-MARTÍNEZ, M.A.
700 1 $aNAVARRO-CERRILLO, R.
773 $tForests, 2023, Volume 14, Issue 5, article 933. https://doi.org/10.3390/f14050933 -- OPEN ACCESS.
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