A global and local allometric pantropical model
The development of generalized allometric models that allow estimations that are comparable with local models is a great challenge for estimating aerial biomass in tropical forests. The estimates of the parametrized allometric models in the logarithmic space (transformation to logarithmic format) m...
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| Format: | Online |
| Language: | spa |
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Instituto de Ecología, A.C.
2021
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| Online Access: | https://myb.ojs.inecol.mx/index.php/myb/article/view/2446 |
| _version_ | 1799769439764217856 |
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| author | Paz-Pellat, Fernando |
| author_facet | Paz-Pellat, Fernando |
| author_sort | Paz-Pellat, Fernando |
| collection | MYB |
| description | The development of generalized allometric models that allow estimations that are comparable with local models is a great challenge for estimating aerial biomass in tropical forests. The estimates of the parametrized allometric models in the logarithmic space (transformation to logarithmic format) minimizing the squared error of estimation requires the estimation of correction factors for the inverse transformation to the arithmetic space. Additionally, if the objective is the minimization of biases (mean relative error MRE and mean absolute error MAE), then the absolute estimation error can be minimized. In this work, classic allometric models were used, based on the relationship between biomass (B) and normal diameter (D), total height (H) and wood density (ρ), to review the relationships between their parameters. To analyze the proposed allometric relationships, a pantropical public database (4 004 data, 58 sampling sites) was used. The analyzes showed that for global models (all sites) and local (each site) the linear regression model of the relationship B versus ρD2H resulted in the best model (root mean square error or RMSE metric), for which was used as a reference standard. The models parametrized in the logarithmic space for the global estimates resulted with estimation errors greater than the model B = av0 (D2H) with av0 as a linear function with ρ. The estimation of av0 was performed by minimizing the absolute error, resulting in the lowest estimation bias errors (MRE and MAE), with RMSE values comparable to the quadratic error minimization process. For local estimates using allometric models at the site level, the model was used with only av0 (minimization of the absolute error) and changing the correction factor from the simple estimator to that of ratio estimator, resulting in a prediction model with an estimation error comparable to the nonlinear regressions and surpassing the classic allometry models. Since there is no information on aerial biomass in normal forest inventories, the estimation of the ratio correction factor was empirically parameterized by a multivariate linear regression process of data measured in the field, with results comparable to having measurements of aerial biomass on the field. |
| format | Online |
| id | oai:oai.myb.ojs.inecol.mx:article-2446 |
| institution | Madera y Bosques |
| language | spa |
| publishDate | 2021 |
| publisher | Instituto de Ecología, A.C. |
| record_format | ojs |
| spelling | oai:oai.myb.ojs.inecol.mx:article-24462022-11-29T22:21:55Z A global and local allometric pantropical model Un modelo alométrico pantropical global y local Paz-Pellat, Fernando alometría condicionada a campo densidad de madera errores de estimación factores de corrección minimización del error absoluto absolute error minimization correction factors estimation errors field-conditioned allometry wood density The development of generalized allometric models that allow estimations that are comparable with local models is a great challenge for estimating aerial biomass in tropical forests. The estimates of the parametrized allometric models in the logarithmic space (transformation to logarithmic format) minimizing the squared error of estimation requires the estimation of correction factors for the inverse transformation to the arithmetic space. Additionally, if the objective is the minimization of biases (mean relative error MRE and mean absolute error MAE), then the absolute estimation error can be minimized. In this work, classic allometric models were used, based on the relationship between biomass (B) and normal diameter (D), total height (H) and wood density (ρ), to review the relationships between their parameters. To analyze the proposed allometric relationships, a pantropical public database (4 004 data, 58 sampling sites) was used. The analyzes showed that for global models (all sites) and local (each site) the linear regression model of the relationship B versus ρD2H resulted in the best model (root mean square error or RMSE metric), for which was used as a reference standard. The models parametrized in the logarithmic space for the global estimates resulted with estimation errors greater than the model B = av0 (D2H) with av0 as a linear function with ρ. The estimation of av0 was performed by minimizing the absolute error, resulting in the lowest estimation bias errors (MRE and MAE), with RMSE values comparable to the quadratic error minimization process. For local estimates using allometric models at the site level, the model was used with only av0 (minimization of the absolute error) and changing the correction factor from the simple estimator to that of ratio estimator, resulting in a prediction model with an estimation error comparable to the nonlinear regressions and surpassing the classic allometry models. Since there is no information on aerial biomass in normal forest inventories, the estimation of the ratio correction factor was empirically parameterized by a multivariate linear regression process of data measured in the field, with results comparable to having measurements of aerial biomass on the field. El desarrollo de modelos alométricos generalizados que permitan realizar estimaciones comparables con modelos locales, es un gran reto para la realización de estimaciones de la biomasa aérea en los bosques tropicales. Las estimaciones de los modelos alométricos parametrizados en el espacio logarítmico (transformación a formato logarítmico) minimizando el error cuadrático de estimación requieren de la estimación de factores de corrección para la transformación inversa al espacio aritmético. Adicionalmente, si el objetivo es la minimización de sesgos (error relativo medio, ERM y error absoluto medio, EAM), entonces se puede minimizar el error absoluto de estimación. En este trabajo se usaron modelos alométricos clásicos, basados en la relación entre la biomasa (B) y el diámetro normal (D), altura total (H) y densidad de la madera (ρ), para revisar las relaciones entre sus parámetros. Para analizar las relaciones alométricas planteadas se utilizó una base de datos pública pantropical (4004 datos, 58 sitios de muestreo). Los análisis mostraron que para modelos globales (todos los sitios) y locales (cada sitio) el modelo de regresión lineal de la relación B versus ρD2H resultó en el mejor modelo (métrica de la raíz del error cuadrático medio o RECM), por ello fue usado como estándar de referencia. Los modelos parametrizados en el espacio logarítmico para las estimaciones globales resultaron con errores de estimación mayores al modelo B = av0 (D2H) con av0 como función lineal con ρ. La estimación de av0 fue realizada minimizando el error absoluto, resultando en los menores errores de sesgos de estimación (EAR y EAM), con valores del RECM comparables al proceso de minimización del error cuadrático. Para las estimaciones locales, usando modelos alométricos a nivel de sitio, se utilizó el modelo con solo av0 (minimización del error absoluto) y cambiando el factor de corrección del estimador simple al de razones, resultando en un modelo de predicción con error de estimación comparables al de las regresiones no lineales y superando los modelos de alometría clásicos. Dado que no se cuenta con información de la biomasa aérea en los inventarios forestales normales, la estimación del factor de corrección de razones fue parametrizado en forma empírica por un proceso de regresión lineal multivariada de datos medidos en campo con resultados comparables a contar con mediciones de campo de la biomasa aérea. Instituto de Ecología, A.C. 2021-12-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artículo evaluado por pares application/pdf text/xml https://myb.ojs.inecol.mx/index.php/myb/article/view/2446 10.21829/myb.2021.2742446 Madera y Bosques; Vol. 27 No. 4 (2021): Special issue. Winter 2021 Madera y Bosques; Vol. 27 Núm. 4 (2021): Número especial. Invierno 2021 2448-7597 1405-0471 spa https://myb.ojs.inecol.mx/index.php/myb/article/view/2446/2272 https://myb.ojs.inecol.mx/index.php/myb/article/view/2446/2302 10.21829/myb.2018.242433 10.21829/myb.2019.251510 http://creativecommons.org/licenses/by-nc-sa/4.0 |
| spellingShingle | Paz-Pellat, Fernando A global and local allometric pantropical model |
| title | A global and local allometric pantropical model |
| title_full | A global and local allometric pantropical model |
| title_fullStr | A global and local allometric pantropical model |
| title_full_unstemmed | A global and local allometric pantropical model |
| title_short | A global and local allometric pantropical model |
| title_sort | global and local allometric pantropical model |
| url | https://myb.ojs.inecol.mx/index.php/myb/article/view/2446 |
| work_keys_str_mv | AT pazpellatfernando aglobalandlocalallometricpantropicalmodel AT pazpellatfernando unmodeloalometricopantropicalglobalylocal AT pazpellatfernando globalandlocalallometricpantropicalmodel |
