Spatial approach for modeling litter carbon in forests under management for timber production
Forest floor or mulch is the carbon stock that regulates most of the functional processes of forest ecosystems, directly influencing soil fertility and site productivity. The forest floor carbon content is highly variable in space and time; therefore, obtaining accurate assessment of the carbon cont...
| Hlavní autoři: | , , , , |
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| Médium: | Online |
| Jazyk: | spa |
| Vydáno: |
Instituto de Ecología, A.C.
2021
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| On-line přístup: | https://myb.ojs.inecol.mx/index.php/myb/article/view/e2712122 |
| _version_ | 1799769406484512768 |
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| author | Pérez-Vázquez, Zaira Rosario Ángeles-Pérez, Gregorio Chávez-Vergara, Bruno Valdez-Lazalde, José René Ramírez-Guzmán, Martha Elva |
| author_facet | Pérez-Vázquez, Zaira Rosario Ángeles-Pérez, Gregorio Chávez-Vergara, Bruno Valdez-Lazalde, José René Ramírez-Guzmán, Martha Elva |
| author_sort | Pérez-Vázquez, Zaira Rosario |
| collection | MYB |
| description | Forest floor or mulch is the carbon stock that regulates most of the functional processes of forest ecosystems, directly influencing soil fertility and site productivity. The forest floor carbon content is highly variable in space and time; therefore, obtaining accurate assessment of the carbon contained in this stock represents a significant methodological challenge at any scale. In this study, four spatial modeling methods were compared to map forest floor carbon content in a temperate forest. The methods were ordinary kriging, generalized linear model, generalized additive model and random forest. Carbon content estimates were made for 2013 and 2018. The predictor variables represent the spatial, canopy and topographic structure present in the study area. All models were evaluated by ten-fold cross validation and the mean absolute error, root mean square error and the coefficient of determination (R2) were determined. The performance of the methods was, in decreasing order, random forest, generalized additive model, generalized linear model and ordinary kriging. The ordinary kriging method reflected the degree of spatial dependence of carbon content, but the spatial estimates were unrealistic (R2 ≤ 0.35). Generalized additive model and generalized linear model showed good performance (R2 ≥ 0.70), but higher overestimation; random forest obtained the best fit (R2 ≥ 0.86) to model carbon content in both years evaluated. It is concluded that random forest is a promising method with high potential to improve estimates of forest floor carbon at landscape scale. |
| format | Online |
| id | oai:oai.myb.ojs.inecol.mx:article-2122 |
| 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-21222022-11-29T22:30:17Z Spatial approach for modeling litter carbon in forests under management for timber production Enfoque espacial para modelación de carbono en el mantillo de bosques bajo manejo forestal maderable Pérez-Vázquez, Zaira Rosario Ángeles-Pérez, Gregorio Chávez-Vergara, Bruno Valdez-Lazalde, José René Ramírez-Guzmán, Martha Elva almacenes de carbono geoestadística modelo aditivo generalizado modelo lineal generalizado random forest variabilidad espacial carbon stocks geostatistics generalized additive model generalized lineal model forest floor random forest spatial variability Forest floor or mulch is the carbon stock that regulates most of the functional processes of forest ecosystems, directly influencing soil fertility and site productivity. The forest floor carbon content is highly variable in space and time; therefore, obtaining accurate assessment of the carbon contained in this stock represents a significant methodological challenge at any scale. In this study, four spatial modeling methods were compared to map forest floor carbon content in a temperate forest. The methods were ordinary kriging, generalized linear model, generalized additive model and random forest. Carbon content estimates were made for 2013 and 2018. The predictor variables represent the spatial, canopy and topographic structure present in the study area. All models were evaluated by ten-fold cross validation and the mean absolute error, root mean square error and the coefficient of determination (R2) were determined. The performance of the methods was, in decreasing order, random forest, generalized additive model, generalized linear model and ordinary kriging. The ordinary kriging method reflected the degree of spatial dependence of carbon content, but the spatial estimates were unrealistic (R2 ≤ 0.35). Generalized additive model and generalized linear model showed good performance (R2 ≥ 0.70), but higher overestimation; random forest obtained the best fit (R2 ≥ 0.86) to model carbon content in both years evaluated. It is concluded that random forest is a promising method with high potential to improve estimates of forest floor carbon at landscape scale. El piso forestal o mantillo es el almacén de carbono que regula la mayoría de los procesos funcionales de los ecosistemas forestales, influyendo directamente en la fertilidad del suelo y en la productividad del sitio. El contenido de carbono en el piso forestal es altamente variable en espacio y tiempo; por ello, obtener evaluaciones precisas del carbono contenido en este almacén representa un desafío metodológico importante a cualquier escala. En este estudio, se compararon cuatro métodos de modelación espacial para mapear el contenido de carbono en el piso forestal de un bosque templado. Los métodos fueron kriging ordinario, modelo lineal generalizado, modelo aditivo generalizado y random forest. Las estimaciones del contenido de carbono fueron realizadas para 2013 y 2018. Las variables predictoras representan la estructura espacial, del dosel y topográfica presente en el área de estudio. Todos los modelos fueron evaluados mediante validación cruzada y se determinó el error medio absoluto, el error cuadrático medio y el coeficiente de determinación. El desempeño de los métodos fue, en orden decreciente: random forest, modelo aditivo generalizado, modelo lineal generalizado y kriging ordinario. El método kriging ordinario reflejó el grado de dependencia espacial del contenido de carbono, pero las estimaciones espaciales fueron poco realistas (R2 ≤ 0.35). El modelo aditivo generalizado y el modelo lineal generalizado mostraron buen desempeño (R2 ≥ 0.70), pero mayor sobreestimación; random forest obtuvo el mejor ajuste (R2 ≥ 0.86) para modelar contenido de carbono en ambos años evaluados. Se concluye que random forest es un método prometedor, con gran potencial para mejorar las estimaciones de carbono en el mantillo a escala de paisaje. Instituto de Ecología, A.C. 2021-07-01 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/e2712122 10.21829/myb.2021.2712122 Madera y Bosques; Vol. 27 No. 1 (2021): Spring 2021 Madera y Bosques; Vol. 27 Núm. 1 (2021): Primavera 2021 2448-7597 1405-0471 spa https://myb.ojs.inecol.mx/index.php/myb/article/view/e2712122/2221 https://myb.ojs.inecol.mx/index.php/myb/article/view/e2712122/2240 10.21829/myb.2017.233293 10.21829/myb.2018.243351 Derechos de autor 2021 Madera y Bosques http://creativecommons.org/licenses/by-nc-sa/4.0 |
| spellingShingle | Pérez-Vázquez, Zaira Rosario Ángeles-Pérez, Gregorio Chávez-Vergara, Bruno Valdez-Lazalde, José René Ramírez-Guzmán, Martha Elva Spatial approach for modeling litter carbon in forests under management for timber production |
| title | Spatial approach for modeling litter carbon in forests under management for timber production |
| title_full | Spatial approach for modeling litter carbon in forests under management for timber production |
| title_fullStr | Spatial approach for modeling litter carbon in forests under management for timber production |
| title_full_unstemmed | Spatial approach for modeling litter carbon in forests under management for timber production |
| title_short | Spatial approach for modeling litter carbon in forests under management for timber production |
| title_sort | spatial approach for modeling litter carbon in forests under management for timber production |
| url | https://myb.ojs.inecol.mx/index.php/myb/article/view/e2712122 |
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