Multinomial logit estimation of a diameter growth matrix model of two Mediterranean pine species in Spain

Escalante, Ernesto; Pando, Valentín; Ordóñez, Cristóbal; Bravo, Felipe

doi:10.1007/s13595-011-0088-9

Cited by 6 publications

(7 citation statements)

References 29 publications

(40 reference statements)

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“…The R for the relationship between the number of trees per hectare and the DBH was negative, which indicated that competition among the trees affected the growth in DBH. This finding is similar to those reported by Luo et al [53], with a negative correlation between the number of trees per hectare and DBH, and similar to Escalante et al [54], in which forest stand simulations under fixed conditions indicated that the probabilities of diameter growth increase with site productivity increase, and decrease with an increase in the stand density index.…”

Section: Discussionsupporting

confidence: 92%

“…Since forest growth is affected by many environmental factors, such as topography and soil [52,53], the DBH of trees of the same age may vary considerably in different environments. From the inventory data for forest management planning and design and DEM, this study initially chose eight influencing factors-elevation, slope, slope aspect, thickness of soil layer, humus thickness, age of tree, canopy density, and number of trees per hectare-to analyze the correlation coefficients [54][55][56]. The results (Table 5) show that the six factors (elevation, slope aspect, humus thickness, age of tree, canopy density, and number of trees per hectare) reached a very significant level (p ≤ 0.01), with a relatively large correlation coefficient (r ≥ 0.1) for four factors, namely, the elevation, age of the tree, canopy density, and the number of trees per hectare, which were included in further modeling and prediction by MLR and GRNN.…”

Section: Correlation Analysismentioning

confidence: 99%

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Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

Zhou

et al. 2019

Forests

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The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R2 were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.

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Section: Discussionsupporting

confidence: 92%

Section: Correlation Analysismentioning

confidence: 99%

Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

Zhou

et al. 2019

Forests

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“…(Monfort, 2014), un Quercus robur puede duplicar ese grosor (Barrio y Diéguez-Aranda, 2005). El Pinus pinaster puede alcanzar a esa altura y edad los 50 cm (Barrio et al 2006), aunque otros cálculos, también para el Pinus silvestris, rebajan esa cifra (Escalante et al 2011). Según Mackay (1964) a un Quercus robur que a la altura de 1,3 metros tuviera un grosor de 10 ISSN 0211-1608, ISSN Digital: 2530 cm o menos (el 90% en La Draga) se le calcula una edad inferior a los 30 años, con un efecto de madera vieja inapreciable.…”

Section: Precauciones En La Gestión De Una Base De Datos Radiocronolóunclassified

La cronología de la Prehistoria de la Península Ibérica y los Sistemas de Información Geográfica del registro arqueológico

Ruiz

Mejías-García

2019

CuPAUAM

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ResumenEsta contribución da a conocer un proyecto colaborativo que combina un repositorio C14 de la Prehistoria de la Península Ibérica y un Sistema de Información Arqueológica Compartida -SIAC-. Está estructurada en cinco apartados: génesis, construcción, visor, reflexión, explotación y un caso práctico: el análisis de las cavidades con registros funerarios. El proyecto ofrece hoy más de 8.400 dataciones de carbono 14 de la Península Ibérica (y de Baleares), sobre la que es posible realizar búsquedas complejas cuyas respuestas se devuelven para un consumo libre. Se accede al repositorio desde dos direcciones web: https://sites.google.com/ehu.eus/c14peninsulaiberica/dataciones-14 y http://arqgeogis.us.es/siac/. Como proyecto colaborativo cualquier usuario puede proponer modificar o añadir nuevas dataciones. Además se aprovechan las fortalezas del software GIS para ofrecer salidas cartográficas de las consultas. Informamos las características del repositorio y reflexionamos sobre su potencial. AbstractThe text presents a collaborative project that combines a C14 database of the Prehistory of the Iberian Peninsula and the Shared Archaeological Information System -SIAC-. It is structured in five sections: genesis, construction, visor, reflection and exploitation and a case study: the analysis of caves with funeral records. The project offers, today, more than 8,400 c14 dates from the Iberian Peninsula (and the Balearic Islands): complex queries are possible, offering the answers in standard formats for use by the researcher. The database is accessed from two web addresses: https://sites.google.com/ehu.eus/c14peninsulaiberica/dataciones-14 and http://arqgeogis.us.es/SIAC/. It is a collaborative project, and any user can propose new dates or make changes. On the other hand, thanks to GIS software, cartographic outputs of the queries are offered. We also inform about the characteristics of the repository and we reflect on its potential.

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“…To date, only two size-class models have been developed in Spain (Sánchez Orois and Rodríguez Soalleiro, 2002;Escalante et al, 2011), both based on a transition matrix growth model. Also, some of the whole-stand models developed in Spain can be mathematically disaggregated using a diameter distribution function, which provides more detailed information about stand structure and volume (e.g., Río and Montero, 2001;Río et al, 2005;Diéguez-Aranda et al, 2006a;Castedo-Dorado et al, 2007a;Cabanillas, 2010).…”

Section: Modeling Approachesmentioning

confidence: 99%

Growth and yield models in Spain: Historical overview, Contemporary Examples and perspectives

Bravo

Álvarez‐González

Rı́o³

et al. 2011

For. syst.

Self Cite

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In this paper we present a review of forest models developed in Spain in recent years for both timber and non timber production and forest dynamics (regeneration, mortality). Models developed are whole stand, size (diameter) class and individual-tree. The models developed to date have been developed using data from permanent plots, experimental sites and the National Forest Inventory. In this paper we show the different sub-models developed so far and the friendly use software. Main perspectives of forest modeling in Spain are presented.Key words: timber production; non-wood production; recruitment; modeling; forest. Resumen Modelos de crecimiento y producción en España: historia, ejemplos contemporáneos y perspectivasEn el presente trabajo se presenta una revisión sobre los modelos forestales desarrollados en España durante los úl-timos años, tanto para la producción maderable como no maderable y, para la dinámica de los bosques (regeneración, mortalidad). Se presentan modelos tanto de rodal completo como de clases diamétricas y de árbol individual. Los modelos desarrollados hasta la fecha se han desarrollado a partir de datos procedentes de parcelas permanentes, ensayos y el Inventario Forestal Nacional. En el trabajo se muestran los diferentes submodelos desarrollados hasta la fecha,

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Multinomial logit estimation of a diameter growth matrix model of two Mediterranean pine species in Spain

Cited by 6 publications

References 29 publications

Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

La cronología de la Prehistoria de la Península Ibérica y los Sistemas de Información Geográfica del registro arqueológico

Growth and yield models in Spain: Historical overview, Contemporary Examples and perspectives

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