Resumen de tesis doctoral

Growth Evaluation of a Conifer Forest (Pinus Cooperí Blanco) using a Neural Net Backpropagation Trained with Distance Independent Competition Measures

Estimación del Crecimiento de un Bosque de Pino (Pinus Cooperí Blanco) por medio de una Red Neuronal Backpropagation Entrenada con Índices de Competencia Independientes de la Distancia

Graduated: Jesús Celis Porras
Centro de Investigación en Computación del IPN
Av. Juan de Dios Bátiz s/n Esq. Miguel Othón de Mendizábal
C.P. 07738 México D.F
jcelisp@yahoo.com.mx

Adviser: Juan Luis Díaz de León
Centro de Investigación en Computación del IPN
Av. Juan de Dios Bátiz s/n Esq. Miguel Othón de Mendizábal
C.P. 07738 México D.F
jdiaz@pollux.cic.ipn.mx

Co–adviser: J. Alberto Gallegos Infante
Instituto Tecnológico de Durango.
Felipe Pescador 1830 Ote. CP 34000, Durango, Dgo. México
jinfantel@starmedia.com

Graduated in September 4, 2006

Abstract

To make a decision about irregular forest handling practices is very difficult cause of some characteristics like age, natural life size diversity, and spatial distribution. A very important factor to fix growth forest is the competition about natural resources, so competition between trees should be considered to develop growth model. This is possible making use of parameters building with tree dimensions like diameter high, canopy extent, top high. These parameters are the distance independent competition measures. This research shows results product to use of backpropagation neural net trained with distance independent competition measures to forecast diameter and high growth. In this work we develop a growth model of a natural mixed forest of Pinus Cooperí Blanco, endemic specie of mountain region of Durango State, Mexico. This specie has been barely studied and is very important in wood exploitation production, because is used in timber wood production, and triplay fabrication.

Key Words: Pinus Cooperí Blanco, backpropagation neural net, independent distance competition measures.

Resumen

La toma de decisiones en el manejo de bosques irregulares se dificulta en gran medida por las características como: una alta complejidad por su diversidad de edad, tamaño y distribución espacial. Una forma de conceptuar el problema es visualizar el bosque como un ecosistema, donde su estudio se basa en las interrelaciones de los organismos y su medio ambiente. Un factor que determina el crecimiento de un bosque es la competencia que existe entre los individuos de la población por recursos, por lo que la competencia entre los individuos de un bosque debe ser considerada en el desarrollo del modelo de crecimiento. Esto se logra haciendo uso de parámetros basados en las dimensiones de los árboles; como son los índices de competencia independientes de la distancia. En esta investigación se muestran los resultados obtenidos de la utilización de una. de una red neuronal backpropagation, entrenada con índices de competencias independientes de la distancia en la predicción del crecimiento en diámetro y altura de un bosque de la especie de pino Pinus Cooperi Blanco, árbol poco estudiado, sin embargo de una gran importancia en su explotación por su aprovechamiento en la producción de madera y uso en la producción de hojas de triplay.

Palabras clave: Pinus Cooperí Blanco, red neuronal backpropagation, índices de competencia independientes de la distancia.

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