Predicción de variables dasométricas del Inventario Forestal Nacional mediante datos LIDAR con técnicas de minera de datos
Fecha
2018Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Trabajo Fin de Máster/Master Amaierako Lana
Impacto
|
nodoi-noplumx
|
Resumen
The management of forest resources is essential for the
development of our society. This requires a forest management
planning based on innovative
studies, according to new
technologies and seeking to lower costs.
In this project, a
methodology has been developed for the extraction of predictive
regression models to determine the ...
[++]
The management of forest resources is essential for the
development of our society. This requires a forest management
planning based on innovative
studies, according to new
technologies and seeking to lower costs.
In this project, a
methodology has been developed for the extraction of predictive
regression models to determine the main dasometric variables of
the beech forest layer with over 70% of the
forest cover density
in
Navarre. For this purpose, data mining techniques and Python as
programming language have been used. The inputs of the work
are: data from the plots of the national forest inventory
(dependent variables) and statistics derived from the LIDAR-PNOA
flight for these same plots (independent variables) obtained with
the LasTools software. The output is the model that best fits the
input data, determined by the methodology used. [--]
Materias
LIDAR,
IFN,
Python,
Data mining,
Fagus sylvatica
Titulación
Máster Universitario en Sistemas de Información Geográfica y Teledetección por la Universidad Pública de Navarra /
Informazio Geografikoko Sistemetako eta Teledetekzioko Unibertsitate Masterra Nafarroako Unibertsitate Publikoan