A neural network approach to identify forest stands susceptible to wind damage
- 26 July 2004
- journal article
- Published by Elsevier BV in Forest Ecology and Management
- Vol. 196 (2-3), 227-243
- https://doi.org/10.1016/j.foreco.2004.02.056
Abstract
No abstract availableKeywords
This publication has 20 references indexed in Scilit:
- Empirical modeling of cutblock edge windthrow risk on Vancouver Island, Canada, using stand level informationForest Ecology and Management, 2001
- Using airflow modelling and spatial analysis for defining wind damage risk classification (WINDARC)Forest Ecology and Management, 2000
- A demonstrator of models for assessing wind, snow and fire damage to forests using the WWWForest Ecology and Management, 2000
- Logistic regression models for wind and snow damage in northern Finland based on the National Forest Inventory dataForest Ecology and Management, 2000
- Management of forests to reduce the risk of abiotic damage — a review with particular reference to the effects of strong windsForest Ecology and Management, 2000
- Integration of component models from the tree, stand and regional levels to assess the risk of wind damage at forest marginsForest Ecology and Management, 2000
- Modelling probability of snow and wind damage using tree, stand, and site characteristics fromPinus sylvestrissample plotsScandinavian Journal of Forest Research, 1998
- Modelling probability of snow and wind damage in Scots pine stands using tree characteristicsForest Ecology and Management, 1997
- A framework for uncertainty assessment of mechanistic forest growth models: a neural network exampleEcological Modelling, 1997
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978