Sentence examples for stands to predict from inspiring English sources

The system operates as a standalone application and allows the end users to apply various fire environment (meteorology, topography, fuel) scenarios in order to estimate the characteristics of fuel complexes in Mediterranean pine stands, to predict the expected fire behavior, as well as to implement and evaluate fuel treatments for reducing the intensity and severity of fires in such ecosystems.

The information about single trees, stands and sites to predict windthrow probability is crucial to many different statistical model approaches such as the binary additive regression model used in this study, a discriminant model by Valinger et al. (1993), a logistic regression model by Kellomäki and Peltola (1998), and a neural network approach by Hanewinkel et al. (2004).

We used stand inventory data to predict forest structure and fuel loads starting after needle fall through one century after bark beetle infestation for three lodgepole pine-dominated forest types (pine, pine with aspen, pine with fir and spruce), and compared simulated effects of no-action and fuel reduction treatments (thinning, broadcast burning).

Because of inherent limitations, modelling approaches such as transition matrices and stand-table projections are no longer recommended to predict stand development in species-rich forests, except where the stand data are available only in summarized form (Vanclay 1994, p.55).

Further research should be undertaken to investigate how well LiDAR data can be used to predict stand characteristics in older thinned stands of Douglas-fir.

The complex dynamics of young stands makes them difficult to predict.

They do not stand accused of failing to predict the earthquake; everyone agrees that would have been impossible.

However, there are limitations to using remotely sensed data to predict stand dimensions across the entire rotation.

A different approach to that taken in this study would be appropriate if one wished to predict stand value and/or create links to sawing simulators.

The objective of the paper is to develop an analytical solution to predict stand-up time of the rock mass surrounding tunnel based on the rheological deformation model using Abel's creep kernel and allowable displacement of the tunnel wall.

We used Principal Component Analysis (PCA) to create integrated explanatory factors of hydrology, soil nutrient status and soil texture for use in linear mixed models to predict stand characteristics.