Random forests (RF) is a popular general purpose classifier that has been shown to outperform many other classifiers on a variety of datasets. The widespread use of random forests can be attributed to several factors, some of which include its excellent empirical performance, scale and unit invariance, robustness to outliers, time and space complexity, and interpretability. While RF has many de...

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophy...

The intermediate disturbance hypothesis (IDH) predicts local species diversity to be maximal at an intermediate level of disturbance. Developed to explain species maintenance and diversity patterns in species-rich ecosystems such as tropical forests, tests of IDH in tropical forest remain scarce, small-scale and contentious. We use an unprecedented large-scale dataset (2504 one-hectare plots an...

Maximum height was assessed for tree species from seven temperate deciduous forests, one subtropical forest and one tropical forest and combined with published tree heights for three other tropical forests. The temperate deciduous forests showed a strong concentration of canopy species and a dearth of subcanopy species. In contrast, the four tropical forests showed more uniform distributions of...

Random forests have emerged as one of the most commonly used nonparametric statistical methods in many scientific areas, particularly in analysis of high throughput genomic data. A general practice in using random forests is to generate a sufficiently large number of trees, although it is subjective as to how large is sufficient. Furthermore, random forests are viewed as "black-box" because of ...

This study explores the importance of different motivations to visit three types of recreational settings--farms, private forests, and state or national parks. Data were collected via a mail-back questionnaire administered to a stratified random sample of households in Missouri (USA). Descriptive and inferential statistics reveal both similarities and discontinuities in motivations for visiting...

Changing and competing land use, where we make use of a growing share of resources, potentially undermines the capacity of forests to provide multiple functions such as timber, biodiversity, recreation and pasture lands. The governance challenge is thus to manage trade-offs between human needs and, at the same time, maintain the capacities of forests to provide us with these needs. Sweden provi...

Random forests are known to be robust for missing and erroneous data as well as irrelevant features. Moreover, even though the forests have many trees, they can utilize the fast building property of decision trees, so they do not require much computing time. In this paper an efficient procedure that utilizes random forests to predict the cylinder bands in rotogravure printing is shown. Even tho...

This week’s exercise explores the fundamentals of sampling theory using virtual forests. Two Pennsylvania old-growth forests were digitized enabling you to employ haphazard, systematic, and random sampling methods while investigating sampling protocols based on sampling areas versus distances. As you sample these virtual forests, you will discover that the efficiencies of sampling methods vary ...