Forestry: Relevant Concepts and Terminology

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Forestry: Relevant Concepts and Terminology

Previously, the definition of natural forest was based on forest naturalness. Forest naturalness is comprised of tree species composition, animal species composition and natural processes in the region. Today, the definition of Swedish natural forest is set by the Nordic Council in collaboration with the Nordic countries and each country’s forest agency or agencies. The definition as stated in de Wit et al. (2013) reads: “In Finland, Norway, and Sweden natural forests are generally considered as forest developed through natural regeneration on untouched forest land or on old, tree covered natural grazing land…” (p. 25)
There is a distinction between natural forest and old grown forest, but a set definition on old grown forest has not been developed. As phrased in de Wit et al. (2013): “The concept of old-growth forest has been much used, especially about old, natural forests in North-America. Still, a common and agreed definition does not seem to have been developed…” (p. 26) de Wit et al. (2013) later states:
However, a pragmatic definition will include the following characteristics: Presence of relatively old trees, that is – large, old, late-successional tree species with ages close to their life-expectancy and a mean age that is half of the age of long- lived, dominating trees (their longevity); structural and compositional features that witness self-replacement through gap-phase dynamics (Wirth et al. 2009). A compact definition of old-growth forests could thus be old, natural forests, i.e., forests significantly older than the normal harvestable age and with structural features characteristic of natural ecological processes and disturbances, with less concern about past human influences that currently have a marginal effect of forest ecosystem structure and function… (p. 26)
A single, precise definition applicable to all forest types may not be possible. In this article, we refer to both old grown forest and natural forest as natural forest. We also refer to any forest other than natural forest as managed forest. A brief history of natural forests, managed forests, and further context can be read in appendix B.

READ PHYSICAL AND CHEMICAL CHARACTERISTICS OF SPING WATERS

Fundamental Concepts of Machine Learning and Decision Trees

Machine learning is, according to Flach (2012): “…the systematic study of algorithms and systems that improve their knowledge or performance with experience.” (p. 3). The key here is improving with experience, like a human learning something new.
Central to machine learning are the concepts of features and tasks and their relation to data, models and learning problems. Features are ways to describe relevant characteristics of objects in a simple and understandable manner. These objects are often our raw, gathered data which may be related to a specific domain. An extracted feature should be understandable without having to go back to the domain object to understand it. A task is a simplified version of a problem to solve. A common example of a task is classification of objects. To solve a task there is often the need of mapping from input data to output data using a model. This can be achieved through solving a learning problem. A learning problem is essentially a description of something we want to achieve and is solved by a learning algorithm. The solution, or output, from the learning problem, produced by a learning algorithm, is the model. (Flach, 2012)
Supervised learning is a subcategory of machine learning or statistical learning where the goal is to produce a concise model that can predict future instances from previously supplied instances (Kotsiantis, 2007). For classification this means that the resulting model can be used to label instances where feature values are known but the instance is unlabeled.

1 Introduction
1.1 Problem descriptio
1.2 Aim and scope .
1.3 Related work .
1.4 Outline
2 Background
2.1 Forestry: Relevant Concepts and Terminology
2.2 Fundamental Concepts of Machine Learning and Decision Trees .
3 Method and implementation
3.1 Labeled data .
3.2 LiDAR data and GIS rasters
3.3 Features for experiment .
3.4 Technology and platforms
3.5 Work process
3.6 Hypotheses .
3.7 Data analysis
4 Results
4.1 Experiment 1
4.2 Experiment 2
5 Analysis
5.1 Experiment 1
5.2 Experiment 2
6 Discussion
6.1 Experiment comparison .
6.2 Analysing results and comparison with previous work
6.3 Future improvements
6.4 Implications of the study
7 Conclusion
References