Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

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Variation in soil nutrient concentrations and bulk density within peatland forest sites

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State Forest Nursery 2013-2014 Seedling Catalog, 2013

The State Forest Nursery welcomes the opportunity to help you with your tree planting needs. Our goal is to provide low cost, native seedlings in order to help make your tree planting successful and affordable. We strive to produce the best stock in the industry, and our staff will do everything they can to help you achieve your planting goals. We want your tree planting to be successful, so please let us know how we can help! You can contact us www.iowatreeplanting.com By planting trees today you will leave a legacy for your children and grandchildren, as well as a legacy for your home state, its people and its habitat. Let us help you leave your mark on the state you love- your children and grandchildren will thank you!

Nature conservation efforts by forest owners : intentions and practice in a Swedish case study

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Heavy-metal pollution and remediation of forest soil around the Harjavalta Cu-Ni smelter, in SW Finland

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Variation in forest fire ignition probability in Finland

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A comparison of one- and two-compartment neighbourhoods in heuristic search with spatial forest management goals

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Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Export of dissolved organic carbon, nitrogen and phosphorus following clear-cutting of three Norway spruce forest growing on drained peatlands in southern Finland

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Objectives and motivations of small-scale forest owners : theoretical modelling and qualitative assessment

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Long-term impacts of forest harvesting related soil disturbance on log product yields and economic potential in a New Zealand forest

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