Height growth patterns of Scots pine and Norway spruce in the coastal areas of western Finland

Karlsson, Kristian

doi:10.1016/s0378-1127(00)00311-x

Cited by 18 publications

(11 citation statements)

References 10 publications

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“…Chapman-Richards, Sloboda (Cieszewski and Strub, 2008). Dominant height growth for various species in Scandinavia has been modeled mainly based on the Chapman-Richards and Hossfeld functions (Hägglund, 1973(Hägglund, , 1974Johansson, 1996;Elfving and Kiviste, 1997;Karlsson, 2000). Current dominant height growth models for Norway spruce and Scots pine in Norway (Tveite, 1977) are based on a fractional function (Strand, 1964).…”

Section: Modelsmentioning

confidence: 99%

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Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors

Sharma

Brunner

Eid

et al. 2011

Forest Ecology and Management

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Section: Modelsmentioning

confidence: 99%

“…Fries, 1969;Hägglund, 1972Hägglund, , 1973Hägglund, , 1974Johansson, 1996;Elfving and Kiviste, 1997) and Finland (e.g. Gustavsen, 1980;Vuokila and Väliaho, 1980;Karlsson, 2000). Data sources were mostly stem analysis and permanent sample plots.…”

Section: Introductionmentioning

confidence: 99%

Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors

Sharma

Brunner

Eid

et al. 2011

Forest Ecology and Management

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“…The stone content is particularly important when planning forest regeneration processes for an area, since a high stone content can cause difficulties for both the soil preparation and the planting (Saksa et al 2018;Rantala et al 2010;Saarinen 2006;Lideskog et al 2014). Knowing the soil stoniness may also help when tree growth, weathering and hydrological models are developed (Eriksson and Holmgren 1996;Karlsson 2000;Coppola et al 2013;Panagos et al 2014). In the context of forest regeneration, the most relevant measure is the stoniness of the soil at depths of 20-30 centimeters.…”

Section: Introductionmentioning

confidence: 99%

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Melander¹,

Ritala²,

Strandström³

2019

Silva Fenn.

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Highlights• An excavator was equipped with an inertial measurement unit for taking automatic measurements of soil stoniness during mounding work. • Supervised machine-learning classifiers were trained utilizing both the automatically measured data and manual stoniness measurements. • The class prediction for the soil stoniness achieved an accuracy of 70% when assigned to constant grid cells. AbstractThe stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20-30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil's stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

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“…Wind creates a mechanical stress on tree stems shifting carbon allocation to stem and roots to enhance stability (Zianis and Mencuccini 2005). Lundqvist and Valinger (1996) found that Scots pine trees are capable of retaining information about mechanical stress on their stems during winter, responding during the following growing season by altering allocation patterns accordingly, increasing diameter but restricting height growth (Karlsson 2000). Moreover, diameter growth was found to be higher when stem number was high, which is a clear demonstration of young stands planted in close spacing having higher growth rates because the availability of nutritional resource over-exceeds the competition stress.…”

Section: Scots Pine Growth Vs Site and Climate Factorsmentioning

confidence: 99%

Effects of climate and site characteristics on Scots pine growth

2011

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Scots pine is a highly diverse species, extended across Europe from Scandinavia to Spain, Italy, Greece and Turkey. It is also a valuable species, used in many commercial monoculture plantations in Great Britain and particularly in Scotland. Because of the diversity of growing environments and its commercial importance, it is necessary to identify the combination of significant factors affecting the observed variability of growth. Temperature, mainly during the growing season, is quite commonly considered as the most important factor in knowledgebased or empirical models. However, in highly oceanic climates like that of Scotland, the impact of temperature may have a less significant impact on growth. Here we argue that other factors, such as incoming winter solar radiation, frost, drought and management also have a significant effect on the growth of Scots pine. In addition, we argue that the already developed Ecological Site Classification knowledge-based model, used as a forest management tool in Great Britain, should be updated to incorporate our findings. Furthermore, we discuss the need to include management impact and possibly more physiological based components in its growth modelling routines, as these would allow the introduction of the effect of winter solar radiation.

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Height growth patterns of Scots pine and Norway spruce in the coastal areas of western Finland

Cited by 18 publications

References 10 publications

Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors

Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Effects of climate and site characteristics on Scots pine growth

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