Matthias Schmidt
Forest Research Institute
19 Papers
60 Citations
Matthias Schmidt is an academic researcher from Forest Research Institute. The author has contributed to research in topics: Regression analysis & Forest management. The author has an hindex of 8, co-authored 19 publications.
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Papers
An inventory-based approach for modeling single- tree storm damage — experiences with the winter storm of 1999 in southwestern Germany
TL;DR: Based on individual tree damage data dating back to the gale "Lothar" (winter 1999) in Baden-Wurttemberg, Germany, a statistical model was developed to estimate the risk of storm damage for individ...
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Assessing risks and uncertainties in forest dynamics under different management scenarios and climate change
TL;DR: Differences in forest dynamics under three management scenarios are characterized, the effect of climate projections on height growth are quantified and uncertainty analysis reveals that height growth of young trees is dominated by the age-trend whereas the climate signal in height increment of older trees is decisive.
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The Silvicultural Decision Support System BWINPro
Jürgen Nagel,Matthias Schmidt +1 more
- 01 Jan 2006
TL;DR: In this paper, a tree growth model was incorporated into a computer software program (BWINPro) which allows for forest growth simulation and strategy development, and this program can be used for permanent plot inventory to calculate future growth, thinning and timber harvest at the enterprise level.
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Quantifying the effect of persistent dryer climates on forest productivity and implications for forest planning: a case study in northern Germany
TL;DR: In this paper, the authors apply a well-established single-tree forest growth simulator to quantify the effect of persistent dryer climates on future forest productivity, and find that Scots pine (Pinus sylvestris L.), European beech (Fagus sylvatica L.), and oak (Quercus robur L. and Quercus petraea (Matt.) Liebl.) in two forest regions in the north German lowlands for a time interval of 60 years until 2070.
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A spatially-explicit count data regression for modeling the density of forest cockchafer ( Melolontha hippocastani ) larvae in the Hessian Ried (Germany)
Matthias Schmidt,Rainer Hurling +1 more
TL;DR: A regression model for predicting the spatial distribution of forest cockchafer larvae in the Hessian Ried region (Germany) is presented, providing a near perfect fit to the data, and can be used to support forest practitioners in their decision making for regeneration and forest protection planning.
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