An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios

Dynamic landscape simulation of the forest requires an initial regeneration stock specific to the characteristics of each simulated stand. Forest inventories, however, are sparse with regard to regeneration. Moreover, statistical regeneration models are rare. We introduce an inventory-based statisti...

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Main Authors: Werner Poschenrieder, Peter Biber, Hans Pretzsch
Format: Article
Language:English
Published: MDPI AG 2018-04-01
Series:Forests
Subjects:
Online Access:http://www.mdpi.com/1999-4907/9/4/212
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spelling doaj-69deba1ac89540698756daa5f23c0db92020-11-24T23:01:12ZengMDPI AGForests1999-49072018-04-019421210.3390/f9040212f9040212An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation ScenariosWerner Poschenrieder0Peter Biber1Hans Pretzsch2Chair of Forest Growth and Yield Science, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, GermanyChair of Forest Growth and Yield Science, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, GermanyChair of Forest Growth and Yield Science, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, GermanyDynamic landscape simulation of the forest requires an initial regeneration stock specific to the characteristics of each simulated stand. Forest inventories, however, are sparse with regard to regeneration. Moreover, statistical regeneration models are rare. We introduce an inventory-based statistical model type that (1) quantifies regeneration biomass as a fundamental regeneration attribute and (2) uses the overstory’s quadratic mean diameter (Dq) together with several other structure attributes and the Site Index as predictors. We form two such models from plots dominated by European beech (Fagus sylvatica L.), one from national forest inventory data and the other from spatially denser federal state forest inventory data. We evaluate the first one for capturing the predictors specific to the larger scale level and the latter one to infer the degree of landscape discretization above which the model bias becomes critical due to yet unquantified determinants of regeneration. The most relevant predictors were Dq, stand density, and maximum height (significance level p < 0.0001). If plot data sets for evaluation differed by the forest management unit in addition to the average diameter, the bias range among them increased from 0.1-fold of predicted biomass to 0.3-fold.http://www.mdpi.com/1999-4907/9/4/212regeneration modellinglandscape scale modellingstand structureinventory dataFagus sylvatica
collection DOAJ
language English
format Article
sources DOAJ
author Werner Poschenrieder
Peter Biber
Hans Pretzsch
spellingShingle Werner Poschenrieder
Peter Biber
Hans Pretzsch
An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios
Forests
regeneration modelling
landscape scale modelling
stand structure
inventory data
Fagus sylvatica
author_facet Werner Poschenrieder
Peter Biber
Hans Pretzsch
author_sort Werner Poschenrieder
title An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios
title_short An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios
title_full An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios
title_fullStr An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios
title_full_unstemmed An Inventory-Based Regeneration Biomass Model to Initialize Landscape Scale Simulation Scenarios
title_sort inventory-based regeneration biomass model to initialize landscape scale simulation scenarios
publisher MDPI AG
series Forests
issn 1999-4907
publishDate 2018-04-01
description Dynamic landscape simulation of the forest requires an initial regeneration stock specific to the characteristics of each simulated stand. Forest inventories, however, are sparse with regard to regeneration. Moreover, statistical regeneration models are rare. We introduce an inventory-based statistical model type that (1) quantifies regeneration biomass as a fundamental regeneration attribute and (2) uses the overstory’s quadratic mean diameter (Dq) together with several other structure attributes and the Site Index as predictors. We form two such models from plots dominated by European beech (Fagus sylvatica L.), one from national forest inventory data and the other from spatially denser federal state forest inventory data. We evaluate the first one for capturing the predictors specific to the larger scale level and the latter one to infer the degree of landscape discretization above which the model bias becomes critical due to yet unquantified determinants of regeneration. The most relevant predictors were Dq, stand density, and maximum height (significance level p < 0.0001). If plot data sets for evaluation differed by the forest management unit in addition to the average diameter, the bias range among them increased from 0.1-fold of predicted biomass to 0.3-fold.
topic regeneration modelling
landscape scale modelling
stand structure
inventory data
Fagus sylvatica
url http://www.mdpi.com/1999-4907/9/4/212
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