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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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 |
work_keys_str_mv |
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1725640366448705536 |