Next Article in Journal
Historic Frequency and Severity of Fire in Whitebark Pine Forests of the Cascade Mountain Range, USA
Previous Article in Journal
Comparing the Quantity and Structure of Deadwood in Selection Managed and Old-Growth Forests in South-East Europe
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Forests 2018, 9(2), 77; https://doi.org/10.3390/f9020077

Designing Wood Supply Scenarios from Forest Inventories with Stratified Predictions

1
Forest Research Institute Baden-Württemberg (FVA), 79110 Freiburg, Germany
2
Faculty of Environment and Natural Resources, University of Freiburg, 79110 Freiburg, Germany
*
Author to whom correspondence should be addressed.
Received: 11 January 2018 / Revised: 31 January 2018 / Accepted: 2 February 2018 / Published: 6 February 2018
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
Full-Text   |   PDF [5747 KB, uploaded 6 February 2018]   |  

Abstract

Forest growth and wood supply projections are increasingly used to estimate the future availability of woody biomass and the correlated effects on forests and climate. This research parameterizes an inventory-based business-as-usual wood supply scenario, with a focus on southwest Germany and the period 2002–2012 with a stratified prediction. First, the Classification and Regression Trees algorithm groups the inventory plots into strata with corresponding harvest probabilities. Second, Random Forest algorithms generate individual harvest probabilities for the plots of each stratum. Third, the plots with the highest individual probabilities are selected as harvested until the harvest probability of the stratum is fulfilled. Fourth, the harvested volume of these plots is predicted with a linear regression model trained on harvested plots only. To illustrate the pros and cons of this method, it is compared to a direct harvested volume prediction with linear regression, and a combination of logistic regression and linear regression. Direct harvested volume regression predicts comparable volume figures, but generates these volumes in a way that differs from business-as-usual. The logistic model achieves higher overall classification accuracies, but results in underestimations or overestimations of harvest shares for several subsets of the data. The stratified prediction method balances this shortcoming, and can be of general use for forest growth and timber supply projections from large-scale forest inventories. View Full-Text
Keywords: aggregated wood supply; national forest inventory; business-as-usual; stratified prediction; Classification and Regression Trees (CART); Random Forest (RF) aggregated wood supply; national forest inventory; business-as-usual; stratified prediction; Classification and Regression Trees (CART); Random Forest (RF)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Kilham, P.; Kändler, G.; Hartebrodt, C.; Stelzer, A.-S.; Schraml, U. Designing Wood Supply Scenarios from Forest Inventories with Stratified Predictions. Forests 2018, 9, 77.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top