4.5 Article

Rule-based vs parametric approaches for developing climate-sensitive site index models: a case study for Scots pine stands in northwestern Spain

Related references

Note: Only part of the references are listed.
Article Forestry

Use of advanced modelling methods to estimate radiata pine productivity indices

Michael S. Watt et al.

Summary: Site productivity indices have been widely used to describe age-normalized height and volume of forest species. In this study, a variety of modeling methods were used to predict Site Index and 300 Index for Pinus radiata D. Don, with non-parametric models like eXtreme Gradient Boosting and random forest outperforming parametric and geospatial models. The use of regression kriging improved prediction accuracy, especially for parametric models, and an ensemble model combining predictions from random forest, XGBoost, and regression model provided the most precise predictions for both Site Index and 300 Index.

FOREST ECOLOGY AND MANAGEMENT (2021)

Article Meteorology & Atmospheric Sciences

WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

Stephen E. Fick et al.

INTERNATIONAL JOURNAL OF CLIMATOLOGY (2017)

Article Forestry

Multi-sensor modelling of a forest productivity index for radiata pine plantations

Michael S. Watt et al.

NEW ZEALAND JOURNAL OF FORESTRY SCIENCE (2016)

Article Forestry

Predicting site index of plantation loblolly pine from biophysical variables

Charles O. Sabatia et al.

FOREST ECOLOGY AND MANAGEMENT (2014)

Article Forestry

Linking climate, gross primary productivity, and site index across forests of the western United States

Aaron R. Weiskittel et al.

CANADIAN JOURNAL OF FOREST RESEARCH (2011)

Article Forestry

Models for supporting forest management in a changing environment

Luis Fontes et al.

Forest Systems (2011)

Article Forestry

Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics

Nicholas L. Crookston et al.

FOREST ECOLOGY AND MANAGEMENT (2010)

Article Multidisciplinary Sciences

Climate change impacts on forestry

Andrei P. Kirilenko et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)

Article Forestry

Predicting lodgepole pine site index from climatic parameters in Alberta

Robert A. Monserud et al.

FORESTRY CHRONICLE (2006)

Article Forestry

Predicting site index with a physiologically based growth model across Oregon, USA

JJ Swenson et al.

CANADIAN JOURNAL OF FOREST RESEARCH (2005)

Article Forestry

Site quality equations for Pinus sylvestris L. plantations in Galicia (northwestern Spain)

U Diéguez-Aranda et al.

ANNALS OF FOREST SCIENCE (2005)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)