Machine Learning Forest Simulator (MLFS): R package for data-driven assessment of the future state of forests

[1]  A. Mäkelä,et al.  Accuracy, realism and general applicability of European forest models , 2022, Global change biology.

[2]  Kai Husmann,et al.  Quantifying the consequences of disturbances on wood revenues with Impulse Response Functions , 2022, Forest Policy and Economics.

[3]  M. Skudnik,et al.  Artificial neural networks as an alternative method to nonlinear mixed-effects models for tree height predictions , 2022, Forest Ecology and Management.

[4]  A. Lanz,et al.  Growing stock monitoring by European National Forest Inventories: Historical origins, current methods and harmonisation , 2022, Forest Ecology and Management.

[5]  C. Paul,et al.  Adaptation strategies for spruce forests—economic potential of bark beetle management and Douglas fir cultivation in future tree species portfolios , 2021, Forestry: An International Journal of Forest Research.

[6]  T. Pukkala,et al.  Self-learning growth simulator for modelling forest stand dynamics in changing conditions , 2021, Forestry: An International Journal of Forest Research.

[7]  A. Hudak,et al.  Tree mortality in western U.S. forests forecasted using forest inventory and Random Forest classification , 2021, Ecosphere.

[8]  M. Bayat,et al.  Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest , 2021, Annals of Forest Science.

[9]  Tharsanee Maganathan,et al.  Machine Learning and Data Analytics for Environmental Science: A Review, Prospects and Challenges , 2020, IOP Conference Series: Materials Science and Engineering.

[10]  E. Zenner,et al.  Future potentials of sustainable wood fuel from forests in Switzerland , 2020 .

[11]  İlker Ercanli,et al.  Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height , 2020, Forest Ecosystems.

[12]  E. Thürig,et al.  Modeling ingrowth for empirical forest prediction systems , 2019, Forest Ecology and Management.

[13]  Wei Li,et al.  On the estimation of tree mortality and liana infestation using a deep self-encoding network , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Rubén Manso,et al.  Simultaneous Prediction of Plot-Level and Tree-Level Harvest Occurrences with Correlated Random Effects , 2018 .

[15]  M. Čurović,et al.  Culture and Silviculture: Origins and Evolution of Silviculture in Southeast Europe , 2018, International Forestry Review.

[16]  L. Hülsmann,et al.  How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model. , 2018, Ecological applications : a publication of the Ecological Society of America.

[17]  H. Lischke,et al.  Predicting individual-tree growth of central European tree species as a function of site, stand, management, nutrient, and climate effects , 2017, European Journal of Forest Research.

[18]  Carsten F. Dormann,et al.  Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure , 2017 .

[19]  Miroslav Svoboda,et al.  Forest disturbances under climate change. , 2017, Nature climate change.

[20]  M. Lindner,et al.  Assessing Impacts of Wood Utilisation Scenarios for a Lithuanian Bioeconomy: Impacts on Carbon in Forests and Harvested Wood Products and on the Socio-Economic Performance of the Forest-Based Sector , 2017 .

[21]  F. Morneau,et al.  Effect of climate and intra- and inter-specific competition on diameter increment in beech and oak stands , 2015 .

[22]  F. Morneau,et al.  Incorporating stochasticity from extreme climatic events and multi-species competition relationships into single-tree mortality models , 2015 .

[23]  Hans Pretzsch,et al.  Representation of species mixing in forest growth models. A review and perspective , 2015 .

[24]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[25]  Lauri Mehtätalo,et al.  Modeling height-diameter curves for prediction , 2015 .

[26]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[27]  S. Searle,et al.  A reassessment of global bioenergy potential in 2050 , 2015 .

[28]  B. Muys,et al.  Ecosystem services of mixed species forest stands and monocultures: comparing practitioners' and scientists' perceptions with formal scientific knowledge , 2014 .

[29]  P. Leitão,et al.  Drivers of forest harvesting intensity patterns in Europe , 2014 .

[30]  S. Luyssaert,et al.  Carbon sequestration: Managing forests in uncertain times , 2014, Nature.

[31]  G. Slaughter,et al.  Prediction of mortality , 2012, 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[32]  José Manuel Benítez,et al.  On the use of cross-validation for time series predictor evaluation , 2012, Inf. Sci..

[33]  G. Kindermann The development of a simple basal area increment model , 2011 .

[34]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[35]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[36]  Ernst-Detlef Schulze,et al.  Forest management and carbon sequestration in wood products , 2009, European Journal of Forest Research.

[37]  F. Bravo,et al.  Modelling ingrowth in mediterranean pine forests: a case study from scots pine (Pinus sylvestris L.) and Mediterranean maritime pine (Pinus pinaster Ait.) stands in Spain , 2008 .

[38]  M. Schelhaas,et al.  Adding natural disturbances to a large-scale forest scenario model and a case study for Switzerland , 2002 .

[39]  Dieter Merkl,et al.  Estimating tree mortality of Norway spruce stands with neural networks , 2001 .

[40]  R. Monserud,et al.  Modeling individual tree mortality for Austrian forest species , 1999 .

[41]  Oscar García,et al.  The state-space approach in growth modelling , 1994 .

[42]  A. Ek,et al.  A Generalized Methodology for Estimating Forest Ingrowth at Multiple Threshold Diameters , 1993, Forest Science.

[43]  William R. Wykoff,et al.  A Basal Area Increment Model for Individual Conifers in the Northern Rocky Mountains , 1990, Forest Science.

[44]  B. Borders,et al.  Projecting Stand Tables: A Comparison of the Weibull Diameter Distribution Method, a Percentile-Based Projection Method, and a Basal Area Growth Projection Method , 1990, Forest Science.

[45]  Mitja Skudnik,et al.  A random forest model for basal area increment predictions from national forest inventory data , 2021 .

[46]  H. Bugmann,et al.  Accurate modeling of harvesting is key for projecting future forest dynamics: a case study in the Slovenian mountains , 2015, Regional Environmental Change.

[47]  V. Olaya,et al.  Chapter 6 Basic Land-Surface Parameters , 2009 .

[48]  H. Salminen,et al.  Evaluating estimation methods for logistic regression in modelling individual-tree mortality , 2003 .

[49]  R. Monserud,et al.  A basal area increment model for individual trees growing in even- and uneven-aged forest stands in Austria , 1996 .