Deriving Forest Stand Information from Small Samples: An Evaluation of Statistical Methods

[1]  F. Krumm,et al.  Balancing disturbance risk and ecosystem service provisioning in Swiss mountain forests: an increasing challenge under climate change , 2023, Regional Environmental Change.

[2]  H. Bugmann,et al.  Tree species admixture increases ecosystem service provision in simulated spruce- and beech-dominated stands , 2022, European Journal of Forest Research.

[3]  M. Bouchard,et al.  Multi‐model projections of tree species performance in Quebec, Canada under future climate change , 2021, Global change biology.

[4]  Rafael Hologa,et al.  Tree Species Classification in a Temperate Mixed Mountain Forest Landscape Using Random Forest and Multiple Datasets , 2021, Remote. Sens..

[5]  E. Thürig,et al.  A Multi-Criteria Decision Support System for Strategic Planning at the Swiss Forest Enterprise Level: Coping With Climate Change and Shifting Demands in Ecosystem Service Provisioning , 2021, Frontiers in Forests and Global Change.

[6]  B. Courbaud,et al.  Evaluating five forest models using multi-decadal inventory data from mountain forests , 2021 .

[7]  Francesca Ieva,et al.  Generalized mixed‐effects random forest: A flexible approach to predict university student dropout , 2021, Stat. Anal. Data Min..

[8]  H. Bugmann,et al.  Stand‐scale climate change impacts on forests over large areas: transient responses and projection uncertainties , 2021, Ecological applications : a publication of the Ecological Society of America.

[9]  Charles O. Sabatia,et al.  Effects of Sample Plot Size and Prediction Models on Diameter Distribution Recovery , 2021, Forest Science.

[10]  Dirk R. Schmatz,et al.  How robust are future projections of forest landscape dynamics? Insights from a systematic comparison of four forest landscape models , 2020, Environmental Modelling & Software.

[11]  Thomas Janssen,et al.  Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification , 2020, Remote. Sens..

[12]  R. Astrup,et al.  Prediction and model-assisted estimation of diameter distributions using Norwegian national forest inventory and airborne laser scanning data , 2020, 2010.07107.

[13]  H. Bugmann,et al.  From small forest samples to generalised uni‐ and bimodal stand descriptions , 2021, Methods in Ecology and Evolution.

[14]  Xiaofei Wang,et al.  Characterizing Tree Spatial Distribution Patterns Using Discrete Aerial Lidar Data , 2020, Remote. Sens..

[15]  G. Jin,et al.  Evaluating individual-based tree mortality modeling with temporal observation data collected from a large forest plot , 2019, Forest Ecology and Management.

[16]  Yuling Chen,et al.  Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle , 2019, Forests.

[17]  A. Larson,et al.  Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern , 2019, Forest Ecology and Management.

[18]  G. Kändler,et al.  Generating Tree-Level Harvest Predictions from Forest Inventories with Random Forests , 2018, Forests.

[19]  R. Seidl,et al.  Legacies of past land use have a stronger effect on forest carbon exchange than future climate change in a temperate forest landscape , 2018, Biogeosciences.

[20]  P. Balandier,et al.  GIS Coop: networks of silvicultural trials for supporting forest management under changing environment , 2018, Annals of Forest Science.

[21]  Jennifer K. Costanza,et al.  Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches , 2018, Forest Ecosystems.

[22]  Anna Barbati,et al.  European Forest Types: toward an automated classification , 2018, Annals of Forest Science.

[23]  S. S. Luna,et al.  Fitting diameter distribution models to data from forest inventories with concentric plot design , 2017 .

[24]  Marco Mina,et al.  Future ecosystem services from European mountain forests under climate change , 2017 .

[25]  S. Magnussen,et al.  Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters , 2016, Annals of Forest Science.

[26]  Jean-Michel Poggi,et al.  VSURF: An R Package for Variable Selection Using Random Forests , 2015, R J..

[27]  Harald Bugmann,et al.  Models for adaptive forest management , 2015, Regional Environmental Change.

[28]  J. Batista,et al.  Modeling Tree Diameter Distributions in Natural Forests: An Evaluation of 10 Statistical Models , 2015 .

[29]  Francis A. Roesch,et al.  Modelling diameter distributions of two-cohort forest stands with various proportions of dominant species: a two-component mixture model approach. , 2014, Mathematical biosciences.

[30]  R. Grote,et al.  Extending a physiological forest growth model by an observation-based tree competition module improves spatial representation of diameter growth , 2013, European Journal of Forest Research.

[31]  Ché Elkin,et al.  A 2 °C warmer world is not safe for ecosystem services in the European Alps , 2013, Global change biology.

[32]  D. Coomes,et al.  Sustainable management, earthquake disturbances, and transient dynamics: modelling timber harvesting impacts in mixed-species forests , 2013, Annals of Forest Science.

[33]  H. Bugmann,et al.  Sensitivity of ecosystem goods and services projections of a forest landscape model to initialization data , 2013, Landscape Ecology.

[34]  H. Bugmann,et al.  Adaptive management for competing forest goods and services under climate change. , 2012, Ecological applications : a publication of the Ecological Society of America.

[35]  T. Spies,et al.  An Individual-Based Process Model to Simulate Landscape-Scale Forest Ecosystem Dynamics , 2012 .

[36]  A. Kangas,et al.  Decision support systems in forest management: requirements from a participatory planning perspective , 2012, European Journal of Forest Research.

[37]  F. Li,et al.  Study on Diameter Distribution of Natural Secondary Forest , 2011 .

[38]  J. D. Malley,et al.  Probability Machines , 2011, Methods of Information in Medicine.

[39]  Alexander Komarov,et al.  Modelling carbon and nitrogen dynamics in forest ecosystems of Central Russia under different climate change scenarios and forest management regimes , 2011 .

[40]  T. Pukkala,et al.  Combining a predicted diameter distribution with an estimate based on a small sample of diameters , 2011 .

[41]  Thomas Rötzer,et al.  Models for supporting forest management in a changing environment , 2011 .

[42]  Zhao Dehai,et al.  Problems of Scaling Plantation Plot Diameter Distributions to Stand Level. , 2008 .

[43]  Timo Pukkala,et al.  Comparison of beta, Johnson’s SB, Weibull and truncated Weibull functions for modeling the diameter distribution of forest stands in Catalonia (north-east of Spain) , 2007, European Journal of Forest Research.

[44]  Oscar García,et al.  Scale and spatial structure effects on tree size distributions : implications for growth and yield modelling , 2006 .

[45]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[46]  K. Gadow,et al.  Is the reverse J-shaped diameter distribution universally applicable in European virgin beech forests? , 2006 .

[47]  A. Zingg,et al.  Evaluation of the forest growth model SILVA along an elevational gradient in Switzerland , 2006, European Journal of Forest Research.

[48]  D. Faber-Langendoen,et al.  Diameter distributions and structural sustainability in forests , 2006 .

[49]  Keith Rennolls,et al.  Tree diameter distribution modelling: introducing the logitlogistic distribution , 2005 .

[50]  Quang V. Cao,et al.  Predicting Parameters of a Weibull Function for Modeling Diameter Distribution , 2004, Forest Science.

[51]  Jerry F. Franklin,et al.  Spatial Aspects of Structural Complexity in Old-Growth Forests , 2004, Journal of Forestry.

[52]  Christian Messier,et al.  Use of a spatially explicit individual-tree model (SORTIE/BC) to explore the implications of patchiness in structurally complex forests , 2003 .

[53]  D. Hedeker A mixed‐effects multinomial logistic regression model , 2003, Statistics in medicine.

[54]  Joshua B. Plotkin,et al.  SAMPLING THE SPECIES COMPOSITION OF A LANDSCAPE , 2002 .

[55]  Hans Pretzsch,et al.  The single tree-based stand simulator SILVA: construction, application and evaluation , 2002 .

[56]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[57]  J. Gove,et al.  A finite mixture of two Weibull distributions for modeling the diameter distributions of rotated-sigmoid, uneven-aged stands , 2001 .

[58]  H. Bugmann A Simplified Forest Model to Study Species Composition Along Climate Gradients , 1996 .

[59]  Jerzy Szwagrzyk,et al.  Spatial patterns of trees in natural forests of East‐Central Europe , 1993 .

[60]  D. W. Scott Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.

[61]  Thomas E. Burk,et al.  Goodness-of-Fit Tests and Model Selection Procedures for Diameter Distribution Models , 1988, Forest Science.

[62]  John W. Moser,et al.  A Generalized Framework for Projecting Forest Yield and Stand Structure Using Diameter Distributions , 1983 .

[63]  P. Marks,et al.  STAND STRUCTURE AND ALLOMETRY OF TREES DURING SELF-THINNING OF PURE STANDS , 1978 .

[64]  H. Schreuder,et al.  Statistical distributions for fitting diameter and height data in even-aged stands , 1977 .

[65]  T. R. Dell,et al.  Quantifying Diameter Distributions with the Weibull Function , 1973 .

[66]  E. Parzen On Estimation of a Probability Density Function and Mode , 1962 .

[67]  M. Rosenblatt Remarks on Some Nonparametric Estimates of a Density Function , 1956 .

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

[69]  Swiss National Forest Inventory – Methods and Models of the Fourth Assessment , 2019, Managing Forest Ecosystems.

[70]  M. Carrer,et al.  Tree spatial patterns and stand attributes in temperate forests: The importance of plot size, sampling design, and null model , 2018 .

[71]  William D. Dijak Landscape Builder: Software for the creation of initial landscapes for LANDIS from FIA data , 2013 .

[72]  L. Mehtätalo,et al.  Parameter recovery vs. parameter prediction for the Weibull distribution validated for Scots pine stands in Finland , 2013 .

[73]  Y. Wiersma,et al.  Predictive species and habitat modeling in landscape ecology : concepts and applications , 2011 .

[74]  A. B. Carey Biocomplexity and restoration of biodiversity in temperate coniferous forest: inducing spatial heterogeneity with variable‐density thinning , 2003 .

[75]  M. Maltamo,et al.  Comparison of beta and weibull functions for modelling basal area diameter distribution in stands of pinus sylvestris and picea abies , 1995 .

[76]  S. Magnussen Diameter distributions in Picea ables described by the Weibull model , 1986 .

[77]  B. Ripley Modelling Spatial Patterns , 1977 .