Recent applications of point process methods in forestry statistics

Forestry statistics is an important field of applied statistics with a long tradition. Many forestry problems can be solved by means of point processes or marked point processes. There, the "points" are tree locations and the "marks" are tree characteristics such as diameter at breast height or degree of damage by environmental factors. Point pro- cess characteristics are valuable tools for exploratory data analysis in forestry, for describing the variability of forest stands and for under- standing and quantifying ecological relationships. Models of point pro- cesses are also an important basis of modern single-tree modeling, that gives simulation tools for the investigation of forest structures and for the prediction of results of forestry operations such as plantation and thinning.

[1]  J. Oliver Estimates of Distance. , 1892, Science.

[2]  E. Odum Fundamentals of ecology , 1972 .

[3]  P. J. Clark,et al.  Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations , 1954 .

[4]  R. M. Newnham The development of a stand model for Douglas fir , 1964 .

[5]  D. J. Strauss A model for clustering , 1975 .

[6]  F. Kelly,et al.  A note on Strauss's model for clustering , 1976 .

[7]  B. Ripley The Second-Order Analysis of Stationary Point Processes , 1976 .

[8]  Spatial distribution development in young tree stands in Lapland. , 1980 .

[9]  D. Stoyan,et al.  On the Second‐Order and Orientation Analysis of Planar Stationary Point Processes , 1981 .

[10]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[11]  Peter J. Diggle,et al.  Bivariate Cox Processes: Some Models for Bivariate Spatial Point Patterns , 1983 .

[12]  Peter J. Diggle,et al.  Statistical analysis of spatial point patterns , 1983 .

[13]  Y. Ogata,et al.  Likelihood Analysis of Spatial Point Patterns , 1984 .

[14]  Y. Ogata,et al.  Estimation of Interaction Potentials of Marked Spatial Point Patterns Through the Maximum Likelihood Method , 1985 .

[15]  Erkki Tomppo,et al.  Models and methods for analysing spatial patterns of trees. , 1986 .

[16]  Jan Lepš,et al.  Models of the development of spatial pattern of an even-aged plant population over time , 1987 .

[17]  D. Tilman Plant Strategies and the Dynamics and Structure of Plant Communities. (MPB-26), Volume 26 , 1988 .

[18]  B. Ripley Statistical inference for spatial processes , 1990 .

[19]  A random field approach to Bitterlich sampling , 1988 .

[20]  R. Gardner,et al.  Quantitative Methods in Landscape Ecology , 1991 .

[21]  P J Diggle,et al.  Second-order analysis of spatial clustering for inhomogeneous populations. , 1991, Biometrics.

[22]  Dietrich Stoyan,et al.  Marked Point Processes in Forest Statistics , 1992, Forest Science.

[23]  Matthias Dobbertin,et al.  A Comparison of Distance-Dependent Competition Measures for Height and Basal Area Growth of Individual Conifer Trees , 1992, Forest Science.

[24]  A. Baddeley,et al.  A non-parametric measure of spatial interaction in point patterns , 1996, Advances in Applied Probability.

[25]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[26]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[27]  Timothy G. Gregoire,et al.  Sampling Methods for Multiresource Forest Inventory , 1993 .

[28]  B. Hambly Fractals, random shapes, and point fields , 1994 .

[29]  P. Diggle,et al.  On parameter estimation for pairwise interaction point processes , 1994 .

[30]  Noel A Cressie,et al.  A space-time survival point process for a longleaf pine forest in Southern Georgia , 1994 .

[31]  Stephen V. Stehman,et al.  The Horvitz-Thompson Theorem as a Unifying Perspective for Probability Sampling: With Examples from Natural Resource Sampling , 1995 .

[32]  Temporal Analogues to Spatial K Functions , 1995 .

[33]  Jianguo Liu,et al.  Individual-based simulation models for forest succession and management , 1995 .

[34]  Gregory S. Biging,et al.  Evaluation of Competition Indices in Individual Tree Growth Models , 1995, Forest Science.

[35]  Dietrich Stoyan,et al.  Estimating Pair Correlation Functions of Planar Cluster Processes , 1996 .

[36]  Stephen L. Rathbun,et al.  Estimation of Poisson Intensity Using Partially Observed Concomitant Variables , 1996 .

[37]  Aila Särkkä,et al.  Parameter Estimation for Marked Gibbs Point Processes Through the Maximum Pseudo-likelihood Method , 1996 .

[38]  S. Pacala,et al.  Forest models defined by field measurements : Estimation, error analysis and dynamics , 1996 .

[39]  Dietrich Stoyan,et al.  On Variograms in Point Process Statistics , 1996 .

[40]  Antti Penttinen,et al.  Statistical opportunities for comparing stand structural heterogeneity in managed and primeval forests: An example from boreal spruce forest in southern Finland , 1996 .

[41]  M. Rudemo,et al.  Stem number estimation by kernel smoothing of aerial photos , 1996 .

[42]  Hans Pretzsch,et al.  Analysis and modeling of spatial stand structures. Methodological considerations based on mixed beech-larch stands in Lower Saxony , 1997 .

[43]  R. Gill,et al.  Kaplan-Meier estimators of distance distributions for spatial point processes , 1997 .

[44]  Rasmus Waagepetersen,et al.  Log Gaussian Cox processes: A statistical model for analyzing stand structural heterogeneity in forestry , 1997 .

[45]  J. Møller,et al.  Log Gaussian Cox Processes , 1998 .

[46]  R. Wolpert,et al.  Poisson/gamma random field models for spatial statistics , 1998 .

[47]  J. Heikkinen,et al.  Non‐parametric Bayesian Estimation of a Spatial Poisson Intensity , 1998 .

[48]  Aila Särkkä,et al.  Modelling interactions between trees by means of field observations , 1998 .

[49]  D. Stoyan,et al.  Non-Homogeneous Gibbs Process Models for Forestry — A Case Study , 1998 .

[50]  Juha Heikkinen,et al.  Bayesian smoothing in the estimation of the pair potential function of Gibbs point processes , 1999 .

[51]  J. Heikkinen,et al.  Modeling a Poisson forest in variable elevations: a nonparametric Bayesian approach. , 1999, Biometrics.

[52]  Dietrich Stoyan,et al.  On Variograms in Point Process Statistics, II: Models of Markings and Ecological Interpretation , 2000 .

[53]  Eva B. Vedel Jensen,et al.  Inhomogeneous Markov point processes by transformation , 2000 .

[54]  Martin Schlather,et al.  On the second-order characteristics of marked point processes , 2001 .

[55]  A. Baddeley Spatial sampling and censoring , 2019, Stochastic Geometry.