Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models

[1]  Terje Gobakken,et al.  Reliability of LiDAR derived predictors of forest inventory attributes: A case study with Norway spruce , 2010 .

[2]  Martin Schroeder,et al.  Climate drivers of bark beetle outbreak dynamics in Norway spruce forests , 2017 .

[3]  Hailemariam Temesgen,et al.  Comparison of Nearest Neighbor Methods for Estimating Basal Area and Stems per Hectare Using Aerial Auxiliary Variables , 2005, Forest Science.

[4]  R. Fay,et al.  Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data , 1979 .

[5]  A. Hudak,et al.  Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data , 2008 .

[6]  S. Magnussen An assessment of three variance estimators for the k-nearest neighbour technique , 2013 .

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  H. Wackernagle,et al.  Multivariate geostatistics: an introduction with applications , 1998 .

[9]  Chris Chatfield,et al.  Statistical Methods for Spatial Data Analysis , 2004 .

[10]  Erkki Tomppo,et al.  Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach , 2004 .

[11]  Andrew O. Finley,et al.  Introduction to Bayesian Methods in Ecology and Natural Resources , 2020 .

[12]  Haavard Rue,et al.  A unified view on Bayesian varying coefficient models , 2018, Electronic Journal of Statistics.

[13]  Sumio Watanabe,et al.  Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory , 2010, J. Mach. Learn. Res..

[14]  Albert R. Stage,et al.  Most Similar Neighbor: An Improved Sampling Inference Procedure for Natural Resource Planning , 1995, Forest Science.

[15]  Fredrik Lagergren,et al.  Storms can cause Europe‐wide reduction in forest carbon sink , 2009 .

[16]  R. Astrup,et al.  Small area estimation of forest attributes in the Norwegian National Forest Inventory , 2012, European Journal of Forest Research.

[17]  Hailemariam Temesgen,et al.  A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications , 2013, PloS one.

[18]  Aki Vehtari,et al.  Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.

[19]  David B. Dunson,et al.  Bayesian data analysis, third edition , 2013 .

[20]  A. Kobler,et al.  The effects of a large-scale ice storm event on the drivers of bark beetle outbreaks and associated management practices , 2018 .

[21]  J. Hyyppä,et al.  Estimation of stem volume using laser scanning-based canopy height metrics , 2006 .

[22]  Thomas Wohlgemuth,et al.  Increasing storm damage to forests in Switzerland from 1858 to 2007 , 2010 .

[23]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[24]  S. Magnussen,et al.  Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory , 2006 .

[25]  Andrew O. Finley,et al.  Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping. , 2018, Statistica Sinica.

[26]  H. Formayer,et al.  Slow and fast drivers of the natural disturbance regime in Central European forest ecosystems , 2013 .

[27]  Ross Nelson,et al.  Estimating forest biomass and volume using airborne laser data , 1988 .

[28]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[29]  E. Næsset,et al.  Estimating tree heights and number of stems in young forest stands using airborne laser scanner data , 2001 .

[30]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[31]  R. Valbuena,et al.  Remote sensing estimates and measures of uncertainty for forest variables at different aggregation levels , 2016 .

[32]  Christoph Gollob,et al.  Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots , 2019, Remote. Sens..

[33]  C. F. Sirmans,et al.  Spatial Modeling With Spatially Varying Coefficient Processes , 2003 .

[34]  L. Vogt Statistics For Spatial Data , 2016 .

[35]  A. Laaksonen,et al.  Increasing large scale windstorm damage in Western, Central and Northern European forests, 1951–2010 , 2017, Scientific Reports.

[36]  Friedrich Leisch,et al.  Automatic Mapping of Forest Stands Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning , 2017 .

[37]  K. Blennow,et al.  The probability of wind damage in forestry under a changed wind climate , 2008 .

[38]  J. Heikkinen,et al.  Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery , 2007 .

[39]  Andrew O. Finley,et al.  Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables , 2016, 1603.07409.

[40]  Montserrat Fuentes,et al.  A New Class of Nonstationary Spatial Models , 2001 .

[41]  E. Næsset Estimating timber volume of forest stands using airborne laser scanner data , 1997 .

[42]  A. Finley,et al.  Hierarchical Bayesian models for small area estimation of forest variables using LiDAR , 2018 .

[43]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .

[44]  F. Freese,et al.  Elementary forest sampling. , 1962 .

[45]  A. Finley,et al.  Hierarchical Bayesian models for small area estimation of county-level private forest landowner population , 2017 .

[46]  Andrew O. Finley,et al.  Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes , 2017, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[47]  P. Guttorp,et al.  Nonparametric Estimation of Nonstationary Spatial Covariance Structure , 1992 .

[48]  Terje Gobakken,et al.  Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations , 2017, Remote Sensing of Environment.

[49]  Gherardo Chirici,et al.  Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data , 2011 .

[50]  M. Maltamo,et al.  The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs , 2007 .

[51]  Roger Woodard,et al.  Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.

[52]  Mikis D. Stasinopoulos,et al.  GAMLSS: A distributional regression approach , 2018 .

[53]  D. Mandallaz,et al.  National forest inventories in the service of small area estimation of stem volume , 2014 .

[54]  Nikolaus Umlauf,et al.  A primer on Bayesian distributional regression , 2018 .

[55]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[56]  Aki Vehtari,et al.  Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC , 2015, Statistics and Computing.

[57]  E. Næsset Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[58]  Andrew K. Finley Data and code for estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models , 2021 .

[59]  M. Maltamo,et al.  Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data , 2006 .

[60]  S. Magnussen,et al.  Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators , 1998 .

[61]  Andrew O. Finley,et al.  Bayesian spatially varying coefficient models in the spBayes R package , 2019, Environ. Model. Softw..

[62]  Gert-Jan Nabuurs,et al.  Natural disturbances in the European forests in the 19th and 20th centuries , 2003 .

[63]  Christoph Gollob,et al.  Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology , 2020, Remote. Sens..

[64]  Andrew O. Finley,et al.  Comparing spatially‐varying coefficients models for analysis of ecological data with non‐stationary and anisotropic residual dependence , 2011 .

[65]  Christoph Gollob,et al.  Towards an Optimization of Sample Plot Size and Scanner Position Layout for Terrestrial Laser Scanning in Multi-Scan Mode , 2020, Forests.

[66]  K. Blennow,et al.  Potential climate change impacts on the probability of wind damage in a south Swedish forest , 2010 .

[67]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .