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Meaningful relationships between forest structure attributes measured in representative field plots on the ground and remotely sensed data measured comprehensively across the same forested landscape facilitate the production of maps of forest attributes such as basal area (BA) and tree density (TD). Because imputation methods can efficiently predict multiple response variables simultaneously, they may be usefully applied to map several structural attributes at the species-level. We compared several approaches for imputing the response variables BA and TD, aggregated at the plot-scale and species-level, from topographic and canopy structure predictor variables derived from discrete-return airborne LiDAR data. The predictor and response variables were associated using imputation techniques based on normalized and unnormalized Euclidean distance, Mahalanobis distance, Independent Component Analysis (ICA), Canonical Correlation Analysis (aka Most Similar Neighbor, or MSN), Canonical Correspondence Analysis (aka Gradient Nearest Neighbor, or GNN), and Random Forest (RF). To compare and evaluate these approaches, we computed a scaled Root Mean Square Distance (RMSD) between observed and imputed plot-level BA and TD for 11 conifer species sampled in north-central Idaho. We found that RF produced the best results overall, especially after reducing the number of response variables to the most important species in each plot with regard to BA and TD. We concluded that RF was the most robust and flexible among the imputation methods we tested. We also concluded that canopy structure and topographic metrics derived from LiDAR surveys can be very useful for species-level imputation. Published by Elsevier Inc.

Data points intensively sampling 46 North American biomes were used to predict the geographic distribution of biomes from climate variables using the Random Forests classification tree. Techniques were incorporated to accommodate a large number of classes and to predict the future occurrence of climates beyond the contemporary climatic range of the biomes. Errors of prediction from the statistical model averaged 3.7%, but for individual biomes, ranged from 0% to 21.5%. In validating the ability of the model to identify climates without analogs, 78% of 1528 locations outside North America and 81% of land area of the Caribbean Islands were predicted to have no analogs among the 46 biomes. Biome climates were projected into the future according to low and high greenhouse gas emission scenarios of three General Circulation Models for three periods, the decades surrounding 2030, 2060, and 2090. Prominent in the projections were (1) expansion of climates suitable for the tropical dry deciduous forests of Mexico, (2) expansion of climates typifying desertscrub biomes of western USA and northern Mexico, (3) stability of climates typifying the evergreen-deciduous forests of eastern USA, and (4) northward expansion of climates suited to temperate forests, Great Plains grasslands, and montane forests to the detriment of taiga and tundra climates. Maps indicating either poor agreement among projections or climates without contemporary analogs identify geographic areas where land management programs would be most equivocal. Concentrating efforts and resources where projections are more certain can assure land managers a greater likelihood of success.

This article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest attribute estimation and mapping. The impetus to writing the yaImpute is a growing interest in nearest neighbor imputation methods for spatially explicit forest inventory, and a need within this research community for software that facilitates comparison among different nearest neighbor search algorithms and subsequent imputation techniques. yaImpute provides directives for defining the search space, subsequent distance calculation, and imputation rules for a given number of nearest neighbors. Further, the package offers a suite of diagnostics for comparison among results generated from different imputation analyses and a set of functions for mapping imputation results.

The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS) simulates fuel dynamics and potential fire behaviour over time, in the context of stand development and management. Existing models of fire behavior and fire effects were added to FVS to form this extension. New submodels representing snag and fuel dynamics were created to complete the linkages.This report contains four chapters. Chapter 1 states the purpose and chronicles some applications of the model. Chapter 2 details the model's content, documents links to the supporting science, and provides annotated examples of the outputs. Chapter 3 is a user's guide that presents options and examples of command usage. Chapter 4 describes how the model was customized for use in different regions.Fuel managers and silviculturists charged with managing fire-prone forests can use the FFE-FVS and this document to better understand and display the consequences of alternative management actions. Keywords The use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Department of Agriculture of any product or serviceYou may order additional copies of this publication by sending your mailing information in label form through one of the following media. Please specify the publication title and number. Collectively, the authors of the papers published in this volume thank the following people. Albert R. Stage encouraged the project team members throughout the project, attended workshops, and reviewed manuscripts. Jim Brown and Stage co-led this work at the beginning, found resources, and mentored our work. To a large degree the lifetime of work from Brown and Stage is merged in this project. The team received extremely helpful reviews on the Model Description from Jane Kappler-Smith, Paul Langowski, and Al Stage. David Atkins, Jamie Barbour, and Paul Stancheff reviewed the Preface. The User's Guide was reviewed by Renee Lundberg and Glenn Christensen, and the FFE Variants chapter was reviewed by Stephanie Rebain, Elizabeth Reinhardt and Nick Crookston. Casey Teske evaluated the model behavior. Stephanie Rebain found and fixed many problems, and greatly improved the model's performance.The following people participated in one or more workshops, held to specify the components of the FFE, to calibrate it for specific regions, or both. The free flow of information and ideas at these workshops was key to the success and adaptation of this model. Introduction ___________________________________________________Fire is now represented in the Forest Vegetation Simulator's (FVS) predictions of forest stand dynamics. At long last! Al Stage (1973) recognized the importance of including disturbance agents in stand projections when he included mountain pine beetle-caused mortality of lodgepole pine in the first release of the FVS parent model, the Prognosis Model for Stand Development.Furthermore, long-term stand dynamics are now included in simulations of fires and fire effects. Fuel managers have a tool, th...

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Spatial climate models were developed for México and its periphery (southern USA, Cuba, Belize and Guatemala) for monthly normals of average, maximum and minimum temperature and precipitation using thin plate smoothing splines of ANUSPLIN software on ca. 3,800 observations. The fit of the model was generally good: the signal was considerably less than one-half of the number of observations, and reasonable standard errors for the surfaces would be less than 1 • C for temperature and 10-15% for precipitation. Monthly normals were updated for three time periods according to three General Circulation Models Climatic Change (2010) 102:595-623 and three emission scenarios. On average, mean annual temperature would increase 1.5 • C by year 2030, 2.3 • C by year 2060 and 3.7 • C by year 2090; annual precipitation would decrease −6.7% by year 2030, −9.0% by year 2060 and −18.2% by year 2090. By converting monthly means into a series of variables relevant to biology (e. g., degree-days > 5 • C, aridity index), the models are directly suited for inferring plant-climate relationships and, therefore, in assessing impact of and developing programs for accommodating global warming. Programs are outlined for (a) assisting migration of four commercially important species of pine distributed in altitudinal sequence in Michoacán State (b) developing conservation programs in the floristically diverse Tehuacán Valley, and (c) perpetuating Pinus chiapensis, a threatened endemic. Climate surfaces, point or gridded climatic estimates and maps are available at http://forest.moscowfsl.wsu.edu/climate/.

This reprinting of the User's Guide to the Prognosis Model describes the Prognosis Model as released in September, 1981 (Version 4.0). Although we will soon release version 5.0, most of the material in this guide will remain applicable to the new version. There will be, however, modifications in the small tree growth models and in the crown-dubbing and crown-changing procedures that improve model behavior. These modifications will necessitate revisions of pages 52, 65-67, and 77-80.These revisions, and descriptions of new features, are contained in a supplement to this guide that will be released with the new version. The new features include:-A regeneration establishment component;-SHRUB and COVER extensions;-An event monitor for dynamic activity scheduling;-A classification algorithm used to shorten the tree record list by combining like records;-Expansion of management options.We have endeavored to make changes in such a way that the procedures for using version 4.0 will operate the same way in version 5.0.