Ronald E. McRoberts

Stratified estimation of forest area using satellite imagery, inventory data, and the k-Nearest Neighbors technique

For two large study areas in Minnesota, USA, stratified estimation using classified Landsat Thematic Mapper satellite imagery as the basis for stratification was used to estimate forest area. Measurements of forest inventory plots obtained for a 12-month period in 1998 and 1999 were used as the source of data for within-stratum estimates. These measurements further served as calibration data for a k-Nearest Neighbors technique that was used to predict forest land proportion for image pixels. The continuum of forest land proportion predictions was separated into strata to facilitate stratified estimation. The k-Nearest Neighbors technique is carefully explained, five precautions are noted, and a plea is made for an objective approach to calibrating the technique. The variances of the stratified forest area estimates were smaller by factors as great as 5 than variances of the arithmetic mean calculated under the assumption of simple random sampling. In addition, when including all plots over a 5-year plot measurement cycle, the forest area precision estimates may be expected to satisfy national standards.

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Abstract Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and model-based inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates

Estimates of forest area were obtained for the states of Indiana, Iowa, Minnesota, and Missouri in the United States using stratified analyses and observations from forest inventory plots measured in federal fiscal year 1999. Strata were created by aggregating the land cover classes of the National Land Cover Data (NLCD), and strata weights were calculated as proportions of strata pixel counts. The analyses focused on improving the precision of unbiased forest area estimates and included evaluation of the correspondence between forest/nonforest aggregations of the NLCD classes and observed attributes of forest inventory plots, evaluation of the utility of the NLCD as a stratification tool, and estimation of the effects on precision of image registration and plot location errors. The results indicate that the combination of NLCD-based stratification of inventory plots and stratified analyses increases the precision of forest area estimates and that the estimates are only slightly adversely affected by image registration and plot location errors.

Advances and emerging issues in national forest inventories

Abstract National forest inventories (NFIs) have a long history, although their current major features date only to the early years of the twentieth century. Recent issues such as concern over the effects of acid deposition, biodiversity, forest sustainability, increased demand for forest data, international reporting requirements and climate change have led to the expansion of NFIs to include more variables, greater diversity in sampling protocols and a generally more holistic approach. This review focuses on six selected topics: (1) a brief historical review; (2) a summary of common structural features of NFIs; (3) a brief review of international reporting requirements using NFI data with an emphasis on approaches to harmonized estimation; (4) an overview of inventory estimation methods that can be enhanced with remotely sensed data; (5) an overview of nearest neighbors prediction and estimation techniques; and (6) a brief overview of several emerging issues including carbon inventories in developing countries and use of lidar data. Although general inventory principles will remain unchanged, sampling designs, plot configurations and measurement protocols will require modification before they can be applied in countries with tropical forests. Technological advances, particularly in the use of remotely sensed data, including lidar data, have led to greater inventory efficiencies, better maps and accurate estimation for small areas.

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

Introduction to Forestry Applications of Airborne Laser Scanning

Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies to provide data for research and operational applications in a wide range of disciplines related to management of forest ecosystems. This chapter starts with a brief historical overview of the early forest-related research on airborne Light Detection and Ranging which was first mentioned in the literature in the mid-1960s. The early applications of ALS in the mid-1990s are also reviewed. The two fundamental approaches to use of ALS in forestry applications are presented – the area-based approach and the single-tree approach. Many of the remaining chapters rest upon this basic description of these two approaches. Finally, a brief introduction to the broad range of forestry applications of ALS is given and references are provided to individual chapters that treat the different topics in more depth. Most chapters include detailed reviews of previous research and the state-of-the-art in the various topic areas. Thus, this book provides a unique collection of in-depth reviews and overviews of the research and application of ALS in a broad range of forest-related disciplines.

A model-assisted k-nearest neighbour approach to remove extrapolation bias

Abstract In applications of the k-nearest neighbour technique (kNN) with real-valued attributes of interest (Y) the predictions are biased for units with ancillary values of X with poor or no representation in a sample of n units. In this article a model-assisted calibration is proposed that reduces unit-level extrapolation bias. The bias is estimated as the difference in model-based predictions of Y given the X-values of the true k nearest units and the k selected reference units. Calibrated kNN predictions are then obtained by adding this difference to the original kNN prediction. The relationship is modelled between Y and X with decorrelated X-variables, variables scaled to the interval [0,1] and Bernstein basis functions to capture changes in Y as a function of changes in X. Three examples with actual forest inventory data from Italy, the USA and Finland demonstrated that calibrated kNN predictions were, on average, closer to their true values than non-calibrated predictions. Calibrated predictions had a range much closer to the actual range of Y than non-calibrated predictions.

Bayesian multivariate process modeling for prediction of forest attributes

This article investigates multivariate spatial process models suitable for predicting multiple forest attributes using a multisource forest inventory approach. Such data settings involve several spatially dependent response variables arising in each location. Not only does each variable vary across space, they are likely to be correlated among themselves. Traditional approaches have attempted to model such data using simplifying assumptions, such as a common rate of decay in the spatial correlation or simplified cross-covariance structures among the response variables. Our current focus is to produce spatially explicit, tree species specific, prediction of forest biomass per hectare over a region of interest. Modeling such associations presents challenges in terms of validity of probability distributions as well as issues concerning identifiability and estimability of parameters. Our template encompasses several models with different correlation structures. These models represent different hypotheses whose tenability are assessed using formal model comparisons. We adopt a Bayesian hierarchical approach offering a sampling-based inferential framework using efficient Markov chain Monte Carlo methods for estimating model parameters.

National Forest Inventories: Contributions to Forest Biodiversity Assessments

1. The need for harmonized estimates of forest biodiversity indicators.- 2. Essential features of forest biodiversity for assessment purposes.- 3. Prospects for harmonized biodiversity assessments using national forest inventory data.- 4. The common NFI database.- 5. Harmonization tests.- 6. Summary and conclusions. Index.

Hierarchical spatial models for predicting tree species assemblages across large domains

Spatially explicit data layers of tree species assemblages, referred to as forest types or forest type groups, are a key component in large-scale assessments of forest sustainability, biodiversity, timber biomass, carbon sinks and forest health monitoring. This paper explores the utility of coupling georeferenced national forest inventory (NFI) data with readily available and spatially complete environmental predictor variables through spatially-varying multinomial logistic regression models to predict forest type groups across large forested landscapes. These models exploit underlying spatial associations within the NFI plot array and the spatially-varying impact of predictor variables to improve the accuracy of forest type group predictions. The richness of these models incurs onerous computational burdens and we discuss dimension reducing spatial processes that retain the richness in modeling. We illustrate using NFI data from Michigan, USA, where we provide a comprehensive analysis of this large study area and demonstrate improved prediction with associated measures of uncertainty.