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The Forest Inventory and Analysis (FIA) program of the USDA Forest Service reports data and information about the Nation's forest resources. Increasingly, users request that FIA data and information be reported and distributed in a geospatial context, and they request access to exact plot locations for their own analyses. However, the FIA program is(More)
The Forest Inventory and Analysis (FIA) program of the USDA Forest Service has initiated a transition from regional, periodic inventories to an enhanced national FIA program featuring annual measurement of a proportion of plots in each state, greater national consistency, and integration with the ground sampling component of the Forest Health Monitoring(More)
USDA Forest Service Forest Inventory and Analysis plot information is widely used for timber inventories, forest health assessments, and environmental risk analyses. With few exceptions, true plot locations are not revealed; the plot coordinates are manipulated to obscure the location of field plots and thereby preserve plot integrity. The influence of(More)
a r t i c l e i n f o New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (k nn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated estimates of the variance of Y. Three(More)
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(More)
science, is a process for investigating nature. It consists of repeatedly cycling through a number of steps, including identifying knowledge gaps, creating knowledge to fill them, and organizing, evaluating, and delivering this knowledge. Much of this effort is directed toward creating abstract models of natural phenomena. The cognitive techniques of AI,(More)
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.(More)
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(More)
In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a(More)