In this chapter, we present a collection of maps designed to portray the composition, structure, ownership, utilization, and spatial patterns of forest resources across the United States. This collection is the first comprehensive compilation of national-scale, forestry-related maps. These maps complement the information presented in the preceding chapters, but the maps and chapters are not directly dependent upon one another. Forest and Rangeland Renewable Resources Planning Act (RPA) tables and summaries provide direct sources for many, but not all, of the maps. Descriptions of the maps are provided below.

The Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program selects site trees for each plot that are used to measure site productivity. The ability of a site to produce wood volume is indicated indirectly by com- paring total tree height with tree age. This comparison assumes that the rate of height growth is strongly related to site quality and is insensitive to basal area, species composition, and stand structure. Research indicates that stand age is often difficult to determine, especially in uneven-aged stands. Furthermore, stands with mixed species compositions and less than full stocking cause problems when using site index as a predictor of site growing capacity. Now that the FIA program has thousands of plots in which volume has been remeasured, other metrics for site quality can be evaluated by noting the observed past growth of the trees on the plot and comparing it with the height-age relationship. This study describes the first steps of this effort.

A spatially explicit dataset of aboveground live forest biomass was made from ground measured inventory plots for the conterminous U.S., Alaska and Puerto Rico. The plot data are from the USDA Forest Service Forest Inventory and Analysis (FIA) program. To scale these plot data to maps, we developed models relating field-measured response variables to plot attributes serving as the predictor variables. The plot attributes came from intersecting plot coordinates with geospatial datasets. Consequently, these models serve as mapping models. The geospatial predictor variables included Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; land cover proportions and other data from the National Land Cover Dataset (NLCD); topographic variables; monthly and annual climate parameters; and other ancillary variables. We segmented the mapping models for the U.S. into 65 ecologically similar mapping zones, plus Alaska and Puerto Rico. First, we developed a forest mask by modeling the forest vs. nonforest assignment of field plots as functions of the predictor layers using classification trees in See5©. Secondly, forest biomass models were built within the predicted forest areas using tree-based algorithms in Cubist©. To validate the models, we compared field-measured with model-predicted forest/nonforest classification and biomass from an independent test set, randomly selected from available plot data for each mapping zone. The estimated proportion of correctly classified pixels for the forest mask ranged from 0.79 in Puerto Rico to 0.94 in Alaska. For biomass, model correlation coefficients ranged from a high of 0.73 in the Pacific Northwest, to a low of 0.31 in the Southern region. There was a tendency in all regions for these models to over-predict areas of small biomass and under-predict areas of large biomass, not capturing the full range in variability. Map-based estimates of forest area and forest biomass compared well with traditional plot-based estimates for individual states and for four scales of spatial aggregation. Variable importance analyses revealed that MODIS-derived information could contribute more predictive power than other classes of information when used in isolation. However, the true contribution of each variable is confounded by high correlations. Consequently, excluding any one class of variables resulted in only small effects on overall map accuracy. An estimate of total C pools in live forest biomass of U.S. forests, derived from the nationwide biomass map, also compared well with previously published estimates.

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km(2) in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r=0.88) was achieved for the prototype mapping zone. (C) 2007 Elsevier Inc. All rights reserved.

Historically, field crews used Global Positioning System (GPS) coordinates to establish and relocate plots, as well as document their general location. During the past 5 years, the increase in Geographic Information System (GIS) capabilities and in customer requests to use the spatial relationships between Forest Inventory and Analysis (FIA) plot data and other GIS layers has increased the value of and requirements on measurements of plot locations. To meet current FIA business requirements, it is essential that GPS locations be accurate.

Historically, field crews used Global Positioning System (GPS) coordinates to establish and relocate plots, as well as document their general location. During the past 5 years, the increase in Geographic Information System (GIS) capabilities and in customer requests to use the spatial relation- ships between Forest Inventory and Analysis (FIA) plot data and other GIS layers has increased the value of and requirements on measurements of plot locations. To meet current FIA business require- ments, it is essential that GPS locations be accurate. The Northeast FIA program (NE-FIA) used Rockwell Precision Lightweight GPS Receivers (PLGRs) in the late 1990s. This moderately priced unit enables accurate navigation and reasonably accurate locations

A land cover map (1993) was combined with an updated forest change detection map (1991–2000) to examine forest harvest activity, mostly on private commercial forest lands. Landsat change detection methods indicated that industrial forest owners harvested a higher percentage of forest than non-industrial owners in a northern Maine study area. In the 1980s, the percentage of forest harvested across all ownership classes (five) was higher, the mean harvest patch size was larger, patches were more compact, and the mean perimeter to area ratios were smaller compared to data from the 1990s. For all patch metrics, there was a significant time period effect but there was no effect among landowners. Larger harvest patch size in the 1980s may be partially explained by extensive salvage logging that occurred in the wake of a massive spruce budworm infestation in the 1970s. Softwood types were dominant (> 80%) in regeneration stands approximately 15–25 years old on all ownerships. Medium spatial resolution Landsat imagery shows promise as a landscape level tool to monitor forest change patterns and trends across multiple ownerships. Key words: remote sensing, Landsat, change detection, harvest intensity, forest regeneration, forest landowners

A forest change detection map was developed to document forest gains and losses during the decade of the 1990s. The effectiveness of the Landsat imagery and methods for detecting Maine forest cover change are indicated by the good accuracy assessment results: forest-no change, forest loss, and forest gain accuracy were 90, 88, and 92%, respectively, and the good correlation of mapped areas of forest cover loss with forest inventory analysis (FIA) tree size class decrease at the county level. The combination of annually collected permanent FIA plots with accurate statewide forest change maps offers a complementary perspective of Maine's forest resource.

Detailed data on tree species drive models that predict risk of insect and disease mortality in forest stands and simulation models for future stand conditions. Application of such models in geospatial analyses requires these data for millions of remotely sensed pixels. However, the vast majority of remotely sensed thematic maps predict a few categories of stand conditions, such as forest type and stage of stand development. Even if the inherent inaccuracies in remotely sensed predictions are ignored, there is considerable variability in tree composition within each category. There is growing interest in k-Nearest Neighbor (k-NN) imputation as an alternative to supervised classification of remotely sensed data. k-NN starts with a set of training sites j, 10<j<10, within the target geographic area. One attractive set of training sites is the field plots measured by the USDA Forest Service’s Forest Inventory and Analysis program. For each pixel i, 10<i<10, which are outside of the training set, k-NN finds 1≤k training sites that are “close” to the i pixel within a feature space formed from remotely sensed and other geospatial data. Then detailed field measurements from those k training sites are used to impute, or predict, the same type of detailed field data for that i pixel. This imputation is separately repeated for each and every pixel in the full target area. The outcome is detailed predictions of tree species, tree size composition and other field measurements for each pixel. The accuracy of k-NN predictions strongly depends upon the distance metric used to measure “closeness” in this feature space, and there are numerous alternatives for that measure. This paper presents and evaluates a new measure that transforms a high-dimensional remotely-sensed feature space into a new space that is optimized to fit a high-dimensional response space, namely tree-level composition at the pixel scale. The advantages of this approach include highly efficient prediction algorithms for risks to forest health and forecasts of future conditions at the pixel scale.

The Food Security Act of 1985 prohibits the disclosure of any information collected by the USDA Forest Service's FIA program that would link individual landowners to inventory plot information. To address this, we developed a technique based on a "swapping" procedure in which plots with similar characteristics are exchanged, and on a "fuzzing" procedure in which the geographic locations of the plots are randomly perturbed by 805 m. A simulation experiment was performed to assess the effects of fuzzing and swapping. Our results indicate the procedures can provide meaningful information and comply with the law. Further refinements of the technique are ongoing.

In the Northeast region, the USDA Forest Service Forest Inventory and Analysis (FIA) program utilizes stratified sampling techniques to improve the precision of population estimates. Recently, interpretation of aerial photographs was replaced with classified remotely sensed imagery to determine stratum weights and plot stratum assignments. However, stratum weights based on remotely sensed data depend on many factors, such as classification algorithm and image selection, County volume estimates and associated variances were calculated over a range of stratum weight scenarios and for various number of strata. Rates of change in estimated values and variances, and their effects on percent sampling error, were examined in relation to different strata configurations.

From: http://gisdata.usgs.net/website/landfire/; Data release status as of 8/2006 NATIONAL DATASETS SUMMARY A major goal of the North American Carbon Program (NACP) is to develop a quantitative scientific basis for regional to continental scale carbon accounting to reduce uncertainties about the carbon cycle component of the climate system. Area-based estimates of terrestrial biomass and carbon are best captured when biophysical measures of horizontal and vertical vegetation structure can be obtained. Given the highly complementary nature and quasi-synchronous data acquisition of the 2000 Shuttle Radar Topography Mission (SRTM), the Landsat-based 2001 National Land Cover Database (NLCD), and data sets from the national LANDFIRE project, an exceptional opportunity exists for exploiting data synergies afforded by the fusion of these high-resolution, spatially explicit data sources. Whereas the thematic layers of the NLCD and LANDFIRE are suitable for characterizing horizontal structure (i.e., cover type, canopy density, etc.), SRTM provides information relating to the vertical structure, i.e., primarily height. Currently, a project funded under the NASA “Carbon Cycle Science” program – “The National Biomass and Carbon Dataset 2000 – NBCD2000” – is underway to generate a high-spatial resolution ecoregional database of circa-2000 vegetation canopy height, aboveground biomass, and carbon stocks for the conterminous U.S. from these data sets. In order to develop regression tree models for spatially extensive estimates of vegetation height and aboveground live dry biomass from the remote sensing data, reference data from the USDA Forest Service Forest Inventory and Analysis (FIA) program, and selected airborne lidar-derived vegetation parameters are used. The estimation of carbon storage in vegetation is based on the established biomass-to-carbon conversion of [carbon = 0.5 * biomass]. The NBCD2000 project follows the same ecoregional mapping zone approach employed by the NLCD and LANDFIRE projects. Results from three mapping zones in montane (western) and coastal (eastern) forests are presented. Final products will include spatially extensive maps with estimation errors. Completion of the NBCD 2000 data set is expected in early 2009.

How accurately can FIA plots, scattered at 1 per 6,000 acres, identify often rare forest land loss, estimated at less than 1 percent per year in the Northeast? Here we explore this question mathematically, empirically, and by comparing FIA plot estimates of forest change with satellite image based maps of forest loss. The mathematical probability of exactly estimating a 5-percent loss within a 600,000-acre forest, where 5 percent has actually been converted, is 18 percent. A GIS experiment in Connecticut, using 452 FIA plots and a satellite-derived forest cover map, where 5 percent of the total forest area was "lost" by 7.5-acre units, indicates that the sample estimates a 5-percent loss 35 percent of the time with a range of estimated loss of 3 to 8 percent. Satellite image classification can probably estimate the amount of forests lost to urbanization more accurately, especially over small areas, while providing a more useful map of forest loss.

We investigate ordinary kriging and three cokriging procedures for making continuous maps of five forest attributes. Both ordinary kriging and cokriging use a primary variable, but cokriging, like multivariate statistics, includes secondary variables. The secondary or ancillary variables are reflectance values and calculated vegetation indices from an August 1996 Landsat Thematic Mapper satellite image. Two methods for comparing the results include examining the residuals and breaking both the estimated and sampled data into classes and then examining the resulting confusion matrix. The comparison statistics are root mean square error, overall accuracy, and kappa statistic. For the cross-validation, an additional statistic was median absolute error. A cross-validation indicated that cokriging had a higher overall accuracy and kappa statistic and a lower median absolute error, while kriging yielded a slightly lower root mean square error. Both procedures captured the same trends in Connecticut. The developed areas around New York City and the I-91 corridor running from New Haven through Hartford into Massachusetts are less forested than the less developed and higher elevation areas in the northwestern portion of the state. Kriging smoothes the maps, missing the fine-scale heterogeneity of the landscape that cokriging detects.