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.
The USDA Forest Service's Forest Inventory and Analysis (FIA) and the Natural Resource Conservation Service's State Soil Geographic (STATSGO) data bases provide valuable natural resource data that can be analyzed at the national scale. When coupled with other data (e.g., climate), these data bases can provide insights into factors associated with current and future ranges of tree species. However, a significant amount of data distillation is needed prior to such analyses. This paper describes the data base and geographic information system (GIS) processing involved with preparing the data for global change research in the eastern United States.
World EnglishesVolume 14, Issue 1 p. 154-162 The Cambridge History of the English Language, Volume II: 1066–1476 CHARLES T. SCOTT, CHARLES T. SCOTT Department of English, University of Wisconsin, Madison, WI 53706, USA.Search for more papers by this author CHARLES T. SCOTT, CHARLES T. SCOTT Department of English, University of Wisconsin, Madison, WI 53706, USA.Search for more papers by this author First published: March 1995 https://doi.org/10.1111/j.1467-971X.1995.tb00346.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume14, Issue1March 1995Pages 154-162 RelatedInformation
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Language LearningVolume 9, Issue 3-4 p. 59-65 PREPARING LITERATURE MATERIALS FOR FOREIGN STUDENTS Charles T. Scott, Charles T. Scott Columbia UniversitySearch for more papers by this author Charles T. Scott, Charles T. Scott Columbia UniversitySearch for more papers by this author First published: December 1959 https://doi.org/10.1111/j.1467-1770.1959.tb01229.xCitations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Citing Literature Volume9, Issue3-4December 1959Pages 59-65 RelatedInformation
Three standard sampling designs have been used for forest monitoring through the years. Simple random sampling typically is applied for small areas. Stratified random sampling often is used in mid-scale assessments when the forest areas are delineated on maps. Double sampling for stratification generally is used only for extensive surveys when strata sizes must be estimated on aerial photographs. Remote sensing (satellite imagery) can be used for stratified random sampling at larger scales, potentially reducing sampling error. The program TabGen was written in Visual Basic 2.0 to analyze surveys using each of these designs. TabGen reads files of survey data that have been expressed on a per hectare basis. The user then selects the row and column categorical variables and the attribute of interest (continuous variable). Tables of means, totals, and areas are produced, including 95% sampling errors for each cell. Users can define multiple filters to control what data are included/excluded from each table. Most forms of forest monitoring are based on sampling designs so that results are unbiased and of known precision. The three most commonly used designs are simple random sampling, stratified random sampling, and double sam pling for stratification. The designs are listed in order of increasing efficiency but also increasing complexity. How ever, each is designed to provide estimates of forest-resource attributes and their precision. These monitoring results generally are provided in the form of one- and two-way tables. For example, a key table might be change estimates for abundance by species and size class. The statistical reports of the Forest Inventory and Analysis (FIA) units of the USDA Forest Service are compi lations of such tables. Although we regularly use such tables, the forest survey and sampling literature describes sampling designs and alternative estimators for a single attribute of interest rather than for tables of them. Software for generating these tables has not been widely available. A FORTRAN program called FINSYS (Forest Inventory System) was developed in the early 1960's and was updated in the early 1980's (Born and Barnard 1983). However, the batch processing mode was retained in the revised version, so it has not been used widely. Survey sampling software packages are available but have not been well accepted by natural resource analysts largely because
