Applications of the United States Forest Inventory and Analysis dataset: a review and future directions

The United States Forest Inventory and Analysis (FIA) program has been monitoring national forest resources in the United States for over 80 years; presented here is a synthesis of research applications for FIA data. A review of over 180 publications that directly utilize FIA data is broken down into broad categories of application and further organized by methodologies and niche research areas. The FIA program provides the most comprehensive forest database currently available, with permanent plots distributed across all forested lands and ownerships in the United States and plot histories dating back to the early 1930s. While the data can be incredibly powerful, users need to understand the spatial resolution of ground-based plots and the nature of the FIA plot coordinate system must be applied correctly. As the need for accurate assessments of national forest resources continues to be a global priority, particularly related to carbon dynamics and climate impacts, such national forest inventories will continue to be an important source of information on the status of and trends in these ecosystems. The advantages and limitations of FIA’s national forest inventory data are highlighted, and suggestions for further expansion of the FIA program are provided.

[1]  M. Hansen,et al.  Latitudinal range shifts of tree species in the United States across multi-decadal time scales , 2015 .

[2]  James M. Dyer Using witness trees to assess forest change in southeastern Ohio , 2001 .

[3]  Xiaoping Zhou,et al.  Ecological and economic determinants of invasive tree species on Alabama forestland , 2008 .

[4]  W. Bridges,et al.  Population dynamics of redbay (Persea borbonia) after laurel wilt disease: an assessment based on forest inventory and analysis data , 2015, Biological Invasions.

[5]  L. Heath,et al.  Estimating down deadwood from FIA forest inventory variables in Maine. , 2002, Environmental pollution.

[6]  Liviu Theodor Ene,et al.  Large-area hybrid estimation of aboveground biomass in interior Alaska using airborne laser scanning data , 2018 .

[7]  S. Malyshev,et al.  Confronting terrestrial biosphere models with forest inventory data. , 2014, Ecological applications : a publication of the Ecological Society of America.

[8]  C. Cieszewski,et al.  Spatial Clusters and Variability Analysis of Tree Mortality , 2006 .

[9]  G. Domke,et al.  From Models to Measurements: Comparing Downed Dead Wood Carbon Stock Estimates in the U.S. Forest Inventory , 2013, PloS one.

[10]  Jeffrey R Dunk,et al.  Estimating habitat value using forest inventory data: The fisher (Martes pennanti) in northwestern California , 2012 .

[11]  Randolph H. Wynne,et al.  Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size , 2002 .

[12]  Hong S. He,et al.  Comparison of historical and current forest surveys for detection of homogenization and mesophication of Minnesota forests , 2012, Landscape Ecology.

[13]  M. D. Nelson,et al.  Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information , 2008 .

[14]  Sarah Parks,et al.  An effective assessment protocol for continuous geospatial datasets of forest characteristics using USFS Forest Inventory and Analysis (FIA) data , 2010 .

[15]  Andrew Kliskey,et al.  Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems , 2014 .

[16]  James N. Long,et al.  Utah State University From the SelectedWorks of James Long 2017 Building the Forest Inventory and Analysis Tree-Ring Data Set , 2017 .

[17]  C. Oswalt,et al.  Status of Black Walnut (Juglans nigra L.) in the Eastern United States in Light of the Discovery of Thousand Cankers Disease , 2013 .

[18]  Jeffrey R Dunk,et al.  Developing and Applying Habitat Models Using Forest Inventory Data: An Example Using a Terrestrial Salamander , 2006 .

[19]  Thomas J. Brandeis,et al.  Adapting the forest inventory and analysis program to a Caribbean Island , 2003 .

[20]  R. Mickler,et al.  Regional estimation of current and future forest biomass. , 2002, Environmental pollution.

[21]  Patrick D. Miles,et al.  Using biological criteria and indicators to address forest inventory data at the state level , 2002 .

[22]  J. Shaw Benefits of a strategic national forest inventory to science and society: the USDA Forest Service Forest Inventory and Analysis program , 2008 .

[23]  Hailemariam Temesgen,et al.  Estimating Cavity Tree Abundance Using Nearest Neighbor Imputation Methods for Western Oregon and Washington Forests , 2008 .

[24]  JiangHuiquan,et al.  Climate- and soil-based models of site productivity in eastern US tree species , 2015 .

[25]  V. Lenin,et al.  The United States of America , 2002, Government Statistical Agencies and the Politics of Credibility.

[26]  Yi‐Chen Wang,et al.  Spatial distribution of forest landscape change in western New York from presettlement to the present , 2009 .

[27]  Yude Pan,et al.  BIOMASS AND NPP ESTIMATION FOR THE MID-ATLANTIC REGION (USA) USING PLOT-LEVEL FOREST INVENTORY DATA , 2001 .

[28]  G. Domke,et al.  Potential increases in natural disturbance rates could offset forest management impacts on ecosystem carbon stocks , 2013 .

[29]  Kenneth B. Pierce,et al.  Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches , 2010 .

[30]  L. Zhang,et al.  A mixture model-based approach to the classification of ecological habitats using Forest Inventory and Analysis data , 2004 .

[31]  Dale D. Gormanson,et al.  Effects of climate on emerald ash borer mortality and the potential for ash survival in North America , 2013 .

[32]  R. Mickler,et al.  Modeling and Spatially Distributing Forest Net Primary Production at the Regional Scale , 2002, Journal of the Air & Waste Management Association.

[33]  M. Gregory,et al.  Mapping gradients of community composition with nearest-neighbour imputation: extending plot data for landscape analysis , 2011 .

[34]  M. D. Nelson,et al.  A comparison of techniques for generating forest ownership spatial products , 2014 .

[35]  D. Chojnacky,et al.  Biomass and carbon data from blue oaks in a California oak savanna , 2014 .

[36]  Maurizio Santoro,et al.  Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band , 2012 .

[37]  James A. Westfall,et al.  Measurement repeatability of a large-scale inventory of forest fuels , 2007 .

[38]  J. N. Long,et al.  Utah State University From the SelectedWorks of James Long 2005 A density management diagram for even-aged ponderosa pine stands , 2017 .

[39]  Grant M. Domke,et al.  Consequences of alternative tree-level biomass estimation procedures on U.S. forest carbon stock estimates , 2012 .

[40]  L. Heath,et al.  Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010 , 2011 .

[41]  H. Andersen,et al.  An Accuracy Assessment of Positions Obtained Using Survey- and Recreational-Grade Global Positioning System Receivers across a Range of Forest Conditions within the Tanana Valley of Interior Alaska , 2009 .

[42]  Xianjun Hao,et al.  Estimating aboveground biomass for different forest types based on Landsat TM measurements , 2009, 2009 17th International Conference on Geoinformatics.

[43]  Jeffrey R Dunk,et al.  Using forest inventory data to assess fisher resting habitat suitability in California. , 2006, Ecological applications : a publication of the Ecological Society of America.

[44]  David L. Evans,et al.  Urbanization's impact on timber harvesting in the south central United States. , 2002, Journal of environmental management.

[45]  Richard H. Waring,et al.  Assessment of site index and forest growth capacity across the Pacific and Inland Northwest U.S.A. with a MODIS satellite-derived vegetation index , 2006 .

[46]  Andrew O. Finley,et al.  An indicator of tree migration in forests of the eastern United States , 2009 .

[47]  J. Burger,et al.  Acid deposition effects on forest composition and growth on the Monongahela National Forest, West Virginia , 2009 .

[48]  M. Ducey,et al.  A comparison of carbon stock estimates and projections for the northeastern United States , 2014 .

[49]  C. Woodall,et al.  What Is the Fire Danger Now? Linking Fuel Inventories with Atmospheric Data , 2005 .

[50]  L. Frelich Old forest in the lake states today and before European settlement , 1995 .

[51]  P. Patterson,et al.  Integrating urban and national forest inventory data in support of rural–urban assessments , 2018, Forestry: An International Journal of Forest Research.

[52]  M. T. Thompson Analysis of conifer mortality in Colorado using Forest Inventory and Analysis's annual forest inventory , 2009 .

[53]  Demetrios Gatziolis,et al.  Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest , 2014, Remote. Sens..

[54]  R. Meentemeyer,et al.  Spatial estimation of the density and carbon content of host populations for Phytophthora ramorum in California and Oregon , 2011 .

[55]  P. E. Schroeder,et al.  SPATIAL PATTERNS OF ABOVEGROUND PRODUCTION AND MORTALITY OF WOODY BIOMASS FOR EASTERN U.S. FORESTS , 1999 .

[56]  C. Woodall,et al.  A technique for conducting point pattern analysis of cluster plot stem-maps , 2004 .

[57]  N. Coops,et al.  Relationships between individual‐tree mortality and water‐balance variables indicate positive trends in water stress‐induced tree mortality across North America , 2017, Global change biology.

[58]  Randall S. Morin,et al.  Mortality rates associated with crown health for eastern forest tree species , 2015, Environmental Monitoring and Assessment.

[59]  John M. Zobel,et al.  Comparison of Forest Inventory and Analysis surveys, basal area models, and fitting methods for the aspen forest type in Minnesota , 2011 .

[60]  Charles O. Sabatia,et al.  Extending a Model System to Predict Biomass in Mixed-Species Southern Appalachian Hardwood Forests , 2013 .

[61]  Ronald E. McRoberts,et al.  Harmonic regression of Landsat time series for modeling attributes from national forest inventory data , 2018 .

[62]  Mark H. Hansen,et al.  The Incidence of Dwarf Mistletoe in Minnesota Black Spruce Stands Detected by Operational Inventories , 2012 .

[63]  Patrick D. Keyser,et al.  A method for integrating the Breeding Bird Survey and Forest Inventory and Analysis databases to evaluate forest bird–habitat relationships at multiple spatial scales , 2007 .

[64]  M. McDill,et al.  Developing a system of annual tree growth equations for the loblolly pine - shortleaf pine type in Louisiana , 2002 .

[65]  G. Wang,et al.  Changes in forest biomass carbon storage in the South Carolina Piedmont between 1936 and 2005 , 2008 .

[66]  Jacek P. Siry,et al.  Potential impacts of increased management intensities on planted pine growth and yield and timber supply modeling in the south , 1999 .

[67]  Francis A. Roesch,et al.  Statistical Properties of Alternative National Forest Inventory Area Estimators , 2012 .

[68]  J. N. Long,et al.  A density management diagram for even-aged Sierra Nevada mixed-conifer stands , 2012 .

[69]  V. Monleon,et al.  Calibrating vascular plant abundance for detecting future climate changes in Oregon and Washington, USA , 2010 .

[70]  James Smith,et al.  Site Productivity and Forest Carbon Stocks in the United States: Analysis and Implications for Forest Offset Project Planning , 2012 .

[71]  Miguel G. Cruz,et al.  Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America , 2003 .

[72]  Xuexia Chen,et al.  Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis Program, Landsat, and LANDFIRE , 2011 .

[73]  J. Eitel,et al.  Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys , 2012 .

[74]  R. D. Ramsey,et al.  Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south‐western USA , 2005 .

[75]  C. Canham,et al.  Regional variation in forest harvest regimes in the northeastern United States. , 2013, Ecological applications : a publication of the Ecological Society of America.

[76]  L. Leefers,et al.  Forest type classification accuracy assessment for Michigan's State and National Forests. , 2012 .

[77]  Using Publicly Available Forest Inventory Data in Climate-Based Models of Tree Species Distribution: Examining Effects of True Versus Altered Location Coordinates , 2013, Ecosystems.

[78]  L. Zhang,et al.  Comparison of Neural Networks and Statistical Methods in Classification of Ecological Habitats Using FIA Data , 2003 .

[79]  Jeffrey G. Masek,et al.  Carbon consequences of forest disturbance and recovery across the conterminous United States , 2012 .

[80]  D. Chojnacky,et al.  Technical Note—Amounts of Down Woody Materials for Mixed-Oak Forests in Kentucky, Virginia, Tennessee, and North Carolina , 2004 .

[81]  C. Witt Characteristics of aspen infected with heartrot: Implications for cavity-nesting birds , 2010 .

[82]  D. Zheng,et al.  Visualizing forest landscapes using public data sources , 2006 .

[83]  Francis A. Roesch,et al.  Anomalous diameter distribution shifts estimated from FIA inventories through time , 2010 .

[84]  J. Rojas‐Sandoval,et al.  Spatial patterns of distribution and abundance of Harrisia portoricensis, an endangered Caribbean cactus , 2013 .

[85]  E. Tomppo National Forest Inventories : pathways for common reporting , 2010 .

[86]  Claude Vidal,et al.  National Forest Inventories Assessment of Wood Availability and Use , 2016 .

[87]  William A. Bechtold,et al.  The enhanced forest inventory and analysis program of the USDA forest service: historical perspective and announcements of statistical documentation , 2005 .

[88]  Jing M. Chen,et al.  ormalized algorithm for mapping and dating forest disturbances and regrowth or the United States , 2011 .

[89]  Web Application to Access and Visualize US Forest Inventory and Analysis Program Down Woody Materials Data , 2013 .

[90]  Yude Pan,et al.  Separating effects of changes in atmospheric composition, climate and land-use on carbon sequestration of U.S. Mid-Atlantic temperate forests , 2009 .

[91]  Michael J. Papaik,et al.  Multi-model analysis of tree competition along environmental gradients in southern New England forests. , 2006, Ecological applications : a publication of the Ecological Society of America.

[92]  D. F. Grigal,et al.  Relative stocking index : a proposed index of site quality , 1994 .

[93]  N. Coops,et al.  Combining a generic process-based productivity model and a statistical classification method to predict the presence and absence of tree species in the Pacific Northwest, U.S.A. , 2009 .

[94]  Richard A. Birdsey,et al.  Relationships between net primary productivity and forest stand age in U.S. forests , 2012 .

[95]  G. Domke,et al.  Net carbon flux of dead wood in forests of the Eastern US , 2014, Oecologia.

[96]  Hong S. He,et al.  A large‐scale forest landscape model incorporating multi‐scale processes and utilizing forest inventory data , 2013 .

[97]  M. Hansen,et al.  Using forest service forest inventory and analysis data to estimate regional oak decline and oak mortality , 2008 .

[98]  Lawrence D. Teeter,et al.  Projecting timber inventory at the product level. , 1999 .

[99]  B. Hanberry Changing eastern broadleaf, southern mixed, and northern mixed forest ecosystems of the eastern United States , 2013 .

[100]  Donald L. Grebner,et al.  Woody biomass availability for bioethanol conversion in Mississippi , 2009 .

[101]  Mark H. Hansen,et al.  Using classified Landsat Thematic Mapper data for stratification in a statewide forest inventory , 2000 .

[102]  Mark H. Hansen,et al.  Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates , 2002 .

[103]  Frederick W. Cubbage,et al.  Estimating harvest costs for fuel treatments in the West , 2008 .

[104]  C. Woodall,et al.  A statistical power analysis of woody carbon flux from forest inventory data , 2013, Climatic Change.

[105]  B. Law,et al.  Biogeosciences Evaluation and improvement of the Community Land Model ( CLM 4 ) in Oregon forests , 2013 .

[106]  Scott L. Powell,et al.  Maintaining the confidentiality of plot locations by exploiting the low sensitivity of forest structure models to different spectral extraction kernels , 2011 .

[107]  M. Hansen,et al.  Farmers’ objectives toward their woodlands in the upper Midwest of the United States: implications for woodland volumes and diversity , 2008, Agroforestry Systems.

[108]  S. Hamburg,et al.  Forest carbon storage: ecology, management, and policy , 2010 .

[109]  G. Domke,et al.  Estimation of merchantable bole volume and biomass above sawlog top in the National Forest inventory of the United States , 2013 .

[110]  S. Fei,et al.  Northward migration under a changing climate: a case study of blackgum (Nyssa sylvatica) , 2014, Climatic Change.

[111]  G. Reams Radial growth trends of loblolly pine in the Virginia Coastal Plain , 1996 .

[112]  Sandra A. Brown,et al.  Aboveground biomass distribution of US eastern hardwood forests and the use of large trees as an indicator of forest development , 1997 .

[113]  J. Prestemon Estimating Tree Grades for Southern Appalachian Natural Forest Stands , 1998, Forest Science.

[114]  A. Ek,et al.  Carbon emissions associated with the procurement and utilization of forest harvest residues for energy, northern Minnesota, USA , 2012 .

[115]  A. Hudak,et al.  Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data , 2008 .

[116]  C. Goodale,et al.  Forest nitrogen sinks in large eastern U.S. watersheds: estimates from forest inventory and an ecosystem model , 2002 .

[117]  D. W. MacFarlane,et al.  Comparing field- and model-based standing dead tree carbon stock estimates across forests of the US , 2012 .

[118]  M. D. Nelson,et al.  Forest Change in the Driftless Area of the Midwest: From a Preferred to Undesirable Future , 2015 .

[119]  Janet L. Ohmann,et al.  Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A. , 2002 .

[120]  F. Thompson,et al.  Estimating cavity tree abundance by stand age and basal area, Missouri, USA , 2003 .

[121]  J. Siry,et al.  Increasing southern pine growth and its implications for regional wood supply , 2003 .

[122]  R. Abt,et al.  Vulnerability of Mid-Atlantic Forested Watersheds to Timber Harvest Disturbance , 2004, Environmental Monitoring & Assessment.

[123]  Joshua J. Puhlick,et al.  Factors influencing ponderosa pine regeneration in the southwestern USA , 2012 .

[124]  A. Prasad,et al.  PREDICTING ABUNDANCE OF 80 TREE SPECIES FOLLOWING CLIMATE CHANGE IN THE EASTERN UNITED STATES , 1998 .

[125]  Jeffrey P. Prestemon,et al.  Linking harvest choices to timber supply , 2000 .

[126]  G. Domke,et al.  Toward inventory-based estimates of soil organic carbon in forests of the United States. , 2017, Ecological applications : a publication of the Ecological Society of America.

[127]  J. Kershaw,et al.  Benchmarking and Calibration of Forest Vegetation Simulator Individual Tree Attribute Predictions Across the Northeastern United States , 2013 .

[128]  David E. Knapp,et al.  Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR , 2018, Forest Ecology and Management.

[129]  L. Heath,et al.  Updated generalized biomass equations for North American tree species , 2014 .

[130]  Ian A. Munn,et al.  Modeling Forest Fire Probabilities in the South Central United States Using FIA Data , 2003 .

[131]  J. Shaw,et al.  Forest Inventory and Analysis (FIA) Annual Inventory Answers the Question: What Is Happening to Pinyon-Juniper Woodlands? , 2005, Journal of Forestry.

[132]  W. K. Moser,et al.  Species choice and the risk of disease and insect attack: evaluating two methods of choosing between longleaf and other pines , 2003 .

[133]  Alan E. Gelfand,et al.  Scaling Integral Projection Models for Analyzing Size Demography , 2013, 1312.7260.

[134]  W. Liu,et al.  Predicting forest successional stages using multitemporal Landsat imagery with forest inventory and analysis data , 2008 .

[135]  J. Kellndorfer,et al.  Modeling Height, Biomass, and Carbon in U.S. Forests from FIA, SRTM, and Ancillary National Scale Data Sets , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[136]  Michael J. Papaik,et al.  Neighborhood analyses of canopy tree competition along environmental gradients in New England forests. , 2006, Ecological applications : a publication of the Ecological Society of America.

[137]  L. Heath,et al.  Estimates of Down Woody Materials in Eastern US Forests , 2004 .

[138]  J. Heikkinen,et al.  Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery , 2007 .

[139]  G. Domke,et al.  Estimating litter carbon stocks on forest land in the United States. , 2016, The Science of the total environment.

[140]  William A. Bechtold,et al.  The enhanced forest inventory and analysis program - national sampling design and estimation procedures , 2005 .

[141]  C. Woodall,et al.  Tracking downed dead wood in forests over time: Development of a piece matching algorithm for line intercept sampling , 2012 .

[142]  F. Thompson,et al.  Change in avian abundance predicted from regional forest inventory data , 2010 .

[143]  Duncan C. Lutes,et al.  Evaluating the performance and mapping of three fuel classification systems using Forest Inventory and Analysis surface fuel measurements , 2013 .

[144]  Andrew O. Finley,et al.  Delineation of forest/nonforest land use classes using nearest neighbor methods , 2004 .

[145]  Debby Beardsley,et al.  Using a mensuration approach with FIA vegetation plot data to assess the accuracy of tree size and crown closure classes in a vegetation map of northeastern California. , 2000 .

[146]  Christopher M. Oswalt,et al.  Data, data everywhere: detecting spatial patterns in fine-scale ecological information collected across a continent , 2015, Landscape Ecology.

[147]  Linda S. Heath,et al.  National inventories of down and dead woody material forest carbon stocks in the United States: Challenges and opportunities , 2008 .

[148]  Bin Zheng,et al.  Financial Assessment of Logging Residue Collection in the Southeast United States , 2012 .

[149]  S. Wang,et al.  Feasibility of High-Density Climate Reconstruction Based on Forest Inventory and Analysis (FIA) Collected Tree-Ring Data , 2013 .

[150]  C. Woodall,et al.  Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage , 2013, Carbon Balance and Management.

[151]  Victor A. Rudis,et al.  Comprehensive regional resource assessments and multipurpose uses of forest inventory and analysis data, 1976 to 2001: a review. , 2003 .

[152]  S. Fei,et al.  Change in oak abundance in the eastern United States from 1980 to 2008 , 2011 .

[153]  R. Morin,et al.  Patterns of exotic plant invasions in Pennsylvania's Allegheny National Forest using intensive Forest Inventory and Analysis plots , 2009 .

[154]  C. Larsen,et al.  An assessment of the optimal scale for monitoring of MODIS and FIA NPP across the eastern USA , 2013, Environmental Monitoring and Assessment.

[155]  B. Bormann,et al.  Site-Specific Douglas-Fir Biomass Equations from the Siskiyou Mountains, Oregon, Compared with Others from the Pacific Northwest , 2014 .

[156]  T. Sohl,et al.  Using the FORE-SCE model to project land-cover change in the southeastern United States , 2008 .

[157]  Ronald E. McRoberts,et al.  Stratified estimation of forest area using satellite imagery, inventory data, and the k-Nearest Neighbors technique , 2002 .

[158]  T. Barrett,et al.  Potential of a national monitoring program for forests to assess change in high-latitude ecosystems. , 2011 .

[159]  W. B. Smith,et al.  Forest inventory and analysis: a national inventory and monitoring program. , 2002, Environmental pollution.

[160]  Mohammad Z. Al-Hamdan,et al.  Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data , 2014, Remote. Sens..

[161]  D. Chojnacky,et al.  Amounts of Down Woody Materials for Mixed-Oak Forests in Kentucky, Virginia, Tennessee, and North Carolina , 2004 .

[162]  Kai Zhu,et al.  Dual impacts of climate change: forest migration and turnover through life history , 2014, Global change biology.

[163]  R. Srinivasan,et al.  Estimating regional forest cover in East Texas using Advanced Very High Resolution Radiometer (AVHRR) data , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[164]  E. Cowling,et al.  Potentials for Mutually Beneficial Collaboration Between FIA Specialists and IEG-40 Pathologists and Geneticists Working on Fusiform Rust , 2013 .

[165]  Bruce McCune,et al.  Effect of inventory method on niche models: Random versus systematic error , 2013, Ecol. Informatics.

[166]  R. Morin,et al.  A cover-based method to assess forest characteristics using inventory data and GIS , 2013 .

[167]  Suming Jin,et al.  Perspectives of Maine Forest Cover Change from Landsat Imagery and Forest Inventory Analysis (FIA) , 2005, Journal of Forestry.

[168]  W. Bechtold,et al.  Changing Stand Structure and Regional Growth Reductions in Georgia's Natural Pine Stands , 1991 .

[169]  Scott L. Powell,et al.  Validation of North American Forest Disturbance dynamics derived from Landsat time series stacks , 2011 .

[170]  R. Birdsey,et al.  Biomass Estimation for Temperate Broadleaf Forests of the United States Using Inventory Data , 1997, Forest Science.

[171]  T. R. Whittier,et al.  Estimation of aboveground forest carbon flux in Oregon: adding components of change to stock-difference assessments , 2014 .

[172]  C. Larsen,et al.  Use of pixel- and plot-scale screening variables to validate MODIS GPP predictions with Forest Inventory and Analysis NPP measures across the eastern USA , 2012 .

[173]  Richard A. Birdsey,et al.  Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region , 2004 .

[174]  P. Hulme,et al.  Distribution modelling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA , 2011 .

[175]  R. Hofstetter,et al.  Survey for Armillaria by Plant Associations in Northern Arizona , 2014 .

[176]  William S. Keeton,et al.  Forest carbon storage in the northeastern United States: Net effects of harvesting frequency, post-harvest retention, and wood products , 2010 .

[177]  L. Hockstad,et al.  Inventory of U.S. Greenhouse Gas Emissions and Sinks , 2018 .

[178]  K. Riitters,et al.  Landscape correlates of forest plant invasions: A high‐resolution analysis across the eastern United States , 2018 .

[179]  Nicholas Skowronski,et al.  Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey , 2007 .

[180]  Hong S. He,et al.  Integration of Satellite Imagery and Forest Inventory in Mapping Dominant and Associated Species at a Regional Scale , 2009, Environmental management.

[181]  Dirk Pflugmacher,et al.  Regional Applicability of Forest Height and Aboveground Biomass Models for the Geoscience Laser Altimeter System , 2008, Forest Science.

[182]  Jianbang Gan,et al.  Availability of logging residues and potential for electricity production and carbon displacement in the USA , 2006 .

[183]  Mark W. Schwartz,et al.  Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginiana , 1999 .

[184]  Chengquan Huang,et al.  Improving estimates of forest disturbance by combining observations from Landsat time series with U.S. Forest Service Forest Inventory and Analysis data , 2014 .

[185]  Michael J. Falkowski,et al.  A review of methods for mapping and prediction of inventory attributes for operational forest management , 2014 .

[186]  D. Zheng,et al.  Forest biomass estimated from MODIS and FIA data in the Lake States: MN, WI and MI, USA , 2007 .

[187]  G. Collatz,et al.  Impacts of disturbance history on forest carbon stocks and fluxes: Merging satellite disturbance mapping with forest inventory data in a carbon cycle model framework , 2014 .

[188]  B. Law,et al.  Evaluation and improvement of the Community Land Model (CLM4) in Oregon forests , 2012 .

[189]  Veronica C. Lessard,et al.  Diameter Growth Models Using Minnesota Forest Inventory and Analysis Data , 2001 .

[190]  F. Stephen,et al.  Oak decline and red oak borer outbreak: impact in upland oak-hickory forests of Arkansas, USA , 2012 .

[191]  R. T. Brooks Abundance, distribution, trends, and ownership patterns of early-successional forests in the northeastern United States , 2003 .

[192]  Carlos Alberto Silva,et al.  Mapping Forest Structure and Composition from Low-Density LiDAR for Informed Forest, Fuel, and Fire Management at Eglin Air Force Base, Florida, USA , 2016 .