National Forest Inventories (NFI's) have been established in many countries to monitor status and trends in forested ecosystems. Sample data collected under these programs are typically intended for reporting on forest characteristics over large geographic areas and time periods, providing valuable information for strategic level planning. When trying to understand forest characteristics at a scale relevant to forest management, the number of NFI plots per project area is typically small, resulting in estimates that lack the precision needed to support good project decisions based on this data. Additionally, areas of interest like those scheduled for treatment (such as fuels, harvest, or wildlife habitat enhancement), or those affected by disturbance events (such as fire, wind, flooding) also typically have a small number of samples.
There is a growing need for NFI forest estimates and information over smaller geographic areas and for shorter time periods. But often, the base-program sampling frame results in too few plots to construct reliable estimates for the small areas or short timeframes using current estimation processes. This Research Topic will explore estimation alternatives through which the NFI analysts and users might produce statistically defensible estimates over domains of interest that are smaller - spatially and/or temporally - without the expense of additional field data collection. This Research Topic gives an opportunity to NFI users, scientists, statisticians and developers to express information needs, highlight methodological advances, and showcase new tools and applications.
We encourage a variety of types of manuscripts for this special issue. Policy and Practice Reviews, or Perspective papers, are encouraged to represent and document different user community needs. Original Research and Methods manuscripts are encouraged to showcase scientific advances. Review or Mini-Review articles are encouraged to summarize the state of knowledge in this field. Data Reports may be contributed to document substantive datasets containing small area estimates. Other types of manuscripts will be considered, with the intent that, collectively, this Research Topic will blend a presentation of user needs, technical advances, and available data and tools.
Authors are invited to submit work in the diverse formats mentioned above that relate to, but are not limited to, the following questions:
• What are the most pressing needs for small area estimates in forest management and planning?
• How can the effects of disturbances in forests (such as fire, wind, flood, insect and disease) be quantified?
• How do we capture statistically significant change through time?
• Which small area models perform the best under different circumstances?
• What datasets support small area estimates?
• How can uncertainty in small area estimates be used in management decisions?
• How can small area estimates be best delivered to meet diverse needs?
Cover Image Credit: Scott Dickson – Forest Inventory and Analysis (FIA), USDA Forest Service
National Forest Inventories (NFI's) have been established in many countries to monitor status and trends in forested ecosystems. Sample data collected under these programs are typically intended for reporting on forest characteristics over large geographic areas and time periods, providing valuable information for strategic level planning. When trying to understand forest characteristics at a scale relevant to forest management, the number of NFI plots per project area is typically small, resulting in estimates that lack the precision needed to support good project decisions based on this data. Additionally, areas of interest like those scheduled for treatment (such as fuels, harvest, or wildlife habitat enhancement), or those affected by disturbance events (such as fire, wind, flooding) also typically have a small number of samples.
There is a growing need for NFI forest estimates and information over smaller geographic areas and for shorter time periods. But often, the base-program sampling frame results in too few plots to construct reliable estimates for the small areas or short timeframes using current estimation processes. This Research Topic will explore estimation alternatives through which the NFI analysts and users might produce statistically defensible estimates over domains of interest that are smaller - spatially and/or temporally - without the expense of additional field data collection. This Research Topic gives an opportunity to NFI users, scientists, statisticians and developers to express information needs, highlight methodological advances, and showcase new tools and applications.
We encourage a variety of types of manuscripts for this special issue. Policy and Practice Reviews, or Perspective papers, are encouraged to represent and document different user community needs. Original Research and Methods manuscripts are encouraged to showcase scientific advances. Review or Mini-Review articles are encouraged to summarize the state of knowledge in this field. Data Reports may be contributed to document substantive datasets containing small area estimates. Other types of manuscripts will be considered, with the intent that, collectively, this Research Topic will blend a presentation of user needs, technical advances, and available data and tools.
Authors are invited to submit work in the diverse formats mentioned above that relate to, but are not limited to, the following questions:
• What are the most pressing needs for small area estimates in forest management and planning?
• How can the effects of disturbances in forests (such as fire, wind, flood, insect and disease) be quantified?
• How do we capture statistically significant change through time?
• Which small area models perform the best under different circumstances?
• What datasets support small area estimates?
• How can uncertainty in small area estimates be used in management decisions?
• How can small area estimates be best delivered to meet diverse needs?
Cover Image Credit: Scott Dickson – Forest Inventory and Analysis (FIA), USDA Forest Service
