2024-03-29T15:28:58Z
http://mcfns.com/index.php/Journal/oai
oai:ojs.mcfns.com:article/115
2011-08-29T10:26:06Z
Journal:FIA
Calculation of Upper Confidence Bounds on Proportion of Area containing Not-sampled Vegetation Types: An Application to Map Unit definition for Existing Vegetation Maps
Patterson, Paul L.
Finco, Mark
Forest inventory, biometrics
Map unit, remote sensing, FIA, dominance type, grid sampling, confidence bounds, mid-level map
This paper explores the information forest inventory data can produce regarding forest types that were not sampled and develops the equations necessary to define the upper confidence bounds on not-sampled forest types. The problem is reduced to a Bernoulli variable. This simplification allows the upper confidence bounds to be calculated based on Cochran (1977). Examples are provided that demonstrate how the resultant equations are relevant to creating mid-level vegetation maps by assisting in the development of statistically defensible map units.
Contemporary Journal Concept Press
U.S. Forest Service
2011-08-01
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed FIA-related Papers
Sampling and estimation
application/pdf
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-98
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 3, No 2: MCFNS August 28, 2011; Pages: 98-101 (4)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-98/MCFNS-3%3A98
Utah, USA
Contemporary
FIA data
oai:ojs.mcfns.com:article/113
2011-08-29T10:26:06Z
Journal:FIA
Consistency Of Forest Presence And Biomass Predictions Modeled Across Overlapping Spatial And Temporal Extents
Nelson, Mark D.
Healey, Sean P.
Moser, Warren Keith
Masek, Jeffrey G.
Cohen, Warren
Remote sensing
Consistency analyses, North American Forest Dynamics, NAFD, Landsat, Random Forests, forest inventory, FIA
We assessed the consistency across space and time of spatially explicit models of forest presence and biomass in southern Missouri, USA, for adjacent, partially overlapping satellite image Path/Rows, and for coincident satellite images from the same Path/Row acquired in different years. Such consistency in satellite image-based classification and estimation is critical to national and continental monitoring programs that depend upon processed satellite imagery, such as the North American Forest Dynamics Program. We tested the interchangeability of particular image acquisitions across time and space in the context of modeling forest biomass and forest presence with a non-parametric Random Forests-based approach. Validation at independent USA national forest inventory plots suggested statistically consistent model accuracy, even when the images used to apply the models were acquired in different years or in different image frames from the images used to build the models. For mapping projects using near-anniversary date imagery and employing careful radiometric correction, advantages of image interchangeability include the ability to build models with more ground data by combining adjacent image frames and the ability to apply models of assessed accuracy to early satellite images for which no corresponding field data may be available.
Contemporary Journal Concept Press
U.S. Forest Service
2011-08-02
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed FIA-related Papers
Image anaysis; Random Forests
application/pdf
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-102
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 3, No 2: MCFNS August 28, 2011; Pages: 102-113 (11)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-102/MCFNS-3%3A102
Missouri, USA
Contemporary
FIA plot data; Landsat image data
oai:ojs.mcfns.com:article/114
2011-08-29T10:26:06Z
Journal:FIA
Errors In Terrain-Based Model Predictions Caused By Altered Forest Inventory Plot Locations In The Southern Appalachian Mountains, USA
Wang, Huei-Jin
Prisley, Stephen P.
Radtke, Philip J.
Coulston, John
Forest inventory, GIS
uncertainty; errors in measurements; GIS; FSQI
Forest modeling applications that cover large geographic areas can benefit from the use of widely-held knowledge about relationships between forest attributes and topographic variables. A noteworthy example involves the coupling of field survey data from the Forest Inventory Analysis (FIA) program of USDA Forest Service with digital elevation model (DEM) data in attempts to explain how topographic characteristics influence forest productivity, vegetation composition, fire behavior, and other phenomena. Because U.S. federal law prohibits the release of actual FIA plot coordinates, only altered coordinates are released to the public. Here, terrain-based variables derived from a 10 m DEM using actual FIA plot locations were compared to those from altered plot locations in a region of the Southern Appalachian Mountains of western North Carolina, USA. Variables examined included simple terrain attributes such as percent slope and azimuth of aspect, and composite attributes such as terrain shape index, flow accumulation, slope position and forest site quality index. Results showed little correspondence between variables from altered plot locations compared to those derived using actual locations. Further, FIA field measurements of percent slope and azimuth of aspect showed little correspondence with corresponding DEM-derived estimates from the actual plot coordinates. In order to effectively link FIA plot data with DEM-derived topographic variables in mountainous regions like the Southern Appalachians, access to actual plot coordinates or terrain variables derived from them may be required.
Contemporary Journal Concept Press
U.S. Forest Service
Virginia Tech
2011-08-02
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed FIA-related Papers
Correlation; Classification
application/pdf
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-114
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 3, No 2: MCFNS August 28, 2011; Pages: 114-123 (9)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-114/MCFNS-3%3A114
North Carolina, USA
Contemporary
FIA data; USGS Digital elevation data
oai:ojs.mcfns.com:article/131
2013-03-21T22:54:34Z
Journal:FIA
Selected Examples of Advanced FIA Data Analysis
Cieszewski, Chris J
McRoberts, Ronald E
Mensuration, Forest Inventory, GIS
Symposium Proceedings, FIA, Forest Inventory, Remote Sensing, GIS, Landsat
This short discussion introduces the contents of the new Special Section on Advanced FIA Data Analysis that has been recently created in the Mathematical and Computational Forestry Natural-Resource Sciences (MCFNS) journal as a result of collaboration between the journal editors and the USDA Forest Service FIA (Forest Inventory Analysis) scientists. This section contains three papers, which originated from presentations at the 9th Annual FIA Symposium held at Park City, Utah on October 21-23, 2008. The papers contained here went through two screening criteria, passed a peer-review process, and were assessed by the journal editors as consistent with the MCRNS journal scope and focus and valuable contributions to the journal contents.
Contemporary Journal Concept Press
2011-08-29
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed FIA-related Papers
GIS analysis, satellite imagery analysis
application/pdf
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-96
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 3, No 2: MCFNS August 28, 2011; Pages: 96-97 (2)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-96/MCFNS.3-96
North American Forestry
Contemporary
Stand and Forest Parameters, Spatial geographical attributes