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Estimating number and size of forest patches from FIA plot data

Forest inventory and analysis (FIA) annual plot data provide for estimates of forest area, type, volume, growth, and other attributes. Estimates of forest landscape metrics, such as those describing abundance, size, and shape of forest patches, however, typically are not derived from FIA plot data but from satellite image-based land cover maps. Associating image-based...

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Author(s):

Mark D. Nelson; Andrew J. Lister; Mark H. Hansen

Year:

2009

Keywords:

Source:

In: McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.; McWilliams, William H., eds. Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16-19; Monterey, CA. Gen. Tech. Report WO-79. Washington, DC: U.S. Department of Agriculture, Forest Service. 159-164.

Measuring Forest Area Loss Over Time Using FIA Plots and Satellite Imagery

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...

Author(s):

Michael L. Hoppus; Andrew J. Lister

Year:

2005

Keywords:

Source:

In: McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.; McWilliams, William H.; Cieszewski, Chris J., eds. Proceedings of the fourth annual forest inventory and analysis symposium; Gen. Tech. Rep. NC-252. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 91-97

Applying inventory methods to estimate aboveground biomass from satellite light detection and ranging (LiDAR) forest height data

Light Detection and Ranging (LiDAR) returns from the spaceborne Geoscience Laser Altimeter (GLAS) sensor may offer an alternative to solely field-based forest biomass sampling. Such an approach would rely upon model-based inference, which can account for the uncertainty associated with using modeled, instead of field-collected, measurements. Model-based methods have...

Author(s):

Sean P. Healey; Paul L. Patterson; Sassan Saatchi; Michael A. Lefsky; Andrew J. Lister; Elizabeth A. Freeman; Gretchen G. Moisen

Year:

2012

Keywords:

statistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring

Source:

In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 404-409.

Post-Modeling Histogram Matching of Maps Produced Using Regression Trees

Spatial predictive models often use statistical techniques that in some way rely on averaging of values. Estimates from linear modeling are known to be susceptible to truncation of variance when the independent (predictor) variables are measured with error. A straightforward post-processing technique (histogram matching) for attempting to mitigate this effect is...

Author(s):

Andrew J. Lister; Tonya W. Lister

Year:

2006

Keywords:

Source:

In: Proceedings of the sixth annual forest inventory and analysis symposium; 2004 September 21-24; Denver, CO. Gen. Tech. Rep. WO-70. Washington, DC: U.S. Department of Agriculture Forest Service. 126p.

Assessing alternative measures of tree canopy cover: Photo-interpreted NAIP and ground-based estimates

In preparation for the development of the National Land Cover Database (NLCD) 2011 tree canopy cover layer, a pilot project for research and method development was completed in 2010 by the USDA Forest Service Forest Inventory and Analysis (FIA) program and Remote Sensing Applications Center (RSAC).This paper explores one of several topics investigated during the NLCD...

Author(s):

Chris Toney; Greg Liknes; Andy Lister; Dacia Meneguzzo

Year:

2012

Keywords:

Source:

In: McWilliams, Will; Roesch, Francis A. eds. 2012. Monitoring Across Borders: 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern Mensurationists. e-Gen. Tech. Rep. SRS-157. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 209-215.

Performance of Hierarchical Abundance Models on Simulated Bat Capture Data

The ability to accurately estimate abundance is crucial to ecologists, conservationists, and managers to provide insight on species status, population trends, and viability. Acoustic detection and occupancy modeling can provide an understanding of resource use for bats, but these methods do not estimate how many bats are in an area, or how these numbers change over...

Author(s):

Kathryn M. Womack-Bulliner; Sybill K. Amelon; Frank R. Thompson; Jaymi J. Lebrun

Year:

2019

Keywords:

N-mixture models, multinom