PE&RS July 2007

This month’s front cover shows three samples from a new, nationwide forest type group dataset produced through a collaborative effort between three USDA Forest Service units – the Remote Sensing Applications Center, Forest Health Monitoring, and Forest Inventory and Analysis (FIA). These data use two nested classifi cations (forest types and forest type groups) to map forest composition, and the data are available for the contiguous United States and Alaska at 250-meter resolution.

The forest type training data were derived from FIA plot data. Nearly one hundred geospatially continuous predictor layers (e.g., MODIS data, DEM derivatives, and DAYMET climate data) were used as independent variables, and classifi cation and regression tree (CART) models were used to produce these datasets. The results are datasets that show the extent, distribution, and composition of the nation’s forests. An accuracy assessment using an independent random holdout was also performed. More information on the datasets and methodology can be found in a peer-reviewed article in this month’s PE&RS. As with any geospatial data, care should be taken when using moderate resolution data in analyses. These data should only be used with data of similar scale and resolution and at 250-m are appropriate for regional to national scale analyses.

Highlight Article

729 Forest Inventory and Analysis in the United States: Remote Sensing and Geospatial Activities (Adobe PDF 202Kb)
Mark Nelson, Gretchen Moisen, Mark Finco, and Ken Brewer

Columns & Updates
733 Grids and Datums — Republic of Panamá (Adobe PDF 148Kb)
735 Mapping Matters
737 Book Review — Introduction to Microwave Remote Sensing (Adobe PDF 176Kb)
739 In Memoriam – Francis Herbert Moffitt, Michael L. Mooney, and Daniel Mostajo
741 Headquarters News (Adobe PDF 152Kb)
744 Industry News

Announcements
828 Call for Papers — Spatial Change Analysis

Departments
732 Certification List
734 New Member List
738 ASPRS Member Champions (Adobe PDF xxxKb)
741 Region of the Month
777 Who’s Who in ASPRS
778 Sustaining Members
781 Instructions to Authors
792 Forthcoming Articles
828 Calendar
852 Classifieds
854 Professional Directory
855 Advertiser Index
856 Membership Application

Yearbook
747 Board of Directors (Adobe PDF 208Kb)
748 Installation of Officers (Adobe PDF 202Kb)
749 ASPRS: The Imaging and Geospatial Information Society (Adobe PDF 56Kb)
750 ASPRS: The Leader in GIScience and Technology for All — President’s Address (Adobe PDF 527Kb)
755 2006-2007 Executive Director Report (Adobe PDF 291Kb)
760 Awards Recipients (Adobe PDF 560Kb)
771 Memorial Address — Ta Liang (Adobe PDF 699Kb)

Click HERE for a .pdf version of the whole yearbook (file size 2.17Mb)

Peer-Reviewed Articles (Click the linked titles to see the full abstract)

783 Patterns in Forest Clearing Along the Appalachian Trail Corridor
David Potere, Curtis Woodcock, Annemarie Schneider, Mutlu Ozdogan, and Alessandro Baccini

The GeoCover Landsat dataset was used to estimate that 75,000 hectares of forest were cleared on a corridor 3,500 km long.

793 Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation
George T. Raber, John R. Jensen, Michael E. Hodgson, Jason A. Tullis, Bruce A. Davis, and Judith Berglund

The empirical relationship between lidar post-spacing and flood zone delineation was examined within a floodplain of the North Carolina piedmont.

805 Building Boundary Tracing and Regularization from Airborne Lidar Point Clouds
Aparajithan Sampath and Jie Shan

Building boundaries can be determined to a precision of 18 to 21 percent of the lidar point spacing by the proposed tracing and regularization approach.

813 Comparison of Segment and Pixel-based Non-parametric Land Cover Classification in the Brazilian Amazon Using Multi-temporal Landsat TM/ETM+ Imagery
Katherine A. Budreski, Randolph H. Wynne, John O. Browder, and James B. Campbell

Accurate land-cover maps were produced using inter-annual, multi-temporal Landsat TM/EMT+ imagery and pixel-based kNN and CART®; segmentation proved unnecessary.

829 Estimating Species Abundance in a Northern Temperate Forest Using Spectral Mixture Analysis
Lucie C. Plourde, Scott V. Ollinger, Marie-Louise Smith, and Mary E. Martin

Spectral mixture analysis is used to classify sugar maple and American beech abundance in a heterogeneous forest in the northeastern U.S.

841 Exploring the Geostatistical Method for Estimating the Signal-to-Noise Ratio of Images
P.M. Atkinson, I.M. Sargent, G.M. Foody, and J. Williams

The use of geostatistics to estimate the ratio of variance in signal to varience in noise.