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Current PE&RS Cover

PE&RS August 2007

This series of images of Redfish Bay, Texas provided by Fugro EarthData, Inc. demonstrates the image classification steps from digital orthoimage to GIS map. This benthic (seafloor) mapping project was performed by Fugro EarthData for the NOAA Coastal Services Center and will support the Texas Seagrass Monitoring Program. The bottom image, a true color digital ortho, displays submerged aquatic vegetation (seagrass and macroalgae), oyster reefs, mangroves and upland islands. The aerial imagery was collected at a 1m pixel resolution in November 2004 using a Leica ADS40 camera as part of the USDA National Agriculture Imaging Program. The middle image depicts habitat polygons, created from the ortho using Definiens Professional, overlaid on top of the ortho. The top image showcases the final benthic habitat map, which incorporates field verification data. This map will be used to help locate, monitor, and protect seagrass beds along the southern Texas coast as well as support other coastal management and environmental issues. For more information call +1-301-948-8550 or email info@earthdata.com.

Highlight Article

861 Using Object-Oriented Classification of ADS40 Data to Map the Benthic Habitats of the State of Texas (Adobe PDF 393Kb)
Kass Green and Chad Lopez

Columns & Updates
869 Grids and Datums — Republic of Estonia (Adobe PDF 119Kb)
871 Mapping Matters
875 Book Review — Resource Management Information Systems: Remote Sensing, GIS and Modelling (Adobe PDF 114Kb)
880 Headquarters News (Adobe PDF 131Kb)
883 Industry News

Announcements
922 Call for Papers — Spatial Change Analysis

Departments
865 New Member List
880 Region of the Month
881 ASPRS Member Champions (Adobe PDF 116Kb)
884 Certification List
887 Who’s Who in ASPRS
888 Sustaining Members
890 Classifieds
891 Instructions to Authors
904 Forthcoming Articles
922 Calendar
966 Professional Directory
967 Advertiser Index
968 Membership Application

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

893 Optimizing Image Resolution to Maximize the Accuracy of Hard Classification
P.K. Bøcher and K.R. McCloy

The relationship between classification accuracy and within class variances is investigated showing that within class variances are a function of image resolution.

905 The Importance of Scale in Object-based Mapping of Vegetation Parameters with Hyperspectral Imagery
Elisabeth A. Addink, Steven M. de Jong, and Edzer J. Pebesma

An investigation of optimal object definition for prediction of biomass and leaf area index.

913 Integrating Fine Scale Information in Super-resolution Land-cover Mapping
Alexandre Boucher and Phaedon C. Kyriakidis

Accounting for additional fine spatial resolution information can lead to super-resolution maps with more realistic spatial patterns.

923 Variability in Soft Classification Prediction and Its Implications for Sub-pixel Scale Change Detection and Super Resolution Mapping
Giles M. Foody and H.T.X. Doan

The impacts of class spectral variability on unmixing the the implications for analyses based on soft classification outputs.

935 Assessing Alternatives for Modeling the Spatial Distribution of Multiple Land-cover Classes at Sub-pixel Scales
Yasuyo Makido, Ashton Shortridge, and Joseph P. Messina

Evaluating three methods for modeling the spatial distribution of multiple land cover classes at sub-pixel scales.

945 Scaling Field Data to Calibrate and Validate Moderate Spatial Resolution Remote Sensing Models
A. Baccini, M.A. Friedl, C.E. Woodcock, and Z. Zhu

An examination of several methods to spatially aggregate biophysical measurements collected in field surveys to the scale of moderate spatial resolution remotely sensed data.

955 Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data
Bing Xu and Peng Gong

Land -use and land-cover classification in an urban rural fringe of the San Francisco Bay Area using EO-1 Hyperion imagery is compared with that using EO-1 ALI imagery, and the application of a computationally efficient segmentation-based feature reduction approach.