PE&RS May 2015 - page 343

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING May 2015 343
H I GHL I GHT ART I C L E
345 Completion of the 2011 National Land Cover Database for
the Conterminous United States – Representing a Decade of
Land Cover Change Information
Collin Homer, Jon Dewitz, Limin Yang, Suming Jin, Patrick Danielson, George
Xian, John Coulston, Nathaniel Herold, James Wickham, and Kevin Megown
PE ER - REV I EWED ART I C L ES
365 Identifying Urban Watershed Boundaries and Area, Fairfax County, Virginia
Tammy E. Parece and James B. Campbell
Identifying often-neglected urban watershed steps to delineate the true area and
boundary of a highly urbanized watershed.
373 Parallel Performance of Typical Algorithms in Remote Sensing based Mapping on a Multi-core
Computer
Jinghui Yang and Jixian Zhang
A straightforward and high-parallelized method is applied to investigate parallel
performance of five categories of typical algorithms in remote sensing based mapping on
two multi-core computers.
387 Evaluation of Lidar-derived DEMs through Terrain Analysis and Field Comparison
Cody P. Gillin, Scott W. Bailey, Kevin J. McGuire, and Stephen P. Prisley
Varying grid resolution of Lidar-derived DEMs showed that watershed boundaries and
upslope accumulation areas were most dependent on DEM processing; their utility in
watershed analyses may be compromised if the best DEM resolution is not chosen for a
given study site.
397 Refining High Spatial Resolution Remote Sensing Image Segmentation for Man-made Objects
through a Collinear and Ipsilateral Neighborhood Model
Min Wang, Yanxia Sun, and Guanyi Chen
A novel neighborhood model for use in high spatial resolution remote sensing image
segmentation.
407 Lidar Detection of the Ten Tallest Trees in the Tennessee Portion of the Great
Smoky Mountains National Park
Chris W. Strother, Marguerite Madden, Thomas R. Jordan, and Andrea Presotto
An automated data mining approach for detecting the tallest trees within the large
LiDAR data set of the Great Smoky Mountains National Park.
PHOTOGRAMME TR I C ENG I NE ER I NG & REMOT E SENS I NG
The official journal for imaging and geospatial information science and technology
May 2015 Volume 81 Number 5
APPLICATIONS
PAPER
COLUMNS
Letter from The President’s Pen 344
Professional Insight—An Interview with 355
Thierry Gregorius
Grids and Datums—Datum Origins with 357
Google EarthTM
Book Review—Thermal Infrared Remote 359
Sensing: Sensors, Methods, Applications
ANNOUNCEMENTS
New Sustaining Members— 361
Hexagon Geospatial and SIIS (SI Imaging
Services)
Pennsylvania Celebrates National 362
Surveyors’ Week
May GeoByte— A Legal Framework for 364
UAVs: How We Get From Here to There?
DEPARTMENTS
Certification 354
Region News 360
ASPRS News 361
New Members 361
Industry News 362
Calendar 364
Forthcoming Articles 386
Who’s Who in ASPRS 415
Sustaining Members 416
Instructions for Authors 418
Membership Application 420
Ohio’s capital city, Columbus, is situated along the Scioto
River and is one of the fastest growing cities in the
state. In 1986, the municipal population was estimated
at 600,000. The latest population estimate for Columbus
from the U.S. Census Bureau is over 820,000.
These two images show Columbus and surrounding
areas in 1986 and again in 2014. The second image
shows the gray urban areas expanding into previous
agricultural land, which is indicated by green patchy
areas. The bright areas throughout the city are retail and
industrial centers. The dark blue spots along the river in
the southern part of the city are wastewater treatment
ponds and other ponds associated with local sand and
gravel quarries.
The historical record provided by Landsat images can be
a useful tool for city managers, planners, and scientists
who are monitoring and documenting the changes to
Earth’s land cover caused by urban expansion.
Sensor: L5 TM, L8 OLI
Path/Row: 19/32
Lat/Long: 40.300/-82.600
Category: World Cities
Download High Resolution
Date Posted: 12/16/2014
This Landsat 8 Image can be viewed at landsat.
usgs.gov/gallery_view.
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