PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
April 2014
287
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
April 2014 Volume 80 Number 4
PE ER - REV I EWED ART I C L ES
Yuki Hamada, Jack A. Gilbert, Peter E. Larsen, and Madeline J. Norgaard
Exploring how biophysical remote sensing may help understand distribution of soil
microbial communities.
Zamir Libohova, James Doolittle, Reed Sims, Thomas Villars, and Larry T. West
The work on mapping subaqueous soils using Ground Penetrating Radar (GPR) in
combination with digital soil mapping (DSM) and field observations.
Katherine E. Williams and Sharolyn J. Anderson
Regression modeling, remotely sensed imagery, lidar, and interpolation techniques to
produce continuous representations of soil moisture.
Zachary P. Sugg, Tobias Finke, David C. Goodrich, M. Susan Moran, and Stephen R. Yool
An object-oriented classification approach using high-resolution imagery developed for
mapping impervious surfaces in arid and semiarid urban areas.
Travis W. Nauman, James A. Thompson, and Craig Rasmussen
Methods of updating a conventional soil map by disaggregating the original polygonal
map units into a field scale raster with DEM and multispectral data using a case study
in the Sonoran Desert, USA.
Shiliang Su, Rui Xiao, and Yuan Zhang
Remote sensing, geographical information systems, and digital soil data to capture the
dynamics of agricultural soil sealing in peri-urban areas and to quantitatively analyze
their relationships with urbanization.
COLUMNS
ANNOUNCEMENTS
DEPARTMENTS
The cover images
reflect the complexity
of soils across various
spatial-temporal scales.
Soil forming factors as
defined and described
by Jenny (1941) and
McBratney (2003) have
been and continue
to be at the center
of soil science. They
express themselves
at different scales varying from global all the
way to molecular level. A Soil-Landscape model
represents a fundamental paradigm of soil mapping
and predictions. Most importantly, Soil landscape
models bring the interactions of soil forming factors
(climate, organisms, parent material, relief and time)
into focus at a human scale; an operational scale.
The introduction and use of remotely sensed data on
characterizing climate, vegetation, topography, and
soil physical, chemical and biological properties has
enabled soil scientists to deepen their understanding
of soil functions. The integration of remotely sensed
data with fundamental understanding of physical
and biological processes and coupled with direct
measurements in a soil system approach allows
for a quantitative, dynamic soil pedology that can
be understood and leveraged at scales relevant to
those who actively manage, change, and sustain
the land. The contributing sources of images are
listed after the references in the highlight article.
