PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
April 2014
309
Special Issue Foreword
Remote Sensing of Soils for Environmental Assessment and Management
Stephen D. DeGloria, James R. Irons, and Larry T. West
The next generation of imaging systems integrated
with complex analytical methods will revolutionize
the way we inventory and manage soil resources across
a wide range of scientific disciplines and application
domains. This special issue highlights those systems
and methods for the direct benefit of environmental
professionals and students who employ imaging and
geospatial information for improved understanding,
management, and monitoring of soil resources.
We solicited articles for this special issue of
Photogrammetric Engineering & Remote Sensing
(
PE&RS
) on the following topics:
•
Imaging and geospatial information for
characterizing dynamic soil properties
•
Airborne topographic lidar for mapping terrain
derivatives and landscape conditions
•
Multi- and hyper-spectral image processing and
analysis for soil survey
•
Radar remote sensing of soils
•
Remote and proximal sensing of soil properties for
digital soil mapping
•
Unmanned Aerial Systems (UAS) for soil
characterization and monitoring
•
Geospatial data fusion for soil inventory, mapping,
and resource management
•
Web-enabled soil assessment and monitoring
We received a number of manuscripts which were
subjected to the standard peer-review process for papers
submitted to
PE&RS
. From that set, we selected six
papers for publication in this special issue. The special
issue Highlight Article complements these six papers
by providing an overview of other forms of remotely
and proximally sensed data and related geospatial
information for soil investigations.
In the first paper, “Toward Linking Aboveground
Vegetation Properties and Soil Microbial Communities
Using Remote Sensing,” Hamada
et al
. provide an
extensive literature review on the importance of
advancing our understanding of the spatial distribution
of soil microorganisms which contribute significantly
to the functioning of terrestrial ecosystems. They posit
that remote sensing and attendant imaging technologies,
when integrated with soil microbial research findings,
can enhance our ability to map the spatial distribution of
these communities at landscape scale. They advocate for
a new research paradigm to integrate biophysical remote
sensing with soil microbial community biogeography
through standardization of taxonomy, improve strategies
to scale and correlate observed surface properties with
characteristics of subsurface microbial communities,
and promote interdisciplinary collaborations.
In the second paper, “Mapping the Subaqueous Soils
of Lake Champlain’s Missisquoi Bay using Ground-
Penetrating Radar, Digital Soil Mapping and Field
Measurements,” Libohova
et al.
convey the importance
of mapping soils in subaqueous environments to
improve understanding of depositional environments
in fresh water systems. They focus on characterizing
chemical and physical properties using ground
penetrating radar and laboratory analyses and relating
those properties to selected subaqueous depositional
landscapes and aquatic vegetation types using digital
soil mapping techniques. Several landscape units
were defined based on interpretation of radar data
in conjunction with subaqueous soil properties,
geomorphic setting, and differences in water depth.
The authors argue that such data from active sensors
when combined with terrain analysis and limited field
sampling can be used to map subaqueous soils in other
freshwater lakes and ponds in temperate latitudes.
In the third paper, “Geostatistical Methods
for Predicting Soil Moisture Continuously in a
Subalpine Basin,” Williams and Anderson explore
the use of spatial statistical methods to map the
spatial distribution of soil moisture conditions in
a mountainous landscape. They apply regression
modeling and interpolation methods to optimally
combine remotely sensed imagery and lidar data for
predicting the spatial distribution of a soil property key
to understanding alpine ecosystems. Their approach
is well-suited to characterizing soil properties and
advancing our understanding of local variations of
soil moisture conditions under short-range terrain
differences as controlled by slope position. This paper
is an excellent example of how regression modeling
with remotely sensed predictor variables is being used
to estimate soil properties in diverse landscapes.
In the fourth paper, “Mapping Impervious Surfaces
Using Object-oriented Classification in a Semiarid Urban
Region,” Sugg
et al
. address the challenge of mapping
impervious surfaces in urban areas where traditional
methods tend to yield unreliable results. Employing
remotely sensed imagery of high spatial resolution, they
successfully mapped impervious surfaces using advanced
image classification techniques without relying on
spectral indicators common to mapping such surfaces. By
attaining high classification accuracy, they demonstrate
a more efficient methodology comparable to manual
interpretation of high resolution imagery for monitoring
impervious surfaces associated with urban growth in arid
and semi-arid environments. This mapping approach
holds promise for hydrologic modeling and watershed
management at variable spatial scales.
