PNNL-13501 - Pacific Northwest National Laboratory

Extension of Site Soil/Plant Physical Properties to Landscape and Regional Scales Using
Remote Sensing
Study Control Number: PN00044/1451
Chris J. Murray, Eileen M. Perry
Terrestrial carbon sequestration research requires estimates of plant and soil characteristics across a landscape. Field
measurements, however, are generally limited in number and represent individual sites. Remote sensing imagery may
provide continuous data that can be used to interpolate the limited site data across landscapes. A successful approach to
interpolation would have applications in many areas of environmental sciences.
Project Description
The purpose of this project was to develop new
capabilities in scaling site measurements or modeled point
data over an entire landscape. Remote sensing datasets
were used to derive both 2 m and 15 m estimates of plant
biomass over farm and range test sites. The 2 m
resolution datasets were used to evaluate three approaches
to interpolation of the 2 m data over a landscape: linear
regression, ordinary kriging, and Gaussian simulation
with locally varying means. For a 46-ha farm test site,
ordinary kriging yielded the best results, in terms of both
root mean square error and the simulated green biomass
images produced. The kriging results did improve with
the number of 2 m samples used; 100 was the minimum
number of samples required, and significant improvement
was seen with 200 and 400 samples. The investigators
feel that boundary effects may be the reason that the
Gaussian simulation with locally varying means did not
perform as well as ordinary kriging for this test case.
Introduction
Our Laboratory is frequently involved in natural resource
research that requires datasets for spatial models, and
measured and modeled physical soil/plant characteristics
at landscape to regional scales. Murray et al. (1999)
developed geostatistical methods to interpolate shrubsteppe
transect data to landscape scale based on
continuous plant cover data. Our project objective was to
develop new capabilities for scaling site measurements or
modeled data to the landscape or regional scales. This
research was directed to 1) define the current state of
knowledge, and 2) refine or develop new methods to
extend measured or modeled results from sites (plant or
soil carbon) to landscape or regional scales.
Approach
Our approach was to 1) review relevant research; 2) select
a suitable data set; 3) perform an initial evaluation of data;
and 4) perform interpolation based on linear regression,
ordinary kriging, and Gaussian simulation with locally
varying means.
Results and Accomplishments
The literature survey revealed a keen interest in multiscale
issues within the ecology, geosciences, and remote
sensing communities. However, no prior research (other
than performed by the investigators) was directly
applicable to this project. A dataset was selected that had
relevance to a current rangeland health program.
Airborne imagery at 2 m spatial resolution and satellite
imagery at 25 m resolution were acquired during 1999
near Grand Junction, Colorado. These image sets were
used to derive green biomass based on the normalized
difference vegetation index. The purpose of the research
was to evaluate the ability of different methods to
interpolate biomass site data (represented by randomly
selected pixels from the 2 m imagery) to landscape scale.
The first method was linear regression based on fitting the
2 m pixels to 15 m normalized difference vegetation
index values derived from the satellite imagery. The
second method was ordinary kriging (Goovaerts 1997),
where only the 2 m imagery was used; randomly selected
pixels were used to fit a variogram, and then kriging was
used to interpolate to the landscape scale. Ordinary
kriging was performed using datasets with 100, 200, and
400 randomly selected 2 m pixels; the results are shown
in Figure 1. The third method was Gaussian simulation
with locally varying means (Goovaerts 1997), which used
the satellite imagery to partition the landscape into three
Earth System Science 221