Three chapters are included in Part I. In Chapter 2, Ehlers and Klonus examine data
fusion results of remote sensing imagery with various spatial scales. The scales are
thought to relate to the ground sampling distances (GSDs) of the respective sensors.
They find that for electro-optical sensors GSD or scale ratios of 1:10 (e.g., IKONOS
and SPOT-5 fusion) can still produce acceptable results if the fusion method is based
on a spectral characteristic-preserving technique such as the Ehlers fusion. Using
radar images as a substitute for high-resolution panchromatic data is possible, but only
for scale ratios between 1:6 and 1:20 due to the limited feature recognition in radar
images. In Chapter 3, Quattrochi and Luvall revisit an article published in Landscape
Ecology in 1999 by them and examine the direct or indirect uses of thermal infrared
(TIR) remote sensing data to analyze landscape biophysical characteristics to offer
insights on how these data can be used more robustly for furthering the understanding
and modeling of landscape ecological processes. In Chapter 4, Weng discusses some
important scale-related issues in urban remote sensing. The requirements for mapping
three interrelated entities or substances in the urban space (i.e., material, land cover,
and land use) and their relationships are first examined. Then, the relationship
between spatial resolution and the fabric of urban landscapes is assessed. Next,
the operational scale/optimal scale for the studies of land surface temperature are
reviewed. Finally, the issue of scale dependency of urban phenomena is discussed via
reviewing two case studies, one on land surface temperature (LST) variability across
multiple census levels and the other on multiscale residential population estimation
modeling.
Part II also contains three chapters. Vegetation indices can be used to separate
landscape components into bare soil, water, and vegetation and, if calibrated with
ground data, to quantify biophysical variables such as leaf area index and fractional
cover and physiological variables such as evapotranspiration and photosynthesis. In
Chapter 5, Glenn, Nagler, and Huete use a case study approach to show how remotely
sensed vegetation indices collected at different scales can be used in vegetation
change detection studies. The primary sensor systems discussed are digital phenocams, Landsat and MODIS, which cover a wide range of spatial (1 cm–250 m) and
temporal (15 min–16 days) resolutions/scales. Sources of error and uncertainty
associated with both ground and remote sensing measurements in change studies
are also discussed. In Chapter 6, Wang and Zhang combine plot data and Thematic
Mapped (TM) images to map above-ground forest carbon at a 990-m pixel resolution
in Lin-An, Zhejiang Province, China, by using two upscaling methods: point simple
cokriging point cosimulation and point simple cokriging block cosimulation Their
results suggest that both methods perform well in scaling up the spatial data as well as
in revealing the propagation of input data uncertainties from a finer spatial resolution
to a coarser one. The output uncertainties reflect the spatial variability of the
estimation accuracy caused by the locations of the input data and the values
themselves. In Chapter 7, Yuhong He intends to bridge the gap in spatial scales
through estimating grassland chlorophyll contents from leaf to landscape level using a
simple yet effective canopy integration method. Using data collected in a heterogeneous tall grassland located at Ontario, Canada, Yuhong’s study first scales leaf level
chlorophyll measurements to canopy and landscape levels and then investigates the
6
CHARACTERIZING, MEASURING, ANALYZING, AND MODELING SCALE
fusion results of remote sensing imagery with various spatial scales. The scales are
thought to relate to the ground sampling distances (GSDs) of the respective sensors.
They find that for electro-optical sensors GSD or scale ratios of 1:10 (e.g., IKONOS
and SPOT-5 fusion) can still produce acceptable results if the fusion method is based
on a spectral characteristic-preserving technique such as the Ehlers fusion. Using
radar images as a substitute for high-resolution panchromatic data is possible, but only
for scale ratios between 1:6 and 1:20 due to the limited feature recognition in radar
images. In Chapter 3, Quattrochi and Luvall revisit an article published in Landscape
Ecology in 1999 by them and examine the direct or indirect uses of thermal infrared
(TIR) remote sensing data to analyze landscape biophysical characteristics to offer
insights on how these data can be used more robustly for furthering the understanding
and modeling of landscape ecological processes. In Chapter 4, Weng discusses some
important scale-related issues in urban remote sensing. The requirements for mapping
three interrelated entities or substances in the urban space (i.e., material, land cover,
and land use) and their relationships are first examined. Then, the relationship
between spatial resolution and the fabric of urban landscapes is assessed. Next,
the operational scale/optimal scale for the studies of land surface temperature are
reviewed. Finally, the issue of scale dependency of urban phenomena is discussed via
reviewing two case studies, one on land surface temperature (LST) variability across
multiple census levels and the other on multiscale residential population estimation
modeling.
Part II also contains three chapters. Vegetation indices can be used to separate
landscape components into bare soil, water, and vegetation and, if calibrated with
ground data, to quantify biophysical variables such as leaf area index and fractional
cover and physiological variables such as evapotranspiration and photosynthesis. In
Chapter 5, Glenn, Nagler, and Huete use a case study approach to show how remotely
sensed vegetation indices collected at different scales can be used in vegetation
change detection studies. The primary sensor systems discussed are digital phenocams, Landsat and MODIS, which cover a wide range of spatial (1 cm–250 m) and
temporal (15 min–16 days) resolutions/scales. Sources of error and uncertainty
associated with both ground and remote sensing measurements in change studies
are also discussed. In Chapter 6, Wang and Zhang combine plot data and Thematic
Mapped (TM) images to map above-ground forest carbon at a 990-m pixel resolution
in Lin-An, Zhejiang Province, China, by using two upscaling methods: point simple
cokriging point cosimulation and point simple cokriging block cosimulation Their
results suggest that both methods perform well in scaling up the spatial data as well as
in revealing the propagation of input data uncertainties from a finer spatial resolution
to a coarser one. The output uncertainties reflect the spatial variability of the
estimation accuracy caused by the locations of the input data and the values
themselves. In Chapter 7, Yuhong He intends to bridge the gap in spatial scales
through estimating grassland chlorophyll contents from leaf to landscape level using a
simple yet effective canopy integration method. Using data collected in a heterogeneous tall grassland located at Ontario, Canada, Yuhong’s study first scales leaf level
chlorophyll measurements to canopy and landscape levels and then investigates the
6
CHARACTERIZING, MEASURING, ANALYZING, AND MODELING SCALE