showed that the vegetation index–based sharpening method provided an effective
way to improve the spatial resolution of thermal imagery.
Adaptive choice of spatial and categorical scales in landscape mapping was
demonstrated by Ju et al. (2005). They provided a data-adaptive choice of spatial
scale varying by location jointed with categorical scale by the assistance of a statistical
finite mixture method. Buyantuyev and Wu (2007) systematically analyzed the effects
of thematic resolution on landscape pattern analysis. Two problems need to be
considered in landscape mapping: the multiplicity of classification schemes and the
level of detail of a particular classification. They found that the thematic resolution
had obvious effects on most of the landscape metrics, which indicated that changing
thematic resolution may significantly affect the detection of landscape changes.
However, an increase in spatial resolution may not lead to a better observation since
objects may be oversampled and their features may vary and be confusing (Hsieh
et al., 2001; Aplin and Atkinson, 2004). Although coarse resolution may include
fewer features, imagery with too fine resolution for a specific purpose can be degraded
in the process of image resampling (Ju et al., 2005). Remote sensing data may not be
always be sufficient when specific problems were addressed at specific scales and onground assessment may be needed, since coarser imagery cannot provide sufficient
information about the location and connectivity in specific areas (Ludwig et al., 2007).
Substantial researches have previously been conducted on scale-related issues in
remote sensing studies, as discussed above. This book intends to revisit and
reexamine the scale and related issues. It will also address how new frontiers in
Earth observation technology since 1999—such as very high resolution, hyperspectral, lidar sensing, and their synergy with existing technologies and advances
in remote sensing imaging science such as object-oriented image analysis, data fusion,
and artificial neural networks—have impacted the understanding of this basic but
pivotal issue. The scale-related issues will be examined from three interrelated
perspectives: in landscape properties, patterns, and processes. These examinations
are preceded by a theoretical exploration of the scale issue by a group of authorities in
the field of remote sensing. The concluding section prospects emerging trends in
remote sensing over the next decade(s) and their relationship with scale.
1.2 CHARACTERIZING, MEASURING, ANALYZING,
AND MODELING SCALE
This book consists of 5 parts and 14 chapters, in addition to this introductory chapter.
Part I focuses on theoretical aspects of scale and scaling. Part II deals with the
estimation and measurement of vegetation parameters and ecosystems across various
spatial and temporal scales. Part III examines the effect of scaling on image
segmentation and object extraction from remotely sensed imagery. Part IV exemplifies with case studies on the scale and scaling issues in land cover analysis and in
land–atmosphere interactions. Finally, Part V addresses how new frontiers in Earth
observation technology, such as hyperspectral and lidar sensing, have impacted the
understanding of the scale issue.
CHARACTERIZING, MEASURING, ANALYZING, AND MODELING SCALE
5
way to improve the spatial resolution of thermal imagery.
Adaptive choice of spatial and categorical scales in landscape mapping was
demonstrated by Ju et al. (2005). They provided a data-adaptive choice of spatial
scale varying by location jointed with categorical scale by the assistance of a statistical
finite mixture method. Buyantuyev and Wu (2007) systematically analyzed the effects
of thematic resolution on landscape pattern analysis. Two problems need to be
considered in landscape mapping: the multiplicity of classification schemes and the
level of detail of a particular classification. They found that the thematic resolution
had obvious effects on most of the landscape metrics, which indicated that changing
thematic resolution may significantly affect the detection of landscape changes.
However, an increase in spatial resolution may not lead to a better observation since
objects may be oversampled and their features may vary and be confusing (Hsieh
et al., 2001; Aplin and Atkinson, 2004). Although coarse resolution may include
fewer features, imagery with too fine resolution for a specific purpose can be degraded
in the process of image resampling (Ju et al., 2005). Remote sensing data may not be
always be sufficient when specific problems were addressed at specific scales and onground assessment may be needed, since coarser imagery cannot provide sufficient
information about the location and connectivity in specific areas (Ludwig et al., 2007).
Substantial researches have previously been conducted on scale-related issues in
remote sensing studies, as discussed above. This book intends to revisit and
reexamine the scale and related issues. It will also address how new frontiers in
Earth observation technology since 1999—such as very high resolution, hyperspectral, lidar sensing, and their synergy with existing technologies and advances
in remote sensing imaging science such as object-oriented image analysis, data fusion,
and artificial neural networks—have impacted the understanding of this basic but
pivotal issue. The scale-related issues will be examined from three interrelated
perspectives: in landscape properties, patterns, and processes. These examinations
are preceded by a theoretical exploration of the scale issue by a group of authorities in
the field of remote sensing. The concluding section prospects emerging trends in
remote sensing over the next decade(s) and their relationship with scale.
1.2 CHARACTERIZING, MEASURING, ANALYZING,
AND MODELING SCALE
This book consists of 5 parts and 14 chapters, in addition to this introductory chapter.
Part I focuses on theoretical aspects of scale and scaling. Part II deals with the
estimation and measurement of vegetation parameters and ecosystems across various
spatial and temporal scales. Part III examines the effect of scaling on image
segmentation and object extraction from remotely sensed imagery. Part IV exemplifies with case studies on the scale and scaling issues in land cover analysis and in
land–atmosphere interactions. Finally, Part V addresses how new frontiers in Earth
observation technology, such as hyperspectral and lidar sensing, have impacted the
understanding of the scale issue.
CHARACTERIZING, MEASURING, ANALYZING, AND MODELING SCALE
5