8
VISUALIZING SCALE-DOMAIN
MANIFOLDS: A MULTISCALE
GEO-OBJECT-BASED APPROACH
GEOFFREY J. HAY
8.1 INTRODUCTION
. . . Scale affects everything . . .
Understanding, modeling, managing, and forecasting landscape-based pattern–
process interactions through multiple temporal and spatial scales represent key
research activities in landscape ecology (Wu and Hobbs, 2002; Wu, 2007). To
achieve these activities, ecologists and others are increasingly relying upon and
integrating advances in areas outside traditional ecology, such as geoinformatics,
remote sensing, complexity theory, computer vision, and pattern recognition (Hay
et al., 1997, 2001; Burnett and Blaschke, 2003; Hay and Marceau, 2004; Castilla
et al., 2008; Steiniger and Hay, 2009; Wu and Li, 2009; Powers et al., 2012). The
necessity for doing so is clear: Landscapes are complex systems composed of a large
number of heterogeneous components that interact in a nonlinear way, are hierarchically structured, and are scale dependent (Waldrop, 1992; Nicolis and Prigogine,
1989; Kay and Regier, 2000; Hay et al., 2002a,b; Wu and Marceau, 2002). Remote
sensing technologies represent the only source of large-area data, and geoinformatics,
computer vision, pattern recognition, and complexity theory provide useful concepts,
methods, models, tools, and insight for defining, visualizing, querying, and managing
these data (Hay et al., 2003). Furthermore, since the advent of NASA’s mission to
planet Earth with its plethora of Earth-orbiting sensors and their daily generation of
141
Scale Issues in Remote Sensing, First Edition. Edited by Qihao Weng.
Ó 2014 John Wiley & Sons, Inc. Published 2014 by John Wiley & Sons, Inc.
VISUALIZING SCALE-DOMAIN
MANIFOLDS: A MULTISCALE
GEO-OBJECT-BASED APPROACH
GEOFFREY J. HAY
8.1 INTRODUCTION
. . . Scale affects everything . . .
Understanding, modeling, managing, and forecasting landscape-based pattern–
process interactions through multiple temporal and spatial scales represent key
research activities in landscape ecology (Wu and Hobbs, 2002; Wu, 2007). To
achieve these activities, ecologists and others are increasingly relying upon and
integrating advances in areas outside traditional ecology, such as geoinformatics,
remote sensing, complexity theory, computer vision, and pattern recognition (Hay
et al., 1997, 2001; Burnett and Blaschke, 2003; Hay and Marceau, 2004; Castilla
et al., 2008; Steiniger and Hay, 2009; Wu and Li, 2009; Powers et al., 2012). The
necessity for doing so is clear: Landscapes are complex systems composed of a large
number of heterogeneous components that interact in a nonlinear way, are hierarchically structured, and are scale dependent (Waldrop, 1992; Nicolis and Prigogine,
1989; Kay and Regier, 2000; Hay et al., 2002a,b; Wu and Marceau, 2002). Remote
sensing technologies represent the only source of large-area data, and geoinformatics,
computer vision, pattern recognition, and complexity theory provide useful concepts,
methods, models, tools, and insight for defining, visualizing, querying, and managing
these data (Hay et al., 2003). Furthermore, since the advent of NASA’s mission to
planet Earth with its plethora of Earth-orbiting sensors and their daily generation of
141
Scale Issues in Remote Sensing, First Edition. Edited by Qihao Weng.
Ó 2014 John Wiley & Sons, Inc. Published 2014 by John Wiley & Sons, Inc.