Part IV introduces three case studies on the scale and scaling issues in analysis of
land cover, landscape metrics, and biophysical parameters. Chapter 11 by Liu and
Weng assesses the effect of scaling on the relationship between landscape pattern and
land surface temperature with a case study in Indianapolis, Indiana. A set of spatial
resolutions were compared by using a landscape metric space. They find that the
spatial resolution of 90 m is the optimal scale to study the relationship and think that it
is the operational scale of the urban thermal landscape in Indianapolis. In Chapter 12,
Liang and Weng provide an evaluation of the effectiveness of the triangular prism
fractal algorithm for characterizing urban landscape in Indianapolis based on eight
satellite images acquired by five different sensors: Landsat Multispectral Scanner,
Landsat Thematic Mapper, Landsat Enhanced Thematic Mapper Plus, Advanced
Spaceborne Thermal Emission and Reflection, and IKONOS. Fractal dimensions
computed from the selected original, classified, and resampled images are compared
and analyzed. The potential of fractal measurement in the studies of landscape pattern
characterization and the scale/resolution issues are further assessed. Chapter 13 by
Hong and Zhang provides important insights into the spatiotemporal scales of
remotely sensed precipitation. This chapter first overviews the precipitation measurement methods—both traditional rain gauge and advanced remote sensing measurements; then develops an uncertainty analysis framework that can systematically
quantify the remote sensing precipitation estimation error as a function of space, time,
and intensity; and finally assesses the spatiotemporal scale-based error propagation in
remote sensing precipitation estimates into hydrological prediction.
The last part of this book looks at how new frontiers in Earth observation
technology have transformed our understanding of this foremost issue in remote
sensing. Chapter 14 examines lidar data processing, whereas Chapter 15 explores
hyperspectral remote sensing for land cover mapping. Digital terrain models (DTMs)
are basic products required for a number of applications and decision making.
Nowadays, high-spatial-resolution DTMs are primarily produced through airborne
laser scanners (ALSs). However, the ALS does not directly deliver DTMs; rather it
delivers a dense point cloud that embeds both terrain elevation and height of natural
and human-made features. Hence, discrimination of above-ground objects from
terrain is a basic processing step. This processing step is termed ground filtering
and has proved especially difficult for large areas of varied terrain characteristics. In
Chapter 14, Silvan-C ardenas and Wang revise and extend a filtering method based on
a multiscale signal decomposition termed the multiscale Hermite transform (MHT).
The formal basis of the latter is presented in the context of scale-space theory, a theory
for representing spatial signals. Through the unique properties of the MHT, namely
local spatial rotation and scale-space shifting, the original filtering algorithm was
extended to incorporate higher order coefficients in the multiscale erosion operation.
Additionally, a linear interpolation was incorporated through a truncated Taylor
expansion which allowed improving the ground filtering performance along sloppy
terrain areas. Practical considerations in the operation of the algorithm are discussed
and illustrated with examples. In Chapter 15, Petropoulos, Manevski, and Carlson
assess the potential of hyperspectral remote sensing systems for improving discrimination among similar land cover classes at different scales. The chapter provides first
8
CHARACTERIZING, MEASURING, ANALYZING, AND MODELING SCALE
land cover, landscape metrics, and biophysical parameters. Chapter 11 by Liu and
Weng assesses the effect of scaling on the relationship between landscape pattern and
land surface temperature with a case study in Indianapolis, Indiana. A set of spatial
resolutions were compared by using a landscape metric space. They find that the
spatial resolution of 90 m is the optimal scale to study the relationship and think that it
is the operational scale of the urban thermal landscape in Indianapolis. In Chapter 12,
Liang and Weng provide an evaluation of the effectiveness of the triangular prism
fractal algorithm for characterizing urban landscape in Indianapolis based on eight
satellite images acquired by five different sensors: Landsat Multispectral Scanner,
Landsat Thematic Mapper, Landsat Enhanced Thematic Mapper Plus, Advanced
Spaceborne Thermal Emission and Reflection, and IKONOS. Fractal dimensions
computed from the selected original, classified, and resampled images are compared
and analyzed. The potential of fractal measurement in the studies of landscape pattern
characterization and the scale/resolution issues are further assessed. Chapter 13 by
Hong and Zhang provides important insights into the spatiotemporal scales of
remotely sensed precipitation. This chapter first overviews the precipitation measurement methods—both traditional rain gauge and advanced remote sensing measurements; then develops an uncertainty analysis framework that can systematically
quantify the remote sensing precipitation estimation error as a function of space, time,
and intensity; and finally assesses the spatiotemporal scale-based error propagation in
remote sensing precipitation estimates into hydrological prediction.
The last part of this book looks at how new frontiers in Earth observation
technology have transformed our understanding of this foremost issue in remote
sensing. Chapter 14 examines lidar data processing, whereas Chapter 15 explores
hyperspectral remote sensing for land cover mapping. Digital terrain models (DTMs)
are basic products required for a number of applications and decision making.
Nowadays, high-spatial-resolution DTMs are primarily produced through airborne
laser scanners (ALSs). However, the ALS does not directly deliver DTMs; rather it
delivers a dense point cloud that embeds both terrain elevation and height of natural
and human-made features. Hence, discrimination of above-ground objects from
terrain is a basic processing step. This processing step is termed ground filtering
and has proved especially difficult for large areas of varied terrain characteristics. In
Chapter 14, Silvan-C ardenas and Wang revise and extend a filtering method based on
a multiscale signal decomposition termed the multiscale Hermite transform (MHT).
The formal basis of the latter is presented in the context of scale-space theory, a theory
for representing spatial signals. Through the unique properties of the MHT, namely
local spatial rotation and scale-space shifting, the original filtering algorithm was
extended to incorporate higher order coefficients in the multiscale erosion operation.
Additionally, a linear interpolation was incorporated through a truncated Taylor
expansion which allowed improving the ground filtering performance along sloppy
terrain areas. Practical considerations in the operation of the algorithm are discussed
and illustrated with examples. In Chapter 15, Petropoulos, Manevski, and Carlson
assess the potential of hyperspectral remote sensing systems for improving discrimination among similar land cover classes at different scales. The chapter provides first
8
CHARACTERIZING, MEASURING, ANALYZING, AND MODELING SCALE