Urban remote sensing: monitoring, synthesis and modeling in the urban environment
Gespeichert in:
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2011
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| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XX, 388 S. Ill., graph. Darst., Kt. |
| ISBN: | 9780470749586 |
Internformat
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| 100 | 1 | |a Yang, Xiaojun |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Urban remote sensing |b monitoring, synthesis and modeling in the urban environment |c Xiaojun Yang |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2011 | |
| 300 | |a XX, 388 S. |b Ill., graph. Darst., Kt. | ||
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Datensatz im Suchindex
| _version_ | 1804143844593238016 |
|---|---|
| adam_text | CONTENTS
List of Contributors
xiii
Author s Biography
xvi
Preface
xix
PARTI
INTRODUCTO
1
What is urban remote
Xiaojun Yang
1.1
1.2
1.3
.,4
Introduction
4
Remote sensing and urban studies
5
Remote sensing systems for urban areas
6
Algorithms and techniques for urban attribute
extraction
7
1.5
Urban socioeconomic analyses
7
1.6
Urban environmental analyses
8
17
Urban growth and landscape change modeling
Summary and concluding remarks
9
References
10
PART
2
REMOTE SENSING
SYSTEMS FOR URBAN AREAS
13
2
Use
of archival Landsat imagery to
monitor urban spatial growth
15
Xiaojun Yang
2.1
Introduction
16
2.2
Landsat program and imaging sensors
16
2.3
Mapping urban spatial growth in an American
metropolis
18
2.3.1
Research design
18
2.3.2
Data acquisition and land classification
scheme
19
2.3.3
Image processing of remotely sensed
data
20
2.3.4
Change detection
21
2.3.5
Interpretation and analysis
25
2.3.6
Summary
27
2.4
Discussion
27
2.4.1
A generic urban growth monitoring
workflow
27
2.4.2
Image resolution and land use/cover
classification
27
2.4.3
Image preprocessing
28
2.4.4
Change detection methods
29
Summary and concluding remarks
30
References
зо
3
Limits and challenges of optical
very-high-spatial-resolution
satellite remote sensing for urban
applications
35
Paolo Gamba, Fabio Dell Acqua,
Mattia Stasolla,
Giovanna Trianni
and Gianni Lisini
3.1
Introduction
36
3.2
Geometrical problems
36
3.3
Spectral problems
38
3.4
Mapping limits and challenges
38
3.5
Adding the time factor: VHR and change
detection
39
3.6
A possible way forward
39
3.7
Building damage assessment
43
Conclusions
46
References
47
Potential of hyperspectral remote
sensing for analyzing the urban
environment
49
Sigrid
Roessner,
Karl Segl, Mathias Bochow, Uta Heiden,
Wieke
Heidens
and Hermann
Kaufmann
4.1
4.2
4.3
Introduction
50
Spectral characteristics of urban surface
materials
50
4.2.1
Categories of interest for material
mapping
50
4.2.2
Establishment of urban spectral libraries
52
4.2.3
Determination of robust spectral features
52
Automated identification of urban surface
materials
54
4.3.1
State of the art of automated hyperspectral
image analysis
54
4.3.2
Processing system for automated material
mapping
57
Results and discussion of their potential for urban
analysis
58
References
60
4.4
viii
CONTENTS
5
Very-high
-гі
synthetic aperture radar and urban
areas: looking into details of a
complex environment
ез
Fabio Dell Acqua,
Paolo
Gamba
and Diego
Polli
5.1
Introduction
64
5.2
Before spaceborne high-resolution
SAR 64
5.3
High-resolution
SAR 66
5.3.1
Extraction of single buildings
66
5.3.2
Damage assessment with VHR
SAR
data
67
5.3.3
Vulnerability mapping with VHR
SAR
data
69
Conclusions
70
Acknowledgments
70
References
70
iernai
imagery
75
Jonathan Li and Haiyan Guan
6.1
Lidar-drived building models: related work
76
6.1.1
Building detection
76
6.1.2
Building reconstruction
76
6.2
Our building reconstruction method
77
6.2.1
Our strategy using fused data
77
6.2.2
Building detection
78
6.2.3
Building reconstruction
81
6.3
Results and discussion
85
6.3.1
Datasets
85
6.3.2
Results
85
Concluding remarks
89
Acknowledgments
90
References
90
A
Parameterizing neural network
models to improve land
classification performance
95
Хтојип
Yang and
Libin
Zhou
Introduction
96
Fundamentals of neural networks
96
7.2.1
Neural network types
96
7.2.2
Network topology
98
7.2.3
Neural training
98
7.3
Internal parameters and classification accuracy
100
7.3.1
Experimental design
100
7.3.2
Remotely sensed data and land
classification scheme
101
7.3.3
Network configuration and training
101
7.3.4
Image classification and accuracy
assessment
103
7.3.5
Interpretation and analysis
103
7.3.6
Summary
105
7.4
Training algorithm performance
105
7.4.1
Experimental design
105
7.4.2
Network training and ¡mage
classification
105
7.4.3
Performance evaluation
106
7.5 Toward a systematic approach to image
classification by neural networks
107
Future research directions
108
References
108
зпзиЬрімеІ
spectral mixture
111
Rebecca Powell
8.1
8.2.2
8.2.3
8.2.4
8.2.5
8.2.6
8.3
Introduction
112
Overview of SMA implementation
112
8.2.1
SMA background
112
Endmember selection
114
SMA models
116
Mapping fraction images
117
Modei
complexity
118
Accuracy assessment
118
Two case studies
118
8.3.1
Evolution of urban morphology on a tropical
forest frontier
119
8.3.2
Discriminating urban tree and lawn cover in
a western US city
122
Conclusions
124
Acknowledgments
126
References
126
......
ЂГП
г
urban
ïificafcion
129
Soe
W. Myint
and Douglas Stow
Introduction
130
Object-oriented classification
130
9.2.1
Image segmentation
130
9.2.2
Features
131
9.2.3
Classifiers
132
Data and study area
133
Methodology
134
CONTENTS
9.4.1
Rule-based detection of swimming
pools
134
9.4.2
Nearest neighbor classifier to extract urban
land covers
136
9.S Results and discussion
137
9.5.1
Decision rule set to extract pool
137
9.5.2
Nearest neighbor classifier to extract urban
land covers
138
Conclusion
139
References
140
nhancementof
images on
urban
141
Bruno Aiazzi,
Stefano
Baranti, Luca Capobianco,
Andrea
Garzelli and Massimo
Selva
Introduction
142
10.1.1
Component substitution fusion methods
142
10.1.2
Multiresolution analysis fusion methods
142
10.1.3
Injection model of details
143
10.1.4
Quality assessment
143
Multiresolution fusion scheme
144
Component substitution fusion scheme
144
Hybrid
MRA
-
component substitution method
146
10.S Results
147
Conclusions
152
References
152
the structure and function of urban
areas
155
Victor Mesev
11.1
Introduction
156
11.2
Micro and macro urban remote sensing
156
11.8
The temporal lag challenge
157
11.4
Structural-functional links
157
51.
S
Temporal-structural-functional links
159
11,
β
Empirical measurement of temporal lags
159
Conclusions
161
References
161
163
listic approach to
def
unire
and measuring yrbao sprawl
íes
Amnon Frenkd and Daniel Oremtem
12.4
Introduction
166
12.2
The diversity of definitions of sprawl
166
12.2.1
Definitions describing an urban spatial
development phenomenon
167
12.2.2
Definitions based on consequences of
sprawl; sprawl is as sprawl does
167
12.2.3
Definitions according to the social and/or
economic processes that give rise to
particular urban spatial development
patterns
168
12.2.4
Sprawl redux: focusing on the concerns of
remote sensing experts
168
12.3
Historic forms of urban sprawl
168
12.4
Qualitative dimensions of sprawl and quantitative
variables for measuring them
169
12.4.1
Criteria for a good sprawl measurement
variable
170
12.4.2
What shall we measure?
170
12.4.3
Choosing among the sprawl
measures
173
Conclusion
178
References
178
„i-re
sensing and
lidar
ш
Le Wang
and Jose-Silvan Cardenas
13.1
Introduction
184
13.2
Study sites and data
185
13.3
Methodology
186
13.3.1
Data preprocessing
186
13.3.2
Building extraction
186
13.3.3
Land use classification
186
13.3.4
Population estimation models
187
13.3.5
Accuracy assessment
187
13.4
Results
187
13.4.1
Building detection results
187
13.4.2
Land use classification results
189
13.4.3
Population estimation results
189
Discussion and conclusions
192
Acknowledgments
192
References
192
14.1
14.2
195
James B. Holt and
Hua Lu
Introduction
198
Dasymetric maps, dasymetric mapping, and
areal
interpolation
196
14.2.1
Ancillary data
107
14.2.2
Dasymetric mapping
197
CONTENTS
14.2.3
Origins
197
14.2.4 Dasymetric
mapping variations
198
14.3
Application example: metropolitan Atlanta,
Georgia
200
14.3.1
Data
200
14.3.2
Dasymetric maps
201
14.3.3 Areal
interpolation
203
Conclusions
205
Acknowledgments
208
References
208
15
Who s ¡n the dark-satellite based
estimates of electrification rates
211
Christopher D.Elvidge, Kimberly E. Baugh, Paul
С
Sutton,
Budhendra Bhaduri, Benjamin T. Tuttle, Tilotamma
Ghosh, Daniel Ziskin and Edward H.
Erwin
15.1
Introduction
212
15.2
Methods
212
15.2.1
Data sources
212
15.2.2
Data processing
213
15.3
Results
213
15.4
Discussion
214
Conclusion
223
Acknowledgments
223
References
223
16
Integrating remote sensing and GiS
for environmental justice
research
225
Jeremy Mennis
16.1
Introduction
226
16.2
Environmental justice research
226
16.3
Remote sensing for environmental equity
analysis
227
16.4
Integrating remotely sensed and other spatial data
using
GIS
229
і
8,5
Case study: vegetation and socioeconomic
character in Philadelphia, Pennsylvania
230
Conclusion
234
References
235
PART
5
URBAN
ENVIRONMENTAL ANALYSES 239
17
Remote sensing of high resolution
urban impervious surfaces
241
Changshan Wti and
Fei
Yuan
17.1
Introduction
242
17.2
Impervious surface estimation
242
17.2.1
Pixel-based models
242
17.2.2
Object-based models
243
17.3
Pixel-based models for estimating high-resolution
impervious surface
243
17.3.1
Introduction
243
17.3.2
Study area and data
243
17.3.3
Methodology
244
17.3.4
Results
248
17.4
Object-based models for estimating high-resolution
impervious surface
249
17.4.1
Study area and data preparation
249
17.4.2
Object-oriented classification
249
17.4.3
Results
251
Conclusions
252
References
252
18
Use of impervious surface data
obtained from remote sensing in
distributed hydrological modeling
of urban areas
255
Frank Canters, Okke Batelaan, Tim Van
de Voorde,
Jarosław Chormański and
Boud Verbeiren
18.1
Introduction
256
18.2
Spatially distributed
hydrological
modeling
256
18.3
Impervious surface mapping
257
18.4
The WetSpa model
258
18.4.1
Surface runoff
258
18.4.2
Flow routing
260
18.4.3
Water balance
261
18.5
Impact of different approaches for estimating
impervious surface cover on runoff calculation and
prediction of peak discharges
261
18.5.1
Study area and data
261
18.5.2
Impervious surface mapping
262
18.5.3
Impact of land-cover distribution on
estimation of peak discharges
264
Conclusions
270
Acknowledgments
270
References
270
19
Impacts of urban growth on
vegetation carbon
sequestration
275
Tingting
Zhao
19.1
Introduction
276
19.2
Vegetation productivities and estimation
276
19.2.1
Vegetation productivities
276
19.2.2
Estimation of vegetation productivities
276
19.3
Data and analysis
277
CONTENTS
19.3.1
Identifying urban growth
277
19.3.2
Preparing vegetation maps and light-use
efficiency parameters
279
19.3.3
Estimating
APAR,
GPP and changes in
GPP
279
19.4
Results
280
19.5
Discussion
283
19.5.1
Urban growth in the South Atlantic
division
283
19.5.2
Impacts of urban growth on vegetation
productivities
283
Conclusions
284
Acknowledgments
284
References
285
20
Characterizing biodiversity in
urban areas using remote
Sensing
287
Marcus
Hedblom
and Vila
Mörtberg
20.1
Introduction
288
20.2
Remote sensing methods in urban biodiversity
studies
288
20.2.1
Direct approaches
289
20.2.2
Indirect approaches
289
20.3
Hierarchical levels and definitions of urban
ecosystems
292
20.3.1
Flora and fauna along urban gradients
292
20.3.2
Using remote sensing to quantify
urbanization patterns
293
20.4
Using remote sensing to interpret effects of
urbanization on species distribution
294
20.5
Long-term monitoring of biodiversity in urban
green areas
-
methodology development
295
20.6
Applications in urban planning and
management
296
Conclusions
297
Acknowledgments
300
References
300
21
Urban weather, climate and air
quality modeling: increasing
resolution and accuracy using
improved urban morphology
305
Susanne.
Grossman-Clarke, William I.
Štefanov
and Joseph
A. Zelmder
21.1
Introduction
306
21.2
Physical approaches for the representation of
urban areas in regional atmospheric models
306
21.2.1
Roughness approach
307
21.2.2
Single-layer urban canopy approaches
307
21.2.3
Multilayer urban canopy approaches
307
21.3
Remotely sensed data as input for regional
atmospheric models
307
21.3.1
Urban land use and land cover data
308
21.3.2
Building data
310
21.4
Case studies investigating the effects of
urbanization on weather, climate and air quality
311
21.4.1
Studies investigating effects of urban land
use and land cover on meteorology and air
quality
311
21.4.2
Case study for Phoenix
312
Conclusions
316
Acknowledgments
316
References
316
PART
6
URBAN GROWTH AND
LANDSCAPE CHANGE
MODELING
321
22
Cellular automata and agent base
models for urban studies: from
pixels to cells to hexa-dpi s
згз
Elisabete A.
Silva
22.1
Introduction
324
22.2
Computation: the raster-pixel aproach
324
22.3
Cells: migrating from basic pixels
324
22.4
Agents: joining with cells
327
22.5
Cells and agents in a computer s artificial life
328
22.6
The hexa-dpi: closing the cycle in the digital
age
330
Conclusions
332
References
332
23
Calibrating and validating cellular
automata models of
urbanization
335
PaulM.
Torrens
23.1
Introduction
336
23.2
Calibration
336
23.2.1
Conditional transition rules
336
23.2.2
Weighted transition rules
337
23.2.3
Seeding and initial conditions
337
23.2.4
Specifying the value of calibration
parameters
338
23.2.5
Coupling automata and exogenous
models
338
23.2.6
Automatic calibration
339
CONTENTS
23.3
Validating automata models
339
23.3.1
Pixel matching
339
23.3.2
Feature and pattern recognition
340
23.3.3
Running models exhaustively
341
Conclusions
341
Acknowledgments
342
References
342
24
Agent-based urban
modelingisimulating urban growth
and subsequent landscape change
in suzhou, china
347
Yichun Xie and Xining Yang
24.1
Introduction
348
24.2
Design, construction, calibration, and validation of
ABM
348
24.3
Case study
-
desakota development in Suzhou,
China
350
24.4
The Suzhou Urban Growth Agent Model
351
24.4.1
The model design
351
24.4.2
The model construction
352
24.4.3
The model calibration
352
24.4.4
The model validation
353
Summary and conclusion
354
References
355
25
Ecological modeling in urban
environments: predicting changes
in biodiversity in response to future
urban development
359
Jeffrey Hepinstall-Cymerman
25.1
Introduction
360
25.1.1
Using urban remote sensing to develop land
cover maps for ecological modeling
360
25.1.2
One example of ecological modeling:
modeling species habitat
360
25.1.3
Predicting future land use and land
cover
361
25.1.4
Integrating models to predict future
biodiversity
362
2S.2 Predicting changes in land cover and
avian
biodiversity for an area north of Seattle,
Washington
362
25.2.1
Land cover maps
362
25.2.2
Land use change model
362
25.2.3
Land cover change model
364
25.2.4
Avian biodiversity model
365
25.2.5
Predicted land cover change for study
area
365
25.2.6
Predicted changes in
avian
biodiversity for
study area
365
Conclusions
365
Acknowledgments
367
References
368
26
Rethinking progress in urban
analysis and modeling: models,
metaphors, and meaning
371
Daniel Z.
Sui
26.1
Introduction
372
26.2
Pepper s world hypotheses: the role of root
metaphors in understanding reality
373
26.3
Progress in urban analysis and modeling:
metaphors urban modelers live by
373
26.3.1
Cities as forms
-
the spatial morphology
tradition
374
26.3.2
Cities as machines
-
the social physics
tradition
375
26.3.3
Cities as organisms
-
the social biology
tradition
375
26.3.4
Cities as arenas
-
the spatial event
tradition
376
26.4
Models, metaphors, and the meaning of progress:
further discussions
377
Summary and concluding remarks
377
Acknowledgments
378
Notes
378
References
378
Index
383
|
| any_adam_object | 1 |
| author | Yang, Xiaojun |
| author_facet | Yang, Xiaojun |
| author_role | aut |
| author_sort | Yang, Xiaojun |
| author_variant | x y xy |
| building | Verbundindex |
| bvnumber | BV037236682 |
| classification_rvk | RB 10232 RB 10627 ZI 9560 |
| ctrlnum | (OCoLC)837945025 (DE-599)BVBBV037236682 |
| discipline | Bauingenieurwesen Geographie |
| edition | 1. publ. |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV037236682 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:54:07Z |
| institution | BVB |
| isbn | 9780470749586 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-021150254 |
| oclc_num | 837945025 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-11 DE-634 DE-20 DE-703 DE-473 DE-BY-UBG DE-83 DE-M347 |
| owner_facet | DE-355 DE-BY-UBR DE-11 DE-634 DE-20 DE-703 DE-473 DE-BY-UBG DE-83 DE-M347 |
| physical | XX, 388 S. Ill., graph. Darst., Kt. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Wiley |
| record_format | marc |
| spelling | Yang, Xiaojun Verfasser aut Urban remote sensing monitoring, synthesis and modeling in the urban environment Xiaojun Yang 1. publ. Chichester [u.a.] Wiley 2011 XX, 388 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Stadtplanung (DE-588)4056754-0 gnd rswk-swf Fernerkundung (DE-588)4016796-3 gnd rswk-swf Stadt (DE-588)4056723-0 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Fernerkundung (DE-588)4016796-3 s Stadtplanung (DE-588)4056754-0 s Stadt (DE-588)4056723-0 s b DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021150254&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Yang, Xiaojun Urban remote sensing monitoring, synthesis and modeling in the urban environment Stadtplanung (DE-588)4056754-0 gnd Fernerkundung (DE-588)4016796-3 gnd Stadt (DE-588)4056723-0 gnd |
| subject_GND | (DE-588)4056754-0 (DE-588)4016796-3 (DE-588)4056723-0 (DE-588)4143413-4 |
| title | Urban remote sensing monitoring, synthesis and modeling in the urban environment |
| title_auth | Urban remote sensing monitoring, synthesis and modeling in the urban environment |
| title_exact_search | Urban remote sensing monitoring, synthesis and modeling in the urban environment |
| title_full | Urban remote sensing monitoring, synthesis and modeling in the urban environment Xiaojun Yang |
| title_fullStr | Urban remote sensing monitoring, synthesis and modeling in the urban environment Xiaojun Yang |
| title_full_unstemmed | Urban remote sensing monitoring, synthesis and modeling in the urban environment Xiaojun Yang |
| title_short | Urban remote sensing |
| title_sort | urban remote sensing monitoring synthesis and modeling in the urban environment |
| title_sub | monitoring, synthesis and modeling in the urban environment |
| topic | Stadtplanung (DE-588)4056754-0 gnd Fernerkundung (DE-588)4016796-3 gnd Stadt (DE-588)4056723-0 gnd |
| topic_facet | Stadtplanung Fernerkundung Stadt Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021150254&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT yangxiaojun urbanremotesensingmonitoringsynthesisandmodelingintheurbanenvironment |