Integration of GIS and remote sensing:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2007
|
| Schriftenreihe: | Mastering GIS
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 296 S. Ill., graph. Darst., Kt. |
| ISBN: | 9780470864098 9780470864104 |
Internformat
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| 245 | 1 | 0 | |a Integration of GIS and remote sensing |c ed. by Victor Mesev |
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2007 | |
| 300 | |a XVI, 296 S. |b Ill., graph. Darst., Kt. | ||
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Datensatz im Suchindex
| _version_ | 1804137105288331264 |
|---|---|
| adam_text | Contents
Series
Foreword
xi
Preface
xiii
List of Contributors
xv
1
GIS
and remote sensing integration: in search of a definition
1
Victor Mesev and Alexandra Walrath
1.1
Introduction
1
1.2
In search of a definition
2
1.2.1
Evolutionary integration
4
1.2.2
Methodological integration
5
1.3
Outline of the book
8
1.4
Conclusions
13
2
Integration taxonomy and uncertainty
17
Manfred Ehlers
2.1
Introduction
17
2.2
Taxonomy issues
19
2.2.1
Taxonomy of
GIS
operators
19
2.2.2
Taxonomy of image analysis operators in remote sensing
20
2.2.3
An integrated taxonomy
20
2.3
Uncertainty issues
22
2.3.1
Uncertainty in geographic information
22
2.3.2
Uncertainty in the integration of
GIS
and remote sensing
23
2.4
Modelling positional and thematic error in the integration of remote
sensing and
GIS
27
2.4.1
Positional and thematic uncertainties
27
2.4.2
Problem formulation
28
2.4.3
Modelling positional uncertainty
29
2.4.4
Thematic uncertainties of a classified image
34
2.4.5
Modelling the combined positional and thematic
uncertainties
35
2.5
Conclusions
38
vi
CONTENTS
3
Data fusion
related to
GIS
and remote sensing
43
Paolo
Gamba
and
Fabio Dell Acqua
3.1
Introduction
43
3.2
Why do we need GIS-remote sensing fusion?
43
3.2.1
Remote sensing output to
GIS
44
3.2.2
GIS
input to remote sensing interpretation algorithms
45
3.2.3
Example: urban planning check and update
46
3.3
Problems in GIS-remote sensing data fusion
47
3.3.1
Lack of consistent standards
48
3.3.2
Inconsistency of GIS-remote sensing accuracy, legends and
scales
49
3.3.3
Different nature of the two sources
51
3.3.4
Need for information rather than data fusion
53
3.3.5
Example: population mapping through remote sensing
54
3.4
Present and future solutions
55
3.4.1
MultiscaLe analysis
55
3.4.2
Fusion techniques
57
3.5
Conclusions
60
3.5.1
Integration of remote sensing and
GIS
into a change detection
module
61
4
The importance of scale in remote sensing and
GIS
and its
implications for data integration
69
Peter M. Atkinson
4.1
Introduction
69
4.2
Data models and scales of measurement
70
4.2.1
Raster imagery
70
4.2.2
Vector data
74
4.3
Scales of spatial variation
75
4.3.1
Spatial variation in raster data
, 75
4.3.2
Scales of variation in vector data
79
4.3.3
Processes in the environment
79
4.4
Remote sensing and
GIS data
integration
80
4.4.1
Overlay and regression
80
4.4.2
Remote sensing classification of land cover
84
4.5
Conclusion
87
5
Of patterns and processes: spatial metrics and
geostatistics in urban analysis
93
XiaoHang Liu and Martin
Herold
5.1
Introduction
93
5.2
Geostatistics
95
5.3
Spatial metrics
96
5.4
Examples
100
5.4.1
Data preparation
100
CONTENTS
vii
5.4.2
Linkage from land cover to land use
103
5.4.3
Linking urban form to population density
107
5.4.5
Linking characteristics of spatial patterns and processes
109
5.5
Conclusion
112
6
Using remote sensing and
GIS
integration to identify spatial
characteristics of sprawl at the building-unit level
117
John
Hasse
6.1
Introduction
117
6.2
Sprawl in the remote sensing and
GIS
literature
118
6.2.1
Definitions of sprawl
119
6.2.2
Spatial characteristics of sprawl at a metropolitan level
122
6.2.3
Spatial characteristics of sprawl at a submetropolitan level
125
6.3
Integrating remote sensing and
GIS
for sprawl research
127
6.4
Spatial characteristics of sprawl at a building-unit level
133
6.5
A practical building-unit Level model for analysing sprawl
135
6.5.1
Urban density
138
6.5.2
Leapfrog
138
6.5.3
Segregated land use
140
6.5.4
Highway strip
141
6.5.5
Community node inaccessibility
141
6.5.6
Normalizing municipal sprawl indicator measures
142
6.6
Future benefits of integrating remote sensing and
GIS
in sprawl research
143
7
Remote sensing applications in urban socio-economic
analysis
149
Changshan Wu
7.1
Introduction
149
7.2
Principles of urban socio-economic studies using remote sensing
technologies
150
7.3
Socio-economic information estimation
153
7.3.1
Population estimation
153
7.3.2
Employment estimation
155
7.3.3
GDP estimation
155
7.3.4
Electrical power consumption estimation
156
7.4
Socio-economic activity modelling
157
7.4.1
Population interpolation
157
7.4.2
Socio-economic index generation
158
7.4.3
Understanding and modelling socio-economic
phenomena
159
7.5
Advantages and limitations of remote sensing technologies in socio-
economic applications
167
7.5.1
Socio-economic information estimation
167
7.5.2
Socio-economic information modelling
168
7.6
Conclusions
168
8.1
Introduction
8.2
Research methodology
8.2.1
Study area
8.2.2
Data acquisition and collection
8.2.3
Satellite image processing
8.2.4
Change analysis
8.2.5
Spatial statistical analysis
8.2.6
Dynamic spatial modelling
8.3
Results
;
and discussion
8.3.1
Urban growth
8.3.2
Driving force
8.3.3
Future growth scenario simulation
8.4
Conclusions
viii
CONTENTS
8
Integrating remote sensing,
GIS
and spatial modelling for
sustainable urban growth management
173
Xiaojun Yang
173
175
176
176
178
180
181
182
184
184
187
191
193
9
An
integrative
GIS
and remote sensing model for
place-based urban vulnerability analysis
199
Tarek
Rashed,
John Weeks,
Heien
Coudelis and Martin
Herold
9.1
Introduction
199
9.2
Analysis of urban vulnerability: what is it all about?
201
9.3
A conceptual framework for place-based analysis of urban vulnerability
202
9.4
Integrating
GIS
and remote sensing into vulnerability analysis
205
9.5
A GIS-remote sensing place-based model for urban vulnerability analysis
206
9.6
An illustrative example of model application
208
9.6.1
Study area
209
9.6.2
Data
209
9.6.3
Identifying vulnerability hot spots
210
9.6.4
Deriving remote sensing measures of urban morphology in
Los Angeles
212
9.6.5
Deriving an index of wealth for Los Angeles County
216
9.6.6
Spatial filtering of variables
217
9.6.7
Generating place-based knowledge of urban vulnerability in
Los Angeles
218
9.6.8
To what extent do model results conform to universal knowl¬
edge of vulnerability?
222
9.7
Conclusions
224
10
Using
GIS
and remote sensing for ecological mapping and
monitoring
233
Jennifer A. Miller and John Rogan
10.1
Introduction
233
10.2
Integration of
GIS
and remote sensing in ecological research
237
CONTENTS ix
10.3
10.4
10.5
10.6
10.7
11 Remote
sensing
and GIS
for ephemeral wetland monitoring
and sustainability in southern Mauritania
269
Tara
Shine and Victor Mesev
11.1
Introduction
269
11.1.1
Ephemeral wetlands
269
11.1.2
Remote sensing of ephemeral wetlands
270
11.2
Ephemeral wetlands in Mauritania
272
11.2.1
Data and processing
274
11.2.2
Results
279
11.2.3
Implications for management
283
11.3
Conclusions
284
Index
291
GIS data
used in ecological applications
237
10.3.1
Gradient analysis
238
10.3.2
Climate
240
10.3.3
Topography
241
Remotely sensed data for ecological applications
242
10.4.1
Spectral enhancements
243
10.4.2
Land cover
244
10.4.3
Habitat structure
245
10.4.4
Biophysical processes
246
Species distribution models
247
10.5.1
Biodiversity mapping
251
Change detection
253
10.6.1
Case study: using
GIS
and remote sensing for large-area
change detection and efficient map updating
253
Conclusions
260
|
| adam_txt |
Contents
Series
Foreword
xi
Preface
xiii
List of Contributors
xv
1
GIS
and remote sensing integration: in search of a definition
1
Victor Mesev and Alexandra Walrath
1.1
Introduction
1
1.2
In search of a definition
2
1.2.1
Evolutionary integration
4
1.2.2
Methodological integration
5
1.3
Outline of the book
8
1.4
Conclusions
13
2
Integration taxonomy and uncertainty
17
Manfred Ehlers
2.1
Introduction
17
2.2
Taxonomy issues
19
2.2.1
Taxonomy of
GIS
operators
19
2.2.2
Taxonomy of image analysis operators in remote sensing
20
2.2.3
An integrated taxonomy
20
2.3
Uncertainty issues
22
2.3.1
Uncertainty in geographic information
22
2.3.2
Uncertainty in the integration of
GIS
and remote sensing
23
2.4
Modelling positional and thematic error in the integration of remote
sensing and
GIS
27
2.4.1
Positional and thematic uncertainties
27
2.4.2
Problem formulation
28
2.4.3
Modelling positional uncertainty
29
2.4.4
Thematic uncertainties of a classified image
34
2.4.5
Modelling the combined positional and thematic
uncertainties
35
2.5
Conclusions
38
vi
CONTENTS
3
Data fusion
related to
GIS
and remote sensing
43
Paolo
Gamba
and
Fabio Dell'Acqua
3.1
Introduction
43
3.2
Why do we need GIS-remote sensing fusion?
43
3.2.1
Remote sensing output to
GIS
44
3.2.2
GIS
input to remote sensing interpretation algorithms
45
3.2.3
Example: urban planning check and update
46
3.3
Problems in GIS-remote sensing data fusion
47
3.3.1
Lack of consistent standards
48
3.3.2
Inconsistency of GIS-remote sensing accuracy, legends and
scales
49
3.3.3
Different nature of the two sources
51
3.3.4
Need for information rather than data fusion
53
3.3.5
Example: population mapping through remote sensing
54
3.4
Present and future solutions
55
3.4.1
MultiscaLe analysis
55
3.4.2
Fusion techniques
57
3.5
Conclusions
60
3.5.1
Integration of remote sensing and
GIS
into a change detection
module
61
4
The importance of scale in remote sensing and
GIS
and its
implications for data integration
69
Peter M. Atkinson
4.1
Introduction
69
4.2
Data models and scales of measurement
70
4.2.1
Raster imagery
70
4.2.2
Vector data
74
4.3
Scales of spatial variation
75
4.3.1
Spatial variation in raster data
, 75
4.3.2
Scales of variation in vector data
79
4.3.3
Processes in the environment
79
4.4
Remote sensing and
GIS data
integration
80
4.4.1
Overlay and regression
80
4.4.2
Remote sensing classification of land cover
84
4.5
Conclusion
87
5
Of patterns and processes: spatial metrics and
geostatistics in urban analysis
93
XiaoHang Liu and Martin
Herold
5.1
Introduction
93
5.2
Geostatistics
95
5.3
Spatial metrics
96
5.4
Examples
100
5.4.1
Data preparation
100
CONTENTS
vii
5.4.2
Linkage from land cover to land use
103
5.4.3
Linking urban form to population density
107
5.4.5
Linking characteristics of spatial patterns and processes
109
5.5
Conclusion
112
6
Using remote sensing and
GIS
integration to identify spatial
characteristics of sprawl at the building-unit level
117
John
Hasse
6.1
Introduction
117
6.2
Sprawl in the remote sensing and
GIS
literature
118
6.2.1
Definitions of sprawl
119
6.2.2
Spatial characteristics of sprawl at a metropolitan level
122
6.2.3
Spatial characteristics of sprawl at a submetropolitan level
125
6.3
Integrating remote sensing and
GIS
for sprawl research
127
6.4
Spatial characteristics of sprawl at a building-unit level
133
6.5
A practical building-unit Level model for analysing sprawl
135
6.5.1
Urban density
138
6.5.2
Leapfrog
138
6.5.3
Segregated land use
140
6.5.4
Highway strip
141
6.5.5
Community node inaccessibility
141
6.5.6
Normalizing municipal sprawl indicator measures
142
6.6
Future benefits of integrating remote sensing and
GIS
in sprawl research
143
7
Remote sensing applications in urban socio-economic
analysis
149
Changshan Wu
7.1
Introduction
149
7.2
Principles of urban socio-economic studies using remote sensing
technologies
150
7.3
Socio-economic information estimation
153
7.3.1
Population estimation
153
7.3.2
Employment estimation
155
7.3.3
GDP estimation
155
7.3.4
Electrical power consumption estimation
156
7.4
Socio-economic activity modelling
157
7.4.1
Population interpolation
157
7.4.2
Socio-economic index generation
158
7.4.3
Understanding and modelling socio-economic
phenomena
159
7.5
Advantages and limitations of remote sensing technologies in socio-
economic applications
167
7.5.1
Socio-economic information estimation
167
7.5.2
Socio-economic information modelling
168
7.6
Conclusions
168
8.1
Introduction
8.2
Research methodology
8.2.1
Study area
8.2.2
Data acquisition and collection
8.2.3
Satellite image processing
8.2.4
Change analysis
8.2.5
Spatial statistical analysis
8.2.6
Dynamic spatial modelling
8.3
Results
;
and discussion
8.3.1
Urban growth
8.3.2
Driving force
8.3.3
Future growth scenario simulation
8.4
Conclusions
viii
CONTENTS
8
Integrating remote sensing,
GIS
and spatial modelling for
sustainable urban growth management
173
Xiaojun Yang
173
175
176
176
178
180
181
182
184
184
187
191
193
9
An
integrative
GIS
and remote sensing model for
place-based urban vulnerability analysis
199
Tarek
Rashed,
John Weeks,
Heien
Coudelis and Martin
Herold
9.1
Introduction
199
9.2
Analysis of urban vulnerability: what is it all about?
201
9.3
A conceptual framework for place-based analysis of urban vulnerability
202
9.4
Integrating
GIS
and remote sensing into vulnerability analysis
205
9.5
A GIS-remote sensing place-based model for urban vulnerability analysis
206
9.6
An illustrative example of model application
208
9.6.1
Study area
209
9.6.2
Data
209
9.6.3
Identifying vulnerability hot spots
210
9.6.4
Deriving remote sensing measures of urban morphology in
Los Angeles
212
9.6.5
Deriving an index of wealth for Los Angeles County
216
9.6.6
Spatial filtering of variables
217
9.6.7
Generating place-based knowledge of urban vulnerability in
Los Angeles
218
9.6.8
To what extent do model results conform to universal knowl¬
edge of vulnerability?
222
9.7
Conclusions
224
10
Using
GIS
and remote sensing for ecological mapping and
monitoring
233
Jennifer A. Miller and John Rogan
10.1
Introduction
233
10.2
Integration of
GIS
and remote sensing in ecological research
237
CONTENTS ix
10.3
10.4
10.5
10.6
10.7
11 Remote
sensing
and GIS
for ephemeral wetland monitoring
and sustainability in southern Mauritania
269
Tara
Shine and Victor Mesev
11.1
Introduction
269
11.1.1
Ephemeral wetlands
269
11.1.2
Remote sensing of ephemeral wetlands
270
11.2
Ephemeral wetlands in Mauritania
272
11.2.1
Data and processing
274
11.2.2
Results
279
11.2.3
Implications for management
283
11.3
Conclusions
284
Index
291
GIS data
used in ecological applications
237
10.3.1
Gradient analysis
238
10.3.2
Climate
240
10.3.3
Topography
241
Remotely sensed data for ecological applications
242
10.4.1
Spectral enhancements
243
10.4.2
Land cover
244
10.4.3
Habitat structure
245
10.4.4
Biophysical processes
246
Species distribution models
247
10.5.1
Biodiversity mapping
251
Change detection
253
10.6.1
Case study: using
GIS
and remote sensing for large-area
change detection and efficient map updating
253
Conclusions
260 |
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| id | DE-604.BV022824935 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:41:30Z |
| indexdate | 2024-07-09T21:07:00Z |
| institution | BVB |
| isbn | 9780470864098 9780470864104 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016030228 |
| oclc_num | 76935862 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-634 DE-83 DE-11 |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-634 DE-83 DE-11 |
| physical | XVI, 296 S. Ill., graph. Darst., Kt. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Wiley |
| record_format | marc |
| series2 | Mastering GIS |
| spelling | Integration of GIS and remote sensing ed. by Victor Mesev Chichester [u.a.] Wiley 2007 XVI, 296 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Mastering GIS Sistema de información geográfica Geographic information systems Remote sensing Fernsehsendung (DE-588)4016842-6 gnd rswk-swf Integration (DE-588)4027238-2 gnd rswk-swf Geoinformationssystem (DE-588)4261642-6 gnd rswk-swf Geoinformationssystem (DE-588)4261642-6 s Fernsehsendung (DE-588)4016842-6 s Integration (DE-588)4027238-2 s b DE-604 Mesev, Victor Sonstige oth Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016030228&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Integration of GIS and remote sensing Sistema de información geográfica Geographic information systems Remote sensing Fernsehsendung (DE-588)4016842-6 gnd Integration (DE-588)4027238-2 gnd Geoinformationssystem (DE-588)4261642-6 gnd |
| subject_GND | (DE-588)4016842-6 (DE-588)4027238-2 (DE-588)4261642-6 |
| title | Integration of GIS and remote sensing |
| title_auth | Integration of GIS and remote sensing |
| title_exact_search | Integration of GIS and remote sensing |
| title_exact_search_txtP | Integration of GIS and remote sensing |
| title_full | Integration of GIS and remote sensing ed. by Victor Mesev |
| title_fullStr | Integration of GIS and remote sensing ed. by Victor Mesev |
| title_full_unstemmed | Integration of GIS and remote sensing ed. by Victor Mesev |
| title_short | Integration of GIS and remote sensing |
| title_sort | integration of gis and remote sensing |
| topic | Sistema de información geográfica Geographic information systems Remote sensing Fernsehsendung (DE-588)4016842-6 gnd Integration (DE-588)4027238-2 gnd Geoinformationssystem (DE-588)4261642-6 gnd |
| topic_facet | Sistema de información geográfica Geographic information systems Remote sensing Fernsehsendung Integration Geoinformationssystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016030228&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mesevvictor integrationofgisandremotesensing |