Оглавление
Группа авторов. Urban Remote Sensing
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Urban Remote Sensing. Monitoring, Synthesis, and Modeling in the Urban Environment
List of Contributors
Authors Biography
Preface
PART I. Introduction
CHAPTER 1 Progress in Urban Remote Sensing: An Overview
Abstract
1.1 INTRODUCTION
1.2 ADVANCES IN URBAN REMOTE SENSING
1.3 OVERVIEW OF THE BOOK
1.3.1 SENSORS AND SYSTEMS FOR URBAN AREAS
1.3.2 ALGORITHMS AND TECHNIQUES FOR URBAN ATTRIBUTE EXTRACTION
1.3.3 URBAN SOCIOECONOMIC APPLICATIONS
1.3.4 URBAN ENVIRONMENTAL APPLICATIONS
1.4 SUMMARY AND CONCLUDING REMARKS
REFERENCES
PART II. Sensors and Systems for Urban Areas
CHAPTER 2 Examining Urban Built‐up Volume: Three‐Dimensional Analyses with Lidar and Radar Data
Abstract
2.1 INTRODUCTION
2.2 THREE‐DIMENSIONAL (3D) GEOSPATIAL DATA FOR URBAN REMOTE SENSING
2.3 LIGHT DETECTION AND RANGING (LIDAR) APPROACHES. 2.3.1 BACKGROUND
2.3.2 DATA PROCESSING AND ANALYSIS
2.3.2.1 Case Study: San Antonio, Texas
2.4 RADIO DETECTION AND RANGING (RADAR) APPROACHES. 2.4.1 BACKGROUND
2.4.2 DATA PROCESSING AND ANALYSIS. 2.4.2.1 QuikSCAT SeaWinds Scatterometer
2.4.2.1.1 Dense Sampling Method (DSM) for Built‐up Volume Analysis in Nine United States Cities
2.4.2.2 Synthetic Aperture Radar (SAR)
2.4.2.2.1 Initial SAR Findings
2.4.3 RADAR FOR BUILT‐UP VOLUME: IMPLICATIONS
2.5 A LOOK FORWARD
2.6 CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 3 Opportunities and Challenges of Unmanned Aircraft Systems for Urban Applications
Abstract
3.1 INTRODUCTION
3.2 COMMON UAS MODELS AND SENSORS. 3.2.1 COMMON MODELS
3.2.2 CAMERAS AND SENSORS
3.2.2.1 RGB Cameras
3.2.2.2 Multispectral Sensors
3.2.2.3 Hyperspectral Sensors
3.2.2.4 Thermal Cameras
3.2.2.5 LiDAR
3.3 DATA COLLECTION AND PROCESSING
3.3.1 MISSION PLANNING (PREFLIGHT)
3.3.2 FLIGHT OPERATIONS (IN‐FLIGHT)
3.3.3 DATA PROCESSING (POSTFLIGHT)
3.4 UAS FOR URBAN APPLICATIONS
3.4.1 DISASTER RELIEF EFFORTS
3.4.2 BUILDING INSPECTION
3.4.3 PHYSICAL DISORDER DETECTION
3.4.4 SMART CITIES
3.5 CASE STUDY: MAPPING AN URBAN RECREATION COMPLEX WITH UAS
3.6 MAJOR CHALLENGES AND POSSIBLE SOLUTIONS. 3.6.1 REGULATORY AND LEGAL CHALLENGES
3.6.2 OPERATIONAL CHALLENGES
3.6.3 SPATIAL COVERAGE AND DATA QUALITY
3.7 SUMMARY AND OUTLOOK
REFERENCES
CHAPTER 4 Methods of Social Sensing for Urban Studies
Abstract
4.1 INTRODUCTION
4.2 SENSING FIRST‐ORDER PLACE CHARACTERISTICS
4.2.1 SENSING URBAN LAND USES AND VIBRANCY
4.2.1.1 Temporal Signature Analysis
4.2.1.2 Topic Modeling
4.2.2 SENSING PLACE LOCALE CHARACTERISTICS FROM STREET VIEW IMAGES
4.2.3 SENSING HUMAN SENTIMENT AND EMOTION AT DIFFERENT PLACES
4.3 SENSING SECOND‐ORDER SPATIAL DEPENDENCY AND INTERACTIONS
4.3.1 METHODS FOR SENSING SPATIAL DEPENDENCY
4.3.2 METHODS FOR SENSING SPATIAL INTERACTIONS
4.4 INTEGRATING PLACE CHARACTERISTICS WITH SPATIAL INTERACTIONS
4.5 CONCLUSIONS
REFERENCES
CHAPTER 5 Urban Remote Sensing Using Ground‐Based Street View Images
Abstract
5.1 INTRODUCTION
5.2 LITERATURE REVIEW
5.3 DATA SOURCES OF STREET‐LEVEL IMAGES
5.4 STREET‐LEVEL IMAGE PROCESSING
5.4.1 GEOMETRIC TRANSFORMATION OF STREET‐LEVEL IMAGES
5.4.1.1 Cylindrical Panoramas to Azimuthal Hemispherical Images
5.4.1.2 Cubic Skybox Images to Cylindrical Projection
5.4.1.3 Cylindrical Projection to Perspective Projection
5.4.2 IMAGE SEGMENTATION
5.5 URBAN MAPPING AND MODELING
5.5.1 MAPPING URBAN STREET GREENERY
5.5.2 URBAN FORM ANALYSIS
5.5.3 SOLAR RADIATION MODELING IN STREET CANYONS
5.6 DISCUSSION AND FUTURE RESEARCH DIRECTIONS
REFERENCES
CHAPTER 6 Spatial Distribution of City Tweets and Their Densities
6.1 INTRODUCTION
6.2 NATURAL CITIES, STREET BLOCKS, AND RELATED DISTANCES
6.3 DATA AND DATA PROCESSING
6.4 TWO MAJOR FINDINGS
6.5 IMPLICATIONS OF THE STUDY AND ITS FINDINGS
6.6 CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 7 Integrating Remote Sensing and Social Sensing to Examine Socioeconomic Dynamics: A Case Study of Twitter and Nighttime Light Imagery
7.1 INTRODUCTION
7.2 REMOTE SENSING AND SOCIAL SENSING. 7.2.1 REMOTE SENSING OF NIGHTTIME LIGHTS
7.2.2 CHALLENGES WITH NTL IMAGERY
7.2.3 SOCIAL SENSING
7.2.4 CHALLENGES WITH LBSM
7.3 PEOPLE AND PIXELS 2.0: TOWARD AN INTEGRATION OF REMOTE SENSING AND SOCIAL SENSING
7.3.1 COMPARISON OF LBSM AND DMSP‐OLS FOR SOCIOECONOMIC DYNAMICS
7.3.2 INTEGRATE LBSM AND NTL FOR SOCIOECONOMIC DYNAMICS
7.4 DISCUSSION AND CONCLUSIONS
REFERENCES
PART III. Algorithms and Techniques for Urban Attribute Extraction
CHAPTER 8 Deep Learning for Urban and Landscape Mapping from Remotely Sensed Imagery
8.1 INTRODUCTION
8.2 AN OVERVIEW OF SOME COMMONLY USED DEEP LEARNING MODELS
8.2.1 CONVOLUTION NEURAL NETWORKS (CNNs)
8.2.1.1 Convolution
8.2.1.2 Pooling/Subsampling
8.2.1.3 Fully Connected Layer
8.2.2 RECURRENT NEURAL NETWORKS (RNNs)
8.3 CASE STUDY I: A PATCH‐BASED CNNs MODEL FOR LAND COVER CLASSIFICATION
8.3.1 DATA ACQUISITION AND PREPROCESSING
8.3.2 TRAINING SAMPLE SELECTION
8.3.3 MODEL DESIGN AND TRAINING
8.3.4 COMPARISON WITH SEVERAL OTHER MODELS
8.3.5 RESULTS
8.3.6 SUMMARY
8.4 CASE STUDY II: A PATCH‐BASED RNNs MODEL FOR LAND CLASSIFICATION
8.4.1 DATA ACQUISITION AND PRE‐PROCESSING
8.4.2 TRAINING SAMPLE SELECTION
8.4.3 MODEL DESIGN AND IMPLEMENTATION
8.4.4 RESULTS
8.4.5 SUMMARY
8.5 DISCUSSION AND CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 9 Google Earth Engine Advancing Urban Land Change Science
9.1 INTRODUCTION
9.1.1 TECHNICAL ASPECTS OF GEE
9.1.2 ROLE OF GEE IN URBAN LAND CHANGE SCIENCE
9.1.3 OVERALL STRUCTURE OF THIS CHAPTER
9.2 GEE EVOLUTION IN URBAN STUDIES
9.2.1 URBAN EXTENT MAPPING
9.2.2 URBANIZATION ESTIMATION
9.2.3 URBAN ECOSYSTEM CHARACTERIZATION
9.2.4 CITY ACCESSIBILITY ASSESSMENT
9.3 EXAMPLES
9.3.1 URBAN EXTENT MAPPING: IMPERVIOUS AREA MONITORING
9.3.2 URBAN ECOSYSTEM CHARACTERIZATION: VEGETATION MONITORING
9.4 DISCUSSION
9.4.1 ADVANTAGES OF GEE‐BASED RESEARCH
9.4.2 FUTURE PROSPECT
9.5 CONCLUSIONS
REFERENCES
CHAPTER 10 Use of Image Endmember Libraries for Multi‐Sensor, Multi‐Scale, and Multi‐Site Mapping of Urban Areas
10.1 INTRODUCTION
10.2 IMAGE ENDMEMBER LIBRARIES FOR URBAN MAPPING
10.3 EXTRACTION AND IDENTIFICATION OF IMAGE ENDMEMBER SPECTRA
10.3.1 METHODS FOR AUTOMATED EXTRACTION OF IMAGE ENDMEMBERS
10.3.2 THE LEARNING URBAN IMAGE SPECTRAL ARCHIVE (LUISA) FRAMEWORK
10.4 PRUNING OF IMAGE ENDMEMBER LIBRARIES
10.4.1 EXISTING LIBRARY PRUNING APPROACHES
10.4.2 AUTOMATED MUSIC AND SPECTRAL SEPARABILITY BASED ENDMEMBER SELECTION (AMUSES)
10.5 USE OF IMAGE ENDMEMBER LIBRARIES FOR MEDIUM‐RESOLUTION MAPPING OF URBAN LAND COVER COMPOSITION
10.5.1 MAP‐BASED VERSUS LIBRARY‐BASED TRAINING
10.5.2 SENSOR/SCALE TRANSFERABILITY OF ENDMEMBER SPECTRA
10.6 USE OF MULTI‐SITE SPECTRAL LIBRARIES FOR VIS MAPPING ACROSS CITIES
10.6.1 MULTI‐SOURCE TRAINING INFORMATION FOR GENERALIZED LAND COVER MAPPING
10.6.2 VIS MAPPING ACROSS MULTIPLE CITIES USING MULTI‐SITE SPECTRAL LIBRARIES
10.7 TOWARD A GENERIC URBAN LIBRARY MANAGEMENT TOOL
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 11 Satellite Monitoring of Urbanization and Environmental Impacts in Stockholm, Sweden, Through a Multiscale Approach
11.1 INTRODUCTION
11.2 A BRIEF REVIEW OF REMOTE SENSING‐BASED MULTISCALE ANALYSIS
11.3 STUDY AREAS AND DATA DESCRIPTION
11.3.1 STUDY AREAS
11.3.2 DATA DESCRIPTION
11.3.2.1 Satellite Imagery with Medium Resolution
11.3.2.2 Satellite Imagery with High Resolution
11.3.2.3 Satellite Imagery with Very High Resolution
11.4 METHODOLOGY
11.4.1 IMAGE PROCESSING
11.4.2 IMAGE CLASSIFICATION
11.4.3 LANDSCAPE METRICS
11.4.4 ENVIRONMENTAL INDICATORS. 11.4.4.1 Stockholm County at High Resolution
11.4.4.2 Stockholm City at Very High Resolution
11.4.5 MONITORING OF ECOSYSTEM SERVICES. 11.4.5.1 Stockholm County at Medium Resolution
11.4.5.2 Stockholm County at High Resolution
11.5 RESULTS. 11.5.1 IMAGE CLASSIFICATIONS
11.5.2 LANDSCAPE CHANGE AND ENVIRONMENTAL IMPACT ANALYSIS USING LANDSCAPE METRICS. 11.5.2.1 Urban Growth and Landscape Change Trends
11.5.2.2 Stockholm County at Medium Resolution
11.5.2.3 Stockholm County at High Resolution
11.5.2.4 Stockholm City at Very High Resolution
11.5.3 ASSESSING IMPACT WITH ECOSYSTEM SERVICE AND GREEN INFRASTRUCTURE INDICATORS. 11.5.3.1 Stockholm County at Medium Resolution
11.5.3.2 Stockholm County at High Resolution
11.5.3.3 Stockholm City at Very High Resolution
11.6 DISCUSSION. 11.6.1 COMPARISON OF THE STOCKHOLM INVESTIGATIONS
11.6.2 ADVANTAGES AND LIMITATIONS OF MULTISCALE ANALYSIS IN THE RESEARCH
11.7 CONCLUSIONS
REFERENCES
PART IV. Urban Socioeconomic Applications
CHAPTER 12 Global Monitoring with the Atlas of Urban Expansion
12.1 INTRODUCTION
12.2 ASSEMBLING THE GLOBAL SAMPLE OF CITIES. 12.2.1 THE UNIVERSE OF CITIES
12.2.2 SAMPLING THE UNIVERSE OF CITIES
12.2.3 WEIGHTING THE SAMPLE OF CITIES
12.3 MEASURING URBAN EXTENT
12.3.1 STUDY AREA ASSESSMENT AND POPULATION DATA COLLECTION
12.3.2 LANDSAT DATA COLLECTION AND CLASSIFICATION
12.3.3 URBAN LANDSCAPE ANALYSIS
12.3.4 URBAN CLUSTERS AND THE URBAN EXTENT RULE
12.3.5 DERIVED METRICS AND CHANGE OVER TIME
12.4 URBAN EXTENT GROWTH RATE, POPULATION GROWTH RATE, AND THE CHANGE IN LAND CONSUMPTION PER CAPITA
12.4.1 DISTRIBUTIONS OF URBAN EXTENT AND POPULATION
12.4.2 GROWTH RATES
12.4.2.1 Urban Extent Growth Rates
12.4.2.2 Population Growth Rates
12.4.3 THE RELATIONSHIP OF URBAN EXTENT CHANGE TO POPULATION CHANGE IN CITIES
12.4.3.1 Why Measure Land Consumption Per Capita and Its Change Over Time?
12.4.4 MEASURING CHANGE IN LAND CONSUMPTION PER CAPITA
12.5 ASSESSING ACCURACY WITH URBAN EXTENT LOCALES
12.5.1 GENERATING AND SELECTING URBAN EXTENT LOCALES
12.5.2 LOCALE IMAGERY, DIGITIZATION, AND LABELING
12.5.3 ALIGNING THE DATASETS FOR COMPARISON
12.6 CLASSIFICATION ACCURACY AND MAP AGREEMENT
12.6.1 ACCURACY BASED ON CITY‐LEVEL DATA
12.6.2 COMPARISONS OF ATLAS AND GHSL URBAN EXTENT
12.7 CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 13 Effect of Image Classification Accuracy on Dasymetric Population Estimation
13.1 INTRODUCTION
13.2 ASSESSING THE ACCURACY OF IMAGE CLASSIFICATION
13.3 DASYMETRIC MAPPING AND AREAL INTERPOLATION
13.4 STUDY AREA AND DATA SOURCES
13.5 METHODS. 13.5.1 DEVELOPING A BUILDING CLASSIFIER FROM HIGH‐RESOLUTION AERIAL IMAGERY
13.5.2 DASYMETRIC ESTIMATION
13.6 RESULTS
13.7 CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 14 Mapping the Morphology of Urban Deprivation: The Role of Remote Sensing for Developing a Global Slum Repository
14.1 BACKGROUND
14.2 DEPRIVATION: AN AREA‐BASED AND RELATIVE CONCEPT
14.3 DATA SOURCES: OVERVIEW OF EO‐DATA IN RELATION TO THEIR BENEFITS AND COSTS
14.4 TOWARD A GLOBAL MAPPING PRODUCT: PEOPLE‐PIXELS‐PRIVACY
14.5 MAPPING METHODS: ACHIEVING SCALABILITY AND TRANSFERABILITY
14.6 EXAMPLES OF MAPPING URBAN DEPRIVATION
14.7 CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 15 The City is the Medium and Satellite Imagery Are a Prism: Conceptualizing Urban Conflict Damage Monitoring with Multitemporal Remote Sensing Data
15.1 INTRODUCTION
15.2 OPPORTUNITIES FOR SATELLITE‐BASED MONITORING OF URBAN CONFLICT DAMAGE
15.3 (RE‐)SITUATING URBAN CONFLICT DAMAGE
15.4 THEORIZING CHANGE IN URBAN CONFLICT DAMAGE MONITORING
15.5 CONCLUSIONS
REFERENCES
PART V. Urban Environmental Applications
CHAPTER 16 US Cities in the Dark: Mapping Man‐Made Carbon Dioxide Emissions Over the Contiguous US Using NASA ’s Black Marble Nighttime Lights Product
16.1 INTRODUCTION
16.2 NASA BLACK MARBLE DATA
16.3 CO2 EMISSION MODELING
16.3.1 ODIAC EMISSION MODELING APPROACH
16.3.2 EMISSION DATA
16.4 RESULTS
16.5 EXAMINING BLACK MARBLE NTL DATA AS ESTIMATOR OF CO2 EMISSIONS
16.5.1 COMPARING NTL DATA TO URBAN AND POPULATION DISTRIBUTIONS
16.5.2 COMPARING THE EMISSION RANGE DISTRIBUTIONS RESULTING FROM THREE NTL DATA
16.5.3 ESTIMATING STATE‐LEVEL EMISSIONS
16.5.4 ASSESSING DOWNSCALING ERRORS AT SPATIAL SCALES BEYOND STATE LEVEL
16.5.5 ASSESSING EMISSION REPRESENTATION ERRORS AT CITY LEVEL
16.6 NEW ADVANTAGES IN NTL‐BASED MAPPING, CHALLENGES, AND FUTURE PERSPECTIVES
16.6.1 ADVANTAGES BROUGHT IN AND ASSOCIATED NEW CHALLENGES. 16.6.1.1 No Saturation
16.6.1.2 No Blooming Effect
16.6.1.3 Calibrated Time Series
16.6.1.4 Uncertainty Analysis
16.6.2 FUTURE PERSPECTIVES
16.7 CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 17 Thermal Infrared Imaging of the Urban Landscape to Understand Urban Microclimate
17.1 INTRODUCTION. 17.1.1 URBAN MICROCLIMATE AND SURFACE ENERGY BALANCE
17.1.2 RADIOMETRIC SURFACE TEMPERATURE OBSERVED BY REMOTE SENSING (Tr)
17.1.3 COMPLETE SURFACE TEMPERATURE (Tc)
17.1.4 LINK BETWEEN Tr AND Tc
17.2 METHODOLOGY
17.2.1 RETRIEVAL OF RADIOMETRIC SURFACE TEMPERATURE (TR)
17.2.2 MODELING THE RELATION BETWEEN TC AND TR
17.3 STUDY SITE AND DATA
17.4 RESULTS. 17.4.1 RETRIEVAL OF TR
17.4.2 ESTIMATION AND MONITORING OF TC OVER URBAN AREAS
17.5 DISCUSSION AND CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 18 Monitoring Air Pollution in the Urban Environment by Remote Sensing
18.1 AIR POLLUTION MONITORING PROBLEM: THE COMPLEXITY
18.2 SATELLITE RETRIEVED AOD vs GROUND‐BASED PM2.5 MEASUREMENTS: BASIC DEFINITIONS
18.3 APPLYING DIFFERENT SATELLITE AOD RETRIEVALS OVER THE URBAN ENVIRONMENT: DATA COVERAGE AND SPATIAL RESOLUTION
18.3.1 DATA COVERAGE
18.3.2 RESOLUTION
18.4 SATELLITE‐RETRIEVED AOD COMPARED TO GROUND‐BASED AND OTHER MEASUREMENTS AVAILABLE
18.5 MODELING PM2.5 USING SATELLITE AND LAND USE DATA
18.6 MODEL ACCURACY ASSESSMENT
18.7 LEARNING AIR POLLUTION FROM PUBLICLY AVAILABLE SOURCES: SEVERAL EXAMPLES
18.8 CURRENT AND FUTURE STEPS AND NEW TECHNOLOGIES: BEYOND THE SATELLITE MONITORING
REFERENCES
CHAPTER 19 Characterizing After‐Rain Standing Waters in Urban Built Environments Through a Multilevel Image Analysis
19.1 INTRODUCTION
19.2 MATERIALS AND METHODS
19.2.1 STUDY AREA
19.2.2 MONITORING URBAN STANDING WATER AFTER HEAVY RAIN
19.2.3 MEASURING SPATIOTEMPORAL RAINFALL PATTERNS
19.2.4 QUANTIFYING THE NEIGHBORING URBAN MORPHOLOGIES
19.2.5 STATISTICAL MODELING WITH MULTISCALE IMAGES
19.3 RESULTS. 19.3.1 SPATIAL DISTRIBUTION OF RAINFALL PATTERNS AND NEIGHBORING URBAN MORPHOLOGIES
19.3.2 DESCRIPTIVE STATISTICS
19.3.3 MULTILEVEL MODELING RESULTS SUMMARY
19.3.4 IDENTIFYING SUSCEPTIBLE URBAN AREAS PRONE TO STANDING WATER
19.4 DISCUSSION
19.5 CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 20 Remote Sensing and Urban Green Infrastructure: A Synthesis of Current Applications and New Advances
20.1 INTRODUCTION
20.2 CURRENT APPLICATIONS
20.2.1 AIR POLLUTION AND QUALITY
20.2.2 URBAN HEAT ISLANDS
20.2.3 WATER MANAGEMENT
20.2.4 CARBON SEQUESTRATION AND STORAGE
20.2.5 URBAN BIODIVERSITY
20.2.6 SUMMARY
20.3 RECENT ADVANCES
20.3.1 TRADITIONAL MACHINE LEARNING FOR UGI
20.3.2 DEEP LEARNING FOR UGI
20.3.3 SUMMARY
20.4 CONCLUSIONS
REFERENCES
CHAPTER 21 Remote Sensing for Urban Sustainability Research and Sustainable Development Goals: Green Space, Public Recreation Space, and Urban Climate
21.1 INTRODUCTION. 21.1.1 THE UN SUSTAINABLE DEVELOPMENT GOALS (SDGs) NECESSITATE SCIENTIFICALLY SOUND MEASURES
21.1.2 MEASURING AND MONITORING – THE GLOBAL INDICATOR FRAMEWORK FOR THE SDGs
21.1.3 SCIENTIFIC RESEARCH ON SUSTAINABLE DEVELOPMENT GOALS (SDGs)
21.2 REMOTE SENSING AND EARTH OBSERVATION. 21.2.1 TERMINOLOGY
21.2.2 FROM PIXEL VALUES TO INFORMATION
21.2.3 IS AI/MACHINE LEARNING THE SOLUTION OF THE FUTURE?
21.2.4 THE ROLE OF INTERNATIONAL ORGANIZATIONS
21.3 REMOTE SENSING FOR SDGs. 21.3.1 INTERNATIONAL COLLABORATION AND FRAMEWORKS
21.3.2 POTENTIALS, ADVANTAGES, AND CHALLENGES IN USING REMOTE SENSING‐DERIVED INFORMATION AS SDG MEASUREMENTS
21.3.3 EXAMPLE OF URBAN GREEN SPACES (SDG 11.7)
21.3.4 ONGOING AND FUTURE CHALLENGES FOR SCIENTISTS
21.4 AN ANALYSIS OF THE SCIENTIFIC LITERATURE IN THE FIELD OF SUSTAINABILITY
21.4.1 CONTRIBUTIONS OF REMOTE SENSING TO SDG RESEARCH
21.4.2 MONITORING URBAN GREEN SPACES BY MEANS OF REMOTE SENSING
21.4.3 RESEARCH RELATED TO URBAN CLIMATE
21.4.4 URBAN REMOTE SENSING AND URBAN HEAT ISLAND RESEARCH
21.4.5 URBAN HEAT ISLAND RESEARCH IN THE CONTEXT OF REMOTE SENSING
21.5 DISCUSSION. 21.5.1 GREEN SPACE VERSUS PUBLIC SPACE
21.5.2 COMBINING REMOTE SENSING AND GIS DATA
21.6 SUMMARY AND CONCLUSIONS
REFERENCES
Index. A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
Y
WILEY END USER LICENSE AGREEMENT
