A Survey of the Evolution of Remote Sensing Imaging Systems and Urban Remote Sensing Applications

The increasingly diverse nature of sensor systems and imagery products, as well as their commercial availability, have led to a broad set of applications resulting in rich, interdisciplinary topics that come under the umbrella of urban remote sensing. This chapter reviews the development of remote sensing systems, their contribution to the emergence of urban remote sensing, and how they have given rise to the pursuit of novel approaches to the study of urban environments.

[1]  J. Weeks,et al.  The Fertility Transition in Egypt: Intraurban Patterns in Cairo , 2004 .

[2]  P. Falkowski,et al.  Brown Tide blooms in Long Island’s coastal waters linked to interannual variability in groundwater flow , 1997 .

[3]  Jinmu Choi,et al.  A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images , 2004 .

[4]  P. Sutton Modeling population density with night-time satellite imagery and GIS , 1997 .

[5]  J. Weeks,et al.  Revealing the Anatomy of Cities through Spectral Mixture Analysis of Multispectral Satellite Imagery: A Case Study of the Greater Cairo Region, Egypt. , 2001 .

[6]  D. Stow,et al.  THE EFFECT OF TRAINING STRATEGIES ON SUPERVISED CLASSIFICATION AT DIFFERENT SPATIAL RESOLUTIONS , 2002 .

[7]  J. R. Jensen,et al.  Remote Sensing of Urban/Suburban Infrastructure and Socio‐Economic Attributes , 2011 .

[8]  C. Lo,et al.  CHINESE URBAN POPULATION ESTIMATES , 1977 .

[9]  John R. Jensen Introductory Digital Image Processing , 2004 .

[10]  K. Chen,et al.  An approach to linking remotely sensed data and areal census data , 2002 .

[11]  Dan G. Blumberg,et al.  New frontiers: Remote sensing in social science research , 1997 .

[12]  Christopher D. Elvidge,et al.  Satellite inventory of human settlements using nocturnal radiation emissions: a contribution for the global toolchest , 1997 .

[13]  C. Lo Application of LandSat TM data for quality of life assessment in an urban environment , 1997 .

[14]  M. Ridd Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[15]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[16]  Martino Pesaresi,et al.  Recognizing Settlement Structure using Mathematical Morphology and Image Texture , 2001 .

[17]  O. Dikshit,et al.  Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh , 2001 .

[18]  Christopher D. Elvidge,et al.  Trends in night-time city lights and vegetation indices associated with urbanization within the conterminous USA , 2004 .

[19]  P. Longley,et al.  Remote Sensing and Urban Analysis , 2001 .

[20]  C. Lo Automated population and dwelling unit estimation from high-resolution satellite images: a GIS approach , 1995 .

[21]  Barbara Entwisle,et al.  Patterns of Urban Land Use as Assessed by Satellite Imagery: An Application to Cairo, Egypt , 2005 .

[22]  Marie-Françoise Courel,et al.  Application of Remote Sensing to the Urban Expansion Analysis for Nouakchott, Mauritania , 2003 .

[23]  Thierry Ranchin,et al.  Improving the spatial resolution of remotely-sensed images by means of sensor fusion: a general solution using the ARSIS method , 2000 .

[24]  J. Iisaka,et al.  Population estimation from Landsat imagery , 1982 .

[25]  C. Elvidge,et al.  A Technique for Using Composite DMSP/OLS "City Lights"Satellite Data to Map Urban Area , 1997 .

[26]  Sachio Kubo,et al.  Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing , 2001 .

[27]  S. Barr,et al.  Inferring Urban Land Use by Spatial and Structural Pattern Recognition , 2001 .

[28]  C. P. Lo Urban Indicators of China from Radiance-Calibrated Digital DMSP-OLS Nighttime Images , 2002 .

[29]  P. Hardin,et al.  Remote sensing/GIS integration to identify potential low-income housing sites , 2000 .

[30]  Dongmei Chen,et al.  Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case , 2004 .

[31]  D. Stow,et al.  Measuring temporal compositions of urban morphology through spectral mixture analysis: toward a soft approach to change analysis in crowded cities , 2005 .

[32]  Michael P. Prisloe,et al.  Development of a geospatial model to quantify, describe and map urban growth , 2003 .

[33]  Dar A. Roberts,et al.  A Comparison of Nighttime Satellite Imagery and Population Density for the Continental United States , 1997 .

[34]  S. Walsh,et al.  Approaches for Linking People, Place, and Environment for Human Dimensions Research , 2003 .

[35]  A. Tatem,et al.  Measuring urbanization pattern and extent for malaria research: A review of remote sensing approaches , 2004, Journal of Urban Health.

[36]  P. Gong,et al.  Validation of urban boundaries derived from global night-time satellite imagery , 2003 .

[37]  C. Kontoes,et al.  The potential of kernel classification techniques for land use mapping in urban areas using 5m-spatial resolution IRS-1C imagery , 2000 .

[38]  Barbara Entwisle,et al.  Population, Land Use, and Environment: Research Directions , 2005 .

[39]  Pietro Alessandro Brivio,et al.  Urban Pattern Characterization through Geostatistical Analysis of Satellite Images , 2001 .

[40]  S. Galea,et al.  Urban health: a new discipline , 2003, The Lancet.

[41]  Douglas A. Stow,et al.  Category identification of changed land-use polygons in an integrated image processing/geographic information system , 1992 .

[42]  R. Welch,et al.  Spatial resolution requirements for urban studies , 1982 .

[43]  M. Ramsey,et al.  Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers , 2001 .

[44]  D. T. Lindgren DWELLING UNIT ESTIMATION WITH COLOR-IR PHOTOS , 1971 .

[45]  Waldo R. Tobler,et al.  Satellite confirmation of settlement size coefficient , 1968 .

[46]  D. Roberts,et al.  Census from Heaven: An estimate of the global human population using night-time satellite imagery , 2001 .

[47]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[48]  Jack T. Harvey,et al.  Estimating census district populations from satellite imagery: Some approaches and limitations , 2002 .

[49]  C. Small Estimation of urban vegetation abundance by spectral mixture analysis , 2001 .

[50]  Toby N. Carlson,et al.  Applications of remote sensing to urban problems , 2003 .

[51]  L. Haddad,et al.  Are urban poverty and undernutrition growing? Some newly assembled evidence , 1999 .

[52]  Margaret E. Gardner,et al.  Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm , 2004 .

[53]  Michael E. Hodgson,et al.  Forest impact estimated with NOAA AVHRR and Landsat TM data related to an empirical hurricane wind-field distribution , 2001 .

[54]  Paul C. Sutton,et al.  A scale-adjusted measure of Urban sprawl using nighttime satellite imagery , 2003 .

[55]  Ahmet B. Orun,et al.  Automated identification of man-made textural features on satellite imagery by Bayesian networks , 2004 .

[56]  C E Ogrosky,et al.  POPULATION ESTIMATES FROM SATELLITE IMAGERY , 1975 .