Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

Remote Sensing

Elizabeth A. Wentz¹  Sharolyn Anderson⁷  Michail Fragkias⁵  Maik Netzband²  Victor Mesev³  Soe W. Myint¹  Dale Quattrochi⁴  Atiqur Rahman⁶ 

[1]School of Geographical Sciences and Urban Planning, Arizona State University, Coor Hall, 5th Floor, 975 S. Myrtle Ave., Tempe, AZ 85287, USA

[2]Department of Geography, Ruhr-University Bochum, Universitaetsstrasse 150, 44801 Bochum, Germany

[3] E-Mail:

[4]Department of Geography, Florida State University, 323 Bellamy Building, 113 Collegiate Loop, Tallahassee, FL 32306, USA

[5] E-Mail:

[6]Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL 35812, USA

[7]E-Mail:

[8]Department of Economics, College of Business and Economics (COBE), Boise State University, 1910 University, Boise, ID 83725, USA

[9] E-Mail:

[10]Department of Geography, Jamia Millia Islamia University, New Delhi 110 025, India

[11]E-Mail:

[12]School of Natural and Built Environments, University of South Australia, Mawson Lakes Campus, Adelaide, SA 5001, Australia

[13] E-Mail:

关键词: urban mapping;    environmental indices;    social indices;    climate modeling;    socioeconomic modeling;   

DOI  :  10.3390/rs6053879

来源: mdpi

【 摘 要 】

This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland