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Remote Sens. 2014, 6(10), 9458-9474; doi:10.3390/rs6109458

Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples

1,2,* , 3
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4
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1,2,* , 5
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6 and 1,2
1
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
2
Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China
3
School of Survey and Mapping, Henan Polytechnic University, Jiaozuo 454003, China
4
GIPSA-lab, Grenoble Institute of Technology, F-38400 Grenoble, France
5
Remote Sensing Laboratory, University of Trento, I-38123 Trento, Italy
6
Jiangsu Key laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou 221116, China
*
Authors to whom correspondence should be addressed.
Received: 2 January 2014 / Revised: 15 September 2014 / Accepted: 22 September 2014 / Published: 2 October 2014
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Abstract

Remote sensing imagery has been widely used in urban growth and environment analysis with many effective and advanced strategies being developed. However, most of these approaches are separated from each other. There is an urgent need to combine different modules into some practical processing chains. Firstly, we present a comprehensive analysis of key processing chains in applying remote sensing images to urban environment analysis from such aspects as Land Use/Land Cover (LULC), urban landscape ecology, Urban Heat Islands (UHIs), vegetation and water monitoring, change detection, urban ecological security assessment and urban environmental mapping. Secondly, an integrated system, namely Urban Environment Analysis System (UEAS), is implemented based on the aforementioned processing chains to analyze urban environment using multi-temporal and multi-source remotely sensed data. Several case studies are demonstrated to confirm the effectiveness of the integrated system and the combined processing chains. The contributions of this paper lie in introducing ensemble learning to urban environment remote sensing, combining remote sensing derived information with thematic models for urban environment assessment, and developing an integrated system for urban environment analysis. View Full-Text
Keywords: urban remote sensing; Urban Environment Analysis System (UEAS); Land Use/Land Cover (LULC) classification; Urban Heat Islands (UHIs); Land Surface Temperature (LST); Change Detection (CD) urban remote sensing; Urban Environment Analysis System (UEAS); Land Use/Land Cover (LULC) classification; Urban Heat Islands (UHIs); Land Surface Temperature (LST); Change Detection (CD)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Du, P.; Liu, P.; Xia, J.; Feng, L.; Liu, S.; Tan, K.; Cheng, L. Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples. Remote Sens. 2014, 6, 9458-9474.

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