Sensors 2009, 9(3), 1768-1793; doi:10.3390/s90301768
Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling
1
State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
2
Graduate University of Chinese Academy of Sciences, Beijing, 100049, China
3
TRIO/LSIIT(UMR7005 CNRS)/ENSPS, Bld Sebastien Brant, BP10413, 67412 Illkirch, France
*
Author to whom correspondence should be addressed.
Received: 18 December 2008 / Revised: 7 March 2009 / Accepted: 12 March 2009 / Published: 13 March 2009
(This article belongs to the Section Remote Sensors)
Abstract
With the development of quantitative remote sensing, scale issues have attracted more and more the attention of scientists. Research is now suffering from a severe scale discrepancy between data sources and the models used. Consequently, both data interpretation and model application become difficult due to these scale issues. Therefore, effectively scaling remotely sensed information at different scales has already become one of the most important research focuses of remote sensing. The aim of this paper is to demonstrate scale issues from the points of view of analysis, processing and modeling and to provide technical assistance when facing scale issues in remote sensing. The definition of scale and relevant terminologies are given in the first part of this paper. Then, the main causes of scale effects and the scaling effects on measurements, retrieval models and products are reviewed and discussed. Ways to describe the scale threshold and scale domain are briefly discussed. Finally, the general scaling methods, in particular up-scaling methods, are compared and summarized in detail.Keywords:
Scale effects; Scaling; Scale domain; Scale threshold; Remote Sensing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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MDPI and ACS Style
Wu, H.; Li, Z.-L. Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling. Sensors 2009, 9, 1768-1793.