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Scale issues in remote sensing: a review on analysis, processing and modeling.

Hua Wu1, Zhao-Liang Li

  • 1State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China;

Sensors (Basel, Switzerland)
|May 11, 2012
PubMed
Summary
This summary is machine-generated.

Quantitative remote sensing faces scale discrepancy challenges. This study addresses scaling issues in data analysis, processing, and modeling for improved remote sensing applications.

Keywords:
Remote SensingScale domainScale effectsScale thresholdScaling

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Area of Science:

  • Earth and Space Sciences
  • Geosciences
  • Environmental Sciences

Background:

  • Quantitative remote sensing is advancing, highlighting significant scale issues.
  • A discrepancy exists between remote sensing data sources and analytical models.
  • These scale issues impede data interpretation and model application.

Purpose of the Study:

  • To analyze scale issues in remote sensing from perspectives of analysis, processing, and modeling.
  • To offer technical guidance for addressing scale challenges in remote sensing.
  • To define scale and related terminology.

Main Methods:

  • Review and discussion of scale effects on measurements, retrieval models, and products.
  • Exploration of methods to describe scale thresholds and domains.
  • Comparison and summarization of general scaling methods, focusing on up-scaling.

Main Results:

  • Identification of scale discrepancy as a critical challenge in quantitative remote sensing.
  • Analysis of the causes and impacts of scale effects.
  • Evaluation of various scaling techniques, particularly up-scaling.

Conclusions:

  • Addressing scale issues is crucial for advancing remote sensing research.
  • Understanding scale effects is vital for accurate data interpretation and model application.
  • Effective scaling methods are essential for leveraging remote sensing data across different scales.