Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China

• Published in: Advances in space research Vol. 52; no. 8; pp. 1437 - 1449
• Main Authors: Xu, Hanqiu, Huang, Shaolin, Zhang, Tiejun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.10.2013
• Subjects: ASTER; Built-up land; Comparison; IBI; Landsat-7 ETM; IBI; Built-up land; ASTER; Comparison; Landsat-7 ETM
• ISSN: 0273-1177; 1879-1948
• DOI: 10.1016/j.asr.2013.07.026

•Comparison ability in built-up land measurements between the ASTER and Landsat-7 ETM+ sensors.•Find out the quantitative relationships between the two sensors.•Quantify the differences in mapping built-up lands between both sensors. Worldwide urbanization has accelerated expansion of urban built-up lands and resulted in substantial negative impacts on the global environments. Precisely measuring the urban sprawl is becoming an increasing need. Among the satellite-based earth observation systems, the Landsat and ASTER data are most suitable for mesoscale measurements of urban changes. Nevertheless, to date the difference in the capability of mapping built-up land between the two sensors is not clear. Therefore, this study compared the performances of the Landsat-7 ETM+ and ASTER sensors for built-up land mapping in the coastal areas of southeastern China. The comparison was implemented on three date-coincident image pairs and achieved by using three approaches, including per-band-based, index-based, and classification-based comparisons. The index used is the Index-based Built-up Index (IBI), while the classification algorithm employed is the Support Vector Machine (SVM). Results show that in the study areas, ETM+ and ASTER have an overall similar performance in built-up land mapping but also differ in several aspects. The IBI values determined from ASTER were consistently higher than from ETM+ by up to 45.54% according to percentage difference. The ASTER also estimates more built-up land area than ETM+ by 5.9–6.3% estimated with the IBI-based approach or 3.9–6.1% with the SVM classification. The differences in the spectral response functions and spatial resolution between relative spectral bands of the two sensors are attributed to these different performances.