Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images
Main Article Content
Abstract
The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.
Keywords:
bare land, classification, urban indices, NDBaI, Sentinel 2 MSI, Landsat 8.
References
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[4] G. Xian, M. Crane, Assessments of urban growth in the Tampa Bay wateshed using remote sensing data, Remote Sensing of Environment 97(2005) 203-215.
[5] M.K. Ridd, Exploring a V-I-S (vegetation-imprevious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities, International Journal of Remote Sensing 16(12) (1994) 2165- 2185.
[6] H.Q. Xu, Extraction of urban built -up land features from Landsat imagery using a thematic oriented index combination technique, Photogrammetric Engineering & Remote Sensing 73(12) (2007) 1381-1391.
[7] Y. Zha, J. Gao, S. Ni, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing 24(3) (2003) 583-594.
[8] H.Q. Xu, A study on information extraction of water body with the modified mormalized difference water index (MNDWI), Journal of Remote Sensing 9(5) (2008) 511-517.
[9] N.H.K. Linh, Automatic establishment of urban land distribution map using IBI index from Landsat TM image: Case study in Hue city - Thua Thien Hue province, National Conference on GIS Application (2011) 205-212 (in Vietnamese).
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[13] A. Sekertekin, S. Abdikan, A. Marangoz, The acquisition of impervious surface area from Landsat 8 satellite sensor data using urban indices: a comparative analysis, Environmental Monitoring and Assessment 190(7) (2018) 1-13.
[14] V. Bramhe, S. Ghosh, P. Garg, Extraction of built-up area by combining textural features and spectral indices from Landsat 8 multispectral image, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42(5) (2018) 727-733.
[15] S. Gadal, W. Ouerghemmi, Multi-level morphometric characterization of built-up areas and change detection in Siberian sub-arctic urban area: Yakutsk, International Journal of Geo-Information 8 (2019) 129-149.
[16] H. Zhao, X. Chen, Use of Normalized Difference Bareness Index in quickly mapping bare areas from TM/ETM+, International Geoscience and Remote Sensing Symposium (IGARSS) 3 (2005) 1666-1668. https://doi.org/10.1109/IGARSS.2005. 1526319.
[17] D. Ghosh, A. Mandal, R. Majumder, P. Patra, G. Bhunia, Analysis for mapping of built-up area using remotely sensed indices – A case study of Rajarhat block in Barasat Sanda sub-division in West Bengal (India), Journal of Landscape Ecology 11(2) (2018) 67-76.
[18] M. Firozijaei, A. Sehighi, M. Kiavarz, S. Qureshi, D. Haase, S. Alavipanah, Automated built-up extraction index: a new technique for mapping surface built-up areas using Landsat 8 OLI imagery, Remote Sensing 11 (2019). https://doi. org/10.3390/rs11171966.
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[20] N. Pahlevan, S. Sarkar, B. Franz, S. Balasubramanian, J. He, Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and validations, Remote Sensing of Environment 201 (2017) 47–56.