Evaluation of Built - Up Areas Using Modified Built - Up Index with Landsat 8 and Sentinel - 2A Data

Authors

  • Wilawan Prasomsup Faculty of Railway Systems and Transportation, Rajamangala University of Technology Isan
  • Tinn Thirakultomorn Faculty of Railway Systems and Transportation, Rajamangala University of Technology Isan
  • Athiwat Phinyoyang Faculty of Railway Systems and Transportation, Rajamangala University of Technology Isan
  • Nalinee Nakutnok Faculty of Engineering and Technology, Rajamangala University of Technology Isan
  • Saharat Pidnguheluxm Faculty of Engineering and Technology, Rajamangala University of Technology Isan

Keywords:

Modified Built - up Index, built - up area, Landsat 8, Sentinel - 2A

Abstract

Background and Objectives : The Modified Built-up Index (MBUI) has been developed only for land use classification from Landsat 8 satellite imagery, but it has not been applied to data with higher spatial resolution. Therefore, this research aims to apply MBUI with Landsat 8 and Sentinel - 2A satellite images in the Bangkok Metropolitan Region to assess and verify the accuracy of built-up areas obtained from data with spatial resolution. They are different for use as guidelines in selecting satellite image data to determine built-up areas.

Methodology : The NDVI, NDBI, and MNDWI values were converted to integer values (0 and 254) using the same threshold condition for every image. A random sampling method was used to determine the sample point locations, and the accuracy was assessed using simple and multivariate analytical statistics.

Main Results : The results found that the Landsat 8 and Sentinel - 2A satellite images had built-up areas of 2,956.79 and 2,906.61 km2, respectively, and had KHAT values of 75.36% and 79.71%, respectively.

Conclusions : The NDVI, NDBI, and MNDWI transformation conditions developed for Landsat 8 satellite images can be applied to Sentinel - 2A data for higher spatial resolution and accuracy.

References

Anderson, J.R. (1971). Land use classification schemes used in selected recent geographic applications of remote sensing. Photogrammetric Engineering, 37(4), 379 - 387.

As - syakur, A.R., Adnyana, I.W.S., Arthana, I.W., & Nuarsa, I.W. (2012). Enhanced Built - Up and Bareness Index (EBBI) for Mapping Built - Up and Bare Land in an Urban Area. Remote Sensing, 4(10), 2957 - 2970.

Bouzekri, S., Lasbet, A.A., Lachehab, A. (2005). A New Spectral Index for Extraction of Built - Up Area Using Landsat - 8 Data. Journal of the Indian Society of Remote Sensing, 43(4), 867 - 873.

Cao, R., Chen, Y., Shen, M., Chen, J., Zhou, J., Wang, C., & Yang, W. (2018). A simple method to improve the quality of NDVI time - series data by integrating spatiotemporal information with the Savitzky - Golay filter. Remote Sensing of Environment, 217, 244 - 257.

Dousset, B., & Gourmelon F. (2003). Satellite multi - sensor data analysis of urban surface temperatures and land cover. ISPRS Journal of Photogrammetry and Remote Sensing, 58(1 - 2), 43 - 54.

He, C., Shi, P., Xie, D., & Zhao, Y. (2010) Improving the Normalized Difference Built - Up Index to Map Urban Built - Up Areas Using a Semiautomatic Segmentation Approach. Remote Sensing Letters, 1(4), 213 - 221.

Hutasavi, S., & Chen, D. (2021). Multi - Temporal Mapping of Built - up Areas Using Daytime and Nighttime Satellite Images Based on Google Earth Engine Platform. International Journal of Civil and Architectural Engineering, 15(6), 345 - 353.

Ji, L., Zhang, L. , & Wylie, B. (2009) Analysis of Dynamic Thresholds for the Normalized Difference Water Index. Photogrammetric Engineering and Remote Sensing, 75(11), 1307 - 1317.

Kawamura, M., Jayamana, S., & Tsujiko, Y. (1996). Relation between Social and Environmental Conditions in Colombo Sri Lanka and the Urban Index Estimated by Satellite Remote Sensing Data. International Archives of Photogrammetry and Remote Sensing, 31(Part B7), 321 - 326.

Land Development Department (LDD). Ministry of Agriculture and Cooperatives. (2023). Land Use. https://dinonline .ldd.go.th/ (in Thai)

Landis J.R., & Koch G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159 - 174.

McFeeters, S.K. (1996) The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17, 1425 - 1432.

Maqsoom, A., Aslam, B., Yousafzai, A. Ullah,S., & Imran, M. (2022). Extracting built - up areas from spectro - textural information using machine learning. Soft Computing, 26, 7789 - 7808.

Prasomsup, W., Piyatadsananon, P., Aunphoklang, W., & Boonrang, A. (2020). Extraction Technic for Built - up Area Classification in Landsat 8 Imagery. International journal of environmental science and development, 11(1), 15 - 20.

Rouse, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. In Third ERTS - 1 Symposium. (pp. 309 - 317). Washington, D.C.: Goddard Space Flight Center.

Shao, Z., Tian, Y., & Shen, X. (2014). BASI: a new index to extract built - up areas from high - resolution remote sensing images by visual attention model. Remote Sensing Letters, 5(4), 305 - 314.

Szabó, S, Gacsi, Z., & Boglárka, B. (2016). Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories. Landscape and Environment, 10(3 - 4), 194 - 202.

USGS. (U.S. Geological Survey). (2023). Landsat Normalized Difference Vegetation Index. https://www.usgs.gov/ landsat - missions/landsat - normalized - difference - vegetation - index.

Vaddiraju, S.C., T, R., & Savitha, C. (2022). Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques. Environmental Monitoring and Assessment, 194, 258.

Xu, H. (2005). A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI). Journal of Remote Sensing, 9, 589 - 595.

Xue, J., & Su, B. (2017). Significant Remote Sensing Vegetation Indices A Review of Developments and Applications. Journal of Sensors, 2017, Article ID 1353691.

Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built - up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583 - 594.

Downloads

Published

2024-06-05

How to Cite

Prasomsup, W., Thirakultomorn, T. . ., Phinyoyang, A. ., Nakutnok , N., & Pidnguheluxm, S. . (2024). Evaluation of Built - Up Areas Using Modified Built - Up Index with Landsat 8 and Sentinel - 2A Data. Burapha Science Journal, 29(2), 510–526. Retrieved from https://li05.tci-thaijo.org/index.php/buuscij/article/view/381

Issue

Section

Research Articles