FIGURE 1. Urban area of Daegu City in South Korea, selected as the study area
FIGURE 2. Flowchart showing the procedure for evaluating the different thresholds for detecting urban areas from the NDBI and UI images generated from the given Landsat-8 satellite image
FIGURE 3. NDBI and UI images generated using the SWIR1, SWIR2, and NIR bands of the given Landsat-8 satellite image: (a) NDBI image; and (b) UI image
FIGURE 4. Given Landsat-8 satellite image and the urban areas separately detected from the NDBI image by evaluating the different thresholds (-0.4, -0.2, and 0): (a) the given Landsat-8 satellite image; (b) the urban area detected from the NDBI image by evaluating the threshold -0.4; (c) the urban area detected from the NDBI image by evaluating the threshold -0.2; and (d) the urban area detected from the NDBI image by evaluating the threshold 0
FIGURE 4. Continued
FIGURE 5. Given Landsat-8 satellite image and the urban areas separately detected from the UI image by evaluating the different thresholds (-0.4, -0.2 and 0): (a) the given Landsat-8 satellite image; (b) the urban area detected from the UI image by evaluating the threshold –0.4; (c) the urban area detected from the UI image by evaluating the threshold –0.2; and (d) the urban area detected from the UI image by evaluating the threshold 0
FIGURE 6. Checkpoints manually digitized based on the given Landsat-8 satellite image
FIGURE 7. Example of the misclassification errors that occurred in the areas where the bare soil areas were classified into urban areas: (a) the given Landsat-8 image showing the bare soil areas; (b) the urban area detected from the NDBI image by evaluating the threshold –0.2; and (c) the urban area detected from the UI image by evaluating the threshold –0.4
FIGURE 7. Continued
FIGURE 8. Example of the misclassification errors that occurred in the areas where the high-rise apartments were classified into other areas: (a) the given Landsat-8 image showing the high-rise apartments; (b) the urban area detected from the NDBI image by evaluating the threshold –0.2; and (c) the urban area detected from the UI image by evaluating the threshold –0.4
TABLE 1. Accuracy of each urban map separately generated from the NDBI and UI images by evaluating the different thresholds (-0.4, -02, and 0): (a) Accuracy of the urban map generated from the NDBI image by evaluating the threshold –0.4; (b) Accuracy of the urban map generated from the NDBI image by evaluating the threshold –0.2; (c) Accuracy of the urban map generated from the NDBI image by evaluating the threshold 0; (d) Accuracy of the urban map generated from the UI image by evaluating the threshold –0.4; (e) Accuracy of the urban map generated from the UI image by evaluating the threshold –0.2; and (f) Accuracy of the urban map generated from the UI image by evaluating the threshold 0
References
- Bhatti, S. and N. Tripathi. 2014. Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing 51(4):445-467. https://doi.org/10.1080/15481603.2014.939539
-
Choung, Y.J. and J.M. Kim. 2019. Study of the Relationship between Urban Expansion and
$PM_{10}$ Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea. Applied Sciences 9(6):1098. https://doi.org/10.3390/app9061098 - Corbane, C., J.F. Faure, N. Baghdadi, N. Villeneuve and M. Petit. 2008. Rapid Urban Mapping Using SAR/Optical Imagery Synergy. Sensors 8(11):7125-7143. https://doi.org/10.3390/s8117125
- Han, R., P. Liu, H. Wang, L. Yang, H. Zhang and C. Ma. 2017. An Improved Urban Mapping Strategy Based on Collaborative Processing of Optical and SAR Remotely Sensed Data. Mathematical Problems in Engineering 2017:1-9.
- Hu, T., J. Yang, X. Li and P. Gong. 2016. Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sensing 8(2):1-18.
- Jensen, J.R. 2016. Introductory Digital Image Processing: A Remote Sensing Perspective (4th Edition). Pearson Series in Geographic Information Science. London, United Kingdom, 656 pp.
- Kim, J.I., K.W. Hwang, H.W. Chung and C.H. Yeo. 2004. Urban Growth Analysis Through Satellite Image and Zonal Data. Journal of the Korean Association of Geographic Information Studies 7(3):1-12.
- Kim, J.I. and J.H. Kwon. 2009. Identifying Urban Spatial Structure through GIS and Remote Sensing Data. Journal of the Korean Association of Geographic Information Studies 12(2):44-51.
- Kim, Y.S., K.J. Lee, J.W. Ryu and J.H. Kim. 2003. Landuse Classification Nomenclature for Urban Growth Analysis Using Satellite Imagery. Journal of the Korean Association of Geographic Information Studies 6(3):83-94.
- Li, H., C. Wang, C. Zhong, A. Su, C. Xiong, J. Wang and J. Liu. 2017a. Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index. Remote Sensing 9(3): 1-15.
- Li, H., C. Wang, C. Zhong, Z. Zhang and Q. Liu. 2017b. Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters. Remote Sensing 9(7): 1-23.
- National Geographic. Urban area. https://www.nationalgeographic.org/encyclopedia/urban-area/ (assessed on March 25, 2019).
- Sertel, E. and S. Akay. 2015. High resolution mapping of urban areas using SPOT-5 images and ancillary data. International Journal of Environment and Geoinformatics 2(2): 63-76. https://doi.org/10.30897/ijegeo.303545
- United States Geological Survey (USGS). 2019. Landsat Missions. https://www.usgs.gov/land-resources/nli/landsat (assessed on March 25, 2019).