Built-up area extraction using Landsat 8 OLI imagery

Figures & data

Figure 1. Study area: Lahore, Pakistan (A) towns in Lahore city and (B) towns selected for this study in Landsat-8 OLI false-colour composite imagery of 18 May 2013.

Figure 2. High-pass convolution filter kernel.

Figure 3. Landsat-8 OLI image in true-colour composite (A) before resolution merging (30 m spatial resolution) and (B) after resolution merging (15 m spatial resolution).

Figure 4. Reflectance of built-up, vegetation and water areas in (A) optical bands 2–7 (DN value) and (B) thermal bands 10–11 (temperature in degree Celsius) of Landsat-8 OLI image.

Figure 5. Scatter plots showing the correlation between (A) band 6 (x-axis) and band 7 (y-axis), and (B) band 10 (x-axis) and band 11 (y-axis) of the Landsat-8 OLI image.

Figure 6. The resultant images of (A) NDBI, (B) NDVI and (C) MNDWI derived from the Landsat-8 OLI image.

Figure 7. Scatter plots showing the correlation between (A) NDBI (x-axis) and NDVI (y-axis), (B) NDBI (x-axis) and MNDWI (y-axis), and (C) NDVI (x-axis) and MNDWI (y-axis).

Table 1. The Pearson’s R values showing the degree of relationship among NDBI, NDVI and MNDWI.

	NDBI	NDVI	MNDWI
NDBI	1	–	–
NDVI	0.130*	1	–
MNDWI	−0.298*	−0.770*	1

Note: *correlation is significant at the level of 0.01.

Figure 8. Map showing (A) a continuous image of built-up areas derived by BAEM, and (B) segmented output of BAEM (built-up area extracted through segmentation of (A) using the threshold value from DFPS).

Table 2. Results of the double-window flexible pace search for the determination of the optimal threshold value.

Range: 255–5 Pace 50		Range: 105–5 Pace 20		Range: 85–45 Pace 8		Range: 85–69 Pace 2		Range: 85–81 Pace 1
Threshold	Success rate	Threshold	Success rate	Threshold	Success rate	Threshold	Success rate	Threshold	Success rate
255	0	105	0	85	76.92	85	76.92	85	76.92
205	0	85	76.92	77	38.46	83	93.72	84	92.31
155	0	65	26.92	69	7.36	81	84.61	83	93.72
105	0	45	3.27	61	7.12	79	65.38	82	88.46
55	3.27	25	3.27	53	3.27	77	38.46	81	84.61
5	3.27	5	3.27	45	3.27	75	19.23
						73	9.61
						71	8.23
						69	7.36

Table 3. Error matrix for NDBI method and BAEM.

Classified data	Reference data
(NDBI method)	Non-built-up area	Built-up area	Classified total	User’s accuracy (%)
Non-built-up area	44	2	46	95.65
Built-up area	55	99	154	64.29
Reference total	99	101	200
Producer’s accuracy (%)	44.44	98.02
Overall accuracy NDBI method = 143/200 (71.50%)
Classified data (BAEM)
Non-built-up area	50	1	51	98.04
Built-up area	38	111	149	74.50
Reference total	88	112	200
Producer’s accuracy (%)	56.82	99.11
Overall accuracy BAEM = 161/200 (80.50%)

Table 4. Summary of the accuracy assessment of the images derived by NDBI method and BAEM.

	Overall accuracy (%)	Omission error (%)	Commission error (%)	k coefficient
NDBI method	71.50	1.98	35.71	0.43
BAEM	80.50	0.89	25.5	0.59

Figure 9. Comparison of the results from the NDBI approach and BAEM at a sample location (A) OLI image in false-colour composite, (B) output from NDBI approach and (C) output from BAEM. (Black rectangles in each panel show the places where the NDBI approach wrongly extracted other land covers as built-up areas.)

Figure 10. (A) OLI image in false-colour composite, (B) output from NDBI approach and (C) output from BAEM. (Black rectangles in each panel show the places that produced obvious commission errors in both methods.)