Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery
Abstract
:1. Introduction
2. Test Sites and Cities
3. Data and Methods
3.1. Landsat Images and Reference Data
3.2. Methods
3.2.1. Image Preprocessing
3.2.2. Formulation of the Automated Built up Extraction Index (ABEI)
3.2.3. Pure-Pixel Selection
3.2.4. Improved Gravitational Search Algorithm
3.2.5. Classification, Threshold Optimization and Per-Pixel Accuracy Assessment
3.2.6. Mixed Pixel (Heterogeneous Region) Accuracy Assessment
3.2.7. The Performance Evaluation of ABEI Using Cross-Validation Analysis
4. Results
4.1. ABEI
4.2. Built-Up Extraction Maps
4.3. Classification Accuracy and Edge Pixel Effects
4.4. Optimal Threshold and Its Variability
4.5. Cross-Validation Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Cohen, B. Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technol. Soc. 2006, 28, 63–80. [Google Scholar] [CrossRef]
- Bhatti, S.S.; Tripathi, N.K. Built-up area extraction using landsat 8 oli imagery. Gisci. Remote Sens. 2014, 51, 445–467. [Google Scholar] [CrossRef]
- Firozjaei, M.K.; Sedighi, A.; Argany, M.; Jelokhani-Niaraki, M.; Arsanjani, J.J. A geographical direction-based approach for capturing the local variation of urban expansion in the application of ca-markov model. Cities 2019, 93, 120–135. [Google Scholar] [CrossRef]
- Sudhira, H.; Ramachandra, T.; Jagadish, K. Urban sprawl: Metrics, dynamics and modelling using gis. Int. J. Appl. Earth Obs. Geoinf. 2004, 5, 29–39. [Google Scholar] [CrossRef]
- Seto, K.C.; Güneralp, B.; Hutyra, L.R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA 2012, 109, 16083–16088. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Bhatta, B. Causes and consequences of urban growth and sprawl. In Analysis of Urban Growth and Sprawl from Remote Sensing Data; Springer: New York, NY, USA, 2010; pp. 17–36. [Google Scholar]
- As-syakur, A.; Adnyana, I.; Arthana, I.W.; Nuarsa, I.W. Enhanced built-up and bareness index (ebbi) for mapping built-up and bare land in an urban area. Remote Sens. 2012, 4, 2957–2970. [Google Scholar] [CrossRef]
- Bouzekri, S.; Lasbet, A.A.; Lachehab, A. A new spectral index for extraction of built-up area using landsat-8 data. J. Indian Soc. Remote Sens. 2015, 43, 867–873. [Google Scholar] [CrossRef]
- He, C.; Shi, P.; Xie, D.; Zhao, Y. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens. Lett. 2010, 1, 213–221. [Google Scholar] [CrossRef][Green Version]
- Kaimaris, D.; Patias, P. Identification and area measurement of the built-up area with the built-up index (bui). Int. J. Adv. Remote Sens. GIS 2016, 5, 1844–1858. [Google Scholar] [CrossRef]
- Waqar, M.M.; Mirza, J.F.; Mumtaz, R.; Hussain, E. Development of new indices for extraction of built-up area & bare soil from landsat data. Open Access Sci. Rep. 2012, 1, 4. [Google Scholar]
- Xu, H. A new index for delineating built-up land features in satellite imagery. Int. J. Remote Sens. 2008, 29, 4269–4276. [Google Scholar] [CrossRef]
- Guindon, B.; Zhang, Y.; Dillabaugh, C. Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sens. Environ. 2004, 92, 218–232. [Google Scholar] [CrossRef]
- Griffiths, P.; Hostert, P.; Gruebner, O.; van der Linden, S. Mapping megacity growth with multi-sensor data. Remote Sens. Env. 2010, 114, 426–439. [Google Scholar] [CrossRef]
- Weng, Q. A remote sensing? Gis evaluation of urban expansion and its impact on surface temperature in the zhujiang delta, china. Int. J. Remote Sens. 2001, 22, 1999–2014. [Google Scholar]
- Jat, M.K.; Garg, P.K.; Khare, D. Monitoring and modelling of urban sprawl using remote sensing and gis techniques. Int. J. Appl. Earth Obs. 2008, 10, 26–43. [Google Scholar] [CrossRef]
- Firozjaei, M.K.; Kiavarz, M.; Alavipanah, S.K.; Lakes, T.; Qureshi, S. Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-markov chain modelling: A case of babol city, iran. Ecol. Indic. 2018, 91, 155–170. [Google Scholar] [CrossRef]
- Sharma, R.C.; Tateishi, R.; Hara, K.; Gharechelou, S.; Iizuka, K. Global mapping of urban built-up areas of year 2014 by combining modis multispectral data with viirs nighttime light data. Int. J. Digit. Earth 2016, 9, 1004–1020. [Google Scholar] [CrossRef]
- Schneider, A.; Friedl, M.A.; Potere, D. Mapping global urban areas using modis 500-m data: New methods and datasets based on ‘urban ecoregions’. Remote Sens. Env. 2010, 114, 1733–1746. [Google Scholar] [CrossRef]
- Su, Y.; Chen, X.; Wang, C.; Zhang, H.; Liao, J.; Ye, Y.; Wang, C. A new method for extracting built-up urban areas using dmsp-ols nighttime stable lights: A case study in the pearl river delta, southern china. Gisci. Remote Sens. 2015, 52, 218–238. [Google Scholar] [CrossRef]
- Zhang, J.; Li, P.; Wang, J. Urban built-up area extraction from landsat tm/etm+ images using spectral information and multivariate texture. Remote Sens. 2014, 6, 7339–7359. [Google Scholar] [CrossRef]
- Ettehadi Osgouei, P.; Kaya, S.; Sertel, E.; Alganci, U. Separating built-up areas from bare land in mediterranean cities using sentinel-2a imagery. Remote Sens. 2019, 11, 345. [Google Scholar] [CrossRef]
- Chen, Y.; Lv, Z.; Huang, B.; Jia, Y. Delineation of built-up areas from very high-resolution satellite imagery using multi-scale textures and spatial dependence. Remote Sens. 2018, 10, 1596. [Google Scholar] [CrossRef]
- Kumar, A.; Pandey, A.C.; Jeyaseelan, A. Built-up and vegetation extraction and density mapping using worldview-ii. Geocarto Int. 2012, 27, 557–568. [Google Scholar] [CrossRef]
- Xu, H. Extraction of urban built-up land features from landsat imagery using a thematicoriented index combination technique. Photogramm. Eng. Remote Sens. 2007, 73, 1381–1391. [Google Scholar] [CrossRef]
- Feyisa, G.L.; Meilby, H.; Fensholt, R.; Proud, S.R. Automated water extraction index: A new technique for surface water mapping using landsat imagery. Remote Sens. Env. 2014, 140, 23–35. [Google Scholar] [CrossRef]
- Robinson, N.; Allred, B.; Jones, M.; Moreno, A.; Kimball, J.; Naugle, D.; Erickson, T.; Richardson, A. A dynamic landsat derived normalized difference vegetation index (ndvi) product for the conterminous united states. Remote Sens. 2017, 9, 863. [Google Scholar] [CrossRef]
- Choi, H.; Bindschadler, R. Cloud detection in landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision. Remote Sens. Env. 2004, 91, 237–242. [Google Scholar] [CrossRef]
- Zha, Y.; Gao, J.; Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from tm imagery. Int. J. Remote Sens. 2003, 24, 583–594. [Google Scholar] [CrossRef]
- Moghaddam, M.H.R.; Sedighi, A.; Fayyazi, M.A. Applying mndwi index and linear directional mean analysis for morphological changes in the zarriné-rūd river. Arab. J. Geosci. 2015, 8, 8419–8428. [Google Scholar] [CrossRef]
- Cleve, C.; Kelly, M.; Kearns, F.R.; Moritz, M. Classification of the wildland–urban interface: A comparison of pixel-and object-based classifications using high-resolution aerial photography. Comput. Environ. Urban Syst. 2008, 32, 317–326. [Google Scholar] [CrossRef]
- Kawamura, M.; Jayamana, S.; Tsujiko, Y. Relation between social and environmental conditions in colombo sri lanka and the urban index estimated by satellite remote sensing data. Int. Arch. Photogramm. Remote Sens. 1996, 31, 321–326. [Google Scholar]
- Zhao, H.; Chen, X. Use of Normalized Difference Bareness Index in Quickly Mapping Bare Areas from Tm/Etm+. In Proceedings of the International geoscience and remote sensing symposium, Seoul, South Korea, 29 July 2005; p. 1666. [Google Scholar]
- Rikimaru, A. Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow. Presented Pap. 18th Acrs 1997, 1, 91–102. [Google Scholar]
- Sukristiyanti, R. Suharyadi; jatmiko, rh evaluasi indeks urban pada citra landsat multitemporal dalam ekstraksi kepadatan bangunan. J. Ris. Geol. Dan Pertamb. 2007, 17, 1–10. [Google Scholar] [CrossRef]
- Moghaddam, M.H.R.; Sedighi, A.; Fasihi, S.; Firozjaei, M.K. Effect of environmental policies in combating aeolian desertification over sejzy plain of iran. Aeolian Res. 2018, 35, 19–28. [Google Scholar] [CrossRef]
- Cooley, T.; Anderson, G.P.; Felde, G.W.; Hoke, M.L.; Ratkowski, A.J.; Chetwynd, J.H.; Gardner, J.A.; Adler-Golden, S.M.; Matthew, M.W.; Berk, A. Flaash, a Modtran4-Based Atmospheric Correction Algorithm, Its Application and Validation. IEEE Int. Geosci. Remote Sens. Symp. 2002, 3, 1414–1418. [Google Scholar]
- Berk, A.; Conforti, P.; Kennett, R.; Perkins, T.; Hawes, F.; Van Den Bosch, J. Modtran® 6: A major upgrade of the modtran® radiative transfer code. In Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland, 24–27 June 2014; pp. 1–4. [Google Scholar]
- Richards, J.A.; Richards, J. Remote Sensing Digital Image Analysis; Springer: New York, NY, USA, 1999; Volume 3. [Google Scholar]
- Firozjaei, M.K.; Daryaei, I.; Sedighi, A.; Weng, Q.; Alavipanah, S.K. Homogeneity distance classification algorithm (hdca): A novel algorithm for satellite image classification. Remote Sens. 2019, 11, 546. [Google Scholar] [CrossRef]
- De Leeuw, J.; Jia, H.; Yang, L.; Liu, X.; Schmidt, K.; Skidmore, A. Comparing accuracy assessments to infer superiority of image classification methods. Int. J. Remote Sens. 2006, 27, 223–232. [Google Scholar] [CrossRef]
- Ezimand, K.; Kakroodi, A.; Kiavarz, M. The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature. Int. J. Remote Sens. 2018, 39, 1–22. [Google Scholar] [CrossRef]
Test Site and City | Centre Point Coordinate (Lon, Lat-WGS84) | Country | Area (Km2) | Mean Alt. (m) | Climate | Population (2019) |
---|---|---|---|---|---|---|
Test sites | ||||||
Babol | 52.68, 36.54 | Iran | 2.0 | 3 | Humid | >5600 |
Naqadeh | 45.38, 36.95 | Iran | 0.5 | 1325 | Semi-humid | >1500 |
Masjed Soleyman | 49.28, 31.96 | Iran | 3.4 | 240 | Dry and semi-arid | >6500 |
Kashmar | 58.46, 35.24 | Iran | 5.4 | 1060 | Warm and dry | >7000 |
Bam | 58.33, 29.10 | Iran | 1.3 | 1083 | Warm and dry | >2800 |
European cities | ||||||
Rome | 12.45, 41.85 | Italy | 631.7 | 50 | Mediterranean | >4,234,000 |
Riga | 24.09, 56.94 | Latvia | 558.0 | 10 | Humid continental | >633,000 |
Madrid | −3.70, 40.41 | Spain | 2332.3 | 650 | Mediterranean and semi-arid | >6,559,000 |
Athens | 23.72, 37.97 | Greece | 2142.2 | 80 | Mediterranean | >3,154,000 |
Brussels | 4.38, 50.85 | Belgium | 608.1 | 60 | Oceanic | >2,065,000 |
Porto | −8.60, 41.16 | Portugal | 481.4 | 80 | Mediterranean | >1,309,000 |
Lyon | 4.83, 45.76 | France | 1143.6 | 175 | Humid subtropical | >1,704,000 |
Ciechanow | 20.60, 52.82 | Poland | 81.1 | 151 | Humid subtropical | >44,000 |
Hamburg | 10.02, 53.60 | Germany | 1097.5 | 10 | Oceanic | >1,791,000 |
Budapest | 19.07, 47.59 | Hungary | 3664.3 | 120 | Oceanic and Humid subtropical | >1,763,000 |
Bucharest | 26.10, 44.42 | Romania | 1385.7 | 85 | Humid continental | >1,812,000 |
Test Site | Landsat Scene | Reference Data and Sources | ||
---|---|---|---|---|
Acquisition Date | Path | Row | ||
Babol | 1 May 2015 | 164 | 35 | Google Earth™ image acquired on 29 April 2015, ©Digital Globe |
Naqadeh | 13 August 2014 | 169 | 34 | Google Earth™ image acquired on 10 August 2014, ©Digital Globe |
Masjed Soleyman | 15 August 2016 | 165 | 38 | Google Earth™ image acquired on 14 August 2016, ©Digital Globe |
Kashmar | 14 October 2016 | 160 | 35 | Google Earth™ image acquired on 14 August 2016, ©CNES/Airbus |
Bam | 6 May 2018 | 159 | 40 | Google Earth™ image acquired on 9 May 2018, ©Digital Globe |
Cities | ||||
Rome | 17 July 2015 | 191 | 031 | High Resolution Layer Imperviousness (HRLI) for the 2015 reference year, European Environment Agency |
Riga | 9 May 2015 | 188 | 020 | |
Madrid | 7 July 2015 | 201 | 032 | |
Athens | 26 August 2015 | 180 | 031 | |
Brussels | 11 September 2015 | 199 | 025 | |
Porto | 12 July 2015 | 204 | 031 | |
Lyon | 12 August 2015 | 197 | 028 | |
Ciechanow | 4 August 2015 | 189 | 023 | |
Hamburg | 21 August 2015 | 196 | 023 | |
Budapest | 29 August 2015 | 188 | 027 | |
Bucharest | 3 August 2015 | 189 | 029 |
Test Site | Babol | Naqadeh | Masjed Soleyman | Kashmar | Bam | |||||
---|---|---|---|---|---|---|---|---|---|---|
Kc | OA | Kc | OA | Kc | OA | Kc | OA | Kc | OA | |
ABEI | 0.93 | 94.01 | 0.96 | 97.62 | 0.94 | 95.95 | 0.92 | 93.34 | 0.9 | 92.04 |
UI | 0.87 | 89.21 | 0.90 | 92.54 | 0.82 | 83.02 | 0.82 | 85.89 | 0.69 | 73.88 |
NDBI | 0.84 | 86.16 | 0.86 | 87.12 | 0.78 | 79.92 | 0.79 | 80.65 | 0.66 | 86.34 |
IBI | 0.73 | 74.32 | 0.74 | 75.23 | 0.68 | 68.91 | 0.70 | 71.36 | 0.59 | 74.23 |
NBAI | 0.78 | 75.65 | 0.79 | 80.32 | 0.70 | 71.24 | 0.69 | 70.54 | 0.60 | 61.63 |
BRBA | 0.74 | 75.36 | 0.73 | 74.53 | 0.69 | 70.32 | 0.70 | 71.25 | 0.58 | 59.41 |
NDBaI | 0.73 | 74.63 | 0.75 | 75.96 | 0.69 | 71.01 | 0.68 | 69.69 | 0.53 | 54.74 |
BI | 0.75 | 76.81 | 0.78 | 79.86 | 0.62 | 63.45 | 0.66 | 67.31 | 0.55 | 56.81 |
EBBI | 0.82 | 83.12 | 0.81 | 82.31 | 0.76 | 77.98 | 0.75 | 76.85 | 0.64 | 65.45 |
BAEM | 0.81 | 82.54 | 0.79 | 80.45 | 0.75 | 76.31 | 0.76 | 77.65 | 0.63 | 64.85 |
NBI | 0.75 | 76.75 | 0.79 | 80.23 | 0.70 | 71.65 | 0.63 | 63.87 | 0.57 | 58.96 |
BUI | 0.74 | 75.96 | 0.75 | 76.87 | 0.71 | 72.26 | 0.65 | 66.23 | 0.60 | 61.32 |
Test Sites | Χ2 | p-Value | |
---|---|---|---|
Classifier | UI | UI | |
Babol | ABEI | 26.03 | 0 |
Naqadeh | ABEI | 20.04 | 0 |
Masjed Soleyman | ABEI | 288 | 0 |
Kashmar | ABEI | 749 | 0 |
Bam | ABEI | 27.03 | 0 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Firozjaei, M.K.; Sedighi, A.; Kiavarz, M.; Qureshi, S.; Haase, D.; Alavipanah, S.K. Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery. Remote Sens. 2019, 11, 1966. https://doi.org/10.3390/rs11171966
Firozjaei MK, Sedighi A, Kiavarz M, Qureshi S, Haase D, Alavipanah SK. Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery. Remote Sensing. 2019; 11(17):1966. https://doi.org/10.3390/rs11171966
Chicago/Turabian StyleFirozjaei, Mohammad Karimi, Amir Sedighi, Majid Kiavarz, Salman Qureshi, Dagmar Haase, and Seyed Kazem Alavipanah. 2019. "Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery" Remote Sensing 11, no. 17: 1966. https://doi.org/10.3390/rs11171966