Research Article
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Year 2023, Volume: 10 Issue: 1, 1 - 8, 19.03.2023

Zelal Kaya Adalet Dervisoglu

https://doi.org/10.30897/ijegeo.1214001
Cited By: 2

Abstract

Keywords

Spectral Indices NDTI NDVI GEE Urban Mapping Esenyurt

References

  • Bouzekri, S., Lasbet, A. A., Lachehab, A. (2015). 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.
  • Colwell, J. E. (1974). Vegetation canopy reflectance. Remote Sensing Environment, 3(3), 175-183.
  • Çelik, O. İ., Çelik, S., Gazioğlu, C. (2022). Evaluation on 2002-2021 CHL-A Concentrations in the Sea of Marmara with GEE Enhancement of Satellite Data, International Journal of Environment and Geoinformatics, 9(4), 68-77. doi.10.30897/ ijegeo.1066168
  • Das, S., Angadi, D. P. (2022). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: A micro-level study. GeoJournal, 87(3), 2101-2123.
  • Duan, Y., X. Shao, Y. Shi, H. Miyazaki, K. Iwao, R. Shibasaki.(2015). Unsupervised global urban area mapping via automatic labeling from ASTER and PALSAR satellite images. Remote Sensing 7(2):2171–2192.
  • 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.
  • Hidayati, I. N., Suharyadi, R., Danoedoro, P. (2018). Exploring spectral index band and vegetation indices for estimating vegetation area. Indonesian Journal of Geography, 50(2), 211-221.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
  • Javed, A., Cheng, Q., Peng, H., Altan, O., Li, Y., Ara, I., ... & Saleem, N. (2021). Review of Spectral Indices for Urban Remote Sensing. Photogrammetric Engineering & Remote Sensing, 87(7), 513-524.
  • Jieli, C., Manchun, L. I., Yongxue, L. I. U., Chenglei, S., Wei, H. U. (2010). Extract residential areas automatically by new built-up index. In 2010 18th International Conference on Geoinformatics (pp. 1-5). IEEE.
  • 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. Int. Arch. Photogramm. Remote Sens, 31, 321-326.
  • Kebede, T. A., Hailu, B. T., Suryabhagavan, K. V. (2022). Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia. Environmental Challenges, 8, 100568.
  • Liu, X., G. Hu, Y. Chen, X. Li, X. Xu, S. Li, F. Pei, S. Wang. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth engine platform. Remote Sensing of Environment, 209:227–239.
  • Magidi, J., Ahmed, F. (2019). Assessing urban sprawl using remote sensing and landscape metrics: A case study of City of Tshwane, South Africa (1984–2015). The Egyptian Journal of Remote Sensing and Space Science, 22(3), 335-346.
  • Misra, M., Kumar, D., Shekhar, S. (2020). Assessing machine learning based supervised classifiers for built-up impervious surface area extraction from sentinel-2 images. Urban Forestry & Urban Greening, 53, 126714. Netzband, M., W. L. Stefanov, C. Redman. (2007). Applied Remote Sensing for Urban Planning, Governance and Sustainability. Springer.
  • Otsu, N. (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979, doi: 10.1109/TSMC.1979.4310076.
  • Trianni, G., Lisini, G., Angiuli, E., Moreno, E. A., Dondi, P., Gaggia, A., Gamba, P. (2015). Scaling up to national/regional urban extent mapping using Landsat data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3710-3719.
  • Url-1:https://data.tuik.gov.tr/Search/Search?text= n%C3%Bcfus
  • Url-2: https://sentinels.copernicus.eu/web/sentinel/user -guides/sentinel-2-msi
  • Url-3: https://www.usgs.gov/landsat-missions/landsat-5
  • Url-4: https://www.usgs.gov/landsat-missions/landsat-8
  • Url-5: https://earthengine.google.com/faq/
  • Url-6: https://www.usgs.gov/landsat-missions/landsat-enhanced-vegetation-index
  • Url-7: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/evi/#
  • Van Deventer, A. P., Ward, A. D., Gowda, P. H., Lyon, J. G. (1997). Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogrammetric engineering and remote sensing, 63, 87-93.
  • Wang, R., Wan, B., Guo, Q., Hu, M., Zhou, S. (2017). Mapping regional urban extent using NPP-VIIRS DNB and MODIS NDVI data. Remote Sensing, 9(8), 862.
  • Waqar, M. M., Mirza, J. F., Mumtaz, R., Hussain, E. (2012). Development of new indices for extraction of built-up area & bare soil from landsat data. Open Access Sci. Rep, 1(1), 4.
  • Yousefi, J. (2011). Image binarization using otsu thresholding algorithm. Ontario, Canada: University of Guelph.
  • 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.
  • Zhang, Z., Wei, M., Pu, D., He, G., Wang, G., Long, T. (2021). Assessment of annual composite images obtained by Google Earth engine for urban areas mapping using random forest. Remote Sensing, 13(4), 748.

Determination of Urban Areas Using Google Earth Engine and Spectral Indices; Esenyurt Case Study

Year 2023, Volume: 10 Issue: 1, 1 - 8, 19.03.2023

Zelal Kaya Adalet Dervisoglu

https://doi.org/10.30897/ijegeo.1214001
Cited By: 2

Abstract

Identifying impervious surfaces for monitoring urban expansion is important for the sustainable management of land resources and the protection of the environment. Remote sensing provides an important data source for urban land use/land cover mapping, and these data can be analyzed with various techniques for different purposes. If the aim is to extract information easily and rapidly, using spectral indices is the most appropriate solution, and there are many indices created for this purpose. The study carried out on the Google Earth Engine (GEE) platform, Esenyurt, the most populous district of Istanbul, was investigated using Sentinel 2 MSI image, with eight urban spectral indices and three vegetation indices. In addition, classification was made, and the results were evaluated. As a result of the urban index applications, it has been seen that the roofs are more or less mixed with the bare soil areas, and Normalized Difference Tillage Index (NDTI)gives the best results. Accuracy assessment is performed for index results and classification using the same points, and due to the urban area density in the application area, it is determined as 0.95% and 0.95% for NDTI and Normalized Difference Vegetation Index (NDVI), and 97% for classification, respectively. In GEE, a high (-0.79) negative correlation is observed between May mean values and 2007-2022 population data when the NDVI time series was applied to the entire area within the district borders using Landsat 5 and Landsat 8 images between 1990-2022. The rapidly increasing population in the district leads to rapid urbanization, and green areas are disappearing at the same rate.

Keywords

Spectral Indices NDTI NDVI GEE Urban Mapping Esenyurt

References

  • Bouzekri, S., Lasbet, A. A., Lachehab, A. (2015). 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.
  • Colwell, J. E. (1974). Vegetation canopy reflectance. Remote Sensing Environment, 3(3), 175-183.
  • Çelik, O. İ., Çelik, S., Gazioğlu, C. (2022). Evaluation on 2002-2021 CHL-A Concentrations in the Sea of Marmara with GEE Enhancement of Satellite Data, International Journal of Environment and Geoinformatics, 9(4), 68-77. doi.10.30897/ ijegeo.1066168
  • Das, S., Angadi, D. P. (2022). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: A micro-level study. GeoJournal, 87(3), 2101-2123.
  • Duan, Y., X. Shao, Y. Shi, H. Miyazaki, K. Iwao, R. Shibasaki.(2015). Unsupervised global urban area mapping via automatic labeling from ASTER and PALSAR satellite images. Remote Sensing 7(2):2171–2192.
  • 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.
  • Hidayati, I. N., Suharyadi, R., Danoedoro, P. (2018). Exploring spectral index band and vegetation indices for estimating vegetation area. Indonesian Journal of Geography, 50(2), 211-221.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
  • Javed, A., Cheng, Q., Peng, H., Altan, O., Li, Y., Ara, I., ... & Saleem, N. (2021). Review of Spectral Indices for Urban Remote Sensing. Photogrammetric Engineering & Remote Sensing, 87(7), 513-524.
  • Jieli, C., Manchun, L. I., Yongxue, L. I. U., Chenglei, S., Wei, H. U. (2010). Extract residential areas automatically by new built-up index. In 2010 18th International Conference on Geoinformatics (pp. 1-5). IEEE.
  • 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. Int. Arch. Photogramm. Remote Sens, 31, 321-326.
  • Kebede, T. A., Hailu, B. T., Suryabhagavan, K. V. (2022). Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia. Environmental Challenges, 8, 100568.
  • Liu, X., G. Hu, Y. Chen, X. Li, X. Xu, S. Li, F. Pei, S. Wang. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth engine platform. Remote Sensing of Environment, 209:227–239.
  • Magidi, J., Ahmed, F. (2019). Assessing urban sprawl using remote sensing and landscape metrics: A case study of City of Tshwane, South Africa (1984–2015). The Egyptian Journal of Remote Sensing and Space Science, 22(3), 335-346.
  • Misra, M., Kumar, D., Shekhar, S. (2020). Assessing machine learning based supervised classifiers for built-up impervious surface area extraction from sentinel-2 images. Urban Forestry & Urban Greening, 53, 126714. Netzband, M., W. L. Stefanov, C. Redman. (2007). Applied Remote Sensing for Urban Planning, Governance and Sustainability. Springer.
  • Otsu, N. (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979, doi: 10.1109/TSMC.1979.4310076.
  • Trianni, G., Lisini, G., Angiuli, E., Moreno, E. A., Dondi, P., Gaggia, A., Gamba, P. (2015). Scaling up to national/regional urban extent mapping using Landsat data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3710-3719.
  • Url-1:https://data.tuik.gov.tr/Search/Search?text= n%C3%Bcfus
  • Url-2: https://sentinels.copernicus.eu/web/sentinel/user -guides/sentinel-2-msi
  • Url-3: https://www.usgs.gov/landsat-missions/landsat-5
  • Url-4: https://www.usgs.gov/landsat-missions/landsat-8
  • Url-5: https://earthengine.google.com/faq/
  • Url-6: https://www.usgs.gov/landsat-missions/landsat-enhanced-vegetation-index
  • Url-7: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/evi/#
  • Van Deventer, A. P., Ward, A. D., Gowda, P. H., Lyon, J. G. (1997). Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogrammetric engineering and remote sensing, 63, 87-93.
  • Wang, R., Wan, B., Guo, Q., Hu, M., Zhou, S. (2017). Mapping regional urban extent using NPP-VIIRS DNB and MODIS NDVI data. Remote Sensing, 9(8), 862.
  • Waqar, M. M., Mirza, J. F., Mumtaz, R., Hussain, E. (2012). Development of new indices for extraction of built-up area & bare soil from landsat data. Open Access Sci. Rep, 1(1), 4.
  • Yousefi, J. (2011). Image binarization using otsu thresholding algorithm. Ontario, Canada: University of Guelph.
  • 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.
  • Zhang, Z., Wei, M., Pu, D., He, G., Wang, G., Long, T. (2021). Assessment of annual composite images obtained by Google Earth engine for urban areas mapping using random forest. Remote Sensing, 13(4), 748.
There are 30 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Zelal Kaya This is me 0000-0002-3225-449X

Adalet Dervisoglu 0000-0001-7455-4282

Publication Date March 19, 2023
Published in Issue Year 2023 Volume: 10 Issue: 1

Cite

APA Kaya, Z., & Dervisoglu, A. (2023). Determination of Urban Areas Using Google Earth Engine and Spectral Indices; Esenyurt Case Study. International Journal of Environment and Geoinformatics, 10(1), 1-8. https://doi.org/10.30897/ijegeo.1214001

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