Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera)
DOI:
https://doi.org/10.11137/1982-3908_2022_45_44537Keywords:
Built-up area, approach multi – index, Google Earth Engine, SVM algorithm, Sentinel 2AAbstract
The fundamental difficulty in mapping urban areas, especially in semi-arid and arid environments, is the separation of built-up areas from bare lands, owing to their similar spectral characteristics. Accordingly, this study aims to identify the suitable spectral index that can provide high differentiation, between urban areas and bare lands, in semi-arid areas of three cities of the province of Djelfa, namely, Djelfa, Messaad, and Ain Oussera (Algerian central highlands), through a selection of four spectral indices including Urban Index (BUI), Band ratio for built-up area (BRBA), Normalized Difference Tillage Index (NDTI) and Dry Bare-soil Index (DBSI). In order to increase the mapping accuracy of the built-up in studied areas, a multi-index approach has been applied focusing on identifying an adequate combination of spectral indices of remote sensing that provides the highest performance compared to the images of sentinel 2A. The multi-index approach was developed using three spectral indices combinations and was created using a layer stack process. For forming bare land layer stacking data, both NDTI and DBSI indices were used, while the built-up area layer stacking data was made with both BUI and BRBA indices. The main process was carried out on the Cloud Computing Platform based on geospatial data of Google Earth Engine (GEE) and using machine learning classification by the Support Vector Machine (SVM) algorithm, based on imagery from sentinel 2A acquired during the dry season. The results indicated that the thresholds of the built-up areas are difficult to delineate and distinguish from bare land efficiently with a single index. The obtained results also revealed that the use of multi-index including BUI index provided the best results as they showed the highest effects with NDTI index and DBSI index compared to BRBA index, where the overall accuracies of the multi-index (DBSI/ NDTI/ BUI) were 98.7% in Djelfa, 96.5% in Messaad, and 97.87 % in Ain Oussera, and the kappa coefficients were 97.3%, 85.4%, and 95.3% respectively. These results show that this multi-index is effective and reliable and can be considered for use in other areas with similar characteristics.
References
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 Sens, vol. 4, no. 10, pp. 2957-70, DOI:10.3390/rs4102957.
Bourcier, A. 1994. 'Télédétection et combinaison d’informations géographiques en mode image, Application à l’estuaire de la Seine', PhD thesis, University of Rouen, Rouen.
Bramhe, V.S., Ghosh, S.K. & Garg, P.K. 2018, 'Extraction of built-up area by combining textural features and spectral indices from landsat-8 multispectral image', ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII, no. 5, pp. 727-33, DOI:10.5194/isprs-archives-xlii-5-727-2018.
Cabral, P. 2007, 'Délimitation d‘aires urbaines à partir d‘une image Landsat ETM+ : comparaison de méthodes de classification', Canadian Journal of Remote Sensing, vol. 33, no. 5, pp. 422-30, DOI:10.5589/m07-039.
Caloz, R & Collet, C. 2001, Traitement d’images numériques de télédétection, Production d’images non spectrales, Propriétés des indices, Précis de la télédétection, Presses de l’Université du Québec, Québec.
Chen, Y., Lv, Z., Huang, B. & Jia, Y. 2018, 'Delineation of built-up areas from very high-resolution satellite imagery using multi-scale textures and spatial dependence', Remote Sensing, vol. 10, no. 10, 1596, DOI:10.3390/rs10101596.
Dennison, P.E. & Roberts, D.A. 2003, 'Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE', Remote Sensing of Environment, vol. 87, no. 2-3, pp. 123-35, DOI:10.1016/S0034-4257(03)00135-4.
Deventer, A., 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, vol. 63, no. 1, pp. 87-93.
Eskandari, I., Navid, H. & Rangzan, K. 2016, 'Evaluating spectral indices for determining conservation and conventional tillage systems in a vetch-wheat rotation', International Soil and Water Conservation Research, vol. 4, no. 2, pp. 93-8, DOI:10.1016/j.iswcr.2016.04.002.
Fakhri, F. & Gkanatsios, I. 2021, 'Integration of Sentinel-1 and Sentinel-2 data for change detection: A case study in a war conflict area of Mosul city', Remote Sensing Applications: Society and Environment, vol. 22, 100505, DOI:10.1016/j.rsase.2021.100505.
Firman, H., Frank, Y. & Kavinda, G. 2018, Qgis basic training, Geo informatics center AIT, viewed 12 January 2021, <https://docs.sigro.org/qgis-basic-training/en/setting-up.html>.
Firozjaei, M.K., Sedighi, A., Kiavarz, M., Qureshi, S., Haase, D. & Alavipanah, S.K. 2019, 'Automated built-up extraction index: A new technique for mapping surface built-up areas using LANDSAT 8 OLI Imagery', Remote Sensing, vol. 11, no. 17, 1966, DOI:10.3390/rs11171966.
Flanagan, M. & Civco, D.L. 2001, 'Subpixel impervious surface mapping', ASPRS Annual Convention, vol. 23, no. 27, pp. 13-25.
Goetz, S.J., Wright, R.K., Smith, A.J., Zinecker, E. & Schaub, E. 2003, 'IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region', Remote Sensing of Environment, vol. 88, no. 1-2, pp. 195-208, DOI:10.1016/j.rse.2003.07.010.
Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephennen, N. & Wolff, E. 2017, 'An open-source semi-automated processing chain for urban object-based classification', Remote Sensing, vol. 9, no. 4, 358, DOI:10.3390/rs9040358.
Hazaymeh, K., Mosleh, M.K., & Al-Rawabdeh, A.M. 2019, 'A combined PCA-SIs classification approach for delineating built-up area from remote sensing data', PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol. 87, no. 3, pp. 91-102, DOI:10.1007/s41064-019-00071-2.
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, vol. 1, no. 4, pp. 213-21, DOI:10.1080/01431161.2010.481681.
Heiden, U., Segl, K., Roessner, S. & Kaufmann, H. 2007, 'Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data', Remote Sensing of Environment, vol. 111, no. 4, pp. 537-52, DOI:10.1016/j.rse.2007.04.008.
Herold, M., Scepan, J. & Clarke, K.C. 2002, 'The use of remote sensing and landscape metrics to describe structures and changes in urban land uses', Environment and Planning A: Economy and Space, vol. 34, no. 8, pp. 1443-58, DOI:10.1068/a3496.
Hidayati, N.I., Suharyadi, R. & Danoedoro, P. 2018, 'Developing an extraction method of urban built-up area based on remote sensing imagery transformation index', Forum Geografi, vol. 32, no. 1, pp. 96-106, DOI:10.23917/forgeo.v32i1.5907.
Hu, X. & Weng, Q. 2009, 'Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks', Remote Sensing of Environment, vol. 113, no. 10, pp. 2089-102, DOI:10.1016/j.rse.2009.05.014.
Jieli, C., Manchun, L., Yongue, L., Chenglei, S. & Wei, H. 2010, 'Extract residential areas automatically by new built-up index', paper presented to the 18th International Conference on Geoinformatics, Geoinformatics, Beijing, China, 18-20 June.
Kaimaris, D. & Patias, P. 2016, 'Identification and area measurement of the built-up area with the Built-up Index (BUI)', International Journal of Advanced Remote Sensing and GIS, vol. 5, no. 1, pp. 1844-58, DOI:10.23953/cloud.ijarsg.64.
Kaşıkçı, Z., Çelik, N. & Sarıyılmaz, F.B. 2020, 'Türkiye Uzaktan Algılama Dergisi Çok zamanlı uydu görüntüler i ile arazi örtüsü ve araz i kullanımı değ i ş iminin belirlenmesi: E lmalı H avzası, İ stanbul Determination of land use and land cover change with time series images: Elmalı Basin, Istanbul', Turkish Journal of Remote Sensing, vol. 2, no. 1, pp. 16-21.
Kawamura, M., Sanath, J. & Yuji, T. 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, vol. 31, no. B7, pp. 321-6.
Kayman, Ö. & Sunar, F. 2015, 'Spektral İndekslerin Landsat TM Uydu Verileri Kullanılarak Arazi Örtüsü/Kullanımı Sınıflandırmasına Etkisi: İstanbul, Beylikdüzü İlçesi, Arazi Kullanımı Değişimi', paper presented to the VIII Türkiye Ulusal Fotogrametri Ve Uzaktan Algilama Birliği, Konya.
Kimwatu, D.M., Mundia, C.N. & Makokha, G.O. 2021, 'Developing a new socio-economic drought index for monitoring drought proliferation: a case study of Upper Ewaso Ngiro River Basin in Kenya', Environmental Monitoring and Assessment, vol. 193, no. 4, 213, DOI:10.1007/s10661-021-08989-0.
Kumar, A., Pandey, A.C. & Jeyaseelan, A.T. 2012, 'Built-up and vegetation extraction and density mapping using worldview II', Geocarto International, vol. 27, no. 7, pp. 557-68, DOI:10.1080/10106049.2012.657695.
Lee, J.A., Lee, S.S. & Chi, K.H. 2010, 'Development of an urban classification method using a built-up index', paper presented to the International Conference on Electric Power Systems, High Voltages, Electric Machines, International Conference on Remote Sensing - Proceedings, pp 39-43.
Lee, J.K., Acharya, T.D. & Lee, D.H. 2018, 'Exploring land cover classification accuracy of landsat 8 image using spectral index layer stacking in Hilly region of South Korea', Sensors and Materials, vol. 30, no. 12, pp. 2927-41, DOI:10.18494/SAM.2018.1934.
Linares, S. & Picone, N. 2018, 'Application of remote sensing and cellular automata model to analyze and simulate urban density changes', in Q. Weng, D. Quattrochi & P.E. Gamba (eds), Urban remote sensing, 2nd edn, CRC Press, Boca Raton, pp. 213-31.
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F. & Wang, S. 2018, 'High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform', Remote Sensing of Environment, vol. 209, pp. 227-39, DOI:10.1016/j.rse.2018.02.055.
Lu, D. & Weng, Q. 2006, 'Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA', Remote Sensing of Environment, vol. 104, no. 2, pp. 157-67, DOI:10.1016/j.rse.2005.11.015.
Lu, D., Moran, E. & Hetrick, S. 2011, 'Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 298-306, DOI:10.1016/j.isprsjprs.2010.10.010.
Luo, X., Peng, Y. & Gao, Y. 2017, 'An improved optimal segmentation threshold algorithm and its application in the built-up quick mapping', Journal of the Indian Society of Remote Sensing, vol. 45, pp. 953-64, DOI:10.1007/s12524-016-0656-4.
Lynch, P. & Blesius, L. 2019, 'Urban remote sensing: Feature extraction “literature review”', GEO, vol. G896, pp. 1-18.Majeed, H.M.S., Ahmed, R.K., Hameed, T.M. & Amin, R.A.M. 2020, 'Effect of urban expansion on the agriculturre lands of Miqdadiya city, Diyala, Iraq', Plant Archives, vol. 20, no. 2, pp. 4027-31.
Maulik, U. & Chakraborty, D. 2017, 'Remote sensing image classification: A survey of support-vector-machine-based advanced techniques', IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 1, pp. 33-52, DOI:10.1109/MGRS.2016.2641240.
Osgouei, E.P., Kaya, S., Sertel, E. & Alganci, U. 2019, 'Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery', Remote Sensing, vol. 11, no. 3, 345, DOI:10.3390/rs11030345.
Pandey, D. & Tiwari, K.C. 2020, 'Extraction of urban built-up surfaces and its subclasses using existing built-up indices with separability analysis of spectrally mixed classes in AVIRIS-NG imagery', Advances in Space Research, vol. 66, no. 8, pp. 1829-45, DOI:10.1016/j.asr.2020.06.038.
Patel, N. & Mukherjee, R. 2015, 'Extraction of impervious features from spectral indices using artificial neural network', Arabian Journal of Geosciences, vol. 8, no. 6, pp. 3729-41, DOI:10.1007/s12517-014-1492-x.
Qiu, C., Schmitt, M., Mou, L., Ghamisi, P. & Zhu, X. 2018, 'Feature importance analysis for local climate zone classification using a residual convolutional neural network with multi-source datasets', Remote Sensing, vol. 10, no. 10, 1572, DOI:10.3390/rs10101572.
Quemada, M. & Daughtry, C.S.T. 2016, 'Spectral indices to improve crop residue cover estimation under varying moisture conditions', Remote Sensing, vol. 8, no. 8, 660, DOI:10.3390/rs8080660.
Rasul, A., Balzter, H., Ibrahim, G.R.F., Hameed, H.M., Wheeler, J., Adamu, B., Ibrahim, S. & Najmaddin, P.M. 2018, 'Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates', Land, vol. 7, no. 3, 81, DOI:10.3390/land7030081.
Rouibah, K. & Belabbas M. 2020, 'Applying multi-index approach from Sentinel-2 imagery to extract urban areas in dry season (semi-arid land in Northeast Algeria)', Revista de Teledetección: Asociación Española de Teledetección, vol. 56, pp. 89-101, DOI:10.4995/raet.2020.13787.
Sariyilmaz, F., Musaoglu, N. & Tanik, A. 2017, 'Investigation of land use/cover changes of sazlidere basin by using band ratio for built-up area (BRBA)', Fresenius Environmental Bulletin, vol. 26, no. 1, pp. 39-45.
Scott, A.J. & Storper, M. 2015, 'The nature of cities: the scope and limits of urban theory', International Journal of Urban and Regional Research, vol. 39, no. 1, pp. 1-15, DOI:10.1111/1468-2427.12134.
Shao, Y. & Lunetta, R.S. 2012, 'Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 70, pp. 78-87, DOI:10.1016/j.isprsjprs.2012.04.001.
Sharma, S., Dhakal, K., Wagle, P. & Kilic, A. 2020, 'Retrospective tillage differentiation using the Landsat‐5 TM archive with discriminant analysis', Agrosystems, Geosciences & Environment, vol. 3, no. 1, e2000, DOI:10.1002/agg2.20000.
Sonmez, N.K. & Slater, B. 2016, 'Measuring intensity of tillage and plant residue cover using remote sensing', European Journal of Remote Sensing, vol. 49, no. 1, pp. 121-35, DOI:10.5721/EuJRS20164907.
Suliman, A. & Zhang, Y. 2018, 'Stereo-based building roof mapping in urban off-nadir VHR satellite images: Challenges and solutions', in Q. Weng, D. Quattrochi & P.E. Gamba (eds), Urban remote sensing, 2nd edn, CRC Press, Boca Raton, pp. 54-80.
Sultana, S. & Satyanarayana, A.N.V. 2020, 'Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000 – 2018 over a sub-tropical Indian City', Sustainable Cities and Society, vol. 52, 101846, DOI:10.1016/j.scs.2019.101846.
Sun, Z., Xu, R., Du, W., Wang, L. & Lu, D. 2019, 'High-resolution urban land mapping in China from sentinel 1A/2 imagery based on Google Earth Engine', Remote Sensing, vol. 11, no. 7, 752, DOI:10.3390/rs11070752.
Thapa, R.B. & Murayama, Y. 2009, 'Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan', Applied Geography, vol. 29, no. 1, pp. 135-44, DOI:10.1016/j.apgeog.2008.08.001.
Valdiviezo-N, J.C., Téllez-Quiñones, A., Salazar-Garibay, A., López-Caloca, A.A. 2018, 'Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion', Journal of the Optical Society of America A, vol. 35, no. 1, pp. 35-44, DOI:10.1364/JOSAA.35.000035.
Vermeulen, L.M., Munch, Z. & Palmer, A. 2021, 'Fractional vegetation cover estimation in southern African rangelands using spectral mixture analysis and Google Earth Engine', Computers and Electronics in Agriculture, vol. 182, 105980, DOI:10.1016/j.compag.2020.105980.
Viganò, P., Arnsperger, C., Corte, M.B., Cogato lanza, E. & Cavalieri, C. 2017, 'Rethinking urban form Switzerland as a ‘‘Horizontal Metropolis’’', Urban Planning, vol. 2, no. 1, pp. 88-9.
Waqar, M.M., Mirza, J.F., Mumtaz,R. & Hussain, E. 2012, 'Development of new indices for extraction of built-up area and bare soil from landsat', Open Access Scientific Reports, vol. l, no. 1, pp. 1-4, DOI:10.4172/scientificreports.136.
Ward, D., Phinn, S.R. & Murray, A.T. 2000, 'Monitoring growth in rapidly urbanizing areas using remotely sensed data', The Professional Geographer, vol. 52, no. 3, pp. 371-86, DOI:10.1111/0033-0124.00232.
Xi, Y., Thinh, N.X. & Li, C. 2019, 'Preliminary comparative assessment of various spectral indices for built-up land derived from Landsat-8 OLI and Sentinel-2A MSI imageries', European Journal of Remote Sensing, vol. 52, no. 1, pp. 240-52, DOI:10.1080/22797254.2019.1584737.
Xiao, R., Ouyang, Z., Zheng, H., Li, W., Schienke, E.W. & Wang, X. 2007, 'Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China', Journal of Environmental Sciences, vol. 19, no. 2, pp. 250-6, DOI:10.1016/s1001-0742(07)60041-2.
Xu, H. 2008, 'A new index for delineating built-up land features in satellite imagery', International Journal of Remote Sensing, vol. 29, no. 14, pp. 4269-76, DOI:10.1080/01431160802039957.
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, vol. 24, no. 3, pp. 583-94, DOI:10.1080/01431160304987.
Zhang, J. & Foody, G.M. 2001, 'Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches', International Journal of Remote Sensing, vol. 22, no. 4, pp. 615-28, DOI:10.1080/01431160050505883.
Downloads
Additional Files
- location map of the study area
- Schematic methodology for the extraction of built-up area with the application of spectral indices and GEE platform with SVM classifier.
- Result of the used spectral indices, False color composite (RGB 8 – 4 - 3) from Sentinel 2 images
- simplified spectral signatures represented by mean of major categories of land cover for the spectral indices images
- . Binary image of SVM classifier in (a) Djelfa city (b) Messaad city (c) Ain Oussera city
- Answer letter
Published
Issue
Section
License
This journal is licensed under a Creative Commons — Attribution 4.0 International — CC BY 4.0, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, content on this site is licensed under a license 