Evaluating metropolises grow and their impact on the around villages using Object-Oriented Images Analysis method by using Sentinel-2 & Landsat data
Abstract
Development of the margin of metropolitan cities is always challenging with regard to the continuous urbanization. The forecast of future changes in the rural landscape is one of the most important issues to be considered in the process of sustainable rural development. The apparent characteristics of rural landscape changes are the result of the interaction between several natural and human factors. Landscape analysis, as well as the identification of best management strategies, can be improved when the useful information on its changes is available over a wide period of time to assess the impact of the changes it has existed. In this study, we tried to extract the changes in the selected villages of the Ardabil metropolitan area by using Landsat-7 and Sentinel-2 images. This study was conducted using supervised classification methods and the best method was chosen based on the overall accuracy 98.91, and high Kappa coefficient 0.96. The results showed that the changes area of settlement area in a village from 2000, as compared to 2018, is about approximately 5.1 Km2. Worth noting that, in this study, by increasing the efficiency of the classification of satellite images of Sentinel-2 comparison with Landsat-7, the accuracy of classification has also improved.
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Bontemps, S., Arias, M., Cara, C., Dedieu, G., et al. (2015, July). Sentinel-2 for agriculture: Supporting global agriculture monitoring. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). pp. 4185-4188. https://doi.org/10.1109/IGARSS.2015.7326748
Bramhe, V. S., Ghosh, S. K., & Garg, P. K. (2018). Extraction of built-up areas using convolutional neural networks and transfer learning from sentinel-2 satellite IMAGES. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. 42(3). 10.5194/isprs-archives-XLII-3-79-2018
Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological modelling, 168(3), 233-249. https://doi.org/10.1016/S0304-3800(03)00139-X
Dannebeck, S., Hoppe, A., Küster, H., & McCracken, D. (2009). Factors affecting cultural landscapes: An overview. In K. Krzywinski, M. O’Connell, & H. Küster (Eds.), Cultural landscapes of Europe. Fields of demeter, haunts of pan (47–54). Bremen: Aschenbeck Media.
Dörnhöfer, K., Göritz, A., Gege, P., Pflug, B., & Oppelt, N. (2016). Water constituents and water depth retrieval from Sentinel-2A—A first evaluation in an oligotrophic lake. Remote Sensing, 8(11), 941. https://doi.org/10.3390/rs8110941
Drăguţ, L., & Eisank, C. (2012). Automated object-based classification of topography from SRTM data. Geomorphology, 141, 21-33. https://doi.org/10.1016/j.geomorph.2011.12.001
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8(4), 354. https://doi.org/10.3390/rs8040354
Fernández-Manso, A., Fernández-Manso, O., & Quintano, C. (2016). SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. International journal of applied earth observation and geoinformation, 50, 170-175.d oi:https://doi.org/10.1016/j.jag.2016.03.005
Fletcher, K. (2012). Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services (ESA SP-1322/2 March 2012). ESA). ESA Communications, Noordwijk.
Friedl, M. A., McIver, D. K., Hodges, J. C., Zhang, X. Y., et al. (2002). Global land cover mapping from MODIS: algorithms and early results. Remote sensing of Environment, 83(1-2), 287-302. https://doi.org/10.1016/S0034-4257(02)00078-0
Gao, B. C., & Li, R. R. (2017). Removal of thin cirrus scattering effects in Landsat 8 OLI images using the cirrus detecting channel. Remote Sensing, 9(8), 834. https://doi.org/10.3390/rs9080834
Greenhough, J., Remedios, J. J., Sembhi, H., & Kramer, L. J. (2005). Towards cloud detection and cloud frequency distributions from MIPAS infra-red observations. Advances in Space Research, 36(5), 800-806. https://doi.org/ 10.1016/j.asr.2005.04.096
Greco, S., Infusino, M., De Donato, C., Coluzzi, R., et al. (2018). Late spring frost in Mediterranean beech forests: extended crown dieback and short-term effects on moth communities. Forests, 9(7), 388. https://doi.org/10.3390/f9070388
Hidayati, I. N., Suharyadi, R., & Danoedoro, P. (2018, April). Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index. In Forum Geografi (Vol. 32, No. 1, pp. 96-108). https://doi.org/ 10.23917/forgeo.v32i1.5907
Huang, H., Roy, D. P., Boschetti, L., Zhang, H. K., et al. (2016). Separability analysis of Sentinel-2A multi-spectral instrument (MSI) data for burned area discrimination. Remote Sensing, 8(10), 873. https://doi.org/10.3390/rs8100873
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213. https://doi.org/ 10.1016/S0034-4257(02)00096-2
Imani, B. (2014). Physical-spatial Transformation in the rural settlements around Ardabil (1975-2011), Ph.D. thesis at Shahid Beheshti University, Tehran, Iran.
Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8(3), 166. https://doi.org/10.3390/rs8030166
Kaufman, Y. J. (1987). The effect of subpixel clouds on remote sensing. International Journal of Remote Sensing, 8(6), 839-857. https://doi.org/10.1080/01431168708948693
Liu, T., & Yang, X. (2015). Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Applied Geography, 56, 42-54. https://doi.org/10.1016/j.apgeog.2014.10.002
Nakajima, T. Y., Tsuchiya, T., Ishida, H., Matsui, T. N., & Shimoda, H. (2011). Cloud detection performance of spaceborne visible-to-infrared multispectral imagers. Applied optics, 50(17), 2601-2616. https://doi.org/10.1364/AO.50.002601
Nazmfar, H., & Jafarzadeh, J. (2018). Classification of satellite images in assessing urban land use change using scale optimization in object-oriented processes (a case study: Ardabil city, Iran). Journal of the Indian Society of Remote Sensing, 46(12), 1983-1990. https://doi.org/10.1007/s12524-018-0850-7
Paul, F., Winsvold, S. H., Kääb, A., Nagler, T., & Schwaizer, G. (2016). Glacier remote sensing using Sentinel-2. Part II: Mapping glacier extents and surface facies, and comparison to Landsat 8. Remote Sensing, 8(7), 575. https://doi.org/10.3390/rs8070575
Radoux, J., Chomé, G., Jacques, D. C., Waldner, F., Bellemans, N., Matton, N., ... & Defourny, P. (2016). Sentinel-2’s potential for sub-pixel landscape feature detection. Remote Sensing, 8(6), 488. https://doi.org/10.3390/rs8060488
Sandker, M., Campbell, B. M., Ruiz-Pérez, M., Sayer, J. A., Cowling, R., Kassa, H., & Knight, A. T. (2010). The role of participatory modeling in landscape approaches to reconcile conservation and development. Ecology and Society, 15(2). https://doi.org/10.5751/ES-03400-150213
Sentinel, E. S. A. (2). Team, 2007. GMES Sentinel-2 Mission Requirements. EOP-SM/1163/MR.dr, issue 2.1. Available online: http://esamultimedia.esa.int/docs/GMES/Sentinel-2_MRD.pdf
Singh, R., & Gupta, R. (2016, August). Improvement of classification accuracy using image fusion techniques. In 2016 International Conference on Computational Intelligence and Applications (ICCIA) (pp. 36-40). IEEE. https://doi.org/ 10.1109/ICCIA.2016.21
Song, X., Yang, C., Wu, M., Zhao, C., Yang, G., Hoffmann, W. C., & Huang, W. (2017). Evaluation of sentinel-2A satellite imagery for mapping cotton root rot. Remote Sensing, 9(9), 906. https://doi.org/10.3390/rs9090906
Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B., & Nõges, T. (2016). First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sensing, 8(8), 640. https://doi.org/10.3390/rs8080640
Topaloğlu, R. H., Sertel, E., & Musaoğlu, N. (2016). Assessment of Classification Accuracies Of Sentinel-2 and Landsat-8 Data For Land Cover/Use Mapping. International archives of the photogrammetry, remote sensing & spatial Information Sciences, 41. https://doi.org/10.5194/isprsarchives-XLI-B8-1055-2016
United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. General Assembley 70 session. https://sustainabledevelopment.un.org/post2015/transformingourworld (accessed December 2, 2016)
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. JOSA A, 35(1), 35-44. https://doi.org/10.1364/JOSAA.35.000035
Woodcock, C. E., Allen, R., Anderson, M., Belward, A., et al. (2008). Free access to Landsat imagery. Science, 320(5879), 1011-1011.
Yesou, H., Pottier, E., Mercier, G., Grizonnet, M., et al. (2016, July). Synergy of Sentinel-1 and Sentinel-2 imagery for wetland monitoring information extraction from continuous flow of sentinel images applied to water bodies and vegetation mapping and monitoring. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 162-165). IEEE. https://doi.org/10.1109/IGARSS.2016.7729033
Zhan, X., Sohlberg, R. A., Townshend, J. R. G., Di Miceli, C., et al. (2002). Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment, 83(1-2), 336-350. https://doi.org/10.1016/S0034-4257(02)00081-0
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