Comparing the Capability of Sentinel 2 and Landsat 8 Satellite Imagery in Land Use and Land Cover Mapping Using Pixel-based and Object-based Classification Methods

Authors

10.22052/deej.2018.7.25.25

Abstract

Introduction: Having accurate and up-to-date information on the status of land use and land cover change is a key point to protecting natural resources, sustainable agriculture management and urban development. Preparing the land cover and land use maps with traditional methods is usually time and cost consuming. Nowadays satellite imagery provides the possibility to prepare these maps in less time and with greater accuracy. In order to prepare land use and land cover maps using satellite imagery, different classification methods are used. In a general division, these methods can be divided into two groups of pixel-based and object-based classification methods. Despite the fact that the Sentinel 2 satellite has recently been launched and its images have been freely available to users, there has not been enough research on the use of this satellite imagery in land use and land cover mapping. Therefore, in present research, we tried to investigate the capability of Sentinel 2 satellite imagery in land use and land cover mapping of Bastam basin in Lorestan province, using pixel-based and object-based classification methods and compare it with Landsat 8 satellite imagery.
Material and methods:  In order to conduct this research, training samples were collected by field survey, using existing maps, creation of false color images and Google Earth software in six classes including garden, bare land, forest, agriculture, residential and river. The 90, 280, 680, 59, 180, 85 and 70 training samples were collected in the river, agriculture, forest, garden, residential, bare land and pasture classes, respectively. The 70% of collected samples were used as training samples and 30% of the rest were used as test samples. After performing the necessary preprocessing, images classified using the Maximum likelihood, Minimum distance, Mahalanobis distance algorithms of pixel-based method and the Nearest neighbor algorithm of object-based method. The object-based classification method consists of two main stages of segmentation and classification. In this research, Multi Resolution Segmentation algorithm was used for segmentation. Classification was done by the Nearest Neighbor method in eCognition software. Accuracy assessment of classification results was performed using test samples and overall accuracy, Kappa coefficient, user accuracy and producer accuracy indices.
Results: The overall accuracy and Kappa coefficient for Sentinel 2 satellite images were classified using Maximum likelihood, Mahalanobis distance and Minimum distance were calculated (85.2% and 0.77), (88.77% and 81.8) and (71.1% and 0.56) respectively. For Landsat 8 satellite images were classified using Maximum likelihood, Mahalanobis distance and Minimum distance the overall accuracy and kappa coefficient were observed (84.10% and 76%), (82.83% and 0.73) and (63.50% and 48.3%) respectively, which indicates a relatively higher potential Sentinel 2 image compared to Landsat 8 images in the pixel classification of land use and land cover. In addition, the results of the object-based classification by the Nearest neighbor method showed that the Sentinel 2 images with overall accuracy 89.70% and kappa coefficient 0.83 had better performance than Landsat 8 images with overall accuracy 88% and Kappa coefficient 0.81.
Discussion and conclusion: According to the results, it can be concluded that of Sentinel 2 satellite images have a better relative performance than Landsat 8 satellite imagery in the preparation of land use and land cover maps, this seems to be due to the higher spatial resolution of the Sentinel 2 satellite (10-meter pixel in near infrared and visible bands) compared to Landsat 8 (30-meter pixel in near infrared and visible bands). The highest level of user and producer accuracy were observed in the forest land cover, it seems that the reason for this is the vast forest area in the study area and as a result of increasing the number of training samples in this land cover. Comparison of the performance of the pixel-based and object-based classification methods showed that the use of the object-based classification method improves the results of land use and land cover classification. Finally, it is important to note that, despite the better performance of Sentinel 2 satellite imagery and object- based classification method than Landsat 8 satellite images and pixel-based classification method, but the results of Landsat 8 satellite imagery, and pixel-based method was acceptable as well.

Keywords


1. Adam, H.E., Csaplovics, E., Elhaja, M. E., 2016. A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan. In: IOP Conference Series: Earth and Environmental Science, pp. 1-10. 2. Ahmadpour, A., Solaimani, K., Shokri, M., Ghorbani, J., 2014. Comparison of three common methods in supervised classification of satellite data for vegetation studies. RS and GIS journal for natural resources 5, 77-89 (in Persian). 3. Al-Ahmadi, F.S., Hames S.A., 2009. Comparison of Four Classification Methods to Extract Land Use and Land Cover from Raw Satellite Image for Some Remote Arid Areas, Kingdom of Saudi Arabia, JKAU. Earth Science 20, 167-191. 4. Araya Y. H., Hergatren. C., 2008. A comparison of pixel and object-based land cover classification: a case study of the Asmara region, Eritrea. Geo Environment and Landscape Environment 100, 233-243. 5. Arekhi, S., Adibnejad, 2011. Efficiency assessment of the of Support Vector Machines for land use classification using Landsat ETM+ data (Case study: Ilam Dam Catchment). Iranian Journal of Rangeland and Desert Research 18, 420-440 (in Persian). 6. Aslami, F., Ghorbani, A., Sobhani, B., Panahandeh, M. 2015. Comparing artificial neural network, support vector machine and object-based methods in preparation land use/cover mapsusing landSat-8 images. RS and GIS journal for natural resources 6, 1-14 (inPersian). 7. Blaschke, T., Strobl, J., 2001. What’s wrong with pixels? some recent developments interfacing remote Sensing and GIS. GIS-Zeitschrift für Geoinformations systeme 6, 12-17. 8. Definiens., 2006. Definiens Professional 5 Reference Book, Definiens AG, Germany. 240 pp. 9. De Kok, Schneider, R.T, Baatz, M, Ammer, U., 1999. Object based image analysis of high-resolution data in the Alpine forest area, Proceeding of Joint WSFISPRS WG I/1, I/3, and IV/4: Sensors and Mapping from Space, Hanover, pp. 27-30. 10. Fathian, F., Morid, S., Arshad, S., 2013. Trend Assessment of Land Use Changes Using Remote Sensing Technique and its Relationship with Streamflows Trend (Case Study: The East Sub Basins of Urmia Lake). Journal of Water and Soil 27, 642-655 (in Persian). 11. Fathizad, H., Tazeh, M., Kalantari, M., 2015. Assessment of pixel-based classification (ARTMAP fuzzy Neural Networks and Decision Tree) and Object-Oriented methods for land use mapping (Case study: Meymeh, Ilam province). Arid biome 5, 69-82 (in Persian). 12. Feizizadeh, B., Helali, H., 2010. Comparison Pixel-Based, Object-Oriented Methods and Effective Parameters in Classification Land Cover/ Land Use of West Province Azerbaijan. Physical Geography Research Quarterly 42, 73-84 (in Persian). 13. Ghafari, S., Moradi. H.R., Modarres, R., 2018. Comparison of object-oriented and pixel-based classification methods for land use mapping (Case study: Isfahan-Borkhar, Najafabad and Chadegan plains. Journal of RS and GIS for Natural Resources, 9, 40-57. 14. Gholoobi, M., Tayyebi, A., Taleyi, M., Tayyebi, A.H., 2010. Comparing pixel based and object based approaches in land use classification in mountainous areas. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8, Kyoto Japan, pp.1-6. 15. Huth, J., Kuenzer, C., Wehrmann, T., Gebhardt, S., Tuan, V.Q, Dech, S., 2012. Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification. Remote sensing 4, 2530-2553. 16. Kim, M., Madden, M., Warner, T., 2009. Forest type mapping using object-specific texture measures from multispectral IKONOS imagery: segmentation quality and image classification issues. Photogrammetric Engineering & Remote Sensing 75, 819-829. 17. Kim, M., 2009. Object- based Spatial Classification of Forest Vegetation with IKONOS Imagery. A dissertation Ph.D thesis. University of Georgia, Athens, Georgia, 133 pp. 18. Lima, T.A., Beuchle, R., Langner, A., Grecchi, R. C., Griess, V.C., Achard, F., 2019. Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon. Remote sensing 11, 1-21. 19. Marangoz, A.M., Sekertekin, A., Akcin, H., 2017. Analysis of land use land cover classification results derived from Sentinel-2 image. 17th International Multidisciplinary Scientific GeoConference SGEM, pp. 3-8. 20. Mercier, A., Betbeder, J., Rumiano, F., Baudry, J., Gond, V., Blanc, L., Bourgoin, C., Cornu, G., Ciudad, C., Marchamalo, M., Poccard-Chapuis, R., Hubert-Moy, L., 2019. Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes. Remote sensing, 11, 1-20. 21. Mokhtari,, M., Najafi, A., 2015. Comparison of Support Vector Machine and Neural Network Classification Methods in Land Use Information Extraction through Landsat TM Data. Journal of Water and Soil Science 19, 35-45 (in Persian). 22. Schwarz, M., Steinmeier, C., Waser, L., 2002. Detection of storm losses in Alpine forest areas by different methodical approaches using high-resolution satellite data. In G. Bégni (Ed.), Observing our environment from space: New solutions for a new millennium, pp. 251-257. 23. Sekertekin, A., Marangoz, A.M., Akcin, H., 2017. Pixel-based classification analysis of land use land cover using Sentinel-2 and Landsat 8 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W6, 2017 4th International GeoAdvances Workshop, Safranbolu, Karabuk, Turkey, pp. 91-93. 24. Sharma, R.C., Hara, K., Tateishi, R., 2017. High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach Land, 6, 1-11. 25. Shataee, Sh., Darvishsefat, A.A., Sobhani, H., 2007. Comparison of pixel-based and object-based approaches forforest type mapping using satellite data. Journal of the Iranian Natural Resources 60, 869-881 (in Persian). 26. Soffianian, A., Madanian, M.A., 2011. Comparison of Maximum Likelihood and Minimum Distance to Mean Classifiers in Preparing Land Cover Map (A Case Study: Isfahan Area). Journal of Water and Soil Science 15, 253-264 (in Persian). 27. Topaloglu, R. H., Sertel, E., Musaoglu, 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, pp. 1055-1059. 28. Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D., Bui, D.T., 2018. Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sensing 10, 1-21. 29. Yaghoub Zadeh, B., 2014. Climate analysis of Aleshtar region, Selseleh division, Lorestan. Proceedings of the Meteorological Services of Lorestan Province, pp. 1-17 (in Persian).