Advertisement

Land cover mapping using Sentinel-1 SAR and Landsat 8 imageries of Lagos State for 2017

  • Esther Oluwafunmilayo MakindeEmail author
  • Edward Oluwasegun Oyelade
Current trends in Economy, Sustainable Development, and Energy
  • 10 Downloads

Abstract

For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos State. In this work, a land cover appearance for Lagos State is examined using Sentinel-1 synthetic aperture radar (SAR) and Land Satellite 8 (Landsat 8) imageries. To this aim, a Sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 and a Landsat 8 Operational Land Imager (OLI) for 2017 over Lagos State were acquired and analysed. The Sentinel-1 imagery was calibrated and terrain corrected using a SRTM 3Sec DEM. Maximum likelihood classification algorithm was performed. A supervised pixel-based imagery classification to classify the dataset using training points selected from RGB combination of VV and VH polarizations was applied. Accuracy assessment was performed using test data collected from high-resolution imagery of Google Earth to determine the overall classification accuracy and Kappa coefficient. The Landsat 8 was orthorectified and maximum likelihood classification algorithm also performed. The results for Sentinel-1 include an RGB composite of the imagery, classified imagery, with overall accuracy calculated as 0.757, while the kappa value was evaluated to be about 0.719. Also, the Landsat 8 includes a RBG composite of the imagery, classified imagery, but an overall accuracy of 0.908 and a kappa value of 0.876. It is concluded that Sentinel 1 SAR result has been effectively exploited for producing acceptable accurate land cover map of Lagos State with relevant advantages for areas with cloud cover. In addition,  the Landsat 8 result reported a high accuracy assessment values with finer visual land cover map appearance.

Keywords

Sentinel-1 Landsat 8 Synthetic aperture radar (SAR) imagery Remote sensing Maximum likelihood algorithm Land cover Lagos State 

Notes

Supplementary material

11356_2019_5589_MOESM1_ESM.docx (24 kb)
ESM 1 (DOCX 28 kb)

References

  1. Abdikan S, Sanli FB, Ustuner M, Calò F (2016) Land cover mapping using sentinel-1 SAR Data. Int Arch Photogramm Remote Sens Spat Inf Sci XLI-B7:757–762.  https://doi.org/10.5194/isprsarchives-XLI-B7-757-2016 CrossRefGoogle Scholar
  2. Abuelgasim A, Ammad R (2019) Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data. Remote Sens Appl Soc Environ 13:415–425.  https://doi.org/10.1016/j.rsase.2018.12.010 Google Scholar
  3. Balzter H, Cole B, Thiel C, Schmullius C (2015) Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sens 7(11):14876–14898CrossRefGoogle Scholar
  4. Ban Y, Gong P, Giri C (2015) Global land cover mapping using Earth observation satellite data: recent progresses and challenges. ISPRS J Photogramm Remote Sens 103(1):1–6.  https://doi.org/10.1016/j.isprsjprs.2015.01.001 CrossRefGoogle Scholar
  5. Banqué X, Lopez-Sanchez JM, Monells D, Ballester D, Duro J, Koudogbo F (2015) Polarimetry-Based land cover classification with sentinel-1 data,” In: Ouwehand L (ed) POLinSAR 2015: The 7th International Workshop on Science and Applications of SAR Polarimetric Interferometry held 26-30, Frascanti, Italy. European Space AgencyGoogle Scholar
  6. Bargiel D, Herrmann S (2011) Multi-temporal land-cover classification of agricultural areas in two european regions with high resolution spotlight TerraSAR-X data. Remote Sens 3(5):859–877CrossRefGoogle Scholar
  7. Castañeda IS, Mulitza S, Schefuß E, Lopes S, Raquel A, Sinninghe D, Jaap S, Schouten S (2009) Wet phases in the Sahara/Sahel region and human migration patterns in North Africa. Proc Natl Acad Sci U S A 106(48):20159–20163.  https://doi.org/10.1073/pnas.0905771106 CrossRefGoogle Scholar
  8. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46.  https://doi.org/10.1016/0034-4257(91)90048-B CrossRefGoogle Scholar
  9. Congalton RG, Gu J, Yadav K, Thenkabail P, Ozdogan M (2014) Global land cover mapping: a review and uncertainty analysis. Remote Sens 6(12):12070–12093CrossRefGoogle Scholar
  10. Crabbe RA, Janouš JD, Dařenová E, Pavelka M (2019) Exploring the potential of LANDSAT-8 for estimation of forest soil CO2 efflux. Int J Appl Earth Obs Geoinf 77:42–52.  https://doi.org/10.1016/j.jag.2018.12.007 CrossRefGoogle Scholar
  11. Djamai N, Fernandes R, Weiss M, McNairn H, Goïta K (2019) Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables using Sentinel-2/MSI and Landsat-8/OLI data. Remote Sens Environ 22:416–430.  https://doi.org/10.1016/j.rse.2019.03.020 CrossRefGoogle Scholar
  12. Dostálová A, Hollaus M, Milenković M, Wagner W (2016) Forest area derivation from sentinel-1 data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci III-7:227–233.  https://doi.org/10.5194/isprs-annals-III-7-227-2016 CrossRefGoogle Scholar
  13. ESA (2016) Observing the Earth. http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-1/Instrument. Erdas Imagine, ERDAS Imagine V9.1. ERDAS, Atlanta, 2006
  14. Falcucci A, Maiorano L, Boitani L (2007) Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc Ecol 22(4):617–631.  https://doi.org/10.1007/s10980-006-9056-4 CrossRefGoogle Scholar
  15. Fonteh LM, Fonkou T, Cornelius LM, Russell M, Abel R, Moses C (2016) Assessing the utility of Sentinel-1 C band synthetic aperture radar imagery for land use land cover classification in a tropical coastal systems when compared with Landsat 8. J Geogr Inf Syst 8(4):495–505.  https://doi.org/10.4236/jgis.2016.84041 Google Scholar
  16. Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114(1):168–182.  https://doi.org/10.1016/j.rse.2009.08.016 CrossRefGoogle Scholar
  17. Geomatics (2014) P.C.I. Geomatics. Using PCI software. Richmond Hill, p 540Google Scholar
  18. Geymen A, Baz I (2008) Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area. Environ Monit Assess 136(1–3):449–459.  https://doi.org/10.1007/s10661-007-9699-x Google Scholar
  19. Hashemian MS, Abkar AA, Fatemi SB (2004) Study of sampling methods for accuracy assessment of classified remotely sensed data. Proceedings of the 20th International Society for Photogrammetry and Remote Sensing Congress, Istanbul, Turkey. ISPRS 20th congress, 1682-1750.Google Scholar
  20. Irons JR, Dwyer JL, Barsi JA (2012) The next Landsat satellite: the Landsat data continuity mission. Remote Sens Environ 122:11–21.  https://doi.org/10.1016/j.rse.2011.08.026 CrossRefGoogle Scholar
  21. Landsat Science (2019). Landsat Program. https://landsat.gsfc.nasa.gov/landsat-data-continuity-mission/
  22. Liu H, Weng Q (2013) Landscape metrics for analysing urbanization-induced land use and land cover changes. Geocarto Int 28(7):582–593.  https://doi.org/10.1080/10106049.2012.752530 CrossRefGoogle Scholar
  23. Longepe N, Rakwatin P, Isoguchi O, Shimada M, Uryu Y, Yulianto K (2011) Assessment of ALOS PALSAR 50 m orthorectified FBD data for regional land cover classification by support vector machines. IEEE Trans Geosci Remote Sens 49(6):2135–2150CrossRefGoogle Scholar
  24. Makinde EO, Womiloju AA, Ogundeko MO (2017) The geospatial modelling of carbon sequestration in Oluwa Forest, Ondo State, Nigeria. Eur J Remote Sens 50(1):397–413.  https://doi.org/10.1080/22797254.2017.1341819 CrossRefGoogle Scholar
  25. Niu X, Ban Y (2013) Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. Int J Remote Sens 34(1):1–26.  https://doi.org/10.1080/01431161.2012.700133 CrossRefGoogle Scholar
  26. Omodanisi EO (2013) Resultant land use and land cover change from oil spillage using remote sensing and GIS. Res J Appl Sci Eng Technol , Indexed platform: Elsevier (Scopus). . ISSN: 2040-7459; e-ISSN: 2040-7467, USA 6(11):2032–2040CrossRefGoogle Scholar
  27. Shaharum NSN, Shafri HZM, Gambo J, Abidin FAZ (2018) Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sens Appl Soc Environ 10:24–35.  https://doi.org/10.1016/j.rsase.2018.01.002 Google Scholar
  28. Tahoun M, Shabayek AE, Nassar M, Giovenco MM, Reulke R et al (2016) Satellite image matching and registration: a comparative study using invariant local features. In: Awad A, Hassaballah M (eds) Image feature detectors and descriptors. Studies in computational intelligence, vol 630. Springer, Cham.  https://doi.org/10.1007/978-3-319-28854-3_6 Google Scholar
  29. Ullmann T, Schmitt A, Roth A, Duffe J, Dech S, Hubberten HW, Baumhauer R (2014) Land cover characterization and classification of Arctic tundra environments by means of polarized synthetic aperture X- and C-band radar (PolSAR) and Landsat 8 multispectral imagery - Richards Island, Canada. Remote Sens 6(9):8565–8593.  https://doi.org/10.3390/rs60x000x CrossRefGoogle Scholar
  30. Vaglio Laurin G, Chan JC-W, Chen Q, Ja L, Coomes DA, Guerriero L et al (2014) Biodiversity mapping in a Tropical West African forest with airborne hyperspectral data. PLoS ONE 9(6):e97910.  https://doi.org/10.1371/journal.pone.0097910 CrossRefGoogle Scholar
  31. Van der Sande CJ, de Jong SM, de Roo APJ (2003) A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int J Appl Earth Obs Geoinf 4(3):217–229CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Environment DepartmentUniversity of YorkYorkUK
  2. 2.Department of Surveying and Geoinformatics, Faculty of EngineeringUniversity of LagosLagosNigeria

Personalised recommendations