Assessing the Capability of Sentinel-2A Data for Mapping Seagrass Percent Cover in Jerowaru, East Lombok

https://doi.org/10.22146/ijg.28407

Muhammad Afif Fauzan(1*), Ignatius S. W. Kumara(2), Rifka N. Yogyantoro(3), Satrio W. Suwardana(4), Nurul Fadhilah(5), Intansania Nurmalasari(6), Santi Apriyani(7), Pramaditya Wicaksono(8)

(1) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(2) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(3) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(4) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(5) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(6) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(7) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(8) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author

Abstract


Remote sensing technology has been widely used in various applications related to natural resources and environment monitoring. In this paper, we evaluated the capability of new Sentinel-2A image to map the distribution and percent cover of seagrass in optically shallow water of Jerowaru coastal area, East Lombok. Seagrass distribution map was produced from radiometrically and geometrically corrected Sentinel-2A image with overall accuracy of 61.9%. Using empirical model, seagrass percent cover was predicted with maximum coefficient of determination (R2) of 0.51 and standard error of estimate (SE) of 19.4%. The results suggest that Sentinel-2A image can be used to perform seagrass mapping time and cost-effectively and can be further improved by incorporating more robust empirical modeling technique.


Keywords


Remote sensing; Sentinel-2; Seagrass; Mapping

Full Text:

PDF


References

Armstrong, R. A. (1993). Remote sensing of submerged vegetation canopies for biomass estimation. International Journal of Remote Sensing, 14(3), 621-627.

Chavez, P., Berlin, G., & Mitchell, W. (1977). Computer Enhancement Techniques of Landsat MSS Digital Images for Landuse/Landcover Assessments. Remote Sensing of Earth Resources, 6, 259.

Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.

[ESA] European Space Agency. (2015). ESA Bulletin 161(1).

Fyfe, S. K. (2003). Spatial and temporal variation in spectral reflectance: are seagrass species spectrally distinct? Limnology and Oceanography, 48, 464-479.

Green E. P., Mumby P. J., Edwards, A. J., Clark, C. D. (2000). Remote Sensing Handbook for Tropical Coastal Management. Coastal Management Sourcebooks 3, UNESCO, Paris.

Goodman, J. A., Purkis, S. J., & Phinn, S. R. (2013). Coral Reef Remote Sensing A Guide for Mapping, Monitoring and Management. (S. R. Phinn, Ed.) Springer.

Hartono. (1994). The use of SPOT image for mangrove inventory in Cimanuk Delta, West Java, Indonesia. Indonesian Journal of Geography, 26(28), 11-26.

Hogarth, P. J. (2015). The Biology of Mangroves and Seagrasses. Oxford: Oxford University Press. Knubdy, A., Nordlund, L. (2011). Remote Sensing of seagrasses in a patchy multi-species environment. International Journal of Remote Sensing, 32(8), 2227-2244.

Lyzenga, D. R. (1981). Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing, 71-82.

McKenzie, L., Finkbeiner, M. A., & Kirkman, H. (2001). Seagrass Mapping Methods. Global Seagrass Research Methods. F. T. Short & R .G. Coles. Amsterdam: Elsevier, 101-122.

Mumby, P. J., Green, EP., Edwards, AJ., Clark, CD. (1997). Measurement of seagrass standing crop using satellite and digital airborne remote sensing. Marine Ecology Progress Series, 159, 51-60.

Nadiarti, Riani, E., Ita D., Sugeng, B., Ari, P., Harald, A. (2012). Challenging for seagrass management in Indonesia. Journal of Coastal Development, 15(3), 234-242.

Pasqualini, V., Pergent-Martini, C., Pergent, G., Agreil, M., Skoufas, G., Sourbes, L., Tsirika, A. (2005). Use of SPOT 5 for mapping seagrasses: An application to Posidonia oceanica. Remote Sensing of Environment, 94, 39-45.

Phinn, S., Roelfsema, C., Dekker, A., Brando. V., Anstee, J. (2008). Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote Sensing of Environment, 112, 3413-3425.

Pu, R., Bell, S. S, Meyer, C. A., Baggert, L. P, Zhao, Y. (2012). Mapping and assessing seagrass along the western coast of Florida using Landsat TM and EO-1 ALI/Hyperion imagery. Estuarine, Coastal, and Shelf Science, 115, 234-245.

Pu, R., Bell, S. S., Meyer, C. A. (2014). Mapping and assessing seagrass bed changes in Central Florida’s west coast using multitemporal Landsat TM imagery. Estuarine, Coastal, and Shelf Science, 149, 68-79.

Roelfsema, C., Kovacs, E.M., Phinn, S. (2014). Field data sets for seagrass biophysical properties for the Eastern Banks, Moreton Bay, Australia, 2004-2014. Scientific Data.

Short, F.T., Coles, R.G. (2001). Global Seagrass Research Methods. Amsterdam: Elsevier.

Topouzelis, K., Spondylidis, S. C., Papakonstantinou, A., Soulakellis, N. (2016). The use of Sentinel-2 imagery for seagrass mapping: Kalloni Gulf (Lesvos Island, Greece) case study. Proceedings of SPIE 9688, Fourth International Conference of Remote Sensing and Geoinformation of the Environment.

[UNEP] United Nations Environment Programme. (2004). Seagrass in the South China sea. UNEP Technical Publication, 3. Bangkok.

Wicaksono, P., Hafizt, M. (2013). Mapping seagrass from space: addressing of complexity of LAI seagrass mapping. European Journal of Remote Sensing, 46, 18-39.

Wicaksono, P. (2015). Remote Sensing Model Development for Seagrass and Mangrove Carbon Stock Mapping. Dissertation. Yogyakarta: Faculty of Geography, Universitas Gadjah Mada.

Wicaksono, P. (2016). Improving the accuracy of multispectral-based benthic habitats mapping using image rotations: the application of principle component analysis and independent component analysis. European Journal of Remote Sensing, 49: 433-463.



DOI: https://doi.org/10.22146/ijg.28407

Article Metrics

Abstract views : 7357 | views : 5534

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 Indonesian Journal of Geography

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web
Analytics IJG STATISTIC