Opportunities provided by remote sensing data for watershed management: example of Konya Closed Basin

Abstract

Remote sensing data provides great opportunities in various steps of watershed management like characterization of watersheds that bear dynamic structure with large land, monitoring the physical variations within the basin, and conducting various scenario analyses to detect the response of the basin. The high resolution capacity of today’s satellite images enables the production of land use/cover data of a basin in shorter period of time. In this study, it is aimed to demonstrate various aspects of remote sensing technology to be used in watershed management studies. For that purpose, MODIS, Landsat and Sentinel satellite data with different spatial resolutions were used to monitor the surface water bodies in Konya Closed Basin (KCB) of Turkey. In addition, high spatial Worldview-3 satellite data were used to extract detailed information about Akgol Wetland located in KCB. A methodology was developed on the utilization of remote sensing technology consisting of 3 main groups; field surveys, satellite images and ancillary data. In the study, 5 different spectral indices were applied to Sentinel 2 data to determine the areas of surface water bodies. Moreover, Support Vector Machine (SVM) method was applied to Worldview-3 satellite image to classify Akgol Wetland and its vicinity. The importance of establishing watershed information system together with a database reflecting the characteristics of watersheds was underlined. Various examples were given from KCB that is known as the largest closed basin of the country with a surface area of 5.426.480 ha. The basin owns 17 water bodies out of which 2 of them are RAMSAR sites. Within the scope of the study, information obtained from optical and synthetic aperture radar (SAR) satellite images in the basin were discussed. More accurate results were achieved by Sentinel 2 than MODIS and Landsat data. In addition, detailed information about the wetland were extracted by means of Worldview-3 data and water bodies were monitored in all weather conditions via Sentinel 1 SAR data.

Keywords

• Konya Closed Basin
• MODIS
• Sentinel
• Landsat
• Worldview-3

References

1. CCIWR. (2016). Climate Change Impact on Water Resources Project, Ministry of Forestry and Water Affairs, General Directorate of Water Management [Online]. Date of access: 21/04/2018 http://iklim.ormansu.gov.tr/Eng/
2. Celebi, M. and Direk, M. (2017). Farmer behaviours and sustainable water management in semiarid Konya Closed Basin in Turkey. International Journal of Advanced Biological and Biomedical Research, 6(1), pp. 441-450.
3. Comert, R, Matcı, D and Avdan, U (2019). Object based burned area mapping with random forest algorithm. International Journal of Engineering and Geosciences, 4 (2), 78-87. DOI: 10.26833/ijeg.455595
4. DMP. (2015). Konya Closed Basin Drought Management Plan Project, Ministry of Forestry and Water Affairs, General Directorate of Water Management. Date of access: 21/04/2018. http://www.suyonetimi.gov.tr.
5. Dursun, S., Onder, S., Acar, R., Direk, M. and Mucevher, O. (2012). Effect of environmental and socioeconomically change on agricultural production in Konya Region. Proceedings of International Conference on Applied Life Sciences (ICALS2012), Turkey, pp. 19-36.
6. EPA. (2008). Handbook for developing watershed plans to restore and protect our waters, United States Environmental Protection Agency, Office of Water Nonpoint Source Control Branch Washington, DC. EPA 841-B-08-002, March 2008.
7. Feyisa, G.L., Meilby, H., Fensholt, R. and Proud, S.R. (2014). Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, pp. 23–35.
8. Forestry Statistics (2010). A Publication of Official Statistics Programme, Republic of Turkey Ministry of Forestry and Water Affairs, Ankara. Heumann, B. W. (2011). An object-based classification of mangroves using a hybrid decision tree-Support vector machine approach. Remote Sensing, 3(11), 2440-2460.

9. Hsu, C.W., Chang, C.c. and Lin, C.J. (2003). A practical guide to support vector classification, Technical Report. Department of Computer Science and Information Engineering, University of National Taiwan, Taipei, 1–12.
10. Huang, C., Chen, Y., Zhang, S. and Wu, J. (2018). Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Reviews of Geophysics, 56 (2), pp. 333-360.
11. Karakus, P., Karabork., H and Kaya, S. (2017). A comparison of the classification accuracies in determining the land cover of Kadirli region of Turkey by using the pixel based and object based classification algorithms. International Journal of Engineering and Geosciences, 2 (2), 52-60.
12. Lamb, B.T, Tzortziou, M. A. and McDonald, K. C. (2019). Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays, Remote Sensing, 11 (20), 2366.
13. Li, L., Vrieling, A., Skidmore, A., Wang, T., Muñoz, A. and Turak, E. (2015). Evaluation of MODIS Spectral Indices for Monitoring Hydrological Dynamics of a Small, Seasonally-Flooded Wetland in Southern Spain, Wetlands, 35, 851-864.
14. Ludwig, C., Walli, A., Schleicher, C., Weichselbaum, J. and Riffler, M. (2019). A highly automated algorithm for wetland detection using multi-temporal optical satellite data, Remote Sensing of Environment, 224, 333-351.
15. McCoy, R. M. (2005). Field methods in remote sensing, pp. 42-114, ISBN: 1593850808, Guilford Press, New York
16. McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), pp. 1425-1432.
17. Musaoglu, N. Tanik, A., Gumusay, U.M., Dervisoglu, A., Bilgilioglu, B.B., Yagmur, N, Bakırman, T., Baran, D. and Gokdag, F, M. (2018). Long-term monitoring of wetlands via remote sensing and GIS: a case study from Turkey, 2nd International Conference on Climate Change, Sri Lanka, Colombo, 15-16 February 2018, Proceedings Vol. 2, p. 11-21.
18. Randhir, O.T. (2007). Watershed Management- Issues and Approaches, IWA publishing, London, UK, p. 146.
19. Sánchez-Aparicio, M., Andrés-Anaya, P., Del Pozo, S. and Lagüela, S. 2020. Retrieving Land Surface Temperature from Satellite Imagery with a Novel Combined Strategy, 12, 277.
20. Shen, L. and Li, C. (2010), June. Water Body Extraction from Landsat ETM Imagery using Adaboost Algorithm. In Proceedings of the 18th International Conference on Geoinformatics, Beijing, China, pp. 1–4.
21. Tanik, A. (2019). Integrated watershed management. Lecture notes of CBM 546E graduate course at ITU Environmental Science, Engineering and Management Programme.
22. UN. (1997). Guidelines and manual on land-use planning and practices in watershed management and disaster reduction, United Nations, ST/ESCAP/1781, Economic and Social Commission for Asia and the Pacific, June 1997.
23. Url 1: http://www.kop.gov.tr/upload/dokumanlar/32.pdf (last accessed 1 April 2019)
24. Vapnik, V. (1999). The nature of statistical learning theory, pp. 133 – 140, ISBN: 0-387-98780-0, Springer science & business media, New York, Berlin, Heidelberg
25. Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), pp. 3025-3033.
26. Yagmur, N., Bilgilioglu, B.B., Musaoglu, N., Erten, E. and Tanik, A. (2018). Temporal changes of lentic system surfaces in Konya Closed Basin, Turkey, 3. ICOCEE 2018, 3rd International Conference on Civil and Environmental Engineering, İzmir, 24-27 April 2018, Conference E-Book Vol.2., pp. 658-668.