Journals / International Journal of Engineering and Geosciences / 2020 / Cilt: 5 - Sayı: 3

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

Pages
120–129
Publication Date
2022-10-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:In this study, it is aimed to demonstrate various aspects of remote sensing technology to be used in watershed management studies.
💡
Conclusion & Impact: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.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Konya Closed Basin · MODIS · Sentinel

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.

🔖

Cite This Article

YAĞMUR, N., MUSAOĞLU, N., TUZCU, A., BİLGİLİOĞLU, B., TANIK, A. (2022). Opportunities provided by remote sensing data for watershed management: example of Konya Closed Basin. International Journal of Engineering and Geosciences, Cilt: 5 - Sayı: 3, 120–129. https://ajindex.com/articles/540703

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  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 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.
  4. 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.
  5. 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.
  6. 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.
  7. Forestry Statistics (2010). A Publication of Official Statistics Programme, Republic of Turkey Ministry of Forestry and Water Affairs, Ankara.
  8. 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 Engineer