Research Article
BibTex RIS Cite

Comparative Investigation of the Effects of Irrigation Ponds on Climate in Agriculture Using Satellite Imagery and Meteorological Data

Year 2024, Volume: 6 Issue: 2, 68 - 84

Abdurahman Yasin Yiğit , Oğuz Şimşek , Halil İbrahim Şenol

https://doi.org/10.51489/tuzal.1551019

Abstract

This study examines the long-term effects of agricultural irrigation ponds on regional climate, vegetation cover, and water resources in the Mersin province, located in Turkey's Mediterranean region, using satellite imagery and meteorological data. The methodology of the study is based on the integration of Landsat satellite data and meteorological datasets from 1985 to 2023. Object-based image processing techniques were employed for land classification to map changes in vegetation and water bodies, and classification accuracy was evaluated using an error matrix and various accuracy metrics. Additionally, drought trends were analyzed using the Standardized Precipitation Index and trend analysis methods such as Mann-Kendall, Spearman Rho, and Sen's Slope. The results reveal that the number of irrigation ponds increased from 51 in 1985 to 1,935 in 2023, contributing to the preservation of vegetation cover as indicated by rising NDVI values. Drought analyses indicate that the adverse effects of drought periods on vegetation cover have been mitigated by these irrigation ponds in the study region. These ponds, with their regulatory effect on the microclimate, are considered a strategic water management tool for the sustainability of agricultural production in semi-arid regions. The findings demonstrate the significant potential of agricultural irrigation ponds for sustainable water resource management, climate change mitigation, and environmental resilience. In this context, it is recommended to increase the number of irrigation ponds in semi-arid and arid regions and adopt climate-friendly approaches in the planning of these structures.

Keywords

Drought Drought analysis Agricultural irrigation ponds Land classification Microclimate regulation

Ethical Statement

Yapılan çalışmada yazarlar, araştırma ve yayın etiğine aykırı bir durum olmadığını ve çalışmanın etik kurul izni gerektirmediğini beyan etmektedir.

Thanks

Bu makalenin özet hali, 12th Clobal Conference on Global Warming (GCGW-2024) sempozyumunda sunulmuştur. Bu çalışma sunulan bildirinin genişletilmiş ve büyük oranda geliştirilmiş halidir. Yazarlar ayrıca Meteorolojik veriler için Meteoroloji Genel Müdürlüğü’ne ve ücretsiz uydu verileri için NASA ve ABD Jeoloji Araştırması'na teşekkür eder.

References

  • Abera, A., Verhoest, N. E., Tilahun, S., Inyang, H., & Nyssen, J. (2021). Assessment of irrigation expansion and implications for water resources by using RS and GIS techniques in the Lake Tana Basin of Ethiopia. Environmental Monitoring and Assessment, 193, 1–17. https://doi.org/10.1007/s10661-021-08924-2
  • Achite, M., Simsek, O., Adarsh, S., Hartani, T., & Caloiero, T. (2023). Assessment and monitoring of meteorological and hydrological drought in semiarid regions: The Wadi Ouahrane basin case study (Algeria). Physics and Chemistry of the Earth, Parts A/B/C, 130, 103386. https://doi.org/10.1016/j.pce.2022.103386
  • Adanalı, T. (2022). Rekreasyon alanlarında kullanılan farklı sulama yöntemlerinin topraktaki tuz dağılımına etkisi [Master's thesis, Tekirdağ Namık Kemal Üniversitesi].
  • Aktaş, Ö. (2014). Impacts of climate change on water resources in Turkey. Environmental Engineering & Management Journal (EEMJ), 13(4), 799–810.
  • Andriushchenko, K., Datsii, O., Aleinikova, O., Abdulla, A. M., & Ali, A. M. (2019). Improvement of the water resources management system at the territorial level. Problems and Perspectives in Management, 17(3), 421–430. https://doi.org/10.21511/ppm.17(3).2019.34
  • Angelakis, A. N., Zaccaria, D., Krasilnikoff, J., Salgot, M., Bazza, M., Roccaro, P., ... & Fereres, E. (2020). Irrigation of world agricultural lands: Evolution through the millennia. Water, 12(5), 1285. https://doi.org/10.3390/w12051285
  • Atici, A., Paksoy, M. F., & Kabadayı, A. (2024). Maden sahalarındaki stok miktarının İHA yardımıyla belirlenmesi. Türkiye Fotogrametri Dergisi, 6(1), 8–13. https://doi.org/10.53030/tufod.1489122
  • Baatz, M. (2000). Multiresolution segmentation: An optimization approach for high-quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung, 12–23.
  • Bandyopadhyay, J., Rahaman, S. H., & Karan, C. (2023). Agricultural potential zone mapping with surface water resource management using geo-spatial tools for Jhargram district, West Bengal, India. Knowledge-Based Engineering and Sciences, 4(1), 1-18.
  • Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258. https://doi.org/10.1016/j.isprsjprs.2003.10.002
  • Bergsjö, J. (2014). Object-based change detection in urban area using KTH-SEG. KTH Royal Institute of Technology, Sweden.
  • Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., ... & Tiede, D. (2014). Geographic object-based image analysis: Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014
  • Blaschke, T., Hay, G. J., Weng, Q., & Resch, B. (2011). Collective sensing: Integrating geospatial technologies to understand urban systems—An overview. Remote Sensing, 3(8), 1743–1776. https://doi.org/10.3390/rs3081743
  • Carleer, A. P., & Wolff, E. (2006). Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features. International Journal of Remote Sensing, 27(6), 1035–1051. https://doi.org/10.1080/01431160500219302
  • Çelebioğlu, T., & Tayanç, M. (2024). A study on precipitation trends in Türkiye via linear regression analysis and non-parametric Mann-Kendall test. Sürdürülebilir Çevre Dergisi, 4(1), 19–28.
  • Definiens. (2012). Definiens Developer XD 2.0.4: Reference Book. Definiens AG, München, Germany. Retrieved from https://www.imperial.ac.uk/media/imperialcollege/medicine/facilities/film/Definiens-Developer-Reference-Book-XD-2.0.4.pdf
  • Erdoğan, A., Görken, M., Kabadayı, A., & Temizel, S. (2022). Evaluation of green areas with remote sensing and GIS: A case study of Yozgat city center. Advanced Remote Sensing Journal (ARSEJ), 2(2), 1–9.
  • Guan, H., Li, J., Yu, Y., Chapman, M., & Wang, C. (2014). Automated road information extraction from mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems, 16(1), 194–205. https://doi.org/10.1109/TITS.2014.2326795
  • Gumus, V., Avsaroglu, Y., & Simsek, O. (2022). Streamflow trends in the Tigris river basin using Mann− Kendall and innovative trend analysis methods. Journal of Earth System Science, 131(1), 34. https://doi.org/10.1007/s12040-022-01837-3
  • Gumus, V., Simsek, O., & Seaid, M. (2023). Investigating recent changes in the wind speed trends over Turkey. Acta Geophysica, 71(3), 1305–1319. https://doi.org/10.1007/s11600-023-00938-4
  • Gupta, N., & Bhadauria, H. S. (2014). Object-based information extraction from high-resolution satellite imagery using eCognition. International Journal of Computer Science Issues (IJCSI), 11(3), 139–144.
  • Gürgülü, H., & Ul, M. A. (2024). Different effects of irrigation water salinity and leaching fractions on pepper (Capsicum annuum L.) cultivation in soilless culture. Agriculture, 14(6), 827. https://doi.org/10.3390/agriculture14060827
  • Hossain, K. T., Salauddin, M., & Tanim, I. A. (2016). Assessment of the dynamics of coastal island in Bangladesh using geospatial techniques: Domar Char. Journal of the Asiatic Society of Bangladesh Science, 42, 219–228.
  • Hossain, M. D., & Chen, D. (2019). Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.019
  • Huang, M., Mu, Z., Zhao, S., & Yang, R. (2024). Ecological water requirement of natural vegetation in the Tarim River Basin based on multi-source data. Sustainability, 16(16), 7034. https://doi.org/10.3390/su16167034
  • Kabadayı, A., & Kaya, Y. (2023). Monitoring shoreline and areal change with UAV data. Intercontinental Geoinformation Days, 7, 153–156.
  • Kartal, V., & Emiroglu, M. E. (2024). Hydrological drought and trend analysis in Kızılırmak, Yeşilırmak and Sakarya Basins. Pure and Applied Geophysics, 1–25. https://doi.org/10.1007/s00024-024-03289-9
  • Kaur, R., Bansal, K., Garg, D., Sardana, R., Vishnubhatla, S., Agrawal, S., ... & Seth, A. (2024, July). Assessing the impact of farm ponds on agricultural productivity in Northern India. In Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (pp. 281–293). https://doi.org/10.1145/3457607.3460648
  • Keskiner, A. D., & Simsek, O. (2024). Evaluation of the sensitivity of meteorological drought in the Mediterranean region to different data record lengths. Environmental Monitoring and Assessment, 196(7), 1–29. https://doi.org/10.1007/s10661-023-10949-y
  • Keskiner, A. D., & Şimşek, O. (2023). Olasılıklı meteorolojik kuraklık analizi: Göller yöresinde bir uygulama. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(1), 160–169.
  • Khatami, R., Mountrakis, G., & Stehman, S. V. (2016). A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 177, 89–100. https://doi.org/10.1016/j.rse.2016.02.028
  • Koutsoyiannis, D. (2011). Scale of water resources development and sustainability: Small is beautiful, large is great. Hydrological Sciences Journal, 56(4), 553–575. https://doi.org/10.1080/02626667.2011.579076
  • López-Felices, B., Aznar-Sánchez, J. A., Velasco-Muñoz, J. F., & Piquer-Rodríguez, M. (2020). Contribution of irrigation ponds to the sustainability of agriculture: A review of worldwide research. Sustainability, 12(13), 5425. https://doi.org/10.3390/su12135425
  • Luo, Y., Qin, J., Xiang, X., & Tan, Y. (2020). Coverless image steganography based on multi-object recognition. IEEE Transactions on Circuits and Systems for Video Technology, 31(7), 2779–2791. https://doi.org/10.1109/TCSVT.2020.2975078
  • Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277–293.https://doi.org/10.1016/j.isprsjprs.2017.06.001
  • Malakar, A., Snow, D. D., & Ray, C. (2019). Irrigation water quality—A contemporary perspective. Water, 11(7), 1482. https://doi.org/10.3390/w11071482
  • Merdan, K. (2024). Türkiye’nin tarım sektörü: Tarımının dünü, bugünü ve yarını. Bingöl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(1), 47–70.
  • Oğuz, H. (2016). LST Calculator: A Python tool for retrieving land surface temperature from Landsat 8 imagery. Environmental Sustainability and Landscape Management, 560, 1–14.
  • Orhan, O., Bilgilioglu, S. S., Kaya, Z., Ozcan, A. K., & Bilgilioglu, H. (2022). Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto International, 37(10), 2795–2820. https://doi.org/10.1080/10106049.2021.1955249
  • Ouma, Y., Nkwae, B., Moalafhi, D., Odirile, P., Parida, B., Anderson, G., & Qi, J. (2022). Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 681–689. https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-681-2022
  • Ozturk, M. Y., & Colkesen, I. (2024). A novel hybrid methodology integrating pixel-and object-based techniques for mapping land use and land cover from high-resolution satellite data. International Journal of Remote Sensing, 45(16), 5640-5678.
  • Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
  • Senthilkumaran, N., & Vaithegi, S. (2016). Image segmentation by using thresholding techniques for medical images. Computer Science & Engineering: An International Journal, 6(1), 1–13. https://doi.org/10.5121/cseij.2016.6101
  • Simsek, O., Bazrafshan, O., & Azhdari, Z. (2024a). A 3-D copula for risk analysis of meteorological drought in the Black Sea Region. Theoretical and Applied Climatology, 155(2), 1185–1200. https://doi.org/10.1007/s00704-023-04541-7
  • Simsek, O., Ceyhunlu, A. I., Ceribasi, G., & Keskiner, A. D. (2024b). Evaluation of long-term meteorological drought in the Aras and Coruh Basins with Crossing Empirical Trend Analysis. Physics and Chemistry of the Earth, Parts A/B/C, 135, 103611. https://doi.org/10.1016/j.pce.2024.103611
  • Staccione, A., Broccoli, D., Mazzoli, P., Bagli, S., & Mysiak, J. (2021). Natural water retention ponds for water management in agriculture: A potential scenario in Northern Italy. Journal of Environmental Management, 292, 112849. https://doi.org/10.1016/j.jenvman.2021.112849
  • Şahin, G., & Kahraman, M. (2021). Kent içi tarım uygulamalarında dünyanın en eski örneği: Yedikule Bostanları. Turkish Studies-Social Sciences, 16(1), 401–416.
  • Şen, Z., & Şişman, E. (2024). Risk attachment Sen’s Slope calculation in hydrometeorological trend analysis. Natural Hazards, 120(4), 3239–3252. https://doi.org/10.1007/s11069-023-05852-7
  • Şimşek, O., Soydan Oksal, N. G., Uncu, E. M., Gümüş, V., & Şeker, M. (2024). SYİ yöntemiyle Çoruh havzası uzun dönem (1969–2020) meteorolojik kuraklığının analizi. Politeknik Dergisi, 27(4), 1553–1564.
  • Tehrany, M. S., Pradhan, B., & Jebuv, M. N. (2014). A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International, 29(4), 351–369. https://doi.org/10.1080/10106049.2013.768300
  • Vico, G., Tamburino, L., & Rigby, J. R. (2020). Designing on-farm irrigation ponds for high and stable yield for different climates and risk-coping attitudes. Journal of Hydrology, 584, 124634. https://doi.org/10.1016/j.jhydrol.2020.124634
  • Wei, W., Chen, X., & Ma, A. (2005, July). Object-oriented information extraction and application in high-resolution remote sensing image. In Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'05) (Vol. 6, pp. 3803–3806). https://doi.org/10.1109/IGARSS.2005.1526762
  • Yiğit, A. Y., Kaya, Y., & Şenol, H. İ. (2022). Monitoring the change of Turkey’s tourism city Antalya’s Konyaaltı shoreline with multi-source satellite and meteorological data. Applied Geomatics, 14(2), 223–236. https://doi.org/10.1007/s12518-022-00410-9
  • Zaman, M., Shahid, S. A., & Heng, L. (2018). Irrigation water quality. In Guideline for salinity assessment, mitigation and adaptation using nuclear and related techniques (pp. 113–131). Springer. https://doi.org/10.1007/978-3-319-96190-3_6

Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi

Year 2024, Volume: 6 Issue: 2, 68 - 84

Abdurahman Yasin Yiğit , Oğuz Şimşek , Halil İbrahim Şenol

https://doi.org/10.51489/tuzal.1551019

Abstract

Bu çalışma, Türkiye’nin Akdeniz Bölgesi’nde yer alan Mersin ili örneğinde, tarımsal sulama göletlerinin bölgesel iklim, bitki örtüsü ve su kaynakları üzerindeki uzun vadeli etkilerini uydu görüntüleri ve meteorolojik verilerle incelemektedir. Çalışmanın metodolojisi, 1985-2023 yılları arasındaki Landsat uydu verileri ile meteorolojik veri setlerinin entegrasyonuna dayanmaktadır. Arazi sınıflandırması için nesne tabanlı görüntü işleme teknikleri kullanılarak bitki örtüsünün ve su kütlelerinin değişimleri haritalanmış, sınıflandırma doğruluğu hata matrisi ve farklı doğruluk metrikleriyle değerlendirilmiştir. Ayrıca, kuraklık analizinde Standartlaştırılmış Yağış İndeksi kullanılarak Mann-Kendall, Spearman Rho ve Sen Slope gibi trend analiz yöntemleriyle kuraklık eğilimleri incelenmiştir. Sonuçlar, 1985 yılında 51 olan sulama göleti sayısının 2023'te 1935’e çıktığını ve bu artışın NDVI değerlerindeki yükselişle birlikte bitki örtüsünün korunmasına katkı sağladığını ortaya koymaktadır. Kuraklık analizleri, çalışma bölgesinde kurak dönemlerin bitki örtüsüne olan olumsuz etkilerinin sulama göletleri sayesinde azaldığını göstermektedir. Özellikle mikro iklim üzerinde düzenleyici bir etkisi olan bu göletler, yarı kurak bölgelerde tarımsal üretim sürdürülebilirliği için stratejik bir su yönetimi aracı olarak değerlendirilmektedir. Bu bulgular, tarımsal sulama göletlerinin su kaynaklarının sürdürülebilir yönetimi, iklim değişikliği ile mücadele ve çevresel direnci artırma gibi konularda önemli bir potansiyele sahip olduğunu göstermektedir. Bu kapsamda, yarı kurak ve kurak bölgelerde sulama göletlerinin sayısının artırılması ve bu yapıların planlamasında iklim dostu yaklaşımlar benimsenmesi önerilmektedir.

Keywords

Kuraklık Kuraklık Analizi Tarımsal Sulama Göletleri Arazi Sınıflandırması Mikro İklim Düzenlemesi

References

  • Abera, A., Verhoest, N. E., Tilahun, S., Inyang, H., & Nyssen, J. (2021). Assessment of irrigation expansion and implications for water resources by using RS and GIS techniques in the Lake Tana Basin of Ethiopia. Environmental Monitoring and Assessment, 193, 1–17. https://doi.org/10.1007/s10661-021-08924-2
  • Achite, M., Simsek, O., Adarsh, S., Hartani, T., & Caloiero, T. (2023). Assessment and monitoring of meteorological and hydrological drought in semiarid regions: The Wadi Ouahrane basin case study (Algeria). Physics and Chemistry of the Earth, Parts A/B/C, 130, 103386. https://doi.org/10.1016/j.pce.2022.103386
  • Adanalı, T. (2022). Rekreasyon alanlarında kullanılan farklı sulama yöntemlerinin topraktaki tuz dağılımına etkisi [Master's thesis, Tekirdağ Namık Kemal Üniversitesi].
  • Aktaş, Ö. (2014). Impacts of climate change on water resources in Turkey. Environmental Engineering & Management Journal (EEMJ), 13(4), 799–810.
  • Andriushchenko, K., Datsii, O., Aleinikova, O., Abdulla, A. M., & Ali, A. M. (2019). Improvement of the water resources management system at the territorial level. Problems and Perspectives in Management, 17(3), 421–430. https://doi.org/10.21511/ppm.17(3).2019.34
  • Angelakis, A. N., Zaccaria, D., Krasilnikoff, J., Salgot, M., Bazza, M., Roccaro, P., ... & Fereres, E. (2020). Irrigation of world agricultural lands: Evolution through the millennia. Water, 12(5), 1285. https://doi.org/10.3390/w12051285
  • Atici, A., Paksoy, M. F., & Kabadayı, A. (2024). Maden sahalarındaki stok miktarının İHA yardımıyla belirlenmesi. Türkiye Fotogrametri Dergisi, 6(1), 8–13. https://doi.org/10.53030/tufod.1489122
  • Baatz, M. (2000). Multiresolution segmentation: An optimization approach for high-quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung, 12–23.
  • Bandyopadhyay, J., Rahaman, S. H., & Karan, C. (2023). Agricultural potential zone mapping with surface water resource management using geo-spatial tools for Jhargram district, West Bengal, India. Knowledge-Based Engineering and Sciences, 4(1), 1-18.
  • Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258. https://doi.org/10.1016/j.isprsjprs.2003.10.002
  • Bergsjö, J. (2014). Object-based change detection in urban area using KTH-SEG. KTH Royal Institute of Technology, Sweden.
  • Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., ... & Tiede, D. (2014). Geographic object-based image analysis: Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014
  • Blaschke, T., Hay, G. J., Weng, Q., & Resch, B. (2011). Collective sensing: Integrating geospatial technologies to understand urban systems—An overview. Remote Sensing, 3(8), 1743–1776. https://doi.org/10.3390/rs3081743
  • Carleer, A. P., & Wolff, E. (2006). Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features. International Journal of Remote Sensing, 27(6), 1035–1051. https://doi.org/10.1080/01431160500219302
  • Çelebioğlu, T., & Tayanç, M. (2024). A study on precipitation trends in Türkiye via linear regression analysis and non-parametric Mann-Kendall test. Sürdürülebilir Çevre Dergisi, 4(1), 19–28.
  • Definiens. (2012). Definiens Developer XD 2.0.4: Reference Book. Definiens AG, München, Germany. Retrieved from https://www.imperial.ac.uk/media/imperialcollege/medicine/facilities/film/Definiens-Developer-Reference-Book-XD-2.0.4.pdf
  • Erdoğan, A., Görken, M., Kabadayı, A., & Temizel, S. (2022). Evaluation of green areas with remote sensing and GIS: A case study of Yozgat city center. Advanced Remote Sensing Journal (ARSEJ), 2(2), 1–9.
  • Guan, H., Li, J., Yu, Y., Chapman, M., & Wang, C. (2014). Automated road information extraction from mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems, 16(1), 194–205. https://doi.org/10.1109/TITS.2014.2326795
  • Gumus, V., Avsaroglu, Y., & Simsek, O. (2022). Streamflow trends in the Tigris river basin using Mann− Kendall and innovative trend analysis methods. Journal of Earth System Science, 131(1), 34. https://doi.org/10.1007/s12040-022-01837-3
  • Gumus, V., Simsek, O., & Seaid, M. (2023). Investigating recent changes in the wind speed trends over Turkey. Acta Geophysica, 71(3), 1305–1319. https://doi.org/10.1007/s11600-023-00938-4
  • Gupta, N., & Bhadauria, H. S. (2014). Object-based information extraction from high-resolution satellite imagery using eCognition. International Journal of Computer Science Issues (IJCSI), 11(3), 139–144.
  • Gürgülü, H., & Ul, M. A. (2024). Different effects of irrigation water salinity and leaching fractions on pepper (Capsicum annuum L.) cultivation in soilless culture. Agriculture, 14(6), 827. https://doi.org/10.3390/agriculture14060827
  • Hossain, K. T., Salauddin, M., & Tanim, I. A. (2016). Assessment of the dynamics of coastal island in Bangladesh using geospatial techniques: Domar Char. Journal of the Asiatic Society of Bangladesh Science, 42, 219–228.
  • Hossain, M. D., & Chen, D. (2019). Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.019
  • Huang, M., Mu, Z., Zhao, S., & Yang, R. (2024). Ecological water requirement of natural vegetation in the Tarim River Basin based on multi-source data. Sustainability, 16(16), 7034. https://doi.org/10.3390/su16167034
  • Kabadayı, A., & Kaya, Y. (2023). Monitoring shoreline and areal change with UAV data. Intercontinental Geoinformation Days, 7, 153–156.
  • Kartal, V., & Emiroglu, M. E. (2024). Hydrological drought and trend analysis in Kızılırmak, Yeşilırmak and Sakarya Basins. Pure and Applied Geophysics, 1–25. https://doi.org/10.1007/s00024-024-03289-9
  • Kaur, R., Bansal, K., Garg, D., Sardana, R., Vishnubhatla, S., Agrawal, S., ... & Seth, A. (2024, July). Assessing the impact of farm ponds on agricultural productivity in Northern India. In Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (pp. 281–293). https://doi.org/10.1145/3457607.3460648
  • Keskiner, A. D., & Simsek, O. (2024). Evaluation of the sensitivity of meteorological drought in the Mediterranean region to different data record lengths. Environmental Monitoring and Assessment, 196(7), 1–29. https://doi.org/10.1007/s10661-023-10949-y
  • Keskiner, A. D., & Şimşek, O. (2023). Olasılıklı meteorolojik kuraklık analizi: Göller yöresinde bir uygulama. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(1), 160–169.
  • Khatami, R., Mountrakis, G., & Stehman, S. V. (2016). A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 177, 89–100. https://doi.org/10.1016/j.rse.2016.02.028
  • Koutsoyiannis, D. (2011). Scale of water resources development and sustainability: Small is beautiful, large is great. Hydrological Sciences Journal, 56(4), 553–575. https://doi.org/10.1080/02626667.2011.579076
  • López-Felices, B., Aznar-Sánchez, J. A., Velasco-Muñoz, J. F., & Piquer-Rodríguez, M. (2020). Contribution of irrigation ponds to the sustainability of agriculture: A review of worldwide research. Sustainability, 12(13), 5425. https://doi.org/10.3390/su12135425
  • Luo, Y., Qin, J., Xiang, X., & Tan, Y. (2020). Coverless image steganography based on multi-object recognition. IEEE Transactions on Circuits and Systems for Video Technology, 31(7), 2779–2791. https://doi.org/10.1109/TCSVT.2020.2975078
  • Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277–293.https://doi.org/10.1016/j.isprsjprs.2017.06.001
  • Malakar, A., Snow, D. D., & Ray, C. (2019). Irrigation water quality—A contemporary perspective. Water, 11(7), 1482. https://doi.org/10.3390/w11071482
  • Merdan, K. (2024). Türkiye’nin tarım sektörü: Tarımının dünü, bugünü ve yarını. Bingöl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(1), 47–70.
  • Oğuz, H. (2016). LST Calculator: A Python tool for retrieving land surface temperature from Landsat 8 imagery. Environmental Sustainability and Landscape Management, 560, 1–14.
  • Orhan, O., Bilgilioglu, S. S., Kaya, Z., Ozcan, A. K., & Bilgilioglu, H. (2022). Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto International, 37(10), 2795–2820. https://doi.org/10.1080/10106049.2021.1955249
  • Ouma, Y., Nkwae, B., Moalafhi, D., Odirile, P., Parida, B., Anderson, G., & Qi, J. (2022). Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 681–689. https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-681-2022
  • Ozturk, M. Y., & Colkesen, I. (2024). A novel hybrid methodology integrating pixel-and object-based techniques for mapping land use and land cover from high-resolution satellite data. International Journal of Remote Sensing, 45(16), 5640-5678.
  • Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
  • Senthilkumaran, N., & Vaithegi, S. (2016). Image segmentation by using thresholding techniques for medical images. Computer Science & Engineering: An International Journal, 6(1), 1–13. https://doi.org/10.5121/cseij.2016.6101
  • Simsek, O., Bazrafshan, O., & Azhdari, Z. (2024a). A 3-D copula for risk analysis of meteorological drought in the Black Sea Region. Theoretical and Applied Climatology, 155(2), 1185–1200. https://doi.org/10.1007/s00704-023-04541-7
  • Simsek, O., Ceyhunlu, A. I., Ceribasi, G., & Keskiner, A. D. (2024b). Evaluation of long-term meteorological drought in the Aras and Coruh Basins with Crossing Empirical Trend Analysis. Physics and Chemistry of the Earth, Parts A/B/C, 135, 103611. https://doi.org/10.1016/j.pce.2024.103611
  • Staccione, A., Broccoli, D., Mazzoli, P., Bagli, S., & Mysiak, J. (2021). Natural water retention ponds for water management in agriculture: A potential scenario in Northern Italy. Journal of Environmental Management, 292, 112849. https://doi.org/10.1016/j.jenvman.2021.112849
  • Şahin, G., & Kahraman, M. (2021). Kent içi tarım uygulamalarında dünyanın en eski örneği: Yedikule Bostanları. Turkish Studies-Social Sciences, 16(1), 401–416.
  • Şen, Z., & Şişman, E. (2024). Risk attachment Sen’s Slope calculation in hydrometeorological trend analysis. Natural Hazards, 120(4), 3239–3252. https://doi.org/10.1007/s11069-023-05852-7
  • Şimşek, O., Soydan Oksal, N. G., Uncu, E. M., Gümüş, V., & Şeker, M. (2024). SYİ yöntemiyle Çoruh havzası uzun dönem (1969–2020) meteorolojik kuraklığının analizi. Politeknik Dergisi, 27(4), 1553–1564.
  • Tehrany, M. S., Pradhan, B., & Jebuv, M. N. (2014). A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International, 29(4), 351–369. https://doi.org/10.1080/10106049.2013.768300
  • Vico, G., Tamburino, L., & Rigby, J. R. (2020). Designing on-farm irrigation ponds for high and stable yield for different climates and risk-coping attitudes. Journal of Hydrology, 584, 124634. https://doi.org/10.1016/j.jhydrol.2020.124634
  • Wei, W., Chen, X., & Ma, A. (2005, July). Object-oriented information extraction and application in high-resolution remote sensing image. In Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'05) (Vol. 6, pp. 3803–3806). https://doi.org/10.1109/IGARSS.2005.1526762
  • Yiğit, A. Y., Kaya, Y., & Şenol, H. İ. (2022). Monitoring the change of Turkey’s tourism city Antalya’s Konyaaltı shoreline with multi-source satellite and meteorological data. Applied Geomatics, 14(2), 223–236. https://doi.org/10.1007/s12518-022-00410-9
  • Zaman, M., Shahid, S. A., & Heng, L. (2018). Irrigation water quality. In Guideline for salinity assessment, mitigation and adaptation using nuclear and related techniques (pp. 113–131). Springer. https://doi.org/10.1007/978-3-319-96190-3_6
There are 54 citations in total.

Details

Primary Language Turkish
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Abdurahman Yasin Yiğit 0000-0002-9407-8022

Oğuz Şimşek 0000-0001-6324-0229

Halil İbrahim Şenol 0000-0003-0235-5764

Early Pub Date December 18, 2024
Publication Date
Submission Date September 16, 2024
Acceptance Date November 28, 2024
Published in Issue Year 2024 Volume: 6 Issue: 2

Cite

APA Yiğit, A. Y., Şimşek, O., & Şenol, H. İ. (2024). Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi. Türkiye Uzaktan Algılama Dergisi, 6(2), 68-84. https://doi.org/10.51489/tuzal.1551019
AMA Yiğit AY, Şimşek O, Şenol Hİ. Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi. TUZAL. December 2024;6(2):68-84. doi:10.51489/tuzal.1551019
Chicago Yiğit, Abdurahman Yasin, Oğuz Şimşek, and Halil İbrahim Şenol. “Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri Ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi”. Türkiye Uzaktan Algılama Dergisi 6, no. 2 (December 2024): 68-84. https://doi.org/10.51489/tuzal.1551019.
EndNote Yiğit AY, Şimşek O, Şenol Hİ (December 1, 2024) Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi. Türkiye Uzaktan Algılama Dergisi 6 2 68–84.
IEEE A. Y. Yiğit, O. Şimşek, and H. İ. Şenol, “Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi”, TUZAL, vol. 6, no. 2, pp. 68–84, 2024, doi: 10.51489/tuzal.1551019.
ISNAD Yiğit, Abdurahman Yasin et al. “Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri Ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi”. Türkiye Uzaktan Algılama Dergisi 6/2 (December 2024), 68-84. https://doi.org/10.51489/tuzal.1551019.
JAMA Yiğit AY, Şimşek O, Şenol Hİ. Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi. TUZAL. 2024;6:68–84.
MLA Yiğit, Abdurahman Yasin et al. “Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri Ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi”. Türkiye Uzaktan Algılama Dergisi, vol. 6, no. 2, 2024, pp. 68-84, doi:10.51489/tuzal.1551019.
Vancouver Yiğit AY, Şimşek O, Şenol Hİ. Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi. TUZAL. 2024;6(2):68-84.
 Download Cover Image

Flag Counter