Identification of Agricultural Land Types in Abbas Plain Using Time Series Analysis of Sentinel 2 Satellite Imagery

Subject Areas : Environmental managment

Mahdi Rezaei ¹ , Hosein Agha mhammadi zanjir abad ² , Zahra Azizi ³ , Alireza Vafaee nejhad ⁴ , Saeid Behzadi ⁵

1 - PhD Student, Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Assistant Professor, Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran. *(Corresponding author)
3 - Assistant Professor, Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University of Tehran, Tehran, Iran.
5 - Assistant Professor in Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

Received: 2022-12-07 Accepted : 2023-05-27 Published : 2023-12-22

Keywords: Time series, NDVI, Irrigated Lands, Image, Sentinel,

Abstract :

Background and Objective: Agricultural land is a crucial source of fresh water, with semi-deep and deep wells serving as the primary water supply for irrigated lands. Effective management and monitoring of these lands is essential for sustainable water consumption. However, identifying unlicensed agricultural wells among licensed ones through land visits can be challenging due to various factors such as the large number of lands, weather conditions, and difficult access to remote areas. Material and Methodology: Remote sensing and geographic information systems can provide a quick and highly accurate solution for discovering and monitoring these lands. In this study, we utilized the NDVI index and time series data set of Sentinel 2 images (bands 4 and 8) for March, April, and May to classify a portion of Abbas Dehlran plain's lands in the water year 2022 using the SVM method. We validated our results using cadastre data. Findings: Our classification accuracy based on cadastre maps was 98.4% for irrigated lands and 86.7% for rainfed lands, indicating high accuracy in identifying different types of agricultural land. Discussion and Conclusion: Our study demonstrates that time series algorithms applied to satellite images can effectively identify illegal irrigated lands along with their corresponding water supply wells by evaluating results based on existing land cadastres in the region. This approach can help improve management practices for sustainable water consumption in agricultural areas.

References:

1. A Khalil Diab Al-Gharibawi, Z Azizi., 2021: Analysis of long-term dynamics of Lake Milh based on satellite imagery, Journal of Meteorology and Atmospheric Science, 4(1): 67-74. (In Persian)


2. Alibakhshi T, Azizi Z, Vafaeinezhad A, Aghamohammadi H. 2020. Survey of Area Changes in Water Basins of Shahid Abbaspour Dam Caused by 2019 Floods Using Google Earth Engine. Iranian Journal of Ecohydrology, 7(2): 345-357. (In Persian)


3. I Mohammad Jani, N Yazdanian,  2014. The Analysis of Water Crisis Conjecture in Iran and The Exigent Measures for Its Management, 21(65), 117. (In Persian)


4. Iran Economy, 2016 water crisis: depth, roots and solutions, number 209, pp.20-26. (In Persian)


5. Yazdan Panah M, Zabidi T, Romina F.Z, 2019.Factors influencing the drilling of unauthorized agricultural wells in Dashtestan city. Space economy and rural development. 8 (27):203-222. (In Persian)


6. Chavoshi, H, 2014. Iranian economy, illegal wells. Farda business weekly. 3: 101. (In Persian)


7. Huaqiao, Xing; Bingyao, Chen; Yongyu, Feng; Dongyang, Hou; Xue, Wang; Yawei, Kong; 2022. Mapping irrigated, rainfed and paddy croplands from time-series Sentinel-2 images by integrating pixel-based classification and image segmentation on Google Earth Engine, Geocarto International, 10, 1-20.


8. Kanjir U, Đurić N, Veljanovski T. Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring. ISPRS International Journal of Geo-Information. 2018; 7(10):405. https://doi.org/10.3390/ijgi7100405


9. Erdanaev, E; Kappas, M; Wyss, D; 2022. The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan, International Journal of Geoinformatics, 18, 37-53.


10. Delfan E, Naqvi H, Malik Nia R, Nouraldini S.A, 2017. Investigating the effectiveness of Sentinel 2 satellite images and non-parametric classification methods in preparing land use maps, the first national conference on applied research in science and engineering, Mashhad, Iran. (In Persian)


11. Akhwan Fomenis, S. Dost M., 1398. Investigating the effectiveness of Sentinel 2 satellite images in the preparation of land use maps, the 5th international conference on agricultural and environmental engineering with a sustainable development approach, Shiraz, Iran. (In Persian)


12. Ghodsi, Z., Kheirkhah Zarkesh, M. M., Ghermezcheshmeh, B. (2021). 'Comparison of Accuracy Between Support Vector Machine and Random Forest Classifiers for Land Use and Crop Mapping Using Multi-Temporal Sentinel-2 Images', Iranian Journal of Remote Sensing & GIS, 12(4), pp. 73-92. doi: 10.52547/gisj.12.4. (In Persian)


13. Mousavi Seyedi, S. R., & Miri, S. M. R. (2022). Numerical Simulation of the Performance and Emission of a Diesel Engine with Diesel-biodiesel Mixtures. Journal of Agricultural Machinery, 12(4), 559-574. (In Persian)


14. Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1973) Monitoring Vegetation Systems in the Great Plains with ERTS (Earth Resources Technology Satellite). Proceedings of 3rd Earth Resources Technology Satellite Symposium, Greenbelt, 10-14 December, SP-351, 309-317.


15. S Haydari, A.R Salehi, Z Azizi, S Firoozinezhad, 2010. The pattern of stands Persian oak decline using time series Landsat satellite images (Case Study Basht forest of Iran), Lebanese Scientific Journal, 19(1): 67-73.


16. Snevajs H, Charvat K, Onckelet V, Kvapil J, Zadrazil F, Kubickova H, Seidlova J, Batrlova I. Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems. Remote Sensing. 2022; 14(5):1095. https://doi.org/10.3390/rs14051095


17. Z Azizi, A Najafi, P Fatehi, M Pirbavaghar, 2010. Forest stand volume estimation using satellite IRS_P6 (LISS_IV) data (Case study: Lirehsar, Tonekabon), Iranian Journal of Forest and Poplar Research,18(1): 151-143. (In Persian)


18. M Mafi, Z Azizi, P Karimi, P Alemi Safaval, 2021. Investigating the trend of water level changes in Allahabad wetland by using temporal images, Iranian journal of Ecohydrology, 8(2): 321-329. (In Persian)


19. Kobayashi, N.; Tani, H.; Wang, X.; Sonobe, R. Crop classification using spectral indices derived from Sentinel-2A imagery. J. Inf. Telecommun. 2020, 4, 67–90.


20. Xu, L.; Zhang, H.; Wang, C.; Zhang, B.; Liu, M. Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data. Remote Sens. 2018, 11, 53.