Estimation of vegetation level and agricultural lands of Babolsar city through Sentinel 2 satellite images using NDVI index
Subject Areas :
Plant Zonation
Mohammad reza yousefiroshan
1
,
Hamid Amounia
2
1 - Department of Geography Education, Farhangian University, Tehran, Iran. *(Corresponding Authors)
2 - Department of Geography Education, Farhangian University, Tehran, Iran.
Received: 2021-11-21
Accepted : 2022-06-29
Published : 2023-10-23
Keywords:
Sentinel 2,
satellite imagery,
Crop area,
NDVI Index,
Abstract :
Background and Objective: Quantitative and qualitative monitoring of vegetation is one of the main applications of remote sensing knowledge. Estimating the area of vegetation and agricultural lands in the traditional way is time consuming. Remote sensing is of great importance for providing up-to-date information, duplicate coverage, sensing in different spectral ranges, quantitative and digital sensing of surface phenomena. This study was conducted to estimate the level of vegetation and agricultural lands of Babolsar county through Sentinel 2 satellite images using NDVI index.
Material and Methodology: The present research method is descriptive-analytical based on the data of Sentinel 2 satellite images of 2019. These images were extracted from the USGS website. After extraction, the images are processed in remote sensing software systems and geographic information systems, and the required results have been extracted.
Findings: After receiving the Sentinel 2 satellite images, the bands 2, 3, 4, 8, which have a spatial resolution of 10 meters, after preprocessing and atmospheric corrections, based on IDL programming language, using NDVI index, Sentinel 2 ROI were obtained, then output. Threshold was calculated between a maximum of 1 and a minimum of 0.5 and with a Shape file output in ENVI and Arc Map software, the area of vegetation and the area under cultivation of agricultural lands in Babolsar county were calculated and concluded on the date of obtaining satellite images.
Discussion and Conclusion: The results showed that the area of arable land and vegetation of Babolsar city is estimated to be more than 52%. Overall accuracy of 95% and kappa coefficient of 89% by evaluating the output of NDVI index showed that Sentinel 2 images can be used to extract vegetation and arable lands.
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Luimi, Naeem, Akram Assadaleh, Bagheri Nikroz and Haji Ahmad, Ali (2021), Evaluation of several spectral indices for estimating canola yield using sentinel-2 sensor images, Journal of Agricultural Machinery, Volume 11, Number 2, Second Semester, pp. 464-447. (In Persian)
Canadian Remote Sensing Center (2019), Fundamentals of Applied Remote Sensing, translated by Valizadeh Kamran, Khalil and Mahdavi Fard, Mostafa, Tehran, Satellite Publications. (In Persian)
Daroi, Parasto, Riahi, Vahid, Ziaian Firoozabadi, Parviz and Azizpour, Farhad (2019), Determining the area under cultivation of agricultural products in Lenjanat region using satellite images, Journal of Applied Research in Geographical Sciences, Nineteenth Year, No. 52., Spring, 169-147. (In Persian)
Alavi Panah, Kazem (2010). Application of remote sensing in earth sciences, University of Tehran Press, third edition. (In Persian)
Mcbratney, A., B. Whelan, T. Ancev, and J. Bouma. 2005. Future Directions of Precision Agriculture. Journal of Precision Agriculture 6 (1): 7-23.
Yan, Y., (2003), “Object-based Classification of Remote Sensing Data for change detection”, elsevier.com.
Alipour, Farideh, Mohammad Hossein Aghkhani, Mohammad Hassan Abbaspourfard and Adel Sepehr (2014), Separation of range and area estimation of agricultural crops using ETM + satellite images (Case study: Astan Quds Razavi sample farm), Journal of Agricultural Machinery, 4 (2) , pp. 254-244. (In Persian)
Matkan, Ali Akbar, Davood Ashourlou, Gholampour, Ali, Aghighi, Hossein, Hosseini Asl, Amin and Ashourlou, Morteza (2009), Presenting an index for extracting wheat cultivated lands with remote sensing data, Journal of Agriculture, No. 84: 72-66. (In Persian)
Ashourloo, Morteza, Ali Mohammadi, Abbas, Rezaian Parviz and Ashourloo, Davood (2006), Application of Linear Diagnosis Analysis in Separation of Wheat from Other Products on Satellite Images, Environmental Sciences, Fourth Year, No. 2, pp. 116-101. (In Persian)
Pourkhbaz, Hamidreza, Mohammadyari, Fatemeh, Tavakoli Morteza and Aghdar, Hossein (2014), Preparation of vegetation mapping and monitoring of its changes using remote sensing techniques and GIS (Case study of Behbahan city), Quarterly Journal of Information Geography (Sepehr) Volume 23, Number 92, Pages 34-23. (In Persian)
Falahatkar, Sara (2008), Detection of Isfahan land cover changes using remote sensing and GIS, Master Thesis, Faculty of Natural Resources, Isfahan University of Technology. (In Persian)
Adamchuk, V.I., Perk, R.L, & Schepers, J. S, 2003. Applications of remote sensing in site-specific management, University of Nebraska Cooperative Extension Publication EC, (2003): 03-702.
LI, W. G., Hua, L. I., & ZHAO, L. H. (2011). Estimating rice yield by HJ-1A satellite images. Rice Science, 18(2), 142-147.
Alavi Panah et al. (2006). Investigation of spectral variability of different cover and water phenomena using remote sensing, Journal of Geographical Research, No. 58, pp. 97-81. (In Persian)
Pettorelli, N., Vik, J.O, Mysterud, A, Gaillard, J.M, Tucker, C.J, & Stenseth, N.C, 2005. Using the satellite –derived NDVI to assess ecological responses to environmental change.J, Trends in ecology and evolution. Vol.20 No.9.
Johnson, D. M. 2016. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geo information 52: 65-81.
Alavipanah, S. K. 2016. Fundamentals of modern remote sensing and interpretation of Satellite images and aerial photos. University of Tehran. Tehran. (In Persian)
Raun, W. R., J. B. Solie, M. L. Stone, E. V. Lukina, W. E. Thomason, and J. S. Schepers. 2001. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93: 131-138.
Gonsamo and J. M. Chen, ―Spectral Response Function Comparability Among21Satellite Sensors for Vegetation Monitoring,‖ IEEE Trans. Geosci. Remote Sens., vol. 51, no. 3, pp. 1319–1335, Mar.
Funk, C., Budd, M. E.,(2009). Phenologically-Tuned MODIS NDVI-based production nomaly estimates for Zimbabwe, Remote Sensing of Environment, 113, 115-
Yamani, Mojtaba, Mazidi, Mohammad Ali (2008), Survey of changes in surface and vegetation of Siahkuh desert using remote sensing data, Journal of Geographical Research, No. 64, pp. 12-1. (In Persian)
Shafiei, Hosseini, Hamed, Seyed Mahmoud (2012), Survey of vegetation using satellite data in Sistan region, Journal of Plant Ecophysiology, Third Year, pp. 105-91. (In Persian)
Falahtkar, Samarra, Saberfar Rahimeh, Kia, Seyed Hossein (2015), Analysis of changes in vegetation indices in Landsat satellite sensors (Case study: East junipers of Golestan National Park and Qarkhod Protected Area), Iranian Quarterly Journal of Natural Ecosystems, Year Ninth, first issue, consecutive 31, spring, pages 91-71. (In Persian)
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. 2016. Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m.
Languille, F., A. Gaudel1, B. Vidal, R. Binet, V. Poulain, and T. Trémas, 2017. Sentinel-2B Image Quality commissioning phase results and Sentinel2 constellation performances. Conference on Sensors, Systems, and Next-Generation Satellites XXI Location: Warsaw, POLAND Date: SEP 11-14, 2017.
