Journals / Eurasian Journal of Agricultural Research / 2026 / Volume: 10 Issue: 1

Monitoring Agricultural Vegetation Change in Antalya Using Multi-Temporal NDVI and GIS Analysis

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Publication Date
2026-06-30
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AI Summary & Key Takeaways

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

Quantitative assessments reveal that the regional mean Normalized Difference Vegetation Index (NDVI) declined from 0.51 to 0.43, marking a 15.7% overall reduction.

📌 3 Key Takeaways

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Objective & Hypothesis:This study quantifies the spatiotemporal dynamics of agricultural vegetation in Antalya Province, a critical region hosting approximately 41% to 43% of Türkiye's total greenhouse area and providing 20% of the nation's fresh produce exports.
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Key Findings & Data:Utilizing multi-temporal Landsat imagery and Geographic Information Systems (GIS) spatial analysis between 2013 and 2025, the research identifies a statistically significant degradation and spatial fragmentation of agricultural landscapes.
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Conclusion & Impact:Quantitative assessments reveal that the regional mean Normalized Difference Vegetation Index (NDVI) declined from 0.51 to 0.43, marking a 15.7% overall reduction.
🔬Design:Modeling & Simulation
📍Scope:2013 Verileri
⚙️Variables:Remote Sensing · NDVI · Antalya

Abstract

This study quantifies the spatiotemporal dynamics of agricultural vegetation in Antalya Province, a critical region hosting approximately 41% to 43% of Türkiye's total greenhouse area and providing 20% of the nation's fresh produce exports. Utilizing multi-temporal Landsat imagery and Geographic Information Systems (GIS) spatial analysis between 2013 and 2025, the research identifies a statistically significant degradation and spatial fragmentation of agricultural landscapes. Quantitative assessments reveal that the regional mean Normalized Difference Vegetation Index (NDVI) declined from 0.51 to 0.43, marking a 15.7% overall reduction. This decline is supported by a robust linear regression model demonstrating a steady vegetation loss of -0.0067 NDVI units annually (R² = 0.81, p 0.60) dropped precipitously from 18.5% to 9.7%. Conversely, "low vegetation" classes (<0.20) nearly doubled, expanding from 12.8% to 24.6%. Spatial difference analysis demonstrates that 38.6% of the total agricultural area experienced negative NDVI anomalies exceeding -0.10, with 14.2% enduring severe degradation (<-0.20). In contrast, only 11.8% of the landscape exhibited positive NDVI changes, primarily isolated within intensively managed greenhouse zones. Spatial synthesis strongly correlates the most severe vegetation reductions with rapid peri-urban development and expanding coastal infrastructure. Ultimately, these data-driven findings underscore the immediate threat of uncontrolled urban sprawl on Mediterranean agricultural productivity, highlighting the urgent need for sustainable land-use planning and continuous environmental monitoring.

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Cite This Article

Soydan, O. (2026). Monitoring Agricultural Vegetation Change in Antalya Using Multi-Temporal NDVI and GIS Analysis. Eurasian Journal of Agricultural Research, Volume: 10 Issue: 1. https://ajindex.com/articles/1481464

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References (19)

  1. Aslan N. & Koc-San D. 2020. Spatiotemporal land use change analysis and future urban growth simulation using remote sensing: A case study of Antalya, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020, 657-662.
  2. Aslan N. & Koc-San D. 2023. The effects of land cover changes on land surface temperatures, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023, 1311-1317.
  3. Cinar I., Ardahanlioglu Z.R. & Toy S. 2024. Land use/land cover changes in a Mediterranean summer tourism destination in Turkey, Sustainability, 16(4), 1480.
  4. Celik M.A., Bilik A., Akiner M.E. & Gemeda D.O. 2025. How do land use/cover changes influence air quality in Türkiye? A satellite-based assessment, Land, 14(10), 1945.
  5. Cetin M., Ozenen Kavlak M., Senyel Kurkcuoglu M.A., Bilge Ozturk G., Cabuk S.N. & Cabuk A. 2024. Determination of land surface temperature and urban heat island effects with remote sensing capabilities: the case of Kayseri, Türkiye, Natural Hazards, 120(6), 5509-5536.
  6. Cevik Degerli B. & Cetin M. 2022. Evaluation from rural to urban scale for the effect of NDVI-NDBI indices on land surface temperature, in Samsun, Türkiye, Turkish Journal of Agriculture - Food Science and Technology, 10(12), 2446-2452.
  7. Coslu M. 2025. Spatial evaluation of urban green space-population relationship using Sentinel-2 satellite data and object-based image analysis: A case study Antalya, Turkish Journal of Nature and Science, 14(3), 202-217.
  8. Gungor R., Sanli F.B., Ates A.M. & Yilmaz O.S. 2026. Evaluation of classification accuracy of spectral indices and machine learning algorithms for greenhouse detection using the Google Earth Engine platform, Journal of Agricultural Sciences, 32(1), 158-176.
  9. Ghosh S., Kumar D. & Kumari R. 2022. Assessing spatiotemporal dynamics of land surface temperature and satellite-derived indices for new town development and suburbanization planning, Urban Governance, 2(1), 144-156.
  10. Hassan H.M. & Dakheel H.S. 2023. Using the Normalized Difference Vegetation Index (NDVI) to study the change of vegetation cover in Thi-Qar Governorate, southern Iraq for the period from 1990-2022, Texas Journal of Agriculture and Biological Sciences, 13, 72-85
  11. Inalpulat M. 2024. Prediction of greenhouse area expansion in an agricultural hotspot using Landsat imagery, machine learning and the Markov–FLUS model, Sustainability, 16(19), 8456.
  12. Koc-San D. & Sonmez N.K. 2016. Plastic and glass greenhouses detection and delineation from WorldView-2 satellite imagery, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B7, 257-262.
  13. Kumar V., Bharti B., Singh H.P., Kumar H. & Kujur S.P. 2023. Identification of environmental epidemiology through advanced remote sensing based on NDVI. In: Advanced Remote Sensing for Urban and Landscape Ecology, Springer Nature Singapore, Singapore, pp. 129-142.
  14. Lakshmi V., Kir E.G., Kir A. & Fang B. 2025. Remote sensing-based monitoring of agricultural drought and irrigation adaptation strategies in the Antalya basin, Türkiye, Hydrology, 12(11), 288. Li B., Xing H., Cao D., Yang G. & Zhang H. 2022. Exploring the effects of roadside vegetation on the urban thermal environment using street view images, International Journal of Environmental Research and Public Health, 19(3), 1272.
  15. Mohamed M.A. 2021. Spatiotemporal impacts of urban land use/land cover changes on land surface temperature: A comparative study of damascus and aleppo (syria). Atmosphere, 12(8), 1037. Moussa Kourouma J., Eze E., Negash E., Phiri D., Vinya R., Girma A. & Zenebe A. 2021. Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach, Geomatics, Natural Hazards and Risk, 12(1), 2880-2903.
  16. Ozelkan E. & Eren E. 2025. Assessing urban sprawl and agricultural land loss: A 40-year remote sensing study in Çanakkale, Journal of Contemporary Urban Affairs, 9(2), 402-425.
  17. Serrano J., Shahidian S., Paixão L., Marques da Silva J., Morais T., Teixeira R. & Domingos T. 2021. Spatiotemporal patterns of pasture quality based on NDVI time-series in Mediterranean Montado ecosystem, Remote Sensing, 13(19), 3820.
  18. Sahin M.T., Hadimli H., Cakır C., Yasak U. & Genisyurek F. 2025. The role of urban landscape on land surface temperature: The case of Muratpaşa, Antalya, Land, 14(4), 663.
  19. Unal O., Demir N., Tezgoren G.K. & Unal B. 2024. Determination of the situation of Antalya Province Kundu and Belek tourism region in 1985 and 2022 with satellite images of vegetation areas, land cover type distributions, use class, usage area and occupation status, Eurasian Journal of Medical and Biological Sciences, 4(2), 71-79.