Monitoring Türkiye’s Vegetation Cover with NDVI: Terrestrial and Temporal Perspectives

Authors

DOI:

https://doi.org/10.61326/silvaworld.v3i2.285

Keywords:

MODIS, NDVI, Temporal, Terrestrial, Türkiye

Abstract

This study aims to monitor and analyze the temporal and spatial dynamics of Türkiye's vegetation cover from 2000 to 2023 using MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) data. The primary objective is to assess the changes in vegetation density across various geographical regions of Türkiye and determine how these changes are influenced by environmental factors such as land use and climate variability. NDVI data from July of each year were processed using ArcGIS to classify vegetation into six categories, ranging from water bodies to dense forests. The study reveals significant fluctuations in NDVI values, indicating both vegetation growth and degradation across different regions over time. Key findings include a positive correlation between NDVI values and forested areas, and a negative correlation in regions affected by drought or land use change. These results provide valuable insights into the long-term trends of vegetation dynamics in Türkiye and can help inform future conservation and land management strategies.

References

Aktürk, E., & Güney, K. (2021). Vegetation cover change analysis of phytogeographic regions of Turkey based on CORINE land cover datasets from 1990 to 2018. Kastamonu University Journal of Forestry Faculty, 21(2), 150-164. https://doi.org/10.17475/kastorman.1000406

Aktürk, E. (2024) Seasonal vegetation trends in biomes of Türkiye: A decade-long (2014-2023) analysis using NDVI time series. Bartın Orman Fakültesi Dergisi, 26(3), 1-1. https://doi.org/10.24011/barofd.1468085

Alhajjar, M. (2024). Assessment of the impact of the Syrian conflict on the extent and quality of agricultural land in the Sabkhat Al-Jabbul area (SJA) (Master of Spatial Analysis, Ryerson University).

Ateşoğlu, A. (2021). Konya kapalı havzası uzun dönem bitki örtüsü indeksi verilerinin izlenmesi ve eğilim analizi. Anadolu Tarım Bilimleri Dergisi, 36(2), 346-356. https://doi.org/10.7161/omuanajas.908576 (In Turkish)

Cao, R., Zhao, X., Chen, Y., Chen, J., & Shen, M. (2022). Reconstructing high-spatiotemporal-resolution (30 m and 8-days) NDVI time-series data for the Qinghai–Tibetan plateau from 2000–2020. Remote Sensing, 14(15), 3648. https://doi.org/10.3390/rs14153648

Choubin, B. F., Soleimani, F., Pirnia, A., Sajedi-Hosseini, F., Alilou, H., Rahmati, O., Melesse, A. M., Singh, V. P., & Shahab, H. (2019). Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns. In A. M. Melesse, W. Abtew & G. Senay (Eds.), Extreme hydrology and climate variability: Monitoring, Modelling, adaptation and mitigation (pp. 213-222). Elsevier. https://doi.org/10.1016/B978-0-12-815998-9.00017-8

Dai, S., Zhang, B., Wang, H., Wang, Y., Guo, L., Xingmei, W., & Li, D. (2011). Vegetation cover change and the driving factors over northwest China. Journal of Arid Land, 3(1), 25-33. https://doi.org/10.3724/sp.j.1227.2011.00025

Ding, M. J., Zhang, Y. L., Sun, X. M., Liu, L. S., Wang, Z. F., & Bai, W. Q. (2012). Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009. Chinese Science Bulletin, 58(3), 396-405. https://doi.org/10.1007/S11434-012-5407-5

Essaadia, A., Abdellah, A., Ahmed, A., Abdelouahed, F., & Kamal, E. (2022). The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: Comparison and dynamics. Heliyon, 8(12), e12204. https://doi.org/10.1016/J.HELIYON.2022.E12204

Ghebrezgabher, M. G., Yang, T., Yang, X., & Eyassu Serek, T. (2020). Assessment of NDVI variations in responses to climate change in the Horn of Africa. The Egyptian Journal of Remote Sensing and Space Science, 23(3), 249-261. https://doi.org/10.1016/j.ejrs.2020.08.003

Jiang, S., Chen, X., Smettem, K., & Wang, T. (2021). Climate and land use influences on changing spatiotemporal patterns of mountain vegetation cover in southwest China. Ecological Indicators, 121, 107193. https://doi.org/10.1016/j.ecolind.2020.107193

Kaymak, H. (2020). Morfo-klimatik özelliklerin Sündiken Dağları’nda (Eskişehir) bitki örtüsünün dağılışı üzerindeki etkileri. Türk Coğrafya Dergisi, (75), 17-32. https://doi.org/10.17211/tcd.639024 (In Turkish)

Liu, L., Wang, Y., Wang, Z., Li, D., Zhang, Y., Qin, D., & Li, S. (2019). Elevation-dependent decline in vegetation greening rate driven by increasing dryness based on three satellite NDVI datasets on the Tibetan Plateau. Ecological Indicators, 107, 105569. https://doi.org/10.1016/j.ecolind.2019.105569

Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors, 7(11), 2636-2651. https://doi.org/10.3390/s7112636

Naunyal, M., Khadka, B., & Anderson, J. (2023). Effect of land use and land cover change on plant diversity in the ghodaghodi lake complex, Nepal. Forests, 14(3), 529. https://doi.org/10.3390/f14030529

Omar, M., & Kawamukai, H. (2022). Evaluation of stochastic and artificial neural network models for multi-step lead forecasting of NDVI. Iop Conference Series: Earth and Environmental Science, 1008(1), 012014. https://doi.org/10.1088/1755-1315/1008/1/012014

Tian, X., Consoli, D., Witjes, M., Schneider, F., Parente, L., Şahin, M., Ho, Y. F., Minařík, R., & Hengl, T. (2024). Time-series of Landsat-based bi-monthly and annual spectral indices for continental Europe for 2000–2022. Earth System Science Data Discussions, 2024, 1-49. https://doi.org/10.5194/essd-2024-266

Turgut, H., & Turgut, B. (2022). The effects of landforms and climate on NDVI in Artvin, Türkiye. eco.mont, 14(2), 24-36. https://doi.org/10.1553/eco.mont-14-2s24

Wu, D., Zhao, X., Liang, S., Zhou, T., Huang, K., Tang, B., & Zhao, W. (2015). Time-lag effects of global vegetation responses to climate change. Global Change Biology, 21(9), 3520-3531. https://doi.org/10.1111/gcb.12945

Wu, Y., Li, W., Wang, Q., Liu, Q., Yang, D., Xing, M., Pei, Y., & Yan, S. (2016). Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for Gangu County, China. Arabian Journal of Geosciences, 9, 84. https://doi.org/10.1007/s12517-015-2112-0

Yasin, M. Y., Abdullah, J., Noor, N. M., Yusoff, M. M., & Noor, N. M. (2022). Landsat observation of urban growth and land use change using NDVI and NDBI analysis. Iop Conference Series: Earth and Environmental Science, 1067(1), 012037. https://doi.org/10.1088/1755-1315/1067/1/012037

Zeng, S., Liu, H., & Chen, T. (2014). Dynamic monitoring of vegetation coverage change in Lu county based on TM/OLI-NDVI. International Symposium on Optoelectronic Technology and Application 2014: Optical Remote Sensing Technology and Applications. Beijing.

Zhang, Y., Migliavacca, M., Penuelas, J., & Ju, W. (2021). Advances in hyperspectral remote sensing of vegetation traits and functions. Remote Sensing of Environment, 252, 112121. https://doi.org/10.1016/j.rse.2020.112121

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Published

29-09-2024

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Research Articles