Seasonal Vegetation Trends in Biomes of Türkiye: A Decade-Long (2014-2023) Analysis Using NDVI Time Series

Year 2024, , 230 - 243, 15.08.2024

Emre Aktürk

https://doi.org/10.24011/barofd.1468085

Abstract

This study analyzes Türkiye's biomes' seasonal vegetation trend from 2014 to 2023 using the Normalized Difference Vegetation Index (NDVI) and Google Earth Engine (GEE). Focusing on Mediterranean Forests, Woodlands & Scrub; Temperate Broadleaf & Mixed Forests; Temperate Grasslands, Savannas & Shrublands; and Temperate Coniferous Forests biomes, it aims to illuminate vegetative trends and inform conservation strategies in line with the European Green Deal. Using Landsat 8 Operational Land Imager (OLI) satellite imagery and GEE's computational capabilities, the study efficiently processes large datasets, revealing distinctive vegetative responses to climatic conditions across biomes. Key findings include the resilience of Mediterranean vegetation to drought, stable growth in temperate broadleaf and mixed forests, dynamic seasonal shifts in grasslands, and consistent photosynthetic activity in coniferous forests. The study highlights the importance of continuous monitoring and suggests future research integrating remote sensing and ground observations for ecosystem management under climate change.

Keywords

NDVI, biomes, Türkiye, vegetation trend, google earth engine

Türkiye Biyomlarında Mevsimsel Bitki Örtüsü Trendleri: NDVI Zaman Serileri ile Son On Yılın (2014-2023) Analizi

Abstract

Bu çalışma, Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI) ve Google Earth Engine (GEE) kullanarak 2014-2023 yılları arasında Türkiye biyomlarının mevsimsel bitki örtüsü eğilimini analiz etmeyi amaçlamaktadır. Çalışma, Akdeniz Ormanları, Ağaçlık ve Çalılıklar; Ilıman Geniş Yapraklı ve Karışık Ormanlar; Ilıman Otlaklar, Savanlar ve Çalılıklar; ve Ilıman İğne Yapraklı Ormanlara ait biyomlara odaklanmaktadır. Biyomlar içerisinde bitkisel eğilimlerin incelenmesi ve Avrupa Yeşil Mutabakatı doğrultusunda koruma stratejilerinin incelenmesi temel hedeflerdendir. Landsat 8 Operational Land Imager (OLI) uydu görüntülerini ve GEE'nin veri işleme yeteneklerini kullanan bu çalışma, büyük veri kümelerini analitik bir şekilde işleyerek biyomlar boyunca iklim koşullarına verilen farklı bitkisel tepkileri ortaya çıkarmaktadır. Çalışmanın temel bulgular arasında Akdeniz bitki örtüsünün kuraklığa karşı dayanıklılığı, ılıman geniş yapraklı ve karışık ormanlarda istikrarlı büyüme, otlaklarda dinamik mevsimsel değişimler ve iğne yapraklı ormanlarda tutarlı fotosentetik aktivitelerden söz edilebilir. Çalışma, ekolojik açıdan oldukça önemli bu alanların sürekli izlemenmesinin önemini vurgulamakta ve iklim değişikliği altında ekosistem yönetimi için uzaktan algılama ve yer gözlemlerini entegre eden gelecekteki araştırmaları önermektedir.

Keywords

NDVI, biyom, Türkiye, vejetasyon trendi, google earth engine

References

• Akman, Y., & Ketenoğlu, O. (1987). Vejetasyon ekolojisi. Ankara Üniversitesi Yayinlari, Ankara-Türkiye. Aksoy, N., Tuğ, N. G., & Eminağaoğlu, Ö. (2014). Türkiye'nin vejetasyon yapısı. Türkiye’nin Doğal-Egzotik Ağaç ve Çalıları 1.
• Aktürk, E. (2023). Monitoring forest canopy cover change with ICESat-2 Data in fire-prone areas: A case study in Antalya, Türkiye. Annals of Forest Research, 66(2), 87-99. https://doi.org/10.15287/afr.2023.2987
• Akturk, E., Popescu, S. C., & Malambo, L. (2023). ICESat-2 for canopy cover estimation at large-scale on a cloud-based platform. Sensors, 23(7), 3394. https://doi.org/10.3390/s23073394
• Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., ... & Brisco, B. (2020). Google earth engine cloud computing platform for remote sensing big data appli-cations: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326-5350. https://doi.org/10.1109/JSTARS.2020.3021052
• Andersson, F. A. (2005). Coniferous forests (Vol. 6). Elsevier.
• Atangana, A., Khasa, D., Chang, S., Degrande, A., Atangana, A., Khasa, D., ... & Degrande, A. (2014). Tropical biomes: Their classification, description and importance. Tropical agroforestry, 3-22. https://doi.org/10.1007/978-94-007-7723-1_1
• Bao, G., Bao, Y., Sanjjava, A., Qin, Z., Zhou, Y., & Xu, G. (2015). NDVI‐indicated long‐term vegeta-tion dynamics in Mongolia and their response to climate change at biome scale. International Jour-nal of Climatology, 35(14), 4293-4306. https://doi.org/10.1002/joc.4286
• Beck, P. S., Atzberger, C., Høgda, K. A., Johansen, B., & Skidmore, A. K. (2006). Improved monitor-ing of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote sens-ing of Environment, 100(3), 321-334. https://doi.org/10.1016/j.rse.2005.10.021
• Cao, M., & Woodward, F. I. (1998). Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature, 393(6682), 249-252. https://doi.org/10.1038/30460
• Chu, H., Venevsky, S., Wu, C., & Wang, M. (2019). NDVI-based vegetation dynamics and its re-sponse to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the To-tal Environment, 650, 2051-2062. https://doi.org/10.1016/j.scitotenv.2018.09.115
• Clements, F. E., & Shelford, V. E. (1939). Bio-ecology. Chapman and Hall. Cody, M.L. (1986). Diversity, rarity, and conservation in Mediterranean climate regions. Conserva-tion biology: the science of scarcity, diversity, pp. 123–152.
• Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., ... & Saleem, M. (2017). An ecoregion-based approach to protecting half the terrestrial realm. BioScience, 67(6), 534-545. https://doi.org/10.1093/biosci/bix014
• Eastman, J. R., Sangermano, F., Machado, E. A., Rogan, J., & Anyamba, A. (2013). Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011. Remote Sensing, 5(10), 4799-4818. https://doi.org/10.3390/rs5104799
• Eisfelder, C., Asam, S., Hirner, A., Reiners, P., Holzwarth, S., Bachmann, M., ... & Kuenzer, C. (2023). Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product. Remote Sensing, 15(14), 3616. https://doi.org/10.3390/rs15143616
• Evrendilek, F., & Gulbeyaz, O. (2008). Deriving vegetation dynamics of natural terrestrial ecosystems from MODIS NDVI/EVI data over Turkey. Sensors, 8(9), 5270-5302. https://doi.org/10.3390/s8095270
• Filgueiras, R., Mantovani, E. C., Althoff, D., Fernandes Filho, E. I., & Cunha, F. F. D. (2019). Crop NDVI monitoring based on sentinel 1. Remote Sensing, 11(12), 1441. https://doi.org/10.3390/rs11121441
• Funk, C., & Budde, M. E. (2009). Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sensing of Environment, 113(1), 115-125. https://doi.org/10.1016/j.rse.2008.08.015
• Gemici, Y., Seçmen, Ö., Ekim, T., & Leblebici, E. (1992). Türkiye'de Endemizm Ve İzmir Yöresinin Bazı Endemikleri. Ege Coğrafya Dergisi, 6(1).
• Guha, S., & Govil, H. (2021). Seasonal variability of LST-NDVI correlation on different land use/land cover using Landsat satellite sensor: a case study of Raipur City, India. Environment, Development and Sustainability, 1-17. https://doi.org/10.1007/s10668-021-01811-4
• Hmimina, G., Dufrêne, E., Pontailler, J. Y., Delpierre, N., Aubinet, M., Caquet, B., ... & Soudani, K. (2013). Evaluation of the potential of MODIS satellite data to predict vegetation phenology in dif-ferent biomes: An investigation using ground-based NDVI measurements. Remote sensing of envi-ronment, 132, 145-158. https://doi.org/10.3390/rs5104799
• Hunter, J., Franklin, S., Luxton, S., & Loidi, J. (2021). Terrestrial biomes: a conceptual review. Vege-tation Classification and Survey, 2, 73-85. https://doi.org/10.3897/VCS/2021/61463
• Karlsen, S. R., Stendardi, L., Tømmervik, H., Nilsen, L., Arntzen, I., & Cooper, E. J. (2021). Time-series of cloud-free sentinel-2 ndvi data used in mapping the onset of growth of central spitsbergen, svalbard. Remote Sensing, 13(15), 3031. https://doi.org/10.3390/rs13153031
• Kouadio, L., Newlands, N. K., Davidson, A., Zhang, Y., & Chipanshi, A. (2014). Assessing the per-formance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Re-mote Sensing, 6(10), 10193-10214. https://doi.org/10.3390/rs61010193
• McLeod, A. I. (2005). Kendall rank correlation and Mann-Kendall trend test. R package Kendall, 602, 1-10. Meneses-Tovar, C. L. (2011). NDVI as indicator of degradation. Unasylva, 62(238), 39-46.
• Muller, R. N. (2003). Deciduous Forest Ecosystems. The herbaceous layer in forests of eastern North America, 15.
• Pacifici, F., Longbotham, N., & Emery, W. J. (2014). The importance of physical quantities for the analysis of multitemporal and multiangular optical very high spatial resolution images. IEEE Tran-sactions on Geoscience and Remote Sensing, 52(10), 6241-6256.
• Pan, N., Feng, X., Fu, B., Wang, S., Ji, F., & Pan, S. (2018). Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sensing of Envi-ronment, 214, 59-72. https://doi.org/10.1016/j.rse.2018.05.018
• Panuju, D. R., & Trisasongko, B. H. (2012). Seasonal pattern of vegetative cover from NDVI time-series. Tropical Forests, 255.
• Paruelo, J. M., Jobbágy, E. G., Sala, O. E., Lauenroth, W. K., & Burke, I. C. (1998). Functional and structural convergence of temperate grassland and shrubland ecosystems. Ecological Applications, 8(1), 194-206. https://doi.org/10.1890/1051-0761(1998)008[0194:FASCOT]2.0.CO;2
• Qiu, S., Zhu, Z., Olofsson, P., Woodcock, C. E., & Jin, S. (2023). Evaluation of Landsat image com-positing algorithms. Remote Sensing of Environment, 285, 113375.
• Soudani, K., Hmimina, G., Delpierre, N., Pontailler, J. Y., Aubinet, M., Bonal, D., ... & Dufrêne, E. (2012). Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote sensing of environment, 123, 234-245. https://doi.org/10.1016/j.rse.2012.03.012
• Tilman, D., & Downing, J. A. (1994). Biodiversity and stability in grasslands. Nature, 367(6461), 363-365. https://doi.org/10.1038/367363a0
• Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
• URL-1 (2010). WWF. Mediterranean Forests, Woodlands and Scrub Ecoregions. Can be accessed at: https://web.archive.org/web/20110401124536/http://wwf.panda.org/about_our_earth/ecoregions/about/habitat_types/selecting_terrestrial_ecoregions/habitat12.cfm
• URL-2 (2010). WWF. Temperate Broadleaf and Mixed Forest Ecoregions. Can be accessed at: https://web.archive.org/web/20110401124425/http://wwf.panda.org/about_our_earth/ecoregions/about/habitat_types/selecting_terrestrial_ecoregions/habitat04.cfm
• URL-3 (2010). WWF. Temperate Grasslands, Savannas, and Shrubland Ecoregions. Can be accessed at: https://web.archive.org/web/20110401124312/http://wwf.panda.org/about_our_earth/ecoregions/about/habitat_types/selecting_terrestrial_ecoregions/habitat08.cfm
• URL-4 (2010). WWF. Temperate Coniferous Forest Ecoregions. Can be accessed at: https://web.archive.org/web/20110102145156/http://wwf.panda.org/about_our_earth/ecoregions/about/habitat_types/selecting_terrestrial_ecoregions/habitat05.cfm.
• URL-5 (2015). FAO. The Global Administrative Unit Layers (GAUL) 2015. Can be accessed at: https://developers.google.com/earth-engine/datasets/catalog/DataLicenseGAUL2015.pdf Woodward, S. L. (2008). Introduction to biomes. Bloomsbury Publishing USA.