Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021)

Başak Kafes Demirci; Osman Salih Yılmaz; Füsun Balık Şanlı

doi:10.26833/ijeg.1650786

Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021)

Abstract

The study investigates changes in the İğneada Longoz forests in Kırklareli Province, Türkiye, between 2000 and 2021 using Landsat-7 and Landsat-8 satellite imagery. Classification was performed with the random forest (RF) machine learning algorithm and remote sensing (RS) techniques on the Google Earth Engine (GEE) platform. To better distinguish longoz forests and other forested areas from surrounding features, the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated into the classification alongside standard spectral bands. In the first stage of the classification process, the study area was categorized into seven classes, identifying all forest areas. In the second stage, the area was further divided into nine classes, isolating longoz forests from other forest types. Additionally, NDVI and EVI time series analyses were conducted to evaluate forest phenology. According to the classification results, the initial stage achieved overall accuracy ranging from 79.97% to 90.63%, with an average of approximately 85.00%. The Kappa statistics varied between 0.721 and 0.877. In the second stage, the overall accuracy ranged from 75.92% to 90.04%, while the Kappa statistic was between 0.649 and 0.866.

Keywords

References

Li, H., Jia, M., Zhang, R., Ren, Y., & Wen, X. (2019). Incorporating the plant phenological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine platform. Remote Sensing, 11(21). https://doi.org/10.3390/rs11212479
Diniz, C., Cortinhas, L., Nerino, G., Rodrigues, J., Sadeck, L., Adami, M., & Souza-Filho, P. W. M. (2019). Brazilian mangrove status: Three decades of satellite data analysis. Remote Sensing, 11(7), 808. https://doi.org/10.3390/rs11070808
Parida, B. R., & Kumar, P. (2020). Mapping and dynamic analysis of mangrove forest during 2009–2019 using landsat–5 and sentinel–2 satellite data along Odisha Coast. Tropical Ecology, 61(4), 538–549. https://doi.org/10.1007/s42965-020-00112-7
Twilley, R. R., Rovai, A. S., & Riul, P. (2018). Coastal morphology explains global blue carbon distributions. Frontiers in Ecology and the Environment, 16(9), 503–508.
Cipta, I. M., Sobarman, F. A., Sanjaya, H., & Darminto, M. R. (2021). Analysis of Mangrove Forest Change from Multioral Landsat Imagery Using Google Earth Engine Application: (Case Study: Belitung Archipelago 1990 - 2020). 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2021-Proceeding, 90–95. https://doi.org/10.1109/AGERS53903.2021.9617354
Wan, L., Zhang, H., Lin, G., & Lin, H. (2019). A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image. Annals of GIS, 25(1), 45–55. https://doi.org/10.1080/19475683.2018.1564791
Topaloglu, R. H. (2022). Investigation of Land Use/Land Cover change in Mersin using geographical object-based image analysis (GEOBIA). Advanced Remote Sensing, 2(2), 40–46. https://publish.mersin.edu.tr/index.php/arsej/article/view/247
Gull, A., & Mahmood, S. (2022). Spatio-temporal analysis and trend prediction of land cover changes using markov chain model in Islamabad, Pakistan. Advanced GIS, 2(2), 52–61. https://publish.mersin.edu.tr/index.php/agis/article/view/679

Nsiah, R. A., Mantey, S., & Ziggah, Y. Y. (2024). Assessing the contribution of RGB VIs in improving building extraction from RGB-UAV images. Mersin Photogrammetry Journal, 6(1), 9–21. https://doi.org/10.53093/mephoj.1399083
Jin, Y., Liu, X., Chen, Y., & Liang, X. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong. International Journal of Remote Sensing, 39(23), 8703–8723. https://doi.org/10.1080/01431161.2018.1490976
Cheret, V., & Denux, J.-P. (2011). Analysis of MODIS NDVI time series to calculate indicators of Mediterranean forest fire susceptibility. GIScience & Remote Sensing, 48(2), 171–194.
Chao Rodríguez, Y., El Anjoumi, A., Domínguez Gómez, J. A., Rodríguez Pérez, D., & Rico, E. (2014). Using Landsat image time series to study a small water body in Northern Spain. Environmental Monitoring and Assessment, 186(6), 3511–3522. https://doi.org/10.1007/s10661-014-3634-8
Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., Wu, Z., Sun, R., Lan, G., Xie, G., Clinton, N., & Giri, C. (2017). A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 104–120. https://doi.org/10.1016/j.isprsjprs.2017.07.011
Johansen, K., Phinn, S., & Taylor, M. (2015). Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine. Remote Sensing Applications: Society and Environment, 1, 36–49. https://doi.org/10.1016/j.rsase.2015.06.002
Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2). https://doi.org/10.1016/S0034-4257(02)00096-2
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Hawick, K. A., Coddington, P. D., & James, H. A. (2003). Distributed frameworks and parallel algorithms for processing large-scale geographic data. Parallel Computing, 29(10), 1297–1333.
Başara, A. C., Tabar, M. E., Gülsün, S., & Şişman, Y. (2022). Monitoring Urban Sprawl in Atakum District Using CORINE Data. Advanced Geomatics, 2(2), 49–56. https://publish.mersin.edu.tr/index.php/geomatics/article/view/481
Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123.
Binuyo, O., & Bello, V. A. (2024). Spatial association in students’ residential apartment property characteristics around a university. Advanced GIS, 4(2), 82–99. https://publish.mersin.edu.tr/index.php/agis/article/view/1558
Türk, T., & Demirel, Y. (2024). Automatic detection of active fires and burnt areas in forest areas using optical satellite imagery and deep learning methods. Mersin Photogrammetry Journal, 6(2), 66–78. https://doi.org/10.53093/mephoj.1575877
Mahdavifard, M., Ahangar, S. K., Feizizadeh, B., Kamran, K. V., & Karimzadeh, S. (2023). Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and optical images on Google Earth Engine. International Journal of Engineering and Geosciences, 8(3), 239–250. https://doi.org/10.26833/IJEG.1118542
Yılmaz, O. S., Gülgen, F., Güngör, R., & Kadi, F. (2018). Coğrafi Bilgi Sistemleri ve Uzaktan Algılama Teknikleri İle Arazi Kullanım Değişiminin İncelenmesi, Köprübaşı İlçesi Örneği. Geomatik. https://doi.org/10.29128/geomatik.410987
Güngör, R., Uzar, M., Atak, B., Yılmaz, O. S., & Gümüş, E. (2022). Orthophoto production and accuracy analysis with UAV photogrammetry. Mersin Photogrammetry Journal, 4(1), 1–6. https://doi.org/10.53093/mephoj.1122615
Yakar, M., Yıldız, F., Uray, F., & Metin, A. (2010, June). Photogrammetric Measurement of The Meke Lake and Its Environment with Kite Photographs to Monitoring of Water Level to Climate Change. In ISPRS Commission V Mid-Term Symposium (pp. 613-616).
Yılmaz, O. S. (2023). Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği. Geomatic, 8(1), 42–54. https://doi.org/10.29128/geomatik.1108735
Kadı, F., & Yılmaz, O. S. (2024a). Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye. International Journal of Engineering and Geosciences, 9(2), 147–164. https://doi.org/10.26833/ijeg.1355615
Kadı, F., & Yılmaz, O. S. (2024b). Orman içi açıklıklara ait zamansal değişimlerin uzaktan algılama teknikleri ile Google Earth Engine platformunda tespit edilmesi: Trabzon-Düzköy İlçesi örneği. Geomatik, 9(2), 141–155. https://doi.org/10.29128/geomatik.1363873
Avci, C., Budak, M., Yagmur, N., & Balcik, F. B. (2023). Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences, 8(1), 1–10. https://doi.org/10.26833/IJEG.987605
Yılmaz, O. S., Gülgen, F., & Ateş, A. M. (2023). Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies. International Journal of Engineering and Geosciences, 8(1), 63–75. https://doi.org/10.26833/IJEG.1052556
Yılmaz, O. S., Özkan, G., & Gülgen, F. (2021). Determining highway slope ratio using a method based on slope angle calculation. International Journal of Engineering and Geosciences, 6(2), 98–103. https://doi.org/10.26833/IJEG.704317
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: 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
Friess, D. A., Rogers, K., Lovelock, C. E., Krauss, K. W., Hamilton, S. E., Lee, S. Y., Lucas, R., Primavera, J., Rajkaran, A., & Shi, S. (2019). The State of the World’s Mangrove Forests: Past, Present, and Future. Annual Review of Environment and Resources, 44(April 2021), 89–115. https://doi.org/10.1146/annurev-environ-101718-033302
Vatandaslar, C., Bolat, F., Abdikan, S., Pamukcu-Albers, P., & Satiral, C. (2020). i Forest. IForest - Biogeosciences and Forestry, 17, 277–285. https://doi.org/10.3832/ifor4527-017
Reddy, C. S., Jha, C. S., Diwakar, P. G., & Dadhwal, V. K. (2015). Nationwide classification of forest types of India using remote sensing and GIS. Environmental Monitoring and Assessment, 187(12), 1–30. https://doi.org/10.1007/s10661-015-4990-8
Ghosh, S. M., Behera, M. D., & Paramanik, S. (2020). Canopy height estimation using sentinel series images through machine learning models in a Mangrove Forest. Remote Sensing, 12(9), 1519. https://doi.org/10.3390/RS12091519
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with Erts. NASA Spec, 351, 309.
Eidenshink, J. C. (1992). The 1990 conterminous US AVHRR data set. Photogrammetric Engineering & Remote Sensing, 58(6), 809–813.
Yilmaz, H. M., Yakar, M., Mutluoglu, O., Kavurmaci, M. M., & Yurt, K. (2012). Monitoring of soil erosion in Cappadocia region (Selime-Aksaray-Turkey). Environmental Earth Sciences, 66(1), 75-81.
Yakar, M., Yilmaz, H. M., & Yurt, K. (2010). The effect of grid resolution in defining terrain surface. Experimental Techniques, 34(6), 23-29.
Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205–241. https://doi.org/10.1146/annurev.energy.28.050302.105459
Breiman, L. (2001). Random Forests (Vol. 45).
Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300. https://doi.org/10.1016/J.PATREC.2005.08.011
Bozkaya, A. G. (2013). İğneada Koruma Alanının Uzaktan Algılama ve Coğrafi Bilgi Sistemleri ile Zamansal Değerlendirilmesi ve Geleceğe Yönelik Modellenmesi. İstanbul Teknik Üniversitesi.
Toker, M. (2018). Landsat ve Sentinel Uydu Görüntüleri Kullanılarak Zamansal Değişim Tespiti Analizi: Bir Vaka Çalışması- İğneada Taşkın Ormanı Milli Parkı. İstanbul Teknik Üniversitesi.
Gencal, B., Taş, İ., & Akay, A. E. (2018). Spatiotemporal Change Detection of the Linden Forests in Bursa, Turkey. European Journal of Forest Engineering, 4(2), 50–55. https://doi.org/10.33904/ejfe.495088
Arekhi, M., Goksel, C., Balik Sanli, F., & Senel, G. (2019). Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest. ISPRS International Journal of Geo-Information, 8(2), 56. https://doi.org/10.3390/ijgi8020056

Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

Başak Kafes Demirci
0000-0003-0966-4260
Türkiye

Osman Salih Yılmaz ^*
0000-0003-4632-9349
Türkiye

Füsun Balık Şanlı
0000-0003-1243-8299
Türkiye

Early Pub Date

August 25, 2025

Publication Date

October 1, 2025

Submission Date

March 4, 2025

Acceptance Date

July 14, 2025

Published in Issue

Year 2026 Volume: 11 Number: 1

DOI

https://doi.org/10.26833/ijeg.1650786

IZ

https://izlik.org/JA66RK93FP

Cite

RIS / Bibtex

APA

Kafes Demirci, B., Yılmaz, O. S., & Balık Şanlı, F. (2025). Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021). International Journal of Engineering and Geosciences, 11(1), 78-88. https://doi.org/10.26833/ijeg.1650786

AMA

1.Kafes Demirci B, Yılmaz OS, Balık Şanlı F. Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021). IJEG. 2025;11(1):78-88. doi:10.26833/ijeg.1650786

Chicago

Kafes Demirci, Başak, Osman Salih Yılmaz, and Füsun Balık Şanlı. 2025. “Investigation of Longoz Forests With Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021)”. International Journal of Engineering and Geosciences 11 (1): 78-88. https://doi.org/10.26833/ijeg.1650786.

EndNote

Kafes Demirci B, Yılmaz OS, Balık Şanlı F (October 1, 2025) Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021). International Journal of Engineering and Geosciences 11 1 78–88.

IEEE

[1]B. Kafes Demirci, O. S. Yılmaz, and F. Balık Şanlı, “Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021)”, IJEG, vol. 11, no. 1, pp. 78–88, Oct. 2025, doi: 10.26833/ijeg.1650786.

ISNAD

Kafes Demirci, Başak - Yılmaz, Osman Salih - Balık Şanlı, Füsun. “Investigation of Longoz Forests With Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021)”. International Journal of Engineering and Geosciences 11/1 (October 1, 2025): 78-88. https://doi.org/10.26833/ijeg.1650786.

JAMA

1.Kafes Demirci B, Yılmaz OS, Balık Şanlı F. Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021). IJEG. 2025;11:78–88.

MLA

Kafes Demirci, Başak, et al. “Investigation of Longoz Forests With Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021)”. International Journal of Engineering and Geosciences, vol. 11, no. 1, Oct. 2025, pp. 78-88, doi:10.26833/ijeg.1650786.

Vancouver

1.Başak Kafes Demirci, Osman Salih Yılmaz, Füsun Balık Şanlı. Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021). IJEG. 2025 Oct. 1;11(1):78-8. doi:10.26833/ijeg.1650786