Research Article
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ESRI Land Cover ve Dynamic World arazi örtüsü verilerinin karşılaştırılması: Kıbrıs Adası örneği

Year 2024, Volume: 11 Issue: 1, 19 - 29, 03.05.2024
https://doi.org/10.9733/JGG.2024R0002.T

Abstract

Arazi Kullanımı/Arazi Örtüsü (AK/AÖ) takibi, değişimlerin belirlenmesi insan ve çevresi arasındaki ilişkinin anlaşılması açısından oldukça önemlidir. Uzaktan algılama teknolojilerinin gelişmesi ile birlikte AK/AÖ lokal ve küresel ölçekte takibi daha kolay hale gelmiştir. Bununla birlikte uzaktan algılama verilerinin sınıflandırılmasında birçok sınıflandırma algoritması ve yöntem geliştirilmiştir ve geliştirilmeye devam etmektedir. Sınıflandırma algoritmalarının ve yöntemlerin birbirine karşı avantaj ve dezavantajları bulunmaktadır. Bununla birlikte AK/AÖ tespiti lokal ve küresel ölçekte kullanılabilmektedir. Bu çalışmada küresel ölçekte ücretsiz olarak servis edilen ESRI Land Cover ve Dynamic World verileri karşılaştırılmıştır. Bu iki veri de sınıflandırma için Sentinel-2 görüntüleri kullanmışlardır ve 10 m çözünürlükte AK/AÖ verisi servis etmektedir. Karşılaştırmada Akdeniz’in önemli bir adası olan Kıbrıs adası ele alınmıştır. Karşılaştırma için öncelikle iki veri arasındaki tutarlılığa bakılmıştır. Ardından oluşturulan kontrol noktaları ile hata matrisleri oluşturulmuştur ve genel doğruluklarına bakılmıştır. İki veri arasında su sınıfında %95, tarım arazileri sınıfında %78, yapılaşmış alan sınıfında %79, ağaçlar sınıfında %97, çıplak arazi sınıfında %85 ve sulu bitki örtüsü sınıfında %50 benzerlik bulunmaktadır. Genel doğruluklarına bakıldığında ESRI Land Cover verisi %83.5 iken Dynamic World verisi %84.5 doğruluk vermiştir. Sonuçlar incelendiğinde her iki verinin Kıbrıs adasının AK/AÖ takibinde kullanılabilir olduğu görülmektedir.

References

  • Akbari, M., Mamanpoush, A. R., Gieske, A., Miranzadeh, M., Torabi, M., & Salemi, H. R. (2006). Crop and land cover classification in Iran using Landsat 7 imagery. International Journal of Remote Sensing, 27(19), 4117-4135.
  • Akca, S., & Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265-2274.
  • Bartholome, E., & Belward, A. S. (2005). GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9), 1959-1977.
  • Brovelli, M. A., Molinari, M. E., Hussein, E., Chen, J., & Li, R. (2015). The first comprehensive accuracy assessment of GlobeLand30 at a national level: Methodology and results. Remote Sensing, 7(4), 4191-4212.
  • Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Haertel R., Ilyushchenko S., Schwehr K., Weisse M., Stolle F., Hanson C., Guinan O., Moore R., & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 251.
  • Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., & Mills, J. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7-27.
  • Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482.
  • Coulter, L. L., Stow, D. A., Tsai, Y. H., Ibanez, N., Shih, H. C., Kerr, A., Benza, M., Weeks, J. R., & Mensah, F. (2016). Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery. Remote Sensing of Environment, 184, 396-409.
  • Friedl, M. A., McIver, D. K., Hodges, J. C., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. H., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., & Schaaf, C. (2002). Global land cover mapping from MODIS: algorithms and early results. Remote sensing of Environment, 83(1-2), 287-302.
  • Hansen, M. C., & Reed, B. (2000). A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products. International Journal of Remote Sensing, 21(6-7), 1365-1373.
  • Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use/land cover with Sentinel 2 and deep learning. 2021 IEEE international geoscience and remote sensing symposium IGARSS, 4704-4707.
  • Koç, A., & Yener, H. (2001). Uzaktan Algılama Verileriyle İstanbul Çevresi Ormanlarının Alansal ve Yapısal Değişikliklerinin Saptanması. İstanbul Üniversitesi Orman Fakültesi Dergisi, 51(2). 17-36.
  • Koday, Z. (1995). Kuzey Kıbrıs Türk Cumhuriyeti Devleti'nin Coğrafi Özellikleri. Atatürk Üniversitesi Türkiyat Araştırmaları Enstitüsü Dergisi, (2), 17-45.
  • Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons. Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L. W. M. J., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International journal of remote sensing, 21(6-7), 1303-1330.
  • Muttitanon, W., & Tripathi, N. K. (2005). Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. International Journal of Remote Sensing, 26(11), 2311-2323.
  • Nguyen, H. T. T., Doan, T. M., & Radeloff, V. (2018). Applying random forest classification to map land use/land cover using Landsat 8 OLI. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 363-367.
  • Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12(14), 2291.
  • Ren, H., Cai, G., Zhao, G., & Li, Z. (2018). Accuracy assessment of the globeland30 dataset in jiangxi province. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 1481-1487.
  • Seyrek, E. C., & Uysal, M. (2023). A comparative analysis of various activation functions and optimizers in a convolutional neural network for hyperspectral image classification. Multimedia Tools and Applications, 1-32.
  • Taati, A., Sarmadian, F., Mousavi, A., Pour, C. T. H., & Shahir, A. H. E. (2015). Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak Journal of Science and Technology (WJST), 12(8), 681-687.
  • Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135.
  • Tateishi, R., Hoan, N. T., Kobayashi, T., Alsaaideh, B., Tana, G., & Phong, D. X. (2014). Production of global land cover data–GLCNMO2008. Journal of Geography and Geology, 6(3), 99-122.
  • Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing, 14(16), 4101.
  • Yang, Y., Xiao, P., Feng, X., & Li, H. (2017). Accuracy assessment of seven global land cover datasets over China. ISPRS Journal of Photogrammetry and Remote Sensing, 125, 156-173.
  • Yener, H., Ayhan, K. O. Ç., & Çoban, H. O. (2006). Uzaktan Algılama Verilerinde Sınıflandırma Doğruluğunun Belirlenmesi Yöntemleri. Journal of the Faculty of Forestry Istanbul University, 56(2), 71-88.
  • URL-1: https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2, (Erişim Tarihi: 31 Kasım 2023).
  • URL-2: https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1, (Erişim Tarihi: 7 Aralık 2023)
  • URL-3: https://livingatlas.arcgis.com/landcoverexplorer/#mapCenter=19.228%2C52.406%2C7&mode=step&timeExtent=2017%2C2022 &year=2022&downloadMode=true, (Erişim Tarihi: 21 Şubat 2024).
  • URL-4: https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1, (Erişim Tarihi: 21 Şubat 2024).

Comparison of ESRI Land Cover and Dynamic World: The case of Cyprus Island

Year 2024, Volume: 11 Issue: 1, 19 - 29, 03.05.2024
https://doi.org/10.9733/JGG.2024R0002.T

Abstract

Monitoring of Land Use/Land Cover (LU/LC) and determination of changes are very important in terms of understanding the relationship between human and the environment. With the development of remote sensing technologies, it has become easier to monitor LU/LC at local and global scales. However, many classification algorithms and methods have been developed and continue to be developed in the classification of remote sensing data. Classification algorithms and methods have advantages and disadvantages against each other. However, the detection of LULC can be used locally and globally. In this study, ESRI Land Cover and Dynamic World data, which are freely available on a global scale, were compared. Both of these data utilise Sentinel-2 imagery for classification and provide 10 m resolution LU/LC data. Cyprus island, an important island in the Mediterranean Sea, is considered in the comparison. For the comparison, firstly the consistency between the two data was analysed. Then, error matrices were created with the control points and their overall accuracy was analysed. There is 95% similarity in the water class, 78% in the crops class, 79% in the built area class, 97% in the trees class, 85% in the bare ground class, and 50% in the flooded vegetation class. Considering the general accuracy, ESRI Land Cover data gave an accuracy of 83.5% while Dynamic World data gave an accuracy of 84.5%. When the results are analysed, it is seen that both data can be used in the monitoring of LULC of the Cyprus island.

References

  • Akbari, M., Mamanpoush, A. R., Gieske, A., Miranzadeh, M., Torabi, M., & Salemi, H. R. (2006). Crop and land cover classification in Iran using Landsat 7 imagery. International Journal of Remote Sensing, 27(19), 4117-4135.
  • Akca, S., & Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265-2274.
  • Bartholome, E., & Belward, A. S. (2005). GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9), 1959-1977.
  • Brovelli, M. A., Molinari, M. E., Hussein, E., Chen, J., & Li, R. (2015). The first comprehensive accuracy assessment of GlobeLand30 at a national level: Methodology and results. Remote Sensing, 7(4), 4191-4212.
  • Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Haertel R., Ilyushchenko S., Schwehr K., Weisse M., Stolle F., Hanson C., Guinan O., Moore R., & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 251.
  • Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., & Mills, J. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7-27.
  • Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482.
  • Coulter, L. L., Stow, D. A., Tsai, Y. H., Ibanez, N., Shih, H. C., Kerr, A., Benza, M., Weeks, J. R., & Mensah, F. (2016). Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery. Remote Sensing of Environment, 184, 396-409.
  • Friedl, M. A., McIver, D. K., Hodges, J. C., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. H., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., & Schaaf, C. (2002). Global land cover mapping from MODIS: algorithms and early results. Remote sensing of Environment, 83(1-2), 287-302.
  • Hansen, M. C., & Reed, B. (2000). A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products. International Journal of Remote Sensing, 21(6-7), 1365-1373.
  • Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use/land cover with Sentinel 2 and deep learning. 2021 IEEE international geoscience and remote sensing symposium IGARSS, 4704-4707.
  • Koç, A., & Yener, H. (2001). Uzaktan Algılama Verileriyle İstanbul Çevresi Ormanlarının Alansal ve Yapısal Değişikliklerinin Saptanması. İstanbul Üniversitesi Orman Fakültesi Dergisi, 51(2). 17-36.
  • Koday, Z. (1995). Kuzey Kıbrıs Türk Cumhuriyeti Devleti'nin Coğrafi Özellikleri. Atatürk Üniversitesi Türkiyat Araştırmaları Enstitüsü Dergisi, (2), 17-45.
  • Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons. Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L. W. M. J., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International journal of remote sensing, 21(6-7), 1303-1330.
  • Muttitanon, W., & Tripathi, N. K. (2005). Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. International Journal of Remote Sensing, 26(11), 2311-2323.
  • Nguyen, H. T. T., Doan, T. M., & Radeloff, V. (2018). Applying random forest classification to map land use/land cover using Landsat 8 OLI. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 363-367.
  • Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12(14), 2291.
  • Ren, H., Cai, G., Zhao, G., & Li, Z. (2018). Accuracy assessment of the globeland30 dataset in jiangxi province. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 1481-1487.
  • Seyrek, E. C., & Uysal, M. (2023). A comparative analysis of various activation functions and optimizers in a convolutional neural network for hyperspectral image classification. Multimedia Tools and Applications, 1-32.
  • Taati, A., Sarmadian, F., Mousavi, A., Pour, C. T. H., & Shahir, A. H. E. (2015). Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak Journal of Science and Technology (WJST), 12(8), 681-687.
  • Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135.
  • Tateishi, R., Hoan, N. T., Kobayashi, T., Alsaaideh, B., Tana, G., & Phong, D. X. (2014). Production of global land cover data–GLCNMO2008. Journal of Geography and Geology, 6(3), 99-122.
  • Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing, 14(16), 4101.
  • Yang, Y., Xiao, P., Feng, X., & Li, H. (2017). Accuracy assessment of seven global land cover datasets over China. ISPRS Journal of Photogrammetry and Remote Sensing, 125, 156-173.
  • Yener, H., Ayhan, K. O. Ç., & Çoban, H. O. (2006). Uzaktan Algılama Verilerinde Sınıflandırma Doğruluğunun Belirlenmesi Yöntemleri. Journal of the Faculty of Forestry Istanbul University, 56(2), 71-88.
  • URL-1: https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2, (Erişim Tarihi: 31 Kasım 2023).
  • URL-2: https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1, (Erişim Tarihi: 7 Aralık 2023)
  • URL-3: https://livingatlas.arcgis.com/landcoverexplorer/#mapCenter=19.228%2C52.406%2C7&mode=step&timeExtent=2017%2C2022 &year=2022&downloadMode=true, (Erişim Tarihi: 21 Şubat 2024).
  • URL-4: https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1, (Erişim Tarihi: 21 Şubat 2024).
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Photogrammetry and Remote Sensing
Journal Section Research Article
Authors

Ömer Gökberk Narin 0000-0002-9286-7749

Early Pub Date February 28, 2024
Publication Date May 3, 2024
Submission Date December 28, 2023
Acceptance Date February 5, 2024
Published in Issue Year 2024 Volume: 11 Issue: 1

Cite

APA Narin, Ö. G. (2024). ESRI Land Cover ve Dynamic World arazi örtüsü verilerinin karşılaştırılması: Kıbrıs Adası örneği. Jeodezi Ve Jeoinformasyon Dergisi, 11(1), 19-29. https://doi.org/10.9733/JGG.2024R0002.T
AMA Narin ÖG. ESRI Land Cover ve Dynamic World arazi örtüsü verilerinin karşılaştırılması: Kıbrıs Adası örneği. hkmojjd. May 2024;11(1):19-29. doi:10.9733/JGG.2024R0002.T
Chicago Narin, Ömer Gökberk. “ESRI Land Cover Ve Dynamic World Arazi örtüsü Verilerinin karşılaştırılması: Kıbrıs Adası örneği”. Jeodezi Ve Jeoinformasyon Dergisi 11, no. 1 (May 2024): 19-29. https://doi.org/10.9733/JGG.2024R0002.T.
EndNote Narin ÖG (May 1, 2024) ESRI Land Cover ve Dynamic World arazi örtüsü verilerinin karşılaştırılması: Kıbrıs Adası örneği. Jeodezi ve Jeoinformasyon Dergisi 11 1 19–29.
IEEE Ö. G. Narin, “ESRI Land Cover ve Dynamic World arazi örtüsü verilerinin karşılaştırılması: Kıbrıs Adası örneği”, hkmojjd, vol. 11, no. 1, pp. 19–29, 2024, doi: 10.9733/JGG.2024R0002.T.
ISNAD Narin, Ömer Gökberk. “ESRI Land Cover Ve Dynamic World Arazi örtüsü Verilerinin karşılaştırılması: Kıbrıs Adası örneği”. Jeodezi ve Jeoinformasyon Dergisi 11/1 (May 2024), 19-29. https://doi.org/10.9733/JGG.2024R0002.T.
JAMA Narin ÖG. ESRI Land Cover ve Dynamic World arazi örtüsü verilerinin karşılaştırılması: Kıbrıs Adası örneği. hkmojjd. 2024;11:19–29.
MLA Narin, Ömer Gökberk. “ESRI Land Cover Ve Dynamic World Arazi örtüsü Verilerinin karşılaştırılması: Kıbrıs Adası örneği”. Jeodezi Ve Jeoinformasyon Dergisi, vol. 11, no. 1, 2024, pp. 19-29, doi:10.9733/JGG.2024R0002.T.
Vancouver Narin ÖG. ESRI Land Cover ve Dynamic World arazi örtüsü verilerinin karşılaştırılması: Kıbrıs Adası örneği. hkmojjd. 2024;11(1):19-2.