Google Earth Engine Üzerinde Sentinel-2 Görüntülerinin Arazi Örtüsü Haritalama Doğruluğunun İyileştirilmesi

Yıl 2022, Cilt: 3 Sayı: 2, 150 - 159, 18.09.2022

Çiğdem Şerifoğlu Yılmaz

https://doi.org/10.48123/rsgis.1119572

Cited By: 1

Öz

Yeryüzü nesnelerinin başarılı bir şekilde izlenmesi, planlanması ve yönetimi için yüksek doğruluklu arazi örtüsü bilgisi gereklidir. Ücretsiz erişilebilen uydu görüntüleri sayesinde, birçok uygulamada temel altlık verisi olarak kullanılabilen yüksek doğruluklu tematik haritaların oluşturulmasına yönelik çalışmalar yoğunlaşmıştır. Bulut tabanlı Google Earth Engine (GEE) platformu, bu tür uydu görüntülerine erişmeyi, depolamayı ve işlemeyi kolaylaştırmaktadır. Bu çalışma, Sentinel-2 uydu görüntüsü ile üretilmiş bir arazi örtüsü haritasının doğruluğunu artırmayı amaçlamaktadır. Bu amaçla, ilk aşamada, çalışma alanı Sentinel-2 verilerinin yalnızca geleneksel bantları kullanılarak sınıflandırılmıştır. Daha sonra, sınıflandırma doğruluğunu iyileştirmek için Sentinel-2 görüntüsünün geleneksel bantlarına Sentinel-1 Yapay Açıklıklı Radar (SAR) verisi, Gelişmiş Arazi Gözlem Uydusu (ALOS) 3 boyutlu sayısal yükseklik modeli verileri, çeşitli spektral indeksler ve Gri Seviyeli Eşdizimlilik Matrisi (GLCM) özellikleri eklenerek çok kaynaklı bir sınıflandırma prosedürü geliştirilmiştir. Trabzon ilinin şehir merkezinin çalışma bölgesi olarak seçildiği bu çalışmada, Rastgele Orman (RF) sınıflandırma algoritması kullanılarak üretilen arazi örtüsü haritasının doğruluğu kullanılan yardımcı veriler ile %83.51'den %92.78'e yükseltilmiştir.

Anahtar Kelimeler

Google Earth Engine, Arazi örtüsü haritalama, Görüntü sınıflandırma, Sentinel-2

Improving the Land Cover Mapping Accuracy of the Sentinel-2 Imagery on Google Earth Engine

Öz

Accurate land cover information is necessary for successful monitoring, planning and management of the land cover features. Thanks to free-access satellite images, studies have focused on the creation of more accurate thematic maps, which have been used as a base data in many applications. The cloud-based Google Earth Engine (GEE) service makes it easier to access, store and process these satellite images. This study aims to improve the accuracy of a land cover map produced with the Sentinel-2 satellite image. For this purpose, as the very first step, study site was classified using only traditional bands of the Sentinel-2 data. To improve the classification accuracy, Sentinel-1 Synthetic Aperture Radar (SAR) data, Advanced Land Observing Satellite (ALOS) World 3D data, various spectral indices and gray-level co-occurrence matrix (GLCM) features were added to the traditional bands of the Sentinel-2 data, leading to a multi-source classification process. In this study, where the city center of Trabzon was selected as the study area, the accuracy of the land cover map produced using the Random Forest (RF) classification algorithm was increased from 83.51% to 92.78% with the auxiliary data used.

Anahtar Kelimeler

Google Earth Engine, Land cover mapping, Image classification, Sentinel-2

Kaynakça

• Akar, A., Gökalp, E., Akar, Ö., & Yılmaz, V. (2017). Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images. Geocarto International, 32(9), 990-1003.
• Akar, Ö., & Güngör, O. (2012). Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2), 105-112.
• Amani, M., Salehi, B., Mahdavi, S., Granger, J. E., Brisco, B., & Hanson, A. (2017). Wetland classification using multi-source and multi-temporal optical remote sensing data in Newfoundland and Labrador, Canada. Canadian Journal of Remote Sensing, 43(4), 360-373.
• Ayhan, B., & Kwan, C. (2020). Tree, shrub, and grass classification using only RGB images. Remote Sensing, 12(8), 1333. doi: 10.3390/rs12081333.
• Birhanu, L., Hailu, B. T., Bekele, T., & Demissew, S. (2019). Land use/land cover change along elevation and slope gradient in highlands of Ethiopia. Remote Sensing Applications: Society and Environment, 16, 100260. doi: 10.1016/j.rsase.2019.100260.
• Chen, D., Shevade, V., Baer, A., He, J., Hoffman-Hall, A., Ying, Q., ... & Loboda, T. V. (2021). A disease control-oriented land cover land use map for Myanmar. Data, 6(6), 63-78.
• Chen, W., Liu, L., Zhang, C., Wang, J., Wang, J., & Pan, Y. (2004, September). Monitoring the seasonal bare soil areas in Beijing using multitemporal TM images. In IGARSS 2004 - 2004 IEEE International Geoscience and Remote Sensing Symposium (Vol. 5, pp. 3379-3382). IEEE.
• Chong, L. U. O., Liu, H. J., Lu, L. P., Liu, Z. R., Kong, F. C., & Zhang, X. L. (2021). Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine. Journal of Integrative Agriculture, 20(7), 1944-1957.
• Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment, 37(1), 35-46.
• Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton, FL: CRC press.
• Conners, R. W., Trivedi, M. M., & Harlow, C. A. (1984). Segmentation of a high-resolution urban scene using texture operators. Computer Vision, Graphics, and Image Processing, 25(3), 273-310.
• Coulter, L. L., Stow, D. A., Tsai, Y. H., Ibanez, N., Shih, H. C., Kerr, A., ... & 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.
• Dong, D., Wang, C., Yan, J., He, Q., Zeng, J., & Wei, Z. (2020). Combing Sentinel-1 and Sentinel-2 image time series for invasive Spartina alterniflora mapping on Google Earth Engine: a case study in Zhangjiang Estuary. Journal of Applied Remote Sensing, 14(4), 044504. doi: 10.1117/1.JRS.14.044504.
• Dumitru, C. O., Schwarz, G., Cui, S., & Datcu, M. (2016, May). Improved image classification by proper patch size selection: TerraSAR-X vs. sentinel-1A. In 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), 2016. Proceedings. (pp. 1-4). IEEE.
• Gitelson, A. A., & Merzlyak, M. N. (1998). Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5), 689-692.
• Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27.
• Han-Qiu, X. U. (2005). A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5), 589-595.
• Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621.
• Hu, B., Xu, Y., Huang, X., Cheng, Q., Ding, Q., Bai, L., & Li, Y. (2021). Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery. ISPRS International Journal of Geo-Information, 10(8), 533-549.
• Iftikhar, H., & Khurshid, K. (2011, November). Fusion of gabor filter and morphological operators for the detection of settlement zones in google earth satellite images. In 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). Proceedings. (pp. 232-237). IEEE.
• Ko, B. C., Kim, H. H., & Nam, J. Y. (2015). Classification of potential water bodies using Landsat 8 OLI and a combination of two boosted random forest classifiers. Sensors, 15(6), 13763-13777.
• Kobayashi, N., Tani, H., Wang, X., & Sonobe, R. (2020). Crop classification using spectral indices derived from Sentinel-2A imagery. Journal of Information and Telecommunication, 4(1), 67-90.
• Lee, J. S., Wen, J. H., Ainsworth, T. L., Chen, K. S., & Chen, A. J. (2008). Improved sigma filter for speckle filtering of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 47, 202–213.
• Liu, C., Frazier, P., & Kumar, L. (2007). Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment, 107(4), 606-616.
• Maffei Valero, M. A., Araújo, W. F., Melo, V. F., Augusti, M. L., & Fernandes Filho, E. I. (2022). Land-use and land-cover mapping using a combination of radar and optical sensors in Roraima–Brazil. Engenharia Agrícola, 42(2), e20210142. doi: 10.1590/1809-4430-Eng.Agric.v42n2e20210142/2022.
• Nguyen, C. T., Chidthaisong, A., Kieu Diem, P., & Huo, L. Z. (2021). A modified bare soil index to identify bare land features during agricultural fallow-period in southeast Asia using Landsat 8. Land, 10(3), 231-248.
• Pu, R., Landry, S., & Yu, Q. (2011). Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery. International Journal of Remote Sensing, 32(12), 3285-3308.
• Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77-84.
• Ressel, R., Frost, A., & Lehner, S. (2015). A neural network-based classification for sea ice types on X-band SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3672-3680.
• Rodriguez-Galiano, V. F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P. M., & Jeganathan, C. (2012). Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sensing of Environment, 121, 93-107.
• Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for north sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability, 11(11), 3024-3040.
• Saralioglu, E., & Gungor, O. (2020). Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network. Geocarto International, 37(2), 657-677.
• Shrestha, D. P., Saepuloh, A., & van der Meer, F. (2019). Land cover classification in the tropics, solving the problem of cloud covered areas using topographic parameters. International Journal of Applied Earth Observation and Geoinformation, 77, 84-93.
• Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N., & Mochizuki, K. I. (2017). Mapping crop cover using multi-temporal Landsat 8 OLI imagery. International Journal of Remote Sensing, 38(15), 4348-4361.
• Sun, Z., Xu, R., Du, W., Wang, L., & Lu, D. (2019). High-resolution urban land mapping in China from sentinel 1A/2 imagery based on Google Earth Engine. Remote Sensing, 11(7), 752. doi: 10.3390/rs11070752.
• Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., & Iwamoto, H. (2014). Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4), 71-76.
• Tadono, T., Nagai, H., Ishida, H., Oda, F., Naito, S., Minakawa, K., & Iwamoto, H. (2016). Generation of the 30 M-mesh global digital surface model by ALOS PRISM. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLI-B4(41), 157-162.
• Takaku, J., Tadono, T., & Tsutsui, K. (2014). Generation of High Resolution Global DSM from Alos Prism. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, XL(4), 243-248.
• Takaku, J., Tadono, T., Tsutsui, K., & Ichikawa, M. (2016). Validation of 'AW3D' Global DSM Generated from Alos Prism. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, III(4), 25-31.
• Tassi, A., Gigante, D., Modica, G., Di Martino, L., & Vizzari, M. (2021). Pixel-vs. Object-based landsat 8 data classification in google earth engine using random forest: The case study of maiella national park. Remote Sensing, 13(12), 2299.
• Tonbul, H., Colkesen, I., & Kavzoglu, T. (2022). Pixel-and Object-Based ensemble learning for forest burn severity using USGS FIREMON and Mediterranean condition dNBRs in Aegean ecosystem (Turkey). Advances in Space Research, 69(10), 3609-3632.
• Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L., & Shi, L. (2018). Mapping vegetation and land use types in Fanjingshan National Nature Reserve using google earth engine. Remote Sensing, 10(6), 927-941.
• Ustuner, M., & Balik Sanli, F. (2019). Polarimetric target decompositions and light gradient boosting machine for crop classification: A comparative evaluation. ISPRS International Journal of Geo-Information, 8(2), 97-112.
• Wagle, N., Acharya, T. D., Kolluru, V., Huang, H., & Lee, D. H. (2020). Multi-temporal land cover change mapping using google earth engine and ensemble learning methods. Applied Sciences, 10(22), 8083-8103.
• Weng, Q. (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment, 117, 34-49.
• Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., ... & Zhu, Z. (2019). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127-147.
• Xiao, P., Feng, X., An, R., & Zhao, S. (2010). Segmentation of multispectral high-resolution satellite imagery using log Gabor filters. International Journal of Remote Sensing, 31(6), 1427-1439.
• Yilmaz, V. (2021). Investigation of the performances of advanced image classification‐based ground filtering approaches for digital terrain model generation. Concurrency and Computation: Practice and Experience, 33(13), e6219. doi: 10.1002/cpe.6219.
• Yilmaz, V., Konakoglu, B., Serifoglu, C., Gungor, O., & Gökalp, E. (2018). Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos. Geocarto International, 33(3), 310-320.
• Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594.