Vejetasyon İndeksleri, Ana Bileşenler Analizi ve Google Earth Engine Kullanılarak Tarımsal Alan Sınıflandırması: Söke/Aydın Örneği

Yıl 2023, Cilt: 11 Sayı: 1, 96 - 104, 19.07.2023

Melis İnalpulat , Neslişah Civelek , Metin Uşaklı , Levent Genç

https://doi.org/10.33202/comuagri.1295054

Öz

Arazi kullanım ve arazi örtüsü (AKAÖ) sınıflaması çevresel değişim ve bozunmanın dünya genelinde en çok kullanılan göstergelerinden biri olarak bilinmektedir. AKAÖ sınıflaması için çeşitli algoritmalar ve metotlar var olup, en önemli hususların başında sınıflama haritalarının güvenliği gelmektedir. Çalışma, Google Earth Engine (GEE) platform kullanılarak tarımsal sınıflama için en geliştirici metotu belirlemek için Sentinel-2 görüntülerinin original bantlarının yanında Normalize Edilmiş Farklılık Vejetasyon İndeksi (NDVI), Yeşil NDVI (GNDVI) ve Ana Bileşenler Analizi (ABA) ile üretilmiş AKAÖ haritalarının doğruluğunun değerlendirilmesi üzerine odaklanmıştır. Bunun yanında, seçilen alan içerisindeki kısa dönem AKAÖ değişimlerinin belirlenmesi amaçlanmıştır. Amaçlara ulaşabilmek için, farklı yılların aynı ayında alınmış olan bulutluluk oranı %10’ dan az olan görüntüler kullanılarak AKAÖ2018 ve AKAÖ2022 haritaları eldesi için Mayıs 2018 ve Mayıs 2022 için ortalama görüntüler oluşturulmuştur. Alan rassal orman (RO) algoritması ile zeytin (Z), ekili tarım (E), Dikili tarım (D), orman (O) doğal vejetasyon (DV), yerleşim alanı (Y) ve su yüzeyi (S) olmak üzere yedi ana sınıfa ayrılmıştır. AKAÖ haritalarının güvenilirlikleri sınıf alanı büyüklüğüne göre rastgele dağıtılmış control noktaları göz önünde bulundurularak değerlendirilmiştir. Sınıfların birbirlerine dönüşümleri belirlenmiştir.

Anahtar Kelimeler

Ana Bileşenler Analizi, Google Earth Enine, Sentinel-2, sınıflama doğruluğu, tarımsal alan, vejetasyon indeksleri.

Agricultural Land Classification Using Vegetation Indices, PCA, and Google Earth Engine: Case Study of Söke/Aydın

Öz

Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.

Anahtar Kelimeler

Agricultural Land Classification Using Vegetation Indices, PCA, and Google Earth Engine: Case Study of Söke/Aydın

Kaynakça

• Ahmed, J., Ahmed, M., Laghari, A., Lohana, W., Ali, S., Fatmi, Z., 2009. Public private mix model in enhancing tuberculosis case detection in District Thatta, Sindh, Pakistan. J. Pak. Med. Assoc. 59:(2): 82.
• Balazs, B., Biro, T., Dyke, G., Singh, S.K., Szabo, S., 2018. Extracting water-related features using reflectance data and principal component analysis of Landsat images. Hydrological Sciences Journal, 63(2): 269-284.
• Belcore, E., Piras, M., Wozniak, E., 2020. Specific Alpine environment land cover classification methodology: Google Earth Engine processing for Sentinel-2 data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLIII-B3-2: 663-670.
• 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.
• Congalton, R.G., Green, K., 2009. Assessing the accuracy of remotely sensed data: principles and practices. Photogramm. Rec. https://doi.org/10.1111/j.1477-9730.2010.005742.x.
• Derdouri, A., Wang, R., Murayama, Y., Osaragi, T., 2021. Understanding the links between lulc changes and SUHI in Cities: Insights from two-decadal studies (2001–2020). Remote Sensing. 13: 3654.
• Dutta, V., 2012. Land use dynamics and peri-urban growth characteristics: Reflections on master plan and urban suitability from a sprawling north Indian city. Environment and Urbanization ASIA, 3(2): 277-301.
• El-kawy, A.O.R., Ismail, H.A., Yehia, H.M., Allam, M.A., 2019. Temporal detection and prediction of agricultural land consumption by urbanization using remote sensing. Egypt. J. Remote. Sens. Space Sci. https://doi.org/10.1016/j.ejrs.2019.05.001.
• Estornell, J., Marti-Gavila, J.M., Sebastia, M.T., Mengual, J., 2013. Principal component analysis applied to remote sensing. Modelling in Science Education and Learning. 6(2/7): 83-89.
• Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M., Neigh, C., Reichstein, M., 2013. Trend change detection in NDVI time series: effects of inter-annual variability andmethodology. Remote Sensing.. 5(5): 2113-2144.
• Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202: 18-27.
• Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58: 289-298.
• Hussain, S., Mubeen, M., Karuppannan, S., 2022. Land use and land cover (LULC) change analysis using TM, ETM+ and OLI Landsat images in district of Okara, Punjab, Pakistan. Physics and Chemistry of the Earth. 126: 103117.
• Kara, B, 2019. Agrarian and wetland areas under metropolitan threats: learning from the case of Inciralti, Izmir (Turkey). Applied Ecology and Environmental Research. 17(6): 15087-151102.
• Kesgin-Atak, B., Ersoy-Tonyaloğlu, E., 2020. Monitoring the spatiotemporal changes in regional ecosystem health: A case study in Izmir, Turkey. Environ. Monit. Assess. 192: 385.
• Kesgin-Atak, B., Ersoy-Tonyaloğlu, E., 2020. Evaluation of the effect of land use/land cover and vegetation cover change on land surface temperature: The case of Aydın province. Turkish Journal of Forestry. 21(4): 489-497
• Lasanta, T., Nadal-Romero, E., Arnáez, J., 2015. Managing abandoned farmland tocontrol the impact of re-vegetation on the environment. The state of the art in Europe. Environmental Science & Policy. 52: 99-109.
• Liu, J., Pattey, E., and Jégo, G., 2012. Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment. 123: 347–358.
• Lodato, F., Colonna, N., Pennazza, G., Praticò, S., Santonico, M., Vollero, L., Pollino, M., 2023. Analysis of the spatiotemporal urban expansion of the Rome Coastline through GEE and RF Algorithm, Using Landsat imagery. ISPRS Int. J. Geo-Inf. 12: 141.
• Lu, D., Mausel, P., Brondízio, E., Moran, E., 2004. Change detection techniques. Int. J. Rem. Sens. 25(12): 2365-2401.
• Maity, B., Mallick, S.K., Rudra, S., 2020. Spatiotemporal dynamics of urban landscape in Asansol municipal corporation, West Bengal, India: a geospatial analysis. GeoJournal. https://doi.org/10.1007/s10708-020-10315-z.
• Mallick, K.S., Rudra, S., 2021. Land use changes and its impact on biophysical environment: Study on a river bank. The Egyptian Journal of Remote Sensing and Space Sciences. 24: 1037-1049.
• Parente, L., Taquary, E., Silva, A.P., Souza, C., Ferreira, L., 2019. Next generation mapping: Combining deep learning, cloud computing, and big remote sensing data, Remote Sens., 11(23): 2881.
• Rawat, J.S., Biswas, V., Kumar, M., 2013. Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt. J. Remote Sensing Space Sci. 16(1): 111-117.
• Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the great plains with ERTS (Earth Resources Technology Satellite). Proceedings of 3rd Earth Resources Technology Satellite Symposium.
• Rwanga, S.S., Ndambuki, J.M., 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int. J. Geosci. 8: 611-622.
• Salem, M., Tsurusaki, N., Divigalpitiya, P., 2021. Remote sensing-based detection of agricultural land losses around Greater Cairo since the Egyptian revolution of 2011.
• Schmitt, M., Hughes, L.H., Qiu, C., Zhu, X.X., 2019. Aggregating cloud-free Sentinel-2 Images with Google Earth Engine. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. IV-2/W7: 145–152.
• TÜİK, 2023. Türkiye İstatistik Kurumu, İlçe bazlı bitkisel üretim istatistikleri. https://biruni.tuik.gov.tr/medas/?locale=tr
• Usman, M., Liedl, R., Shahid, M.A., Abbas, A., 2015. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. J. Geosci. 25(12): 1479-1506.
• Vivekananda, G.M., Swathi, R., Sujith, A.V.L.N., 2021. Multi-temporal image analysis for LULC classification and change detection. European Journal of Remote Sensing. 54(Supp.2): 189-199.
• Vizzari, M., 2022. PlanetScope, Sentinel-2, and Sentinel-1 data integration for object-based land cover classification in Google Earth Engine. Remote Sens. 14: 2628.
• Yassine, H., Tout, K., Jaber, M., 2021. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2021, XXIV ISPRS Congress. https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-369-2021