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Dergiler / International Journal of Engineering and Geosciences / 2023 / Cilt: 8 - Sayı: 1

Comparison between random forest and support vector machine algorithms for LULC classification

Cengiz AVCI
Muhammed BUDAK
Nur YAĞMUR
Filiz BALÇIK
Sayfa
1–10
Yayın Tarihi
2023-02-15
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Whereas the water body and wetland have decreased more than 50% between 2015 and 2020, crop sites have increased approximately 50%.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Nowadays, machine learning (ML) algorithms have been widely chosen for classifying satellite images for mapping Earth's surface.
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Temel Bulgular & Veri:Support Vector Machine (SVM) and Random Forest (RF) stand out among these algorithms with their accurate results in the literature.
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Sonuç & Katkı:Whereas the water body and wetland have decreased more than 50% between 2015 and 2020, crop sites have increased approximately 50%.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2015 Verileri
⚙️Değişkenler:Remote Sensing · Supervised Classification · Random Forest

Abstract

Nowadays, machine learning (ML) algorithms have been widely chosen for classifying satellite images for mapping Earth's surface. Support Vector Machine (SVM) and Random Forest (RF) stand out among these algorithms with their accurate results in the literature. The aim of this study is to analyze the performances of these algorithms on land use and land cover (LULC) classification, especially wetlands which have significant ecological functions. For this purpose, Sentinel-2 satellite image, which is freely provided by European Space Agency (ESA), was used to monitor not only the open surface water body but also around Marmara Lake. The performance evaluation was made with the increasing number of the training dataset. 3 different training datasets having 10, 15, and 20 areas of interest (AOI) per class, respectively were used for the classification of the satellite images acquired in 2015 and 2020. The most accurate results were obtained from the classification with RF algorithm and 20 AOIs. According to obtained results, the change detection analysis of Marmara Lake was investigated for possible reasons. Whereas the water body and wetland have decreased more than 50% between 2015 and 2020, crop sites have increased approximately 50%.

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Bu Makaleye Atıf Yap

AVCI, C., BUDAK, M., YAĞMUR, N., BALÇIK, F. (2023). Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences, Cilt: 8 - Sayı: 1, 1–10. https://ajindex.com/articles/875461

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Kaynakça (9)

  1. DeFries, R. S., Foley, J. A., & Asner, G. P. (2004). Land‐use choices: Balancing human needs and ecosystem function. Frontiers in Ecology and the Environment, 2(5), 249-257.
  2. Kavzoglu, T., Tonbul, H., Erdemir, M. Y., & Colkesen, I. (2018). Dimensionality reduction and classification of hyperspectral images using object-based image analysis. Journal of the Indian Society of Remote Sensing, 46(8), 1297-1306.
  3. Ekumah, B., Armah, F. A., Afrifa, E. K., Aheto, D. W., Odoi, J. O., & Afitiri, A. R. (2020). Assessing land use and land cover change in coastal urban wetlands of international importance in Ghana using Intensity Analysis. Wetlands Ecology and Management, 28(2), 271-284.
  4. Basu, T., Das, A., Pham, Q. B., Al-Ansari, N., Linh, N. T. T., & Lagerwall, G. (2021). Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India. Scientific reports, 11(1), 1-22.
  5. Jamal, S., & Ahmad, W. S. (2020). Assessing land use land cover dynamics of wetland ecosystems using Landsat satellite data. SN Applied Sciences, 2(11), 1-24.
  6. Hochreuther, P., Neckel, N., Reimann, N., Humbert, A., & Braun, M. (2021). Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series. Remote Sens. 2021, 13, 205. Shih, H. C., Stow, D. A., & Tsai, Y. H. (2019). Guidance on and comparison of machine learning classifiers for Landsat-based land cover and land use mapping. International Journal of Remote Sensing, 40(4), 1248-1274.
  7. Bangira, T., Alfieri, S. M., Menenti, M., & Van Niekerk, A. (2019). Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11), 1351. Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern recognition letters, 27(4), 294-300.
  8. 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.
  9. Wang, Y., Ma, J., Xiao, X., Wang, X., Dai, S., & Zhao, B. (2019). Long-term dynamic of poyang lake surface water: a mapping work based on the Google earth engine cloud platform. Remote Sensing, 11(3), 313. Wang, C., Jia, M., Chen, N., & Wang, W. (2018). Long-term surface water dynamics analysis based on Landsat imagery and the Google Earth Engine platform: A case study in the middle Yangtze River Basin. Remote Sensing, 10(10), 1635. Amani, M., Mahdavi