Research Article
Year 2023,
Volume: 9 Issue: 2, 150 - 156, 01.01.2024
Abstract
The usage areas of satellite systems and remote sensing technologies are considerably expanding. It is frequently used, especially in determining land use classes and land cover changes, and the thematic maps produced can be presented as a data source. In this study, land use classes were determined and mapped using supervised classification techniques such as maximum likelihood (ML), support vector machines (SVM) linear function, SVM radial function, SVM polynomial function, SVM sigmoid function and artificial neural networks (ANN) using Landsat 8 Operational Land Imager satellite images. For this purpose, Eleman Forest Planning Unit was selected, and the stand map of this area was used as reference data. Both classification success and the McNemar test were used to compare the performance of six different supervised classification techniques. According to the results, kappa and overall accuracy values of all classification techniques were found to be 0.80 and above 80%, respectively. The highest kappa (0.8488) and overall accuracy (89.1442%) values were obtained for the SVM polynomial function. According to the McNemar test results, there was no statistical difference between the most successful SVM polynomial function and ANN method (χ2<3.8414). The SVM polynomial function and ANN approach were found to be the most effective classification methods among the employed supervised classification techniques.
References
- Aksoy, H., Kaptan, S. 2021. Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: A study in Northern Turkey. Environmental monitoring and assessment, 193(8), 507.
- Aksoy, H., Kaptan, S. 2022. Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model. Geocarto International, 37(4), 1183-1202.
- Aliabad, F. A., Zare, M., Solgi, R., Shojaei, S. 2023. Comparison of neural network methods (fuzzy ARTMAP, Kohonen and Perceptron) and maximum likelihood efficiency in preparation of land use map. GeoJournal, 88(2), 2199-2214.
- Anlar, H. C., Günlü, A., Keleş, S., Bulut, S. 2015. SPOT-4 uydu görüntüsü yardımıyla bazı meşcere parametreleri (gelişim çağı ve kapalılık) ve arazi kullanım sınıflarının belirlenmesi; Devrez Planlama Birimi Örneği. Anadolu Orman Araştırmaları Dergisi, 1(1-2), 33-40.
- Anonim 2018. Ankara Orman Bölge Müdürlüğü, Eskipazar Orman İşletme Müdürlüğü, Eleman Orman İşletme Şefliği, Ekosistem Tabanlı Fonksiyonel Orman Amenajman Planı. T. C. Orman Genel Müdürlüğü, Orman İdaresi ve Planlama Dairesi Başkanlığı, Ankara.
- Berberoğlu, S., Şatır, O. 2008. Fuzzy classification of Mediterranean type forest using ENVISAT MERRIS data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B8, pp. 1109-1114. Beijing, China.
- Bolstad, P., Lillesand, T. M. 1991. Rapid maximum likelihood classification. Photogrammetric engineering and remote sensing, 57(1), 67-74.
- Bulut, S., Günlü, A. 2016. Arazi kullanım sınıfları için farklı kontrollü sınıflandırma algoritmalarının karşılaştırılması. Kastamonu University Journal of Forestry Faculty, 16(2), 528-535.
- Bulut, S., Günlü, A. 2019. Determination of total carbon storage using Sentinel-2 and Geographic Information Systems in mixed forests. Anadolu Orman Araştırmaları Dergisi, 5(2), 127-135.
- Bulut, S., Günlü, A., Keleş, S. 2019. Estimation of forest development stage and crown closure using different classification methods and satellite images: A case study from Turkey. Journal of Forest Science, 65(1), 18-26.
- Dash, P., Sanders, S. L., Parajuli, P., Ouyang, Y. 2023. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed. Remote Sensing, 15(16), 4020.
- Demirci, F. 2008. Filyos havzasındaki sediment birikim alanlarının uydu görüntü verileri ve sayısal arazi modeli ile analizi. İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 73 s.
- Dietterich, T. G. 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10(7), 1895-1923.
- Ediş, S., Tuttu, G., Aytaş, İ., Tuttu, U., Özcan, A. U. 2022. Acıçay (Çankırı) Riparian Zonunda zamansal ve mekânsal değişimin analizi. AÇÜ Orman. Fak. Derg. 23(1), 1-10.
- Erbek, F. S., Özkan, C., Taberner, M. 2004. Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International journal of remote sensing, 25(9), 1733-1748.
- Ersoy Mirici, M., Şatır, O., Berberoğlu, S. 2020. Monitoring the Mediterranean type forests and land-use/cover changes using appropriate landscape metrics and hybrid classification approach in Eastern Mediterranean of Turkey. Environmental Earth Sciences, 79(21), 492.
- Geetha, V., Aprameya, K. S., Hinduja, D. M. 2020. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Information Science and Systems, 8, 1-14.
- Göl, C., Günlü, A., Ediş, S., Küçükdöngül, A. 2018. Çorum-Osmancık-Emine Deresi ve yan dere havzaları 1990-2014 yılları havza ıslah çalışmalarının arazi kullanım türü/arazi örtüsüne etkileri. Turkish Journal of Forestry, 19(2), 149-155.
- Haykin, S. 1998. Neural networks: a comprehensive foundation. Prentice Hall PTR.
- Hsu, C. W., Chang, C. C., Lin, C. J. 2003. A practical guide to support vector classification. Taipei, National Taiwan University, 16.
- Jog, S., Dixit, M. 2016. Supervised classification of satellite images. In 2016 Conference on Advances in Signal Processing (CASP) (pp. 93-98). IEEE.
- Kavzoğlu, T., Reis, S., 2008, Performance Analysis of Maximum Likelihood and Artificial Neural Network Classifiers for Training Sets with Mixed Pixels, GIScience and Remote Sensing, 45(3), 330–342.
- Kavzoğlu T., Çölkesen I. 2009. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11, 352–359.
- Kavzoğlu, T., Çölkesen, İ. 2010. Destek vektör makineleri ile uydu görüntülerinin sınıflandırılmasında kernel fonksiyonlarının etkilerinin incelenmesi. Harita Dergisi, 144(7), 73-82.
- Khan, U., Minallah, N., Junaid, A., Gul, K., Ahmad, N. 2015. Parallelepiped and Mahalanobis Distance based Classification for forestry identification in Pakistan. In 2015 International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE.
- Knapp, G. M., Wang, H. P. 1992. Machine fault classification: a neural network approach. International Journal of Production Research, 30(4), 811-823.
- Kulkarni, A. D., Lowe, B. 2016. Random forest algorithm for land cover classification. Computer Science Faculty Publications and Presentations. Paper 1. htp://hdl.handle.net/10950/341.
- Kumar, A., Garg, R. D., Singh, P., Shankar, A., Nayak, S. R., Diwakar, M. 2023. Monitoring the Land Use, Land Cover Changes of Roorkee Region (Uttarakhand, India) Using Machine Learning Techniques. International Journal of Social Ecology and Sustainable Development (IJSESD), 14(1), 1-16.
- Lu, D., Hetrick, S., Moran, E. 2010. Land cover classification in a complex urban-rural landscape with QuickBird imagery. Photogrammetric Engineering & Remote Sensing, 76(10), 1159-1168.
- McNemar, Q. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153-157.
- Şatır, O., Berberoğlu, S. 2012. Land use/cover classification techniques using optical remotely sensed data in landscape planning. Landscape Planning. Rijeka: InTech, 21-54.
- Otukei, J. R., Blaschke, T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
- Özkan, U., Yeşil, A. 2016. Forest stand delineation using Ikonos image and object based image analysis. Journal of the Faculty of Forestry Istanbul University, 66(2), 600-612.
- Paola, J. D., Schowengerdt, R. A. 1995. A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and remote sensing, 33(4), 981-996.
- Roy, A., Chakraborty, S. 2023. Support vector machine in structural reliability analysis: A review. Reliability Engineering & System Safety, 109126.
- Saralioglu, E., Vatandaslar, C. 2022. Land use/land cover classification with Landsat-8 and Landsat-9 satellite images: A comparative analysis between forest-and agriculture-dominated landscapes using different machine learning methods. Acta Geodaetica et Geophysica, 57(4), 695-716.
- Shaharum, N. S. N., Shafri, H. Z. M., Gambo, J., Abidin, F. A. Z. 2018. Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sensing Applications: Society and Environment, 10, 24-35.
- Sisodia, P. S., Tiwari, V., Kumar, A. 2014. Analysis of supervised maximum likelihood classification for remote sensing image. In International conference on recent advances and innovations in engineering (ICRAIE-2014) (pp. 1-4). IEEE.
- Vapnik, V. N. 1963. Pattern recognition using generalized portrait method. Automation and remote control, 24(6), 774-780.
- Vapnik, V. N. 1999. The nature of statistical learning theory. Springer science & business media.
Year 2023,
Volume: 9 Issue: 2, 150 - 156, 01.01.2024
Abstract
Uydu sistemlerinin ve uzaktan algılama teknolojilerinin kullanım alanları oldukça genişlemektedir. Özellikle arazi kullanım sınıfları ile arazi örtü değişimlerinin belirlenmesinde sıklıkla kullanılmakta ve üretilen tematik haritalar veri kaynağı olarak sunulabilmektedir. Bu çalışmada, Landsat 8 Operational Land Imager (OLI) uydu görüntüsü kullanılarak en çok benzerlik (EÇB), destek vektör makineleri (DVM) doğrusal fonksiyon, DVM radyal fonksiyon, DVM polinom fonksiyon, DVM sigmoid fonksiyon ve yapay sinir ağları (YSA) gibi kontrollü sınıflandırma teknikleri ile arazi kullanım sınıfları belirlenmiş ve haritalanmıştır. Bunun için Eleman Orman İşletme Şefliği seçilmiş ve referans veri olması için bu şefliğe ait meşcere haritası kullanılmıştır. Altı farklı kontrollü sınıflandırma tekniğinin performansını karşılaştırmak için hem sınıflandırma başarıları hem de McNemar testi kullanılmıştır. Bulgulara göre bütün sınıflandırma tekniklerine ait kappa ve genel doğruluk değerleri 0.80 ve %80 üzerinde bulunmuştur. En yüksek kappa (0,8488) ve genel doğruluk (%89,1442) değeri ise DVM polinom fonksiyon için elde edilmiştir. McNemar test sonuçlarına göre en başarılı DVM polinom fonksiyon ve YSA metodu arasında istatistiksel olarak bir fark bulunmamıştır (χ2<3,8414). Kullanılan kontrollü sınıflandırma teknikleri arasında en etkili sınıflandırma yöntemlerinin DVM polinom fonksiyon ve YSA metodu olduğu bulunmuştur.
Keywords
Kontrollü sınıflandırma Landsat 8 OLI McNemar testi destek vektör makineleri yapay sinir ağları
References
- Aksoy, H., Kaptan, S. 2021. Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: A study in Northern Turkey. Environmental monitoring and assessment, 193(8), 507.
- Aksoy, H., Kaptan, S. 2022. Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model. Geocarto International, 37(4), 1183-1202.
- Aliabad, F. A., Zare, M., Solgi, R., Shojaei, S. 2023. Comparison of neural network methods (fuzzy ARTMAP, Kohonen and Perceptron) and maximum likelihood efficiency in preparation of land use map. GeoJournal, 88(2), 2199-2214.
- Anlar, H. C., Günlü, A., Keleş, S., Bulut, S. 2015. SPOT-4 uydu görüntüsü yardımıyla bazı meşcere parametreleri (gelişim çağı ve kapalılık) ve arazi kullanım sınıflarının belirlenmesi; Devrez Planlama Birimi Örneği. Anadolu Orman Araştırmaları Dergisi, 1(1-2), 33-40.
- Anonim 2018. Ankara Orman Bölge Müdürlüğü, Eskipazar Orman İşletme Müdürlüğü, Eleman Orman İşletme Şefliği, Ekosistem Tabanlı Fonksiyonel Orman Amenajman Planı. T. C. Orman Genel Müdürlüğü, Orman İdaresi ve Planlama Dairesi Başkanlığı, Ankara.
- Berberoğlu, S., Şatır, O. 2008. Fuzzy classification of Mediterranean type forest using ENVISAT MERRIS data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B8, pp. 1109-1114. Beijing, China.
- Bolstad, P., Lillesand, T. M. 1991. Rapid maximum likelihood classification. Photogrammetric engineering and remote sensing, 57(1), 67-74.
- Bulut, S., Günlü, A. 2016. Arazi kullanım sınıfları için farklı kontrollü sınıflandırma algoritmalarının karşılaştırılması. Kastamonu University Journal of Forestry Faculty, 16(2), 528-535.
- Bulut, S., Günlü, A. 2019. Determination of total carbon storage using Sentinel-2 and Geographic Information Systems in mixed forests. Anadolu Orman Araştırmaları Dergisi, 5(2), 127-135.
- Bulut, S., Günlü, A., Keleş, S. 2019. Estimation of forest development stage and crown closure using different classification methods and satellite images: A case study from Turkey. Journal of Forest Science, 65(1), 18-26.
- Dash, P., Sanders, S. L., Parajuli, P., Ouyang, Y. 2023. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed. Remote Sensing, 15(16), 4020.
- Demirci, F. 2008. Filyos havzasındaki sediment birikim alanlarının uydu görüntü verileri ve sayısal arazi modeli ile analizi. İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 73 s.
- Dietterich, T. G. 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10(7), 1895-1923.
- Ediş, S., Tuttu, G., Aytaş, İ., Tuttu, U., Özcan, A. U. 2022. Acıçay (Çankırı) Riparian Zonunda zamansal ve mekânsal değişimin analizi. AÇÜ Orman. Fak. Derg. 23(1), 1-10.
- Erbek, F. S., Özkan, C., Taberner, M. 2004. Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International journal of remote sensing, 25(9), 1733-1748.
- Ersoy Mirici, M., Şatır, O., Berberoğlu, S. 2020. Monitoring the Mediterranean type forests and land-use/cover changes using appropriate landscape metrics and hybrid classification approach in Eastern Mediterranean of Turkey. Environmental Earth Sciences, 79(21), 492.
- Geetha, V., Aprameya, K. S., Hinduja, D. M. 2020. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Information Science and Systems, 8, 1-14.
- Göl, C., Günlü, A., Ediş, S., Küçükdöngül, A. 2018. Çorum-Osmancık-Emine Deresi ve yan dere havzaları 1990-2014 yılları havza ıslah çalışmalarının arazi kullanım türü/arazi örtüsüne etkileri. Turkish Journal of Forestry, 19(2), 149-155.
- Haykin, S. 1998. Neural networks: a comprehensive foundation. Prentice Hall PTR.
- Hsu, C. W., Chang, C. C., Lin, C. J. 2003. A practical guide to support vector classification. Taipei, National Taiwan University, 16.
- Jog, S., Dixit, M. 2016. Supervised classification of satellite images. In 2016 Conference on Advances in Signal Processing (CASP) (pp. 93-98). IEEE.
- Kavzoğlu, T., Reis, S., 2008, Performance Analysis of Maximum Likelihood and Artificial Neural Network Classifiers for Training Sets with Mixed Pixels, GIScience and Remote Sensing, 45(3), 330–342.
- Kavzoğlu T., Çölkesen I. 2009. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11, 352–359.
- Kavzoğlu, T., Çölkesen, İ. 2010. Destek vektör makineleri ile uydu görüntülerinin sınıflandırılmasında kernel fonksiyonlarının etkilerinin incelenmesi. Harita Dergisi, 144(7), 73-82.
- Khan, U., Minallah, N., Junaid, A., Gul, K., Ahmad, N. 2015. Parallelepiped and Mahalanobis Distance based Classification for forestry identification in Pakistan. In 2015 International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE.
- Knapp, G. M., Wang, H. P. 1992. Machine fault classification: a neural network approach. International Journal of Production Research, 30(4), 811-823.
- Kulkarni, A. D., Lowe, B. 2016. Random forest algorithm for land cover classification. Computer Science Faculty Publications and Presentations. Paper 1. htp://hdl.handle.net/10950/341.
- Kumar, A., Garg, R. D., Singh, P., Shankar, A., Nayak, S. R., Diwakar, M. 2023. Monitoring the Land Use, Land Cover Changes of Roorkee Region (Uttarakhand, India) Using Machine Learning Techniques. International Journal of Social Ecology and Sustainable Development (IJSESD), 14(1), 1-16.
- Lu, D., Hetrick, S., Moran, E. 2010. Land cover classification in a complex urban-rural landscape with QuickBird imagery. Photogrammetric Engineering & Remote Sensing, 76(10), 1159-1168.
- McNemar, Q. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153-157.
- Şatır, O., Berberoğlu, S. 2012. Land use/cover classification techniques using optical remotely sensed data in landscape planning. Landscape Planning. Rijeka: InTech, 21-54.
- Otukei, J. R., Blaschke, T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
- Özkan, U., Yeşil, A. 2016. Forest stand delineation using Ikonos image and object based image analysis. Journal of the Faculty of Forestry Istanbul University, 66(2), 600-612.
- Paola, J. D., Schowengerdt, R. A. 1995. A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and remote sensing, 33(4), 981-996.
- Roy, A., Chakraborty, S. 2023. Support vector machine in structural reliability analysis: A review. Reliability Engineering & System Safety, 109126.
- Saralioglu, E., Vatandaslar, C. 2022. Land use/land cover classification with Landsat-8 and Landsat-9 satellite images: A comparative analysis between forest-and agriculture-dominated landscapes using different machine learning methods. Acta Geodaetica et Geophysica, 57(4), 695-716.
- Shaharum, N. S. N., Shafri, H. Z. M., Gambo, J., Abidin, F. A. Z. 2018. Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sensing Applications: Society and Environment, 10, 24-35.
- Sisodia, P. S., Tiwari, V., Kumar, A. 2014. Analysis of supervised maximum likelihood classification for remote sensing image. In International conference on recent advances and innovations in engineering (ICRAIE-2014) (pp. 1-4). IEEE.
- Vapnik, V. N. 1963. Pattern recognition using generalized portrait method. Automation and remote control, 24(6), 774-780.
- Vapnik, V. N. 1999. The nature of statistical learning theory. Springer science & business media.
There are 40 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Forestry Sciences (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 30, 2023 |
| Publication Date | January 1, 2024 |
| Submission Date | September 27, 2023 |
| Published in Issue | Year 2023 Volume: 9 Issue: 2 |
