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Crop Type Classification using Sentinel 2A-Derived Normalized Difference Red Edge Index (NDRE) and Machine Learning Approach

Year 2024, Volume: 38 Issue: 1, 89 - 105, 14.06.2024

Bere Benjamin Bantchına Kemal Sulhi Gündoğdu

https://doi.org/10.20479/bursauludagziraat.1402043

Abstract

Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have recently gained considerable attention in the scientific community. This study aimed to enhance remote sensing research using high-resolution satellite imagery and a ML approach. To achieve this objective, ML algorithms were employed to demonstrate whether it was possible to accurately classify various crop types within agricultural areas using the Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE). Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), were implemented using Python programming on Google Colaboratory. The target land cover classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT (88%), KNN (87%), SVM (85%), and MLP (82%). These findings showed the possibility of achieving high classification accuracy using NDRE from a few Sentinel 2A images. This study demonstrated the potential enhancement of the application of high-resolution satellite RS data and ML for crop type classification in regions that have received less attention in previous studies.

Keywords

Classification Crop Type Machine Learning NDRE Remote Sensing Sentinel 2A

References

  • Abubakar, G., Wang, K., Shahtahamssebi, A., Xue, X., Belete, M., Gudo, A. and Gan, M. 2020. Mapping maize fields by using multi-temporal Sentinel-1A and Sentinel-2A images in Makarfi, Northern Nigeria, Africa. Sustainability, 12(6):2539. https://doi.org/10.3390/su12062539
  • Arora, A., Sim, C., Severson, D. and Kang, D. 2022. Random forest analysis of impact of abiotic factors on Culex pipiens and Culex quinquefasciatus occurrence. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.773360
  • Bantchina, B. B., Mucan, U. and Gündoğdu, K. S. 2017. Land Availability Analysis in Bursa using Geographic Information Systems. In Proceedings Book, Proceedings of the 5th International Participation Soil and Water Resources Congress, Kırklareli, Turkey, 12–15 September 2017; Atatürk Soil Water and Agricultural Meteorology Research Institute Kırklareli: Merkez, Turkey; Volume 1, pp. 65–74.
  • Cuenca, M., Campo‐Bescós, M. and Álvarez‐Mozos, J. 2020. Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain. Remote Sensing, 12(2):278. https://doi.org/10.3390/rs12020278
  • Cunningham, P. and Delany, S. 2021. K-nearest neighbour classifiers - a tutorial. Acm Computing Surveys, 54(6): 1-25. https://doi.org/10.1145/3459665
  • Fan, J., Zhang, X., Zhao, C., Qin, Z., De Vroey, M., and Defourny, P. 2021. Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources. Remote Sensing, 13(5):911. https://doi.org/10.3390/rs13050911
  • Ghamisi, P., Plaza, J., Chen, Y., Li, J. and Plaza, A. 2017. Advanced spectral classifiers for hyperspectral images: a review. Ieee Geoscience and Remote Sensing Magazine, 5(1):8–32. https://doi.org/10.1109/mgrs.2016.2616418
  • Gündoğdu, K. S. and Bantchina, B. B. 2018. Landsat Uydu Görüntülerinden NDVI Değer Dağılımının Parsel Bazlı Değerlendirilmesi, Bursa Uludağ Üniversitesi Ziraat Fakültesi Çiftlik Arazisi Örneği. Bursa Uludag Üniv. Ziraat Fak. Derg., 32 (2):45–53.
  • Hajian, A., Zomorrodian, H., Styles, P., Greco, F. and Lucas, C. 2011. Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (lolimot). Near Surface Geophysics, 10(3):221–234. https://doi.org/10.3997/1873-0604.2011039
  • Hardisky, M. A., Klemas, V. and Smart, R. M. 1983. The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering & amp; Remote Sensing, 49:77–83.
  • Haykin, S. and Kosko, B. 2009. Gradient-based learning applied to document recognition. https://doi.org/10.1109/9780470544976.ch9
  • Izza, Y., Ignatiev, A. and Marques-Silva, J. 2022. On tackling explanation redundancy in decision trees. Journal of Artificial Intelligence Research, 75:261–321. https://doi.org/10.1613/jair.1.13575
  • Jensen, R. and Cornelis, C. 2008. A new approach to fuzzy-rough nearest neighbour classification., 310–319. https://doi.org/10.1007/978-3-540-88425-5_32
  • Kang, Y., Xinli H., Qingyan, M, Youfeng, Z., Linlin, Z., Miao, L. and Maofan, Z. 2021. Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data. Remote Sensing 13(22): 4522. https://doi.org/10.3390/rs13224522
  • LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the Ieee, 86(11):2278–2324. https://doi.org/10.1109/5.726791
  • Li, J., Shen, Y. and Yang, C. 2020. An adversarial generative network for crop classification from remote sensing time series images. Remote Sensing, 13(1):65. https://doi.org/10.3390/rs13010065
  • Liu, Z., Su, B. and Lv, F. 2022. Intelligent identification method of crop species using improved U-net network in UAV remote sensing image. Scientific Programming, 1–9. https://doi.org/10.1155/2022/9717843
  • Lu, T., Wan, L. and Wang, L. 2022. Fine crop classification in high-resolution remote sensing based on deep learning. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.991173
  • Mahynski, N., Ragland, J., Schuur, S. and Shen, V. 2022. Building interpretable machine learning models to identify chemometric trends in seabirds of the north Pacific Ocean. Environmental Science & Technology, 56(20):14361–14374. https://doi.org/10.1021/acs.est.2c01894
  • Mashaba-Munghemezulu, Z., Chirima, G. and Munghemezulu, C. 2021. Delineating smallholder maize farms from Sentinel-1 coupled with Sentinel-2 data using machine learning. Sustainability, 13(9):4728. https://doi.org/10.3390/su13094728
  • Mazarire, T., Ratshiedana, P., Nyamugama, A., Adam, E. and Chirima, G. 2022. Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. a case study of Free State Province, South Africa. South African Journal of Geomatics, 9(2):333–347. https://doi.org/10.4314/sajg.v9i2.22
  • Muntean, M. and Militaru, F. D. 2023. Metrics for Evaluating Classification Algorithms. In: Ciurea, C., Pocatilu, P., Filip, F.G. (eds) Education, Research and Business Technologies. Smart Innovation, Systems and Technologies, vol 321. Springer, Singapore. https://doi.org/10.1007/978-981-19-6755-9_24
  • Mustak, S., Uday, G., Ramesh, B. and Praveen, B. 2019. Evaluation of the performance of sar and sar-optical fused dataset for crop discrimination. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLII-3/W6, 563–571. https://doi.org/10.5194/isprs-archives-xlii-3-w6-563-2019
  • Ndikumana, E., Minh, D., Baghdadi, N., Courault, D. and Hossard, L. 2018. Deep Recurrent Neural Network for Agricultural Classification Using Multitemporal SAR Sentinel-1 For Camargue, France. Remote Sensing, 8(10):1217. https://doi.org/10.3390/rs10081217
  • Nguyen, H. and Nansen, C. 2020. Hyperspectral remote sensing to detect leafminer‐induced stress in bok choy and spinach according to fertilizer regime and timing. Pest Management Science, 76(6):2208–2216. https://doi.org/10.1002/ps.5758
  • Nidamanuri, R., Garg, P. and Ghosh, S. 2007. Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture, 8(4-5):173–185. https://doi.org/10.1007/s11119-007-9037-x
  • Pech-May, F., Aquino-Santos, R., Ríos-Toledo, G. and Posadas-Durán, J. 2022. Mapping of land cover with optical images, supervised algorithms, and Google Earth engine. Sensors, 22(13):4729. https://doi.org/10.3390/s22134729
  • Pham, B., Nguyen, M., Bui, K., Prakash, I., Chapi, K. and Bui, D. 2019. A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena, 173:302–311. https://doi.org/10.1016/j.catena.2018.10.004
  • Ren, T., Liu, Z., Zhang, L., Liu, D., Xi, X., Kang, Y., Zhao, Y., Zhang, C., Li, S. and Zhang, X. 2020. Early identification of seed maize and common maize production fields using Sentinel-2 images. Remote Sensing, 12(13):2140.
  • Rumelhart, D., Hinton, G. and Williams, R. 1986. Learning representations by back-propagating errors. Nature, 323(6088):533–536. https://doi.org/10.1038/323533a0
  • Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Arnal, A., Andrés, A. and Zurbano, J. 2018. Scalable parcel-based crop identification scheme using Sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sensing, 10(6):911. https://doi.org/10.3390/rs10060911
  • Sonobe, R. 2019. Parcel-based crop classification using multi-temporal Terrasar-x dual polarimetric data. Remote Sensing, 11(10):1148. https://doi.org/10.3390/rs11101148
  • Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N. and Mochizuki, K. 2018. Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. Journal of Applied Remote Sensing, 12(02):1. https://doi.org/10.1117/1.jrs.12.026019
  • Strobl, C., Boulesteix, A., Zeileis, A. and Hothorn, T. 2007. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics, 8(1). https://doi.org/10.1186/1471-2105-8-25
  • Tian, H., Yong-Jiu, W., Cui, T., Zhang, L. and Qin, Y. 2021. Early-season mapping of winter crops using sentinel-2 optical imagery. Remote Sensing, 13(19):3822. https://doi.org/10.3390/rs13193822
  • Ustuner, M., Sanli, F. B., Abdikan, S., Esetlili, M. T. and Kurucu, Y. 2014. Crop type classification using vegetation indices of RapidEye imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014 ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
  • Yang, N., Liu, D., Feng, Q., Xiong, Q., Zhang, L., Ren, T. and Huang, J. 2019. Large-scale crop mapping based on machine learning and parallel computation with grids. Remote Sensing, 11(12):1500. https://doi.org/10.3390/rs11121500
  • Zhou, T., Pan, J., Zhang, P., Wei, S. and Han, T. 2017. Mapping Winter Wheat with Multi-temporal Sar and Optical Images in an Urban Agricultural Region. Sensors, 6(17):1210. https://doi.org/10.3390/s17061210

Sentinel 2A Uydu Görüntüsünden Normalleştirilmiş Fark Kırmızı Kenar İndeksi (NDRE) Kullanılarak Tarımsal Ürünlerin Makine Öğrenme Yöntemleri ile Sınıflandırılması

Year 2024, Volume: 38 Issue: 1, 89 - 105, 14.06.2024

Bere Benjamin Bantchına Kemal Sulhi Gündoğdu

https://doi.org/10.20479/bursauludagziraat.1402043

Abstract

Uzaktan algılama, arazi örtüsü ve bitki türleriyle ilgili kritik bilgilerin edinilmesini sağlayarak tarım alanındaki araştırmalara önemli katkılar sunmaktadır. Son zamanlarda, uzaktan algılama verileri ve makine öğrenimi algoritmaları aracılığıyla arazi örtüsü ve ürün türlerinin sınıflandırılması konusu büyük ilgi çekmektedir. Bu çalışmanın ana amacı, yüksek çözünürlüklü uydu görüntüleri ve makine öğrenimi yaklaşımını kullanarak uzaktan algılama araştırma alanını geliştirmektir. Bu hedefe ulaşmak adına, Sentinel 2A'dan elde edilen Normalleştirilmiş Fark Kırmızı Kenar İndeksi (NDRE) ile tarım alanlarındaki çeşitli ürün türlerinin etkili bir şekilde sınıflandırılmasının mümkün olup olmadığını değerlendirmek amacıyla çeşitli makine öğrenimi yöntemleri kullanılmıştır. Karar Ağaçları (KA), Destek Vektör Makineleri (DVM), Rastgele Orman (RO), K-En Yakın Komşular (KEYK) ve Çok Katmanlı Algılayıcı (ÇKA) dahil olmak üzere beş makine öğrenimi sınıflandırıcı algoritması uygulanmıştır. Analizde değerlendirilen hedef arazi örtüsü sınıfları arasında tahıllar, nadas, yem bitkileri, meyveler, çayır-mera, baklagiller, mısır, şeker pancarı, soğan-sarımsak, ayçiçeği ve karpuz-kavun bulunmaktadır. Elde edilen sınıflandırma modelleri, yüksek doğruluk oranları ile güçlü bir performans sergilemiştir. RF modeli %95'lik genel doğruluk (OA) oranı ve %92'lik Kappa skoru ile en yüksek performans göstermiştir. Bunu sırasıyla %88, %87, %85 ve %82 OA ile KA, KEYK, DVM ve ÇKA takip etmiştir. Bu bulgular, az sayıda Sentinel 2A görüntüsünden NDRE kullanılarak yüksek sınıflandırma doğruluğu elde edilebileceğini göstermektedir. Bu çalışma, yüksek mekânsal çözünürlüğe sahip uydu uzaktan algılama verileri ve makine öğrenimi algoritmalarının, mahsul türü sınıflandırması için potansiyel bir gelişim sağlayabileceğini doğrulamıştır.

Keywords

Makine Öğrenme NDRE Sentinel 2A Sınıflandırma Tarımsal Ürünlerin Uzaktan Algılama

Ethical Statement

Makale araştırma ve yayın etiğine uygun olarak hazırlanmıştır.

References

  • Abubakar, G., Wang, K., Shahtahamssebi, A., Xue, X., Belete, M., Gudo, A. and Gan, M. 2020. Mapping maize fields by using multi-temporal Sentinel-1A and Sentinel-2A images in Makarfi, Northern Nigeria, Africa. Sustainability, 12(6):2539. https://doi.org/10.3390/su12062539
  • Arora, A., Sim, C., Severson, D. and Kang, D. 2022. Random forest analysis of impact of abiotic factors on Culex pipiens and Culex quinquefasciatus occurrence. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.773360
  • Bantchina, B. B., Mucan, U. and Gündoğdu, K. S. 2017. Land Availability Analysis in Bursa using Geographic Information Systems. In Proceedings Book, Proceedings of the 5th International Participation Soil and Water Resources Congress, Kırklareli, Turkey, 12–15 September 2017; Atatürk Soil Water and Agricultural Meteorology Research Institute Kırklareli: Merkez, Turkey; Volume 1, pp. 65–74.
  • Cuenca, M., Campo‐Bescós, M. and Álvarez‐Mozos, J. 2020. Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain. Remote Sensing, 12(2):278. https://doi.org/10.3390/rs12020278
  • Cunningham, P. and Delany, S. 2021. K-nearest neighbour classifiers - a tutorial. Acm Computing Surveys, 54(6): 1-25. https://doi.org/10.1145/3459665
  • Fan, J., Zhang, X., Zhao, C., Qin, Z., De Vroey, M., and Defourny, P. 2021. Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources. Remote Sensing, 13(5):911. https://doi.org/10.3390/rs13050911
  • Ghamisi, P., Plaza, J., Chen, Y., Li, J. and Plaza, A. 2017. Advanced spectral classifiers for hyperspectral images: a review. Ieee Geoscience and Remote Sensing Magazine, 5(1):8–32. https://doi.org/10.1109/mgrs.2016.2616418
  • Gündoğdu, K. S. and Bantchina, B. B. 2018. Landsat Uydu Görüntülerinden NDVI Değer Dağılımının Parsel Bazlı Değerlendirilmesi, Bursa Uludağ Üniversitesi Ziraat Fakültesi Çiftlik Arazisi Örneği. Bursa Uludag Üniv. Ziraat Fak. Derg., 32 (2):45–53.
  • Hajian, A., Zomorrodian, H., Styles, P., Greco, F. and Lucas, C. 2011. Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (lolimot). Near Surface Geophysics, 10(3):221–234. https://doi.org/10.3997/1873-0604.2011039
  • Hardisky, M. A., Klemas, V. and Smart, R. M. 1983. The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering & amp; Remote Sensing, 49:77–83.
  • Haykin, S. and Kosko, B. 2009. Gradient-based learning applied to document recognition. https://doi.org/10.1109/9780470544976.ch9
  • Izza, Y., Ignatiev, A. and Marques-Silva, J. 2022. On tackling explanation redundancy in decision trees. Journal of Artificial Intelligence Research, 75:261–321. https://doi.org/10.1613/jair.1.13575
  • Jensen, R. and Cornelis, C. 2008. A new approach to fuzzy-rough nearest neighbour classification., 310–319. https://doi.org/10.1007/978-3-540-88425-5_32
  • Kang, Y., Xinli H., Qingyan, M, Youfeng, Z., Linlin, Z., Miao, L. and Maofan, Z. 2021. Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data. Remote Sensing 13(22): 4522. https://doi.org/10.3390/rs13224522
  • LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the Ieee, 86(11):2278–2324. https://doi.org/10.1109/5.726791
  • Li, J., Shen, Y. and Yang, C. 2020. An adversarial generative network for crop classification from remote sensing time series images. Remote Sensing, 13(1):65. https://doi.org/10.3390/rs13010065
  • Liu, Z., Su, B. and Lv, F. 2022. Intelligent identification method of crop species using improved U-net network in UAV remote sensing image. Scientific Programming, 1–9. https://doi.org/10.1155/2022/9717843
  • Lu, T., Wan, L. and Wang, L. 2022. Fine crop classification in high-resolution remote sensing based on deep learning. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.991173
  • Mahynski, N., Ragland, J., Schuur, S. and Shen, V. 2022. Building interpretable machine learning models to identify chemometric trends in seabirds of the north Pacific Ocean. Environmental Science & Technology, 56(20):14361–14374. https://doi.org/10.1021/acs.est.2c01894
  • Mashaba-Munghemezulu, Z., Chirima, G. and Munghemezulu, C. 2021. Delineating smallholder maize farms from Sentinel-1 coupled with Sentinel-2 data using machine learning. Sustainability, 13(9):4728. https://doi.org/10.3390/su13094728
  • Mazarire, T., Ratshiedana, P., Nyamugama, A., Adam, E. and Chirima, G. 2022. Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. a case study of Free State Province, South Africa. South African Journal of Geomatics, 9(2):333–347. https://doi.org/10.4314/sajg.v9i2.22
  • Muntean, M. and Militaru, F. D. 2023. Metrics for Evaluating Classification Algorithms. In: Ciurea, C., Pocatilu, P., Filip, F.G. (eds) Education, Research and Business Technologies. Smart Innovation, Systems and Technologies, vol 321. Springer, Singapore. https://doi.org/10.1007/978-981-19-6755-9_24
  • Mustak, S., Uday, G., Ramesh, B. and Praveen, B. 2019. Evaluation of the performance of sar and sar-optical fused dataset for crop discrimination. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLII-3/W6, 563–571. https://doi.org/10.5194/isprs-archives-xlii-3-w6-563-2019
  • Ndikumana, E., Minh, D., Baghdadi, N., Courault, D. and Hossard, L. 2018. Deep Recurrent Neural Network for Agricultural Classification Using Multitemporal SAR Sentinel-1 For Camargue, France. Remote Sensing, 8(10):1217. https://doi.org/10.3390/rs10081217
  • Nguyen, H. and Nansen, C. 2020. Hyperspectral remote sensing to detect leafminer‐induced stress in bok choy and spinach according to fertilizer regime and timing. Pest Management Science, 76(6):2208–2216. https://doi.org/10.1002/ps.5758
  • Nidamanuri, R., Garg, P. and Ghosh, S. 2007. Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture, 8(4-5):173–185. https://doi.org/10.1007/s11119-007-9037-x
  • Pech-May, F., Aquino-Santos, R., Ríos-Toledo, G. and Posadas-Durán, J. 2022. Mapping of land cover with optical images, supervised algorithms, and Google Earth engine. Sensors, 22(13):4729. https://doi.org/10.3390/s22134729
  • Pham, B., Nguyen, M., Bui, K., Prakash, I., Chapi, K. and Bui, D. 2019. A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena, 173:302–311. https://doi.org/10.1016/j.catena.2018.10.004
  • Ren, T., Liu, Z., Zhang, L., Liu, D., Xi, X., Kang, Y., Zhao, Y., Zhang, C., Li, S. and Zhang, X. 2020. Early identification of seed maize and common maize production fields using Sentinel-2 images. Remote Sensing, 12(13):2140.
  • Rumelhart, D., Hinton, G. and Williams, R. 1986. Learning representations by back-propagating errors. Nature, 323(6088):533–536. https://doi.org/10.1038/323533a0
  • Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Arnal, A., Andrés, A. and Zurbano, J. 2018. Scalable parcel-based crop identification scheme using Sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sensing, 10(6):911. https://doi.org/10.3390/rs10060911
  • Sonobe, R. 2019. Parcel-based crop classification using multi-temporal Terrasar-x dual polarimetric data. Remote Sensing, 11(10):1148. https://doi.org/10.3390/rs11101148
  • Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N. and Mochizuki, K. 2018. Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. Journal of Applied Remote Sensing, 12(02):1. https://doi.org/10.1117/1.jrs.12.026019
  • Strobl, C., Boulesteix, A., Zeileis, A. and Hothorn, T. 2007. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics, 8(1). https://doi.org/10.1186/1471-2105-8-25
  • Tian, H., Yong-Jiu, W., Cui, T., Zhang, L. and Qin, Y. 2021. Early-season mapping of winter crops using sentinel-2 optical imagery. Remote Sensing, 13(19):3822. https://doi.org/10.3390/rs13193822
  • Ustuner, M., Sanli, F. B., Abdikan, S., Esetlili, M. T. and Kurucu, Y. 2014. Crop type classification using vegetation indices of RapidEye imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014 ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
  • Yang, N., Liu, D., Feng, Q., Xiong, Q., Zhang, L., Ren, T. and Huang, J. 2019. Large-scale crop mapping based on machine learning and parallel computation with grids. Remote Sensing, 11(12):1500. https://doi.org/10.3390/rs11121500
  • Zhou, T., Pan, J., Zhang, P., Wei, S. and Han, T. 2017. Mapping Winter Wheat with Multi-temporal Sar and Optical Images in an Urban Agricultural Region. Sensors, 6(17):1210. https://doi.org/10.3390/s17061210
There are 38 citations in total.

Details

Primary Language English
Subjects Biosystem, Precision Agriculture Technologies
Journal Section Research Articles
Authors

Bere Benjamin Bantchına 0000-0002-2593-426X

Kemal Sulhi Gündoğdu 0000-0002-5591-4788

Early Pub Date June 11, 2024
Publication Date June 14, 2024
Submission Date December 8, 2023
Acceptance Date March 20, 2024
Published in Issue Year 2024 Volume: 38 Issue: 1

Cite

APA Bantchına, B. B., & Gündoğdu, K. S. (2024). Crop Type Classification using Sentinel 2A-Derived Normalized Difference Red Edge Index (NDRE) and Machine Learning Approach. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi, 38(1), 89-105. https://doi.org/10.20479/bursauludagziraat.1402043
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TR Dizin kriterleri gereği dergimize gönderilecek olan makalelerin mutlaka aşağıda belirtilen hususlara uyması gerekmektedir.

Tüm bilim dallarında yapılan, ve etik kurul kararı gerektiren klinik ve deneysel insan ve hayvanlar üzerindeki çalışmalar için ayrı ayrı etik kurul onayı alınmış olmalı, bu onay makalede belirtilmeli ve belgelendirilmelidir.
Makalelerde Araştırma ve Yayın Etiğine uyulduğuna dair ifadeye yer verilmelidir.
Etik kurul izni gerektiren çalışmalarda, izinle ilgili bilgiler (kurul adı, tarih ve sayı no) yöntem bölümünde ve ayrıca makale ilk/son sayfasında yer verilmelidir.
Kullanılan fikir ve sanat eserleri için telif hakları düzenlemelerine riayet edilmesi gerekmektedir.
Makale sonunda; Araştırmacıların Katkı Oranı beyanı, varsa Destek ve Teşekkür Beyanı, Çatışma Beyanı verilmesi.
Etik Kurul izni gerektiren araştırmalar aşağıdaki gibidir.
- Anket, mülakat, odak grup çalışması, gözlem, deney, görüşme teknikleri kullanılarak katılımcılardan veri toplanmasını gerektiren nitel ya da nicel yaklaşımlarla yürütülen her türlü araştırmalar
- İnsan ve hayvanların (materyal/veriler dahil) deneysel ya da diğer bilimsel amaçlarla kullanılması,
- İnsanlar üzerinde yapılan klinik araştırmalar,
- Hayvanlar üzerinde yapılan araştırmalar,
- Kişisel verilerin korunması kanunu gereğince retrospektif çalışmalar,
Ayrıca;
- Olgu sunumlarında “Aydınlatılmış onam formu”nun alındığının belirtilmesi,
- Başkalarına ait ölçek, anket, fotoğrafların kullanımı için sahiplerinden izin alınması ve belirtilmesi,
- Kullanılan fikir ve sanat eserleri için telif hakları düzenlemelerine uyulduğunun belirtilmesi.



Makale başvurusunda;

(1) Tam metin makale, Dergi yazım kurallarına uygun olmalı, Makalenin ilk sayfasında ve teşekkür bilgi notu kısmında Araştırma ve Yayın Etiğine uyulduğuna ve Etik kurul izni gerektirmediğine dair ifadeye yer verilmelidir. Etik kurul izni gerektiren çalışmalarda, izinle ilgili bilgiler (kurul adı, tarih ve sayı no) yöntem bölümünde ve ayrıca makale ilk/son sayfasında yer verilmeli ve sisteme belgenin yüklenmesi gerekmektedir. (Dergiye gönderilen makalelerde; konu ile ilgili olarak derginin daha önceki sayılarında yayımlanan en az bir yayına atıf yapılması önem arz etmektedir. Dergiye yapılan atıflarda “Bursa Uludag Üniv. Ziraat Fak. Derg.” kısaltması kullanılmalıdır.)

(2) Tam metin makalenin taratıldığını gösteren benzerlik raporu (Ithenticate, intihal.net) (% 20’nin altında olmalıdır),

(3) İmzalanmış ve taratılmış başvuru formu, Dergi web sayfasında yer alan başvuru formunun başvuran tarafından İmzalanıp, taratılarak yüklenmesi , (Ön yazı yerine)

(4) Tüm yazarlar tarafından imzalanmış telif hakkı devir formunun taranmış kopyası,

(5) Araştırmacıların Katkı Oranı beyanı, Çıkar Çatışması beyanı verilmesi Makale sonunda; Araştırmacıların Katkı Oranı beyanı, varsa Destek ve Teşekkür Beyanı, Çatışma Beyanı verilmesi ve sisteme belgenin (Tüm yazarlar tarafından imzalanmış bir yazı) yüklenmesi gerekmektedir.

Belgelerin elektronik formatta DergiPark sistemine https://dergipark.org.tr/tr/login adresinden kayıt olunarak başvuru sırasında yüklenmesi mümkündür. 


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