Land Use Classification in Nakhon Nayok Province Using Machine Learning Glgorithms and Sentinel-2 Image
Abstract
The dynamic of land use can be recognized as the transformation of local socio-economic. Nakhon Nayok Province exhibits the field crop and environmental destination as significant sites, and it further reveals an active transition of local land use. Therefore, acquiring updated land-use information is significant to local management and administration. Recently, satellite imagery has been freely available and incorporated with the classification process using any classifiers. In addition, machine learning classifiers have been introduced to increase the potential of classification. However, applying these classifiers demands experiments to identify the fittest algorithm. This article examined the potential of four machine learning algorithms: analytical neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM), including the standard classifier (maximum likelihood: MLC). Hyperparameters of each machine learning algorithm have been inquired properly. Classification results were then selected to identify the most desirable classifier using overall accuracy, F1-score, kappa coefficient, and Z-statistics. According to the result, the random forest has been reported as the best classifier, which gains 92.00% of overall accuracy—followed by a decision tree, analytical neural network, support vector machine, and maximum likelihood classifier (84.00. 69.00, 65.00, and 63.00 of overall accuracy, respectively). The Z-statistics also confirmed that the random forest had a significant difference compared with other classifiers at a 95% confidence level. Keywords : land use ; classification ; machine learning ; satellite image ; Sentinel-2References
Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. Giscience & Remote Sensing, 57(1), 1-20. doi:10.1080/15481603.2019.1650447
Arora, M. K., & Watanachaturaporn, P. (2004). Support vector machines for classification of multi-and hyperspectral data. In Advanced image processing techniques for remotely sensed hyperspectral data (pp. 237-255): Springer.
Aurélien, G. (2017). Hands-on machine learning with scikit-learn & tensorflow. Geron Aurelien.
Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices: CRC press.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees: CRC press.
Dubeau, P., King, D. J., Unbushe, D. G., & Rebelo, L.-M. (2017). Mapping the Dabus wetlands, Ethiopia, using random forest classification of Landsat, PALSAR and topographic data. Remote Sensing, 9(10), 1056.
Ge, G., Shi, Z., Zhu, Y., Yang, X., & Hao, Y. (2020). Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation, 22, e00971. doi:https://doi.org/10.1016/j.gecco.2020.
e00971
Imran, A. B., Khan, K., Ali, N., Ahmad, N., Ali, A., & Shah, K. (2020). Narrow band based and broadband derived vegetation indices using Sentinel-2 Imagery to estimate vegetation biomass. Global Journal of Environmental Science and Management-Gjesm, 6(1), 97-108. doi:10.22034/gjesm.2020.01.08
Intarat, K., & Sillaparat, S. (2019). Tropical Mangrove Species Classification Using Random Forest Algorithm and Very High-Resolution Satellite Imagery. Burapha Science Journal, 24(2), 742-753. (in Thai)
Jhonnerie, R., Siregar, V. P., Nababan, B., Prasetyo, L. B., & Wouthuyzen, S. (2015). Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24, 215-221
Lennon, R. (2002). Remote sensing digital image analysis: An introduction. United States: Esa/Esrin.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation: John Wiley & Sons.
Majnouni-Toutakhane, A. (2020). Modeling the Land Use Change Process on the South Coast of the Caspian Sea Using Logistic Regression and Artificial Neural Network. Journal of Environmental Accounting and Management, 8(2), 111-123. doi:10.5890/jeam.2020.06.001
Mazzia, V., Khaliq, A., & Chiaberge, M. (2020). Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN). Applied Sciences-Basel, 10(1). doi:10.3390/app10010238
Macintyre, P., van Niekerk, A., & Mucina, L. (2020). Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification. International Journal of Applied Earth Observation and Geoinformation, 85. doi:10.1016/j.jag.2019.101980
Meyer, D., Leisch, F., & Hornik, K. (2003). The support vector machine under test. Neurocomputing, 55(1-2), 169-186.
Piedelobo, L., Hernandez-Lopez, D., Ballesteros, R., Chakhar, A., Del Pozo, S., Gonzalez-Aguilera, D., & Moreno, M. A. (2019). Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin. Agricultural Systems, 171, 36-50. doi:10.1016/j.agsy.2019.01.005
Ramo, R., & Chuvieco, E. (2017). Developing a random forest algorithm for MODIS global burned area classification. Remote Sensing, 9(11), 1193.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. Isprs Journal of Photogrammetry and Remote Sensing, 67, 93-104.
Saboori, M., Torahi, A. A., & Bakhtyari, H. R. R. (2019). Combining multi-scale textural features from the panchromatic bands of high spatial resolution images with ANN and MLC classification algorithms to extract urban land uses. International Journal of Remote Sensing, 40(22), 8608-8634. doi:10.1080/01431161.2019.1620371
Shrestha, A., & Mahmood, A. (2019). Review of Deep Learning Algorithms and Architectures. Ieee Access, 7, 53040-53065. doi:10.1109/access.2019.2912200
Silva, L. P. e., Xavier, A. P. C., da Silva, R. M., & Santos, C. A. G. (2020). Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil. Global Ecology and Conservation, 21, e00811. doi:https://doi.org/10.1016/j.gecco.2019.e00811
Sun, C. L., Bian, Y., Zhou, T., & Pan, J. L. (2019). Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. Sensors, 19(10). doi:10.3390/s19102401
Vasilakos, C., Kavroudakis, D., & Georganta, A. (2020). Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem. Remote Sensing, 12(12). doi:10.3390/rs12122005
Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5), 360-363.
Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., & Wu, X. (2018). Artificial mangrove species mapping using pléiades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing, 10(2), 294.
Watanachaturaporn, P., Arora, M. K., & Varshney, P. K. (2008). Multisource classification using support vector machines: An empirical comparison with decision tree and neural network classifiers. Photogrammetric Engineering and Remote Sensing, 74(2), 239-246. doi:10.14358/pers.74.2.239
Zhang, K. X., Wu, X. L., Niu, R. Q., Yang, K., & Zhao, L. R. (2017). The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environmental Earth Sciences, 76(11). doi:10.1007/s12665-017-6731-5
Zhu, Y., Liu, K., Liu, L., Myint, S. W., Wang, S., Liu, H., & He, Z. (2017). Exploring the potential of worldview-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sensing, 9(10), 1060.
Arora, M. K., & Watanachaturaporn, P. (2004). Support vector machines for classification of multi-and hyperspectral data. In Advanced image processing techniques for remotely sensed hyperspectral data (pp. 237-255): Springer.
Aurélien, G. (2017). Hands-on machine learning with scikit-learn & tensorflow. Geron Aurelien.
Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices: CRC press.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees: CRC press.
Dubeau, P., King, D. J., Unbushe, D. G., & Rebelo, L.-M. (2017). Mapping the Dabus wetlands, Ethiopia, using random forest classification of Landsat, PALSAR and topographic data. Remote Sensing, 9(10), 1056.
Ge, G., Shi, Z., Zhu, Y., Yang, X., & Hao, Y. (2020). Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation, 22, e00971. doi:https://doi.org/10.1016/j.gecco.2020.
e00971
Imran, A. B., Khan, K., Ali, N., Ahmad, N., Ali, A., & Shah, K. (2020). Narrow band based and broadband derived vegetation indices using Sentinel-2 Imagery to estimate vegetation biomass. Global Journal of Environmental Science and Management-Gjesm, 6(1), 97-108. doi:10.22034/gjesm.2020.01.08
Intarat, K., & Sillaparat, S. (2019). Tropical Mangrove Species Classification Using Random Forest Algorithm and Very High-Resolution Satellite Imagery. Burapha Science Journal, 24(2), 742-753. (in Thai)
Jhonnerie, R., Siregar, V. P., Nababan, B., Prasetyo, L. B., & Wouthuyzen, S. (2015). Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24, 215-221
Lennon, R. (2002). Remote sensing digital image analysis: An introduction. United States: Esa/Esrin.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation: John Wiley & Sons.
Majnouni-Toutakhane, A. (2020). Modeling the Land Use Change Process on the South Coast of the Caspian Sea Using Logistic Regression and Artificial Neural Network. Journal of Environmental Accounting and Management, 8(2), 111-123. doi:10.5890/jeam.2020.06.001
Mazzia, V., Khaliq, A., & Chiaberge, M. (2020). Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN). Applied Sciences-Basel, 10(1). doi:10.3390/app10010238
Macintyre, P., van Niekerk, A., & Mucina, L. (2020). Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification. International Journal of Applied Earth Observation and Geoinformation, 85. doi:10.1016/j.jag.2019.101980
Meyer, D., Leisch, F., & Hornik, K. (2003). The support vector machine under test. Neurocomputing, 55(1-2), 169-186.
Piedelobo, L., Hernandez-Lopez, D., Ballesteros, R., Chakhar, A., Del Pozo, S., Gonzalez-Aguilera, D., & Moreno, M. A. (2019). Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin. Agricultural Systems, 171, 36-50. doi:10.1016/j.agsy.2019.01.005
Ramo, R., & Chuvieco, E. (2017). Developing a random forest algorithm for MODIS global burned area classification. Remote Sensing, 9(11), 1193.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. Isprs Journal of Photogrammetry and Remote Sensing, 67, 93-104.
Saboori, M., Torahi, A. A., & Bakhtyari, H. R. R. (2019). Combining multi-scale textural features from the panchromatic bands of high spatial resolution images with ANN and MLC classification algorithms to extract urban land uses. International Journal of Remote Sensing, 40(22), 8608-8634. doi:10.1080/01431161.2019.1620371
Shrestha, A., & Mahmood, A. (2019). Review of Deep Learning Algorithms and Architectures. Ieee Access, 7, 53040-53065. doi:10.1109/access.2019.2912200
Silva, L. P. e., Xavier, A. P. C., da Silva, R. M., & Santos, C. A. G. (2020). Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil. Global Ecology and Conservation, 21, e00811. doi:https://doi.org/10.1016/j.gecco.2019.e00811
Sun, C. L., Bian, Y., Zhou, T., & Pan, J. L. (2019). Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. Sensors, 19(10). doi:10.3390/s19102401
Vasilakos, C., Kavroudakis, D., & Georganta, A. (2020). Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem. Remote Sensing, 12(12). doi:10.3390/rs12122005
Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5), 360-363.
Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., & Wu, X. (2018). Artificial mangrove species mapping using pléiades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing, 10(2), 294.
Watanachaturaporn, P., Arora, M. K., & Varshney, P. K. (2008). Multisource classification using support vector machines: An empirical comparison with decision tree and neural network classifiers. Photogrammetric Engineering and Remote Sensing, 74(2), 239-246. doi:10.14358/pers.74.2.239
Zhang, K. X., Wu, X. L., Niu, R. Q., Yang, K., & Zhao, L. R. (2017). The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environmental Earth Sciences, 76(11). doi:10.1007/s12665-017-6731-5
Zhu, Y., Liu, K., Liu, L., Myint, S. W., Wang, S., Liu, H., & He, Z. (2017). Exploring the potential of worldview-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sensing, 9(10), 1060.
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2022-05-24
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