1. bookVolume 40 (2021): Issue 3 (September 2021)
Journal Details
First Published
24 Aug 2013
Publication timeframe
4 times per year
access type Open Access

Land use land cover mapping using advanced machine learning classifiers

Published Online: 22 Oct 2021
Page range: 286 - 300
Received: 08 Mar 2020
Accepted: 29 May 2020
Journal Details
First Published
24 Aug 2013
Publication timeframe
4 times per year

Due to the recent climate changes such as floods and droughts, there is a need for Land Use Land Cover (LULC) mapping to monitor environmental changes that have effects on ecology, policy management, health and disaster management. As such, in this study, two well-known machine learning classifiers, namely, Support Vector Machine (SVM) and Random Forest (RF), are used for land cover mapping. In addition, two advanced deep learning algorithms, namely, the GAMLP and FSMLP, that are based on the Multi-layer Perceptron (MLP) function are developed in MATLAB programming language. The GAMLP uses a Genetic Algorithm (GA) to optimise parameters of the MLP function and, on the other hand, the FSMLP uses a derivative-free function for optimisation of the MLP function parameters. Three different scenarios using Landsat-8 imagery with spatial resolutions of 30 and 15 m are defined to investigate the effects of data pre-processing on the final predicted LULC map. Results based on the statistical indices, including overall accuracy (OA) and kappa index, show that the developed MLP-based algorithms have relatively high accuracies with higher than 98% correct classification. Besides the statistical indices, final LULC maps are interpreted visually where the GAMLP and FSMLP give the best results for the pre-processed Landsat-8 imagery with a spatial resolution of 15 m, but they have the worst outcomes for the unprocessed Landsat-8 imagery compared to SVM and RF classifiers visually and statistically.


Araki, S., Shima, M. & Yamamoto K. (2018). Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan. Sci. Total Environ., 634, 1269–1277. DOI: 10.1016/j. scitotenv.2018.03.324. Search in Google Scholar

Bégué, A., Arvor, D., Bellon, B., Betbeder, J., de Abelleyra, D., Ferraz, R.P.D., Lebourgeois, V., Lelong, C., Simőes, M. & Verón S.R. (2018). Remote sensing and cropping practices: A review. Remote Sensing, 10, 99. DOI: 10.3390/rs10010099.10.3390/rs10010099 Search in Google Scholar

Belward, A.S. & Skøien J.O. (2015). Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sens ing, 103, 115–128. DOI: 10.1016/j.isprsjprs.2014. Search in Google Scholar

Betts, M.G., Christopher Wolf, W.J., Ripple, B.P., Millers, K.A., Adam Duarte, S.H., Butchart, M. & Levi T. (2017). Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature, 547, 441‒447. DOI: 10.1038/nature23285.10.1038/nature23285 Search in Google Scholar

Bourgeois, M. & Sahraoui Y. (2020). Modelling in the context of an environmental mobilisation: a graph-based approach for assessing the landscape ecological impacts of a highway project. Ekológia (Bratislava), 39(1), 88−100. DOI: 10.2478/eko-2020-007. Search in Google Scholar

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. DOI: 10.1023/a:1010933404324.10.1023/A:1010933404324 Search in Google Scholar

Chang, C., Lo, S. & Yu S. (2006). The parameter optimization in the inverse distance method by genetic algorithm for estimating precipitation. En viron. Monit. Assess., 117, 145–155. DOI: 10.1007/s10661-006-8498-0.10.1007/s10661-006-8498-0 Search in Google Scholar

Chen, Y., Chen, J., Hsieh, S. & Ni P. (2009). The application of remote sensing technology to the interpretation of land use for rainfall-induced landslides based on genetic algorithms and artificial neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sens ing, 2, 87–95.10.1109/JSTARS.2009.2023802 Search in Google Scholar

Cortes, C. & Vapnik V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. DOI: 10.1007/bf00994018.10.1007/BF00994018 Search in Google Scholar

Goodin, D.G., Anibas, K.L. & Bezymennyi M. (2015). Mapping land cover and land use from object-based classification: An example from a complex agricultural landscape. Int. J. Remote Sens., 36(18), 4702–4723. DOI: 10.1080/01431161.2015.1088674.10.1080/01431161.2015.1088674 Search in Google Scholar

Harris, R. & Baumann I. (2015). Open data policies and satellite earth observation. Space Policy, 32, 44−53. DOI: 10.1016/j.spacepol.2015. Search in Google Scholar

Hasegawa, H., Arimura, M. & Tamura T. (2006). Hybrid model of random forests and genetic algorithms for commute mode choice. Analysis, 9. Search in Google Scholar

Hastie, T., Tibshirani, R. & Friedman J. (2009). Random forests. In The elements of statistical learning: Data mining, inference, and prediction (pp. 587–604). New York: Springer. DOI: 10.1007/978-0-387-84858-7_15.10.1007/978-0-387-84858-7_15 Search in Google Scholar

Jamali, A. (2019). Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran. SN Applied Sciences, 1, 1448. DOI: 10.1007/s42452-019-1527-8.10.1007/s42452-019-1527-8 Search in Google Scholar

Jamali, A. (2020a). Improving land use land cover mapping of a neural network with three optimizers of multi-verse optimizer, genetic algorithm, and derivative-free function. The Egyptian Journal of Remote Sensing and Space Science. DOI: 10.1016/j.ejrs.2020. Search in Google Scholar

Jamali, A. (2020b). Land use land cover mapping using advanced machine learning classifiers: A case study of Shiraz city, Iran. Earth Science Infor matics. DOI: 10.1007/s12145-020-00475-4.10.1007/s12145-020-00475-4 Search in Google Scholar

Jamali, A. (2020c). Land use land cover modeling using optimized machine learning classifiers: a case study of Shiraz, Iran. Model. Earth Syst. Envi ron. DOI: 10.1007/s40808-020-00859-x.10.1007/s40808-020-00859-x Search in Google Scholar

Jamali, A., Mahdianpari, M., Brisco, B., Granger, J., Mohammadimanesh, F. & Salehi B. (2021a). Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery. Remote Sensing, 13(11), 2046. DOI: 10.3390/rs13112046.10.3390/rs13112046 Search in Google Scholar

Jamali, A., Mahdianpari, M., Brisco, B., Granger, J., Mohammadimanesh, F. & Salehi B. (2021b). Wetland Mapping Using Multi-Spectral Satellite Imagery and Deep Convolutional Neural Networks: A Case Study in Newfoundland and Labrador, Canada. Canadian Journal of Remote Sensing, 1–18. DOI: 10.1080/07038992.2021.1901562.10.1080/07038992.2021.1901562 Search in Google Scholar

Kavzoglu, T. (2017). Object-oriented random forest for high resolution land cover mapping using quickbird-2 imagery. In S.S.P. Samui, S. Sekhar & V.E. Balas (Eds.), Handbook of neural computation (pp. 607–619). Cambridge: Academic Press. DOI: 10.1016/b978-0-12-811318-9.00033-8.10.1016/B978-0-12-811318-9.00033-8 Search in Google Scholar

Kenderessy P., Kollár, J. & Palaj A. (2020). The impact of historical agricultural landuse on selected site conditions in the traditional landscape of the West Carpathians. Ekológia (Bratislava), 39(4), 343−356. DOI: 10.2478/ eko-2020-0028. Search in Google Scholar

Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 1(5). https://www.jstatsoft.org/v028/i0510.18637/jss.v028.i05 Search in Google Scholar

Kussul, N., Lavreniuk, M., Skakun, S. & Shelestov A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778–782. DOI: 10.1109/ LGRS.2017.2681128.10.1109/LGRS.2017.2681128 Search in Google Scholar

Li, W., Haohuan, F., Le Yu, P., Gong, D.F., Congcong, L. & Clinton N. (2016). Stacked autoencoder-based deep learning for remote-sensing image classification: A case study of African land-cover mapping. Int. J. Remote Sens., 37(23), 5632–5646. DOI: 10.1080/01431161.2016.1246775.10.1080/01431161.2016.1246775 Search in Google Scholar

Li, W., Haohuan, F., Le, Y. & Cracknell A. (2017). Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing, 9(1), 22. DOI: 10.3390/rs9010022.10.3390/rs9010022 Search in Google Scholar

Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S. & Gill E. (2019). The first wetland inventory map of newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine Cloud Computing Platform. Remote Sensing, 11(1), 43. DOI: 10.3390/rs11010043.10.3390/rs11010043 Search in Google Scholar

Mahdianpari, M., Salehi, B., Mohammadimanesh, F. & Motagh M. (2017). Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS Journal of Pho togrammetry and Remote Sensing, 130, 13−31. DOI: 10.1016/j.isprsjprs.2017. Search in Google Scholar

Mansaray, L.R., Wang, F., Huang, J., Yang, L. & Kanu A.S. (2020). Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets. Geocarto International, 35(10), 1088–1108. DOI: 10.1080/10106049.2019.1568586.10.1080/10106049.2019.1568586 Search in Google Scholar

Mas, J.F. & Flores J.J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. Int. J. Remote Sens., 29(3), 617–663. DOI: 10.1080/01431160701352154.10.1080/01431160701352154 Search in Google Scholar

Mohanty, S.P., Hughes, D.P. & Salathé M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. DOI: 10.3389/ fpls.2016.01419. Search in Google Scholar

Mountrakis, G., Jungho, I. & Ogole C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. DOI: 10.1016/j.isprsjprs.2010. Search in Google Scholar

Nitze, I., Barrett, B. & Cawkwell F. (2017). Temporal optimisation of image acquisition for land cover classification with random forest and MODIS Time-series. International Journal of Applied Earth Observation and Geo information, 34, 136–146. DOI: 10.1016/ j.jag.2014.08.001. Search in Google Scholar

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. DOI: 10.1016/j.isprsjprs.2011.11.002- Search in Google Scholar

Rogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C. & Roberts D. (2008). Mapping land-cover modifications over large areas: a comparison of machine learning algorithms. Remote Sens. Environ., 112(5), 2272–2283. DOI: 10.1016/j.rse.2007. Search in Google Scholar

Rouse, J.W., Haas, R.H., Deering, D.W. & Schell J.A. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural veg ffect) of natural vegetation. Progress Report RSC. Search in Google Scholar

Shao, Y. & Lunetta R.S. (2012). Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78–87. DOI: 10.1016/j.isprsjprs.2012. Search in Google Scholar

Skalský, R., Koco, Š., Barančíková, G., Tarasovičová, Z., Halas, J., Koleda, P., Makovníková, J., Gutteková, M., Tobiášová, E., Gömöryová, E. & Takáč J. (2020). Land cover and land use change-driven dynamics of soil organic carbon in North-East Slovakian croplands and grasslands between 1970 and 2013. Ekológia (Bratislava), 39(2), 159−173. DOI: 10.2478/ eko-2020-0012. Search in Google Scholar

Vapnik, V. (1982). Estimation of dependences based on empirical data. New York: Springer Verlag. DOI: 10.1007/0-387-34239-7.10.1007/0-387-34239-7 Search in Google Scholar

Waldrop, M.M. (2016). The chips are down for Moore’s Law. Nature, 530(7589), 144‒147. DOI: 10.1038/530144a.10.1038/530144a Search in Google Scholar

Woznicki, S.A., Baynes, J., Panlasigui, S., Mehaffey, M. & Neale A. (2019). Development of a spatially complete floodplain map of the conterminous United States using random forest. Sci. Total Environ., 647, 942–953. DOI: 10.1016/j.scitotenv.2018.07.353.10.1016/j.scitotenv.2018.07.353 Search in Google Scholar

Yeom, J., Han, Y. & Kim Y. (2013). Separability analysis and classification of rice fields using KOMPSAT-2 high resolution satellite imagery. Research Journal of Chemistry and Environment, 17, 136–144. Search in Google Scholar

Zha, Y., Gao, J. & Ni S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens., 24(3), 583–594. DOI: 10.1080/01431160304987.10.1080/01431160304987 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo