Land use land cover mapping using advanced machine learning classifiers: A case study of Shiraz city, Iran

Abstract

Due to the recent climate changes and their consequences such as flash floods and droughts, there is a need for Land Use Land Cover mapping to monitor environmental changes which have effects on ecology, policy management, health, and disaster management. It should be noticed that recent droughts caused by climate change, and on the other hand, population growth has increased the rate of urbanization in Iran, where people are moving from rural areas to urban areas. In this study, two well-known machine learning classifiers, including Support Vector Machine (SVM) and Complex Tree (CTree), are used for land cover mapping. An advanced supervised algorithm, namely the Derivative-free Multi-layer Perceptron (FDMLP), which is based on the Multi-layer Perceptron (MLP) function, is developed in MATLAB programming language. The FDMLP uses a derivative-free function for the optimization 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 Land Use Land Cover (LULC) maps. A Deep Neural Network (DNN) is used for LULC mapping as well. The FDMLP classifier has outperformed the other two well-known algorithms of the SVM and the CTree for the classification of the pixel-based Landsat-8 imagery and the object-based Landsat-8 imagery with a spatial resolution of 15 m in terms of the overall accuracy and index of kappa. Based on the test data, the DNN classifier for the object-based Landsat-8 imagery with a spatial resolution of 15 m with values of 91.28 and 88.57 percent for the OA and Kappa index has outperformed the other supervised classifiers. The worst results of classification are for the DNN algorithm for the pixel-based Landsat-8 imagery.