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Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery

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Abstract

Frequent human activity and rapid urbanization have led to an assortment of environmental issues. Monitoring land-cover change is critical to efficient environmental management and urban planning. The current study had two objectives. The first was to compare pixel-based random forest (RF) and decision tree (DT) classifier methods and a support vector machine (SVM) algorithm both in pixel-based and object-based approaches for classification of land-cover in a heterogeneous landscape for 2010. The second was to examine spatio-temporal land-cover change over the last two decades (1990–2010) using Landsat data. This study found that the object-based SVM classifier is the most accurate with an overall classification accuracy of 93.54% and a kappa value of 0.88. A post-classification change detection algorithm was used to determine the trend of change between land-cover classes. The most significant change from 1990 to 2010 was caused by the expansion of built-up areas. In addition to the net changes, the rate of annual change for each phenomenon was calculated to obtain a better understanding of the process of change. Between 1990 and 2010, an average of 4.53% of lands turned to the built-up annually and there was an annual decrease of about 0.81% in natural land. If the current trend of change continues, regardless of the actions of sustainable development, drastic declines in natural areas will ensue. The results of this study can be a valuable baseline for land-cover managers in the region to better understand the current situation and adopt appropriate strategies for management of land-cover.

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Acknowledgements

The authors would like to thank the Thüringer Landesanstalt für Umwelt und Geologie, Jena, Germany, for providing digital data and also the US Geological Survey (USGS) and European Space Agency (ESA) for preparing free archive of Landsat Earth-observing satellites images.

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  1. Institute of Ecology, Friedrich Schiller University, 07743, Jena, Germany

    Hamidreza Keshtkar & Winfried Voigt

  2. College of Natural Resources, University of Tehran, Tehran, Iran

    Hamidreza Keshtkar & Esmaeil Alizadeh

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  1. Hamidreza Keshtkar
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Keshtkar, H., Voigt, W. & Alizadeh, E. Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery. Arab J Geosci 10, 154 (2017). https://doi.org/10.1007/s12517-017-2899-y

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