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Accuracy assessment of land cover/land use classifiers in dry and humid areas of Iran

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Abstract

Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic areas in a timely fashion. Knowing the most accurate classification method to produce LCLU maps based on site characteristics is necessary for the environment managers. The aim of this research is to examine the performance of various classification algorithms for LCLU mapping in dry and humid climates (from June to August). Testing is performed in three case studies from each of the two climates in Iran. The reference dataset of each image was randomly selected from the entire images and was randomly divided into training and validation set. Training sets included 400 pixels, and validation sets included 200 pixels of each LCLU. Results indicate that the support vector machine (SVM) and neural network methods can achieve higher overall accuracy (86.7 and 86.6 %) than other examined algorithms, with a slight advantage for the SVM. Dry areas exhibit higher classification difficulty as man-made features often have overlapping spectral responses to soil. A further observation is that spatial segregation and lower mixture of LCLU classes can increase classification overall accuracy.

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Acknowledgments

The authors would like to thank Matthieu Molinier for his valuable suggestions during writing and development of this manuscript. Also, the authors would like to thank the editorial comments and anonymous reviewers for their helpful comments on the previous version of the manuscript.

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Authors and Affiliations

  1. Watershed Science Department, Faculty of Natural Resources, Tarbiat Modares University (TMU), Mazandaran, Iran

    Saleh Yousefi

  2. Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, NY, 13210, USA

    Reza Khatami & Giorgos Mountrakis

  3. College of Natural Resources, Lorestan University, Lorestan, Iran

    Somayeh Mirzaee

  4. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  5. College of Natural Resources, Ardekan University, Ardekan, Iran

    Mehdi Tazeh

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  1. Saleh Yousefi
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  2. Reza Khatami
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  4. Somayeh Mirzaee
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  5. Hamid Reza Pourghasemi
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Correspondence to Hamid Reza Pourghasemi.

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Yousefi, S., Khatami, R., Mountrakis, G. et al. Accuracy assessment of land cover/land use classifiers in dry and humid areas of Iran. Environ Monit Assess 187, 641 (2015). https://doi.org/10.1007/s10661-015-4847-1

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