Mapping Vegetation in Riparian Areas Using Pixel-Based and Object-Based Classification of Sentinel-2 Multi-Temporal Imagery

Document Type : Original Article


1 Ph.D. Student, Dep. of Forestry, Faculty of Natural Resources, Tarbiat Modarres University

2 Associate Prof., Dep. of Forestry, Faculty of Natural Resources, Tarbiat Modarres University

3 Prof. of Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU)

4 Assistant Prof., Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU)


Despite the low area coverage, riparian vegetation presents several ecosystem services. But there is no precise spatial information on these ecosystems in Iran. Considering the lack of such information, mapping and providing a spatial database is essential. Due to the mixture of these vegetation types and other land covers, the detection of these types of vegetation is challenging, and choosing an appropriate classification method is of great significance. This study examines the performance of pixel-based and object-based classification approaches for the detection of these vegetation types using freely available Sentinel-2 imagery. Five different riparian areas in Chaharmahal-va-Bkhtiari province were selected and used for training the classification models. We used random forest algorithm and multi-temporal Sentinel-2 data to perform the classification models. The validation of classification models was based on independent validation points spread across the province. Our results showed that multi-temporal Sentinel-2 imagery has a high capability to map riparian vegetation in the Zagros Mountains. Moreover, the pixel-based classification approach (overall accuracy of 83.9%) represents more accurate results compared to the object-based classification approach (overall accuracy of 76.7%). Overall, this study recommends a pixel-based classification approach to map these vegetation types using freely available multi-temporal Sentinel-2 imagery. Note that it is important to use pure pixels for training the classification models.


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