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Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform

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Abstract

Detailed wetland inventories and information about the spatial arrangement and the extent of wetland types across the Earth’s surface are crucially important for resource assessment and sustainable management. In addition, it is crucial to update these inventories due to the highly dynamic characteristics of the wetlands. Remote sensing technologies capturing high-resolution and multi-temporal views of landscapes are incredibly beneficial in wetland mapping compared to traditional methods. Taking advantage of the Google Earth Engine’s computational power and multi-source earth observation data from Sentinel-1 multi-spectral sensor and Sentinel-2 radar, we generated a 10 m nationwide wetlands inventory map for Iran. The whole country is mapped using an object-based image processing framework, containing SNIC superpixel segmentation and a Random Forest classifier that was performed for four different ecological zones of Iran separately. Reference data was provided by different sources and through both field and office-based methods. Almost 70% of this data was used for the training stage and the other 30% for evaluation. The whole map overall accuracy was 96.39% and the producer’s accuracy for wetland classes ranged from nearly 65 to 99%. It is estimated that 22,384 km2 of Iran are covered with water bodies and wetland classes, and emergent and shrub-dominated are the most common wetland classes in Iran. Considering the water crisis that has been started in Iran, the resulting ever-demanding map of Iranian wetland sites offers remarkable information about wetland boundaries and spatial distribution of wetland species, and therefore it is helpful for both governmental and commercial sectors.

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Availability of data and material

Publicly available datasets were analyzed in this study. These datasets can be found here: https://rslab.ut.ac.ir/.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, Canada

    MohammadAli Hemati & Masoud Mahdianpari

  2. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    MohammadAli Hemati & Mahdi Hasanlou

  3. C-CORE, 1 Morrissey Road, St. John’s, Newfoundland/Labrador, A1B 3X5, Canada

    Masoud Mahdianpari & Fariba Mohammadimanesh

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  1. MohammadAli Hemati
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  2. Mahdi Hasanlou
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  3. Masoud Mahdianpari
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  4. Fariba Mohammadimanesh
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Contributions

Conceptualization: M.A.H., M.H., and M.M. Methodology: M.A.H. Writing original draft preparation: M.H. and M.M. Writing review and editing: M.H., F.M., M.H., and M.M. Visualization: M.A.H., M.H., and F.M. Supervision: M.H. and M.M. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Mahdi Hasanlou.

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Hemati, M., Hasanlou, M., Mahdianpari, M. et al. Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform. Environ Monit Assess 195, 558 (2023). https://doi.org/10.1007/s10661-023-11202-z

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