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
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s10661-023-11202-z.pdf
Reference61 articles.
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2. Adam, E., Mutanga, O., & Rugege, D. (2010). Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review. Wetlands Ecology and Management, 18(3), 281–296. https://doi.org/10.1007/s11273-009-9169-z
3. Alibakhshi, S., Groen, T. A., Rautiainen, M., & Naimi, B. (2017). Remotely-sensed early warning signals of a critical transition in a wetland ecosystem. Remote Sensing, 9(4), 352. https://doi.org/10.3390/rs9040352
4. Amani, M., Kakooei, M., Ghorbanian, A., Warren, R., Mahdavi, S., & Brisco, B., et al. (2022). Forty years of wetland status and trends analyses in the Great Lakes using Landsat archive imagery and Google Earth Engine. Remote Sensing, 14(15), 3778. https://doi.org/10.3390/rs14153778
5. Amani, M., Salehi, B., Mahdavi, S., Granger, J. E., Brisco, B., & Hanson, A. (2017). Wetland classification using multi-source and multi-temporal optical remote sensing data in Newfoundland and Labrador. Canada. Canadian Journal of Remote Sensing, 43(4), 360–373. https://doi.org/10.1080/07038992.2017.1346468
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