Acknowledgements

4. Trends in wetland mapping and monitoring with SAR

76 Wetlands Management - Assessing Risk and Sustainable Solutions

As shown in the previous section, spaceborne SAR remote sensing technology is recognized as essential tool for effective wetland observation. With the presence of global warming and its associated risks on Earth systems, there is an expressed interest in increased temporal and spatial resolution of satellite measurements. Thus, a trend toward increased temporal and spatial resolution of SAR imagery is noted in recent and future SAR missions. The Sentinel-1 SAR mission with its two identical SAR satellites (Sentinel-1A&B) is a good example of a recent SAR mission with a spatial resolution ranging from 5 m to 100 m and a revisit time of 6 days. This high temporal and spatial resolution is expected to be even higher in the near future with the launch of the RCM in late 2018. The RCM is expected to provide SAR imagery in a spatial resolution ranging from 1 m to 100 m, in a revisit time of only 4 days [32]. The increased temporal and spatial resolution would be required to adequately monitor wetlands and characterize the actual implications of climate change. Also, it is expected to further improve our understanding of climate change in wetlands and water quality, allowing ecosystem managers

and decision makers to have sufficient information regarding wetland preservation.

comes to fulfill the requirement for operational wetland monitoring systems.

5. Conclusions

The continuing advancements in computer processing power and software development as well as the trend toward free and open access to remote sensing imagery, such as those from the current Sentinel satellites and the future RCM, are enabling the ingestion of data into a centralized archive. This also supports the application of a standard rapid processing chain to generate analysis-ready wetland products. The provision of analysis-ready products to a wide range of users would revolutionize the role of remote sensing in Earth system science [108].

This chapter highlighted the SAR remote sensing technology and its potential for wetland monitoring and mapping. It was shown that a wide range of wetland applications can be

With the availability of different remote sensing data with various information contents, the application of multi-source data for advanced wetland applications is demonstrated in a number of studies; see for example [2, 44, 61, 67, 106]. In addition to SAR imagery, experiments on the integration of topographic and remote sensing data, such as optical imagery and LiDAR data, were conducted. The ultimate objective of these experiments was the improved mapping accuracy of wetlands. The integration of SAR imagery with optical and topographic data from multiple sensors was shown in [44, 106] to be necessary for improved wetland mapping and classification during the growing season. However, the integration of SAR imagery and LiDAR data did not improve significantly the classification accuracy of wetland in [61, 67]. The modern advances in remote sensing technology and the availability of multi-source information are shifting the manner in which Earth observation data are used for wetland monitoring, indicating the need for automated and efficient techniques. Different studies, such as [2, 44, 61, 106], have highlighted the effectiveness of machine learning algorithms for automated wetland classification. An example of these algorithms is the Random Forest (RF) classification algorithm proposed in [107]. This shift toward the automated machine learning algorithms

Authors would like to thank Mr. Jean Granger and Dr. Bahram Salehi from the Memorial University of Newfoundland and Dr. Koreen Millard form Environment and Climate Change Canada for their help in providing pictures of different wetland classes.
