A New Synergistic Approach for Monitoring Wetlands Using 1 Sentinels -1 and 2 data With Object-based Machine Learning 2 Algorithms

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.envsoft.2018.01.023. This is version 3 of this Preprint.


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Alexander R. Brown, George Petropoulos, Konstantinos P. Ferentinos


In this work the synergistic use of Sentinel-1 and 2 combined with the System for Automated Geoscientific Analyses (SAGA) Wetness Index in the content of land use/cover (LULC) mapping with emphasis in wetlands is evaluated. A further objectivehas been to a new Object-based Image Analysis (OBIA) approach for mapping wetland areas using Sentinel-1 and 2 data, where the latter is also tested against two popular machine learning algorithms (Support Vector Machines - SVMs and Random Forests - RFs). The highly vulnerable iSimangaliso Wetland Park was used as the study site. Results showed that two-part image segmentation could efficiently create object features across the study area. For both classification algorithms, an increase in overall accuracy was observed when the full synergistic combination of available datasets. A statistically significant difference in classification accuracy at all levels between SVMs and RFs was also reported, with the latter being up to 2.4% higher. SAGA wetness index showed promising ability to distinguish wetland environments, and in combination with Sentinel-1 and 2 synergies can successfully produce a land use and land coverclassification in a location where both wetland and non-wetland classes exist.




Earth Sciences, Life Sciences, Other Earth Sciences, Physical Sciences and Mathematics



Published: 2020-03-31 00:09

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GNU Lesser General Public License (LGPL) 2.1

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