Paper Info
Title
A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms.
Abstract
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 objective has been to develop 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 cover classification in a location where both wetland and non-wetland classes exist.
Year
DOI
Venue
2018
10.1016/j.envsoft.2018.01.023
Environmental Modelling & Software
Keywords
Field
DocType
Support Vector Machines,Random Forests,Object-based classification,Sentinel-1,Sentinel-2
Computer science,Support vector machine,Wetland,Algorithm,Image segmentation,Artificial intelligence,Statistical classification,Random forest,Land cover,Machine learning,Land use
Journal
Volume
ISSN
Citations 
104
1364-8152
6
PageRank 
References 
Authors
0.51
18
3
Name
Order
Citations
PageRank
Andrew Whyte160.51
Konstantinos Ferentinos220912.52
George P. Petropoulos315021.16