Quantifying Mangrove Extent Using a Combination of Optical and Radar Images in a Wetland Complex, Western Region, Ghana

Daniel Aja, Michael K. Miyittah () and Donatus Bapentire Angnuureng
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Daniel Aja: Africa Center of Excellence in Coastal Resilience, Center for Coastal Management, University of Cape Coast, Cape Coast P.O. Box UC56, Ghana
Michael K. Miyittah: Africa Center of Excellence in Coastal Resilience, Center for Coastal Management, University of Cape Coast, Cape Coast P.O. Box UC56, Ghana
Donatus Bapentire Angnuureng: Africa Center of Excellence in Coastal Resilience, Center for Coastal Management, University of Cape Coast, Cape Coast P.O. Box UC56, Ghana

Sustainability, 2022, vol. 14, issue 24, 1-17

Abstract: The classification of mangrove forests in tropical coastal zones, based only on passive remote sensing methods, is hampered by mangrove complexities, topographical considerations, and cloud cover effects, among others. This paper reports on a study that combines optical and radar data to address the challenges of distinguishing mangrove stands in cloud-prone regions. The Google Earth Engine geospatial processing platform was used to extract multiple scenes of Landsat surface reflectance Tier 1 and synthetic aperture radar (C-band and L-band). The images were enhanced by creating a feature that removes clouds from the optical data and using speckle filters to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Classification was evaluated using three scenarios: classification of optical data only, classification of radar data only, and combination of optical and radar data. Our results revealed that the scenario that combines optical and radar data performed better. Further analysis showed that about 16.9% and 21% of mangrove and other vegetation/wetland cover were lost between 2009 and 2019. Whereas water body and bare land/built-up areas increased by 7% and 45%, respectively. Accuracy was evaluated based on the three scenarios. The overall accuracy of the 2019 classification was 98.9% (kappa coefficient = 0.979), 84.6% (kappa coefficient = 0.718), and 99.1% (kappa coefficient = 0.984), for classification of optical data only, classification of radar data only, and combination of optical and radar data, respectively. This study has revealed the potential to map mangroves correctly, enabling on-site conservation practices in the climate change environment.

Keywords: algorithm; decision trees; cloud cover; NDVI; radar; tropical coastal zone (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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