Assessing the Utility of Sentinel-1 C Band Synthetic Aperture Radar Imagery for Land Use Land Cover Classification in a Tropical Coastal Systems When Compared with Landsat 8
Cloud cover constitutes a major obstacle to land cover classification in
the humid tropical regions when using optical remote sensing such as Landsat
imagery. The advent of freely available Sentinel-1 C band synthetic aperture
radar (SAR) imagery offers new opportunities for land cover classification in
frequently cloud covered environments. In this study, we investigated the
utility of Sentinel-1 for extracting land use land cover (LULC) information in
the coastal low lying strip of Douala, Cameroon when compared with Landsat
enhanced thematic mapper (TM). We also assessed the potential of integrating Sentinel-1 and Landsat. The major
LULC classes in the region included water, settlement, bare ground, dark
mangroves, green mangroves, swampy vegetation, rubber, coastal forest and other
vegetation and palms. Textural variables including mean, correlation, contrast
and entropy were derived from the Sentinel-1 C band. Various conventional image
processing techniques and the support vector machine (SVM) algorithm were
applied. Only four land cover classes (settlement, water, mangroves and other
vegetation and rubber) could be calibrated and validated using SAR imagery due
to speckles. The Sentinel-1 only classification yielded a lower overall
classification accuracy (67.65% when compared to all Landsat bands (88.7%)).
The integrated Sentinel-1 and Landsat data showed no significant differences in
overall accuracy assessment (88.71% and 88.59%, respectively). The three best
spectral bands (5, 6, 7) of Landsat imagery yielded the highest overall
accuracy assessment (91.96%). in the study. These results demonstrate a lower
potential of Sentinel-1 for land cover classification in the Douala estuary
when compared with cloud free Landsat images. However, comparable results were
obtained when only broad classes were considered.
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