Abstract
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.
Highlights
Information on land use land cover (LULC) is a vital element in forming policies regarding economic, demographic, and environmental issues at national, regional and global scales
The aim of the study was to investigate the utility of Sentinel-1 for extracting land use land cover (LULC) information in the coastal low lying region of Douala, Cameroon when compared to Landsat enhanced thematic mapper (TM), and whether an integrated Sentinel-1/Landsat provide an improved classification of LULC types
The results obtained by employment of Geographic Information System (GIS) and RS applications have highlighted the different LULC types of the study area using all Landsat spectral bands, three best Landsat bands, integrated Landsat and synthetic aperture radar (SAR) images
Summary
Information on land use land cover (LULC) is a vital element in forming policies regarding economic, demographic, and environmental issues at national, regional and global scales. The Douala coastal low lying urban region, like other urban areas in the world, is undergoing rapid changes in LULC. The region is losing its mangrove forest because of conversion to urban settlement and industrial developments. Mapping LULC of this coastal strip in a timely and accurate manner is of immense importance for urban planning, land use planning, conservation of biodiversity and sustainable management of land resources. Reference [1] highlighted the fact that remotely sensed imageries provide an efficient means of obtaining information on temporal trends and spatial distribution of urban areas needed for understanding, modelling and projecting LULC changes. Improvements in satellite image processing and Geographic Information System (GIS), implies that change detection can be and rapidly conducted as in [2] [3]
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