Abstract
Abstract. In this paper it is described a study case of a rapid assessment of change detections for post-cyclone Idai vegetated damage and flood extension estimation by fusion of multi-temporal Landsat and sentinel-1 SAR images. For automated change detection, after disasters, many algorithms have been proposed. To visualize the changes induced by cyclone we tested and compared two automated change detection techniques namely: Principal Components Analysis (PCA), Normalized Difference Vegetation Index (NDVI) and image segmentation. With the image segmentation of multispectral and SAR images, it was possible to visualize the extension of the wet area. For this specific application, PCA was identified as the optimal change detection indicator than NDVI. This study suggested that image segmentation, principal components analysis, and normalized difference vegetation index can be used for change detection of surface water due to flood and disasters especially in prone countries like Mozambique.
Highlights
The landfall of cyclone IDAI in southwest Africa during 14th March to 16th March 2019, considered one of the worst disasters of the last 20 years, has resulted in the destruction of communities in Mozambique, Malawi, and Zimbabwe, with greater severity in the province of Sofala (Mozambique), in the city of Beira and the suburban spots of Dondo and Búzi villages
By using well-established change detection techniques in the literature, this paper aims at detecting the changes that occurred in Beira after landfall of cyclone IDAI
Sentinel-1 synthetic aperture radar (SAR) operates at C-band, in a wavelength of 5.6cm, and the interferometric wide swath (IW) operated in the default mode, which acquires the image in a dual-polarized manner with VV/VH polarization
Summary
The landfall of cyclone IDAI in southwest Africa during 14th March to 16th March 2019, considered one of the worst disasters of the last 20 years, has resulted in the destruction of communities in Mozambique, Malawi, and Zimbabwe, with greater severity in the province of Sofala (Mozambique), in the city of Beira and the suburban spots of Dondo and Búzi villages. The category 4 tropical cyclone IDAI caused the death of citizens and animals, destroyed infrastructure, as well as the different typical land-cover classes of that region After such events, one of the post-catastrophe needs is to identify and quantify losses as well as changes in a different landscape, land cover, and infrastructures, seeking to plan and coordinate the damage restoration. Change detection is a well-studied topic in remote sensing and a wide range of methods have been developed, tested and applied Such methods range from visual comparison to detailed quantitative approaches (Wickware and Howarth, 1981). Visualize the destruction of built-up areas, vegetation and the extent of flooded areas To do this, it is used two remote sensing data sources: Landsat 8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) multitemporal dataset and Sentinel-1 synthetic aperture radar (SAR) and two techniques, namely Principal Component Analysis and Normalized Difference Vegetation Index, are used to perform change detection.
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