The lack of spatially explicit flood hazard mapping has hampered the development of appropriate flood interventions at community levels in Nigeria. This paper reports on a work conducted to develop a local government level flood hazard map to delineation flood vulnerable areas. Flood vulnerability mapping in this work was addressed from a perspective of administrative unit as the unit of investigation. The study is anchored on the Modeling Flow based on Relational Rule for flood assessment using ArcGIS in combination with modified rational model. The output of the modified rational model was integrated into the Geographic Information Systems environment by the arithmetic overlay operation methods. The results show that the delineated areas/sub-catchments however experienced the same rainfall intensity of 414.2 mm/h but the flood extents in the areas are different. For instance, the very high flood risk zone covers about 22.8% of the study area while the low risk zone covers about 44.3% and the possible areas likely to experience seasonal floods with a given rainfall input are mostly below 40 m elevation. The results of this study will be helpful to prioritize development efforts at grassroot in the study location and to formulate flood adaptation strategies.
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.
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