Abstract
Accurate and current land cover information is required to develop strategies for sustainable development and to improve the quality of life in urban areas. This study presents an approach that combines multi-seasonal Sentinel-1 (S1) and Sentinel-2 (S2) data, and a random forest (RF) classifier in order to map land cover in four major urban centers in Zimbabwe. The specific objective of this study was to assess the potential of multi-seasonal (rainy, post-rainy, and dry season) S1, rainy season S2, post-rainy season, dry season S2, multi-seasonal S2, and multi-seasonal composite S1 and S2 data for mapping land cover in urban areas. The study results show that the combination of multi-seasonal S1 and S2 data improve land cover mapping in urban and peri-urban areas relative to only multi-seasonal S1, mono-seasonal S2, and multi-seasonal S2 data. The overall accuracy scores for the multi-seasonal S1 and S2 land cover maps are above 85% for all urban centers. Our results indicate that rainy and post-rainy S2 spectral bands, as well as dry-season S1 VV and VH bands (ascending orbit) are the most important features for land cover mapping. In particular, S1 data proved useful in separating built-up areas from cropland, which is usually problematic when only optical imagery is used in the study area. While there are notable improvements in land cover mapping, some challenges related to the S1 data analysis still remain. Nonetheless, our land cover mapping approach shows a potential to map land cover in other urban areas in Zimbabwe or in Sub-Sahara Africa. This is important given the urgent need for reliable geospatial information, which is required to implement the United Nations Sustainable Development Goals (UN SDGs) and United Nations New Urban Agenda (NUA) programmes.
Highlights
According to the United Nations Human Settlements Programme (UN-Habitat), 55%of the world’s population reside in urban areas, while in 2050 the urban population is expected to reach 68% [1,2]
This study has revealed that the use of S1 data is not effective for mapping land cover in urban and peri-urban areas in the study area
This is attributed to the sensitivity of S1 data to detect different target surfaces and the ability to separate cropland from built-up areas, as well as the capacity of multi-seasonal S2 data to identify phenological changes
Summary
According to the United Nations Human Settlements Programme (UN-Habitat), 55%of the world’s population reside in urban areas, while in 2050 the urban population is expected to reach 68% [1,2]. Given the rapid growth of urban population in Africa, local government authorities—responsible for urban planning and management—are failing to provide adequate housing, basic services (provision of clean water and sanitation), and basic infrastructures such as transport and health facilities [1,7]. This is further worsened by the outbreak of epidemics and global pandemics such as COVID-19, which will impact more vulnerable citizens living in informal settlements [8]
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