Application of change detection techniques driven by expert opinions for small-area studies in developing countries - Tanaka A. Mbendana; Anesu D. Gumbo; Simbarashe Jombo; Ephias Mugari; Evison Kapangaziwiri (Scientific African)
Rapid urbanisation in developing countries, fuelled by population growth and rural-to-urban migration, poses significant challenges for service delivery in under-resourced municipalities. Dangamvura Township in Mutare, Zimbabwe, exemplifies this issue, potentially overwhelming the City of Mutare services. Resource limitations have hindered the quantification of these changes. This study assesses changes in land use and land cover (LULC) in Dangamvura Township, Mutare, Zimbabwe, between 2010 and 2022, using stakeholder-driven methods, Google Earth Pro (GEP) and machine learning in R. A four-tier methodology was applied integrating R and expert validation through GEP to classify and quantify changes in LULC. The analysis identified built-up areas, cropland, and bare land as the main LULC classes. The results from R showed that built-up areas expanded from 3.74 km² in 2010 to 8.64 km² in 2022, the bare land decreased from 3.10 km² to 1.42 km², and the cropland declined from 6.06 km² to 2.84 km². GEP assessments indicated an increase in built-up areas from 5.08 km² to 8.91 km², a reduction in bare land from 3.57 km² to 1.97 km², and a decrease in cropland from 4.25 km² to 2.02 km² over the same period. These findings highlight significant urban expansion and declining agricultural and undeveloped land. The disparities between the R statistical software and the GEP results underscore the importance of integrating expert opinions to validate classifications, particularly in small-area studies with spatial heterogeneity. The results provide valuable insights for urban planning and decision-making, highlighting the need for adaptive strategies to manage urban growth and infrastructure development. The tiered methodology demonstrates the potential of combining advanced remote sensing tools with local knowledge to achieve robust LULC assessments in resource-limited settings, guiding sustainable urban planning and informing policy interventions to address challenges associated with rapid urbanisation in resource-limited countries.