Visual Impact Assessment Index on Landscape Based on Grey Clustering and Shannon Entropy: A Case Study on a Mining Project

Landscape visual impact assessment is a key component of environmental impact studies, as it enables the identification and management of negative effects on the territory. Traditional methods are often subjective, rely on expert judgement, and consider limited criteria. To address these limitations, this study proposes a quantitative index based on the integration of grey clustering and Shannon entropy complemented with Geographic Information System (GIS). This approach allows classification under uncertainty and the objective weighting of indicators related to physiographic, biotic, and anthropic factors of visual quality, fragility, and accessibility. The methodology was applied to an open-pit mine in Peru. Results show that terrain modifications, presence of artificial elements, and the alteration of water bodies significantly affect visual quality, while the absence of restoration measures, observer exposure, and vegetation type increase fragility and reduce landscape resilience. The proposed method provides a robust, transparent, and reproducible framework that overcomes subjectivity in traditional approaches, supporting more reliable environmental planning and management.