GIS and Remote Sensing-Based Assessment and Mapping of Industrial Pollution Impacts: A Case Study of the Kafue River, Zambia

Abstract

This study applies GIS and multi-temporal satellite remote sensing to trace the environmental signature of the February 2025 Sino-Metals tailings dam failure along Zambia's Kafue River, and considers what the resulting evidence implies for the design of a low-cost, satellite-based early-warning monitoring policy. Landsat 8/9 and Sentinel-2 imagery across six temporal windows (three pre-incident, three post-incident) were used to compute the Normalised Difference Turbidity Index (NDTI) and Normalised Difference Vegetation Index (NDVI), with the Normalised Difference Water Index (NDWI) applied as a binary water mask to separate the open-water pixels used for NDTI from the riparian land pixels used for NDVI. Mean NDTI rose sharply from a pre-incident rainy-season-onset value of −0.008 to +0.210 in March 2025, before returning to near-baseline levels (+0.018) within a month and to clear-water conditions (−0.009) by June 2025 a pattern consistent with the river's hydrological flushing capacity. Riparian NDVI, in contrast, declined from a rainy-season peak of 0.345 in December 2024 to 0.313 immediately after the spill and had not returned to that baseline five months later. Because no concurrent in-situ water-chemistry or soil heavy-metal sampling was available, and because only a single pre-incident rainy-season observation exists, these patterns are interpreted as consistent with, rather than statistically confirmatory of, pollution-driven vegetation stress. The differential recovery signatures of NDTI and NDVI are nonetheless proposed as a candidate design feature for a dual-index, satellite-based early-warning trigger for Zambia's Environmental Management Agency (ZEMA) and Water Resources Management Authority (WARMA), and the data, validation, and institutional requirements for operationalising such a system are discussed.