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

Stanley Kapota; Dabwitso  Miti; Musoka  Nyongolo; Penjani Hopkins Nyimbili; Erastus Misheng’u  Mwanaumo; Wellington Didibhuku  Thwala; Masauso  Sakala

doi:10.38027/smart.v3n1-10

Authors

Stanley Kapota University of Zambia Author https://orcid.org/0009-0008-7597-4017
Dabwitso Miti University of Zambia Author https://orcid.org/0009-0000-0703-3743
Musoka Nyongolo University of Zambia Author https://orcid.org/0009-0008-6732-0654
Dr. Penjani Hopkins Nyimbili University of Zambia Author https://orcid.org/0009-0008-1679-4569
Prof. Dr. Erastus Misheng’u Mwanaumo University of Zambia Author https://orcid.org/0000-0002-2911-3207
Prof. Dr. Wellington Didibhuku Thwala Walter Sisulu University Author https://orcid.org/0000-0002-8848-7823
Masauso Sakala University of Zambia Author https://orcid.org/0009-0008-1679-4569

DOI:

https://doi.org/10.38027/smart.v3n1-10

Keywords:

GIS, Remote Sensing, Water Pollution, Kafue River, NDVI, NDTI, Smart Environmental Monitoring, 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.

Author Biographies

Stanley Kapota, University of Zambia

Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Dabwitso Miti, University of Zambia

Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Musoka Nyongolo, University of Zambia

Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Dr. Penjani Hopkins Nyimbili, University of Zambia

Lecturer, Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia

Deputy Centre Leader, National Centre for Uncrewed Aircraft Systems (NACUAS), University of Zambia, Zambia

Assistant Dean (Research), School of Engineering, University of Zambia, Zambia

Research Associate, Built Environment and Information Technology, Faculty of Engineering, Walter Sisulu University, South Africa
Prof. Dr. Erastus Misheng’u Mwanaumo, University of Zambia

Department of Civil and Environmental Engineering, School of Engineering, University of Zambia, Zambia

Centre Leader, National Centre for Uncrewed Aircraft Systems (NACUAS), University of Zambia, Zambia

Built Environment and Information Technology, Faculty of Engineering, Walter Sisulu University, South Africa
Prof. Dr. Wellington Didibhuku Thwala, Walter Sisulu University

Built Environment and Information Technology, Faculty of Engineering, Walter Sisulu University, South Africa
Masauso Sakala, University of Zambia

Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia

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