Correlation of Remote Sensing Data for Assessing Carbon Credits under Climate Change in the Okavango Countries

Author

Isau Alfredo Bernardo Quissindo, University José Eduardo dos Santos, Angola

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/tbc

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

This study implements a cloud-based computational framework to analyse land use/cover (LULC) dynamics, biomass, and carbon sequestration across the Okavango countries - OC (2010-2024). Utilising automated pipelines for multi-sensor Landsat and MODIS imagery, the research applies Pearson's correlation algorithms to quantify interdependencies between spectral indices (NDVI/EVI), algorithmic burned area mapping, and climatic datasets. Results identify distinct data-driven LULC patterns: Angola (croplands/urban mosaics), Namibia (croplands/shrublands), and Botswana (croplands/wetlands). Computational analysis reveals that fire frequency significantly correlates with deforestation in Angola (r ˜ 0.58), while systemic vegetation resilience preserves biomass. By identifying peatlands as critical digital carbon sinks, the Decision Support System integrates anthropogenic and climatic drivers to model ecosystem productivity dynamics, offering a scalable geospatial framework for carbon credit assessment in the OC.

Keywords

MODIS, Data Analysis, LULC Change, Carbon Stock Modeling, Cloud Computing, Okavango Countries

Cite this paper

I. Quissindo (2026) "Correlation of Remote Sensing Data for Assessing Carbon Credits under Climate Change in the Okavango Countries", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/tbc

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