Digital Monitoring of Natural Resources: A Theoretical Framework of Remote Sensing, Artificial Intelligence and Enforcement in Forest, Land and Water Governance
DOI:
https://doi.org/10.68854/tsp26577Keywords:
remote sensing, environmental monitoring, artificial intelligence, deforestation, land use, water resources, environmental governance, enforcement, satellite data, AfricaAbstract
Satellites and machine learning now detect forest clearing within days, map land-use change at field scale and track water bodies continuously, yet the expected gains in protection have been uneven and, in many places, absent. The literature on AI-enabled remote sensing has concentrated on detection accuracy, while the governance question of why better observation does not reliably produce better protection has received little systematic theoretical treatment, particularly for African resource systems. This paper aims to explain that gap and to specify the conditions under which digital monitoring reduces resource degradation. It adopts an integrative conceptual review and theory-building design. The corpus comprises 36 peer-reviewed journal articles published between 2023 and 2026, purposively selected from PubMed and Crossref-indexed literature using stated inclusion criteria and verified individually against Crossref publisher metadata; the analysis combines thematic synthesis with abductive, analytical theory-building. The paper distinguishes three stages of monitoring-based governance, namely detection, attribution and enforcement, and argues that artificial intelligence collapses the cost of detection, modestly reduces the cost of attribution and leaves enforcement untouched, because enforcement depends on the capacity, independence and political will of the enforcing institution. The resulting framework treats the governance value of monitoring as a weakest-link product of the three stages, moderated by the enforcement environment. Eight propositions follow, concerning the downstream migration of bottlenecks, the ranking of forest, land and water by attribution difficulty, the conditional effect of enforcement environments, the leverage of external audiences, the shift of monitoring authority from states to international organisations, NGOs and firms, the displacement of degradation to unmonitored resources and weaker communities, and the complementarity of community enforcement. Nigeria's forest, land, oil-pollution and water governance anchors the argument. The paper recommends that governments and donors fund attribution infrastructure and sanction capacity ahead of further sensing, that national agencies retain processing capacity, and that researchers test the propositions with alert-to-sanction data.
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