Integrating Remote Sensing and Total Economic Valuation: A Spatially Explicit, Degradation-Adjusted Assessment of Ecosystem Services in Kakamega Forest

Martin Mamboleo; Isaiah Wandabwa

Abstract

Kakamega Forest in Western Kenya is a remnant of the Guineo-Congolian rainforest and a biodiversity hotspot. It supports one of the country’s most populous and impoverished regions, yet its value as an economic asset remains underappreciated. This study integrates original spatiotemporal forest health analysis (Landsat 8, SAVI 2013–2023) with synthesized Total Economic Valuation from existing literature to provide a spatially explicit, degradation-informed valuation framework. Our synthesis estimates that the forest generates about KES 8.7–13.6 billion annually (approximately 65–102 million USD), with roughly 79% attributable to indirect regulating services. Applying the observed 24.36% net forest degradation to the indirect use value component (KES 6.8–10.9 billion) under a linear proportional assumption gives an estimated annual loss of KES 1.7–2.7 billion in ecosystem service flows. These estimates are derived from secondary data and benefit transfer. The estimated per-unit-area economic return of conserved forest exceeds that of the most common agricultural conversions, indicating that short-term opportunity-cost arguments for deforestation are not supported by the aggregate valuation. However, the observed resource-use intensity, combined with high local poverty rates, is consistent with a poverty-degradation feedback. Beyond its local relevance, this work provides a replicable methodological template for a spatially explicit, degradation-informed valuation framework in tropical forest contexts worldwide, showing how adaptive management is made possible in a time of fast land-use change by combining remote sensing with economic assessment.

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