New method reveals Nigeria is losing land three times faster than official data shows
A refined satellite monitoring technique detected between 6% and 28% more degraded land in Nigeria's savannah region than conventional assessment methods, challenging the accuracy of SDG progress reports. The findings suggest governments may be systematically underestimating land loss, with serious implications for agricultural investment, food security policy, and climate adaptation funding.
Originaltitel: Fine-scale land degradation assessment in Nigeria's Guinea Savannah: Enhancing SDG 15.3.1 reporting using default and adapted Earth Observation methods
<p>Land degradation (LD) is a major barrier toward achieving the Sustainable Development Goals (SDGs), particularly in Sub-Saharan Africa (SSA), due to challenges in national assessments. A comprehensive spatiotemporal analysis integrating all three UNCCD-recommended indicators at critical subnational scales remains absent in Nigeria, despite global reporting frameworks. In the Nigerian Guinea Savannah (NGS), LD severely threatens livelihoods, ecosystem services, and sustainability efforts. To support SDG 15.3.1, this study aims to provide a comparative assessment and enhanced context for subnational LD patterns and status in the NGS, using available national data and the UNCCD-endorsed Default Method (DM) and its Adapted Method (AM), across defined baseline and monitoring periods. Results reveal that the AM detected between 6% and 28% more degraded areas than the DM, demonstrating greater sensitivity to localized degradation. Based on the LULC indicator, degraded areas declined from 0.70% to 0.38% under the DM, whereas the AM increased from 26.00% to 28.68% of the NGS. Biomass loss showed declining land productivity, from 22.55% to 10.20% under DM and from 5.15% to 3.84% under AM. Both methods confirmed widespread degradation in key frontline states in the NGS, with consistent convergence in degradation patterns. However, the AM proved more capable of identifying degradation masked by coarse-resolution data, especially under prevailing unsustainable conditions such as widespread insecurity-induced land abandonment. This study presents a robust, locally adapted EO-based framework for assessing SDG 15.3.1 in Nigeria and, by extension, across SSA, providing actionable insights to close management gaps and inform policy interventions at multi-levels. </p>