Modeling afforestation suitability in the Cross-Niger transition forest through remote sensing and analytical hierarchy process to advance global goals

Afforestation is a crucial strategy for conserving biodiversity, mitigating climate change, and preventing land degradation to accomplish Sustainable Development Goal (SDG) 15.2. It is the process of establishing forests in previously unforested areas. Accordingly, the study integrates remote sensing and the analytical hierarchy process (AHP) to identify optimal sites for afforestation suitability that support SDGs within the Cross Niger transition forest. It combined sustainable factors or criteria such as land use/land cover, erodibility, soil, rainfall, aspect, slope, elevation, distance to river, distance to road, NDVI, land surface temperature, drainage density, and human disturbance index to model afforestation suitability maps. AHP was employed to assign weights to these factors, for normalization and validation with the receiver operating characteristic (ROC) curve analysis. The final afforestation suitability map was classified as very low (1501.503 km 2 ), low (5297.648 km 2 ), moderate (6971.094 km 2 ), high (5178.869 km 2 ), and very high (2640.442 km 2 ) afforestation suitability. The AUC-ROC value of 0.970 demonstrates that the model has excellent predictive accuracy for afforestation suitability. The study emphasizes the relevance of integrating remote sensing and AHP for well-informed decision-making for afforestation initiatives, thereby supporting global goals including SDG 15.2 within the study area.