dc.description.abstract |
Forest degradation significantly impacts biodiversity, carbon storage, water regulation, and
forest-dependent livelihoods, necessitating effective restoration efforts. Identifying optimal sites
for forest landscape restoration is critical for sustainable forest management. This study
employed a GIS-based Multi-Criteria Decision Analysis (MCDA) using the Analytical Hierarchy
Process (AHP) to assess forest landscape restoration suitability in Ethiopia's Libokemkem
District. Sampling techniques and data collection instruments involved acquiring high-resolution
geospatial data from diverse sources, including ALOS PALSAR DEM (12.5m), PERSIANN-CCS
rainfall (4km), Sentinel-2 LULC (10m), FAO soil maps, and OpenStreetMap for infrastructure.
Ground truth data from Google Earth validated the LULC map, achieving an 85% overall
accuracy and a Kappa coefficient of 74%. Expert judgment, formalized through AHP pairwise
comparisons, determined factor weights. The Consistency Ratio (CR) of 0.056 (5.6%) confirmed
high judgment reliability. LULC (26%), Slope (20%), Distance from Forest Patches (15%), and
Proximity to River (13%) emerged as the most influential factors. Conversely, Distance from
Roads and Settlements (both 3%) had minimal influence. The Weighted Overlay Analysis
revealed that the majority of the district (61.29%) is Moderately Suitable (S2) for restoration,
covering 845.93 km2. Marginally Suitable (S1) areas constitute 38.10% (525.84 km2), while
Highly Suitable (S3) sites are scarce, accounting for only 0.62% (8.49 km2). Negligible areas
were classified as Not Suitable (N). This geospatial framework provides a robust, data-driven
foundation for strategically prioritizing and implementing targeted forest restoration initiatives
across suitable zones. Its application is crucial for enhancing ecological resilience and fostering
sustainable socio-economic benefits in the region. |
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