Ecosystem services are the benefits people obtain from ecosystems, including provisioning, regulating, supporting, and cultural services. The Meta-analysis of this seminar was reviewed to identify, describe, and choose an appropriate ecosystem valuation method. Four major and nine sub-ecosystem services valuation methods were reviewed from scientific literature sources. Direct market-valuation methods are used to estimate ecosystem services via the use of direct market prices and production functions, indirect market valuation methods are used to estimate when direct markets for many ecosystem goods and services do not exist, direct market prices are missing, and stated preference valuation methods are used when no market prices are available, indirect market valuation methods are not possible, and the change in ecosystem services is hypothetical. The indirect market valuation methods can be used to estimate economic values for all types of ecosystem services. Ecosystem service modeling is used in integrating all the spatial and temporal dynamics of ecosystem services. The strength and limitations of each ecosystem services valuations method are summarized to know and choose appropriate valuation methods. The analysis shows different valuation methods exist and have been applied to estimate the values of different ecosystem services, but their appropriateness under specific conditions or limitations is not uniform. Thus, further study on the pros and cons of valuation methods will be needed to choose appropriate ecosystem valuation methods.

The Awash River Basin is essential in combating climate change by absorbing atmospheric carbon. The goal of this study was to determine carbon stocks using the linear correlation between field inventory and remote sensing data in the basin. The regression model was created using each plot studied from field inventory and the corresponding vegetation indices (VI) values obtained from ArcGIS software in the basin’s study area. The linear correlation values for estimated carbon stock with Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were R² = 0.36, R = 0.60, and R² = 0.52, R = 0.72, respectively. In contrast, the values for the Soil-Adjusted Vegetation Index (SAVI) were R² = 0.36 and R = 0.60. The results show that the relationship between carbon stock and EVI is stronger for estimating carbon stock than other indices (NDVI and SAVI). The linear regression model or equation developed for NDVI, EVI, SAVI, and field-level carbon stock was Y = 16.25x–1.093, Y = 8.935x–1.1254, and Y = 12.988x–1.0895, respectively. The estimated carbon stock from the EVI linear regression model was 15,904,158.24 tons, with an average carbon stock value of 111.96 tons per hectare. The findings concluded that EVI has a stronger correlation with carbon stock stand estimation than NDVI and SAVI. This study could serve as a baseline for the estimation of carbon stock using vegetation indices in different and large-scale land use land cover types. High-resolution multispectral imagery and cloud-based geospatial analysis platforms could provide more accurate results.