Assessing of Forest Structure Using Earth Observation Data: A Case Study in Munessa Forest, Oromia Region, Ethiopia - Mulualem Kere (International Journal of Land, 2025)
Understanding forest structure is crucial for estimating carbon emissions associated with forests, assessing forest degradation, and evaluating the success of forest restoration efforts. However, forest structure quantification is limited to the area of interest without considering the whole forest coverage. Forest structure may be easily assessed over a wide area using data from remote sensing. Thus, by combining ground observation with satellite-based light detection and ranging (LiDAR) and Sentinel 2 data, this study seeks to assess the forest structure of Munessa Natural Forest. The plantation tree species were categorized in this study using the object-based image analysis (OBIA) technique. Forest structures such forest height and aboveground biomass density with 7,810 and 2,426 footprint locations were assessed by Global Ecosystem Dynamics Investigation (GEDI) LiDAR data.The result shows that the Munessa Forest has five feature classes and is covered by 69% Natural forest, 4% Pinus patula, 9% Ceupressus lusitanica, 10 % eucalyptus, and 9% shrub. . Across all study plots, the average tree height in the Munessa forest was 43.7 meters per hectare. The GEDI LiDAR-derived estimated forest structures (forest height) correlate (R=0.714) with the field measurement data from sample plots. A new age of large-area methodologies for predicting forest structure in various forest assessments is supported by the more substantial LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI).
Keywords: Earth observation data; Forest structure; Munessa; remote sensing