ASSESSING OF FOREST STRUCTURE USING EARTH OBSERVATION DATA: ACASE STUDY IN MUNESSA FOREST, OROMIA REGION, ETHIOPIA - Mulualem Kere; Biniyam Tesfaw (International Journal of Land, 2024)
Forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. However, forest structure quantification is only limited to the specific area of interest without considering the whole forest coverage. Remote sensing data can easily deliver a large area to assess forest structure. Therefore, this study aims to assess forest structure of Munessa Natural Forest by integrating satellite based light detection and ranging (LiDAR) and Sentinel 2.00 data with ground observation. In this study, classification using object based image analysis (OBIA) technique was used to classify the plantation tree species. Forest structure such as forest height and aboveground biomass density with a total of 7,810 and 2,426 footprint locations were assessed by Global Ecosystem Dynamics Investigation (GEDI) LiDAR data. Field sample plots (n = 17) with 10x10m area were also used for validation and quantification of forest structure. The result shows that the Munessa Forest has five feature classes and is covered by, 69% Natural forest, 4% Pinus patula, 9% Ceupressus lusitanica, 10.00 % eucalyptus and 9% shrub. The mean tree height of Munessa forest was 43.7m and the tree density ranged to 583.30 individuals per hectare across all plots of the sample. Estimated forest structures (forest height) derived from GEDI LiDAR have a correlation (R= 0.714) with sample plots' data captured from field measurements. Global Ecosystem Dynamics Investigation (GEDI) LiDAR data is more significant and supports a new era of large-area approaches for estimating forest structure in different forest assessments.
Keywords: Earth observation data; Forest structure; Munessa; remote sensing