Model Development of the Forest Quality Assessment using Second-Order Confirmatory Factor Analysis

Forest quality plays a crucial role in sustaining the functions of forest ecosystems. This study aims to develop a valid and reliable model for assessing forest quality through six dimensions: forest productivity, forest structure, soil factors, climatic conditions, topography, and anthropogenic factors. Vegetation data were collected from 138 sample plots using a stratified purposive sampling method. Soil, topography, and climate data were obtained from the SoilGrids, DEMNAS, CHIRPS, and NASA POWER websites, respectively. Anthropogenic data were derived from Sentinel-2 imagery. The forest quality assessment model was developed using confirmatory factor analysis (CFA). Results showed that forest structure, forest productivity, soil, and anthropogenic factors are valid and reliable in assessing forest quality, with forest productivity as the primary determinant. However, topographic and climatic factors were not valid for assessing forest quality due to the low variation in topographic and climatic data within the study area. The goodness-of-fit model evaluation indicated a good fit based on criteria including the chi-square, RMSEA, GFI, SRMR, AGFI, TLI, CFI, NFI, and CMIN/DF. Based on the relative weights of each dimension and indicator and using linear additive equations, a mathematical equation for the forest quality index is derived, providing a practical framework for assessing forest quality at the landscape scale, particularly in heterogeneous tropical ecosystems. Keywords: confirmatory factor analysis, forest quality assessment, Rawa Aopa Watumohai National Park, sustainable forest management