Modeling the relationships between some stand parameters and leaf area index values in pure red pine stands (a case study in Güngören Forest Planning Unit)

The sustainable management of forest resources is significant from ecological, economic, and socio-cultural perspectives in contemporary times. In this context, remote sensing technologies have become an essential tool for accurately, rapidly, and widely monitoring the structural characteristics of forest ecosystems. The objective of this study is to predict some stand attributes using variables derived from Leaf Area Index (LAI) in pure Turkish red pine stands located within the boundaries of the Güngören forest planning unit in Anamur Forest Enterprise, Mersin Regional Directorate of Forestry. A total of 238 sample plots were utilized. For each plot, some stand attributes such as number of trees (stem/ha-1), basal area (m2/ha-1), volume (m3/ha-1), and mean diameter (cm) were calculated. In addition, leaf area index (LAI) values were derived from Sentinel-2 satellite imagery for each sample plot. The relationships between stand attributes and LAI were analyzed using multiple regression analysis. The modeling results revealed no statistically significant relationship between mean stand diameter and LAI. However, significant correlations were observed between LAI and the number of trees (R_adj^2= 0.519; Sy.x = 136.2 trees ha⁻¹), stand volume (R_adj^2 = 0.377; Sy.x = 74.7 m³ ha⁻¹), and basal area (R_adj^2= 0.112; Sy.x = 15.5 m² ha⁻¹). The findings derived from the study reveal that the LAI variables obtained from Sentinel-2 satellite image exhibit statistically significant correlations, particularly in the prediction of stand attributes such as tree density and volume. This underscores the potential of utilizing these data as a valuable supplementary tool in forest inventory assessments.