Estimation of Basal Area in Coppice Oak Forests Using Geostatistical Kriging

   Aim. This study evaluates the application of Ordinary Kriging, a geostatistical interpolation method, for estimating basal area index in coppice oak forests of the northern Zagros region, Iran.

   Methodology. The research was conducted in a 6,103-hectare coppice oak forest in northern Zagros, Iran, dominated by Quercusbrantii alongside other oak species (Q. infectoria and Q. libani). A systematic-random sampling grid was employed to establish 136 sample plots (0.1 ha each), where diameter at breast height (DBH) was measured for all trees (DBH ≥ 5 cm) to calculate basal area. Exploratory data analysis was conducted to assess data normality and spatial trends, while variogram analysis was performed to determine the spatial correlation structure. Ordinary Kriging was then applied to predict basal area across the study area, with prediction accuracy evaluated through leave-one-out cross-validation using statistical metrics including mean absolute error (MAE), root mean square error (RMSE), and their relative values.

   Results. The forest exhibited relatively low basal area (14.53 m²/ha) despite high stem density (350 stems/ha), indicating the dominance of young trees and coppice regeneration. Variogram analysis revealed strong spatial dependence (spatial dependence degree = 99.8 %), with an exponential model providing the best fit to the data (r² = 0.676). Ordinary Kriging yielded accurate spatial predictions (MAE = 1.25 m²/ha, RMSE = 3.26 m²/ha), demonstrating its effectiveness for basal area estimation in coppice oak forests.

   Research implications. These findings demonstrate that geostatistical methods such as Ordinary Kriging provide a precise and cost-effective alternative to traditional forest inventories, enhancing sustainable forest management practices. The observed strong spatial dependence of basal area confirms its suitability as a regionalized variable, facilitating the development of optimized sampling strategies for future forest assessments. This geostatistical approach has significant potential to improve forest resource assessment, carbon stock estimation, and conservation planning in ecologically important ecosystems such as the Zagros oak forests.

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