Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index...

Area of Science:

• Forestry
• Ecology
• Geostatistics

Background:

• Forest site index is crucial for assessing forest productivity but is challenging to measure directly.
• Spatial interpolation techniques are vital for estimating ecological and environmental parameters.
• Accurate site index estimation is essential for effective forest management.

Purpose of the Study:

• To estimate the forest site index in a Turkish beech forest ecosystem.
• To compare the performance of various spatial interpolation and modeling techniques for site index prediction.

Main Methods:

• Collected data on soil characteristics, stand parameters, and topography from 70 sample plots.
• Employed multiple regression analysis (MLR), multilayer perceptron (MLP), and radial basis function (RBF) models.
• Integrated these models with kriging (MLRK, MLPK, RBFK) to incorporate spatial autocorrelation.

Main Results:

• Radial Basis Function Kriging (RBFK) achieved the highest prediction accuracy (R² = 0.98), followed closely by Multiple Regression Kriging (MLRK) (R² = 0.96).
• Incorporating krigged residuals significantly enhanced prediction accuracy, particularly for MLR, increasing R² from 0.23 to 0.96.
• Models combined with krigged residuals demonstrated superior performance compared to those without.

Conclusions:

• The Multiple Regression Kriging (MLRK) method substantially improved site index prediction accuracy.
• Combined spatial modeling approaches, especially those using krigged residuals, are effective for estimating forest site index.
• These findings support the development of improved site index maps for sustainable forest management.