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

Alkan Günlü1, Sinan Bulut2, Sedat Keleş1

  • 1Faculty of Forestry, Çankırı Karatekin University, 18200, Çankırı, Turkey.

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Spatial interpolation methods accurately estimated forest site index in a Turkish beech forest. Combining multiple regression analysis with kriging significantly improved prediction accuracy for forest productivity mapping.

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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.