ARTIFICIAL INTELLIGENCE APPLICATIONS FOR PREDICTING SOME STAND ATTRIBUTES USING LANDSAT 8 OLI SATELLITE DATA: A CASE STUDY FROM TURKEY

Forest resources inventory is one of the essential parts of the sustainable forest management. Remote sensing applications have broad usage areas for this aim, since field measurements are costly, time consuming and laborious. Monitoring forest resources with various satellite images has found wide usage areas in forestry. In this study, the relationships between some stand attributes (mean diameter, basal area, stand volume and number of trees) and texture values obtained from Landsat 8 OLI satellite image were investigated for Crimean pine (Pinus nigra J.F. Arnold subsp. pallasiana (Lamb.) Holmboe) stands in Kastamonu region of Turkey. The multiple linear regression analysis and artificial neural networks (ANN) were utilized to fit stand parameters using texture values. To form the ANN architectures, various transfer functions in hidden and output layers and number of nodes ranged from 1 to 20 in hidden layer were used, and a total of 180 architectures were designed for each stand attribute. The results indicated that the regression models had low R-2 values (0.399 for mean diameter, 0.337 for basal area, 0.332 for stand volume, and 0.183 for number of trees), and the most of the ANN models were better than the regression models for predicting stand attributes. The model containing hyperbolic tangent transfer functions in both hidden and output layers for mean diameter (R-2 = 0.593), logistic transfer function in hidden layer and hyperbolic tangent function in output layer for basal area and stand volume (R-2 = 0.632 and 0.650, respectively), and hyperbolic tangent function in hidden layer and linear function in output layer for number of trees (R-2 = 0.610) were the best ANN models. This study concluded that the ANN models developed with Landsat 8 OLI were useful to predict stand parameters better than the regression models in Crimean pine stands located in Kastamonu, Turkey.