Modelling and mapping of soil damage caused by harvesting in Caspian forests (Iran) using CART and RF data mining techniques
Controlling the soil damage caused by forest harvesting has a key role in forest management due to its effect on forest dynamics and productivity, mainly through modifying the physical, mechanical, and hydrological context of soil. This study was conducted to evaluate the soil damage susceptibility in one of the Caspian forests, Iran. For this purpose, two data mining techniques including classification and regression tree (CART) and random forest (RF) were applied. A total of 224 soil damage locations were identified primarily from field surveys. Then, 10 conditioning variables were produced in GIS. For model performance, the outputs of the analyses were compared with the field-verified soil damage locations. Our results show that slope degree, soil type, and slope aspect had the highest weight on soil damage, in the order of their appurtenance. Additionally, according to the relative operating characteristics curve, RF is a more suitable prediction model for soil damage zoning compared to CART. In summary, the findings of this study suggest that soil damage susceptibility mapping is an effective technique for Caspian forests, Iran.
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