The wireless sensor nodes are getting smaller, but Wireless Sensor Networks (WSNs) are getting larger with the technological developments, currently containing thousands of nodes and possibly millions of nodes in the future. Therefore, effective and trustworthy event detection methods for the WSN require robust and intelligent methods of mining hidden patterns in the sensor data, while supporting various kinds of dynamicity. Due to the fact that events are often functions of more than one attribute, data fusion and use of more features can help increasing event detection rate and reducing false alarm rate. In addition, sensor fusion can lead to more accurate and robust event detection by eliminating outliers and erroneous readings of individual sensor nodes and combining individual readings. There is a need for intelligent and energy efficient monitoring methods, made possible by novel data mining and classification methods, and the work reported in this paper involves such a novel energy efficient data mining scheme for forest cover type classification based on random forests and random trees & dual event detection decisions. The experimental validation of the proposed data mining scheme on a publicly available UCI machine learning dataset, shows that the proposed random forest and random tree based approach perform significantly better than the conventional statistical classifiers, such as Naïve Bayes, discriminant classifiers, and can lead towards energy efficient, intelligent monitoring and characterization of large physical environments instrumented using Wireless sensor networks.