An Analysis of Fast Learning Methods for Classifying Forest Cover Types

Abstract

Proper mapping and classification of Forest cover types are integral in understanding the processes governing the interaction mechanism of the surface with the atmosphere. In the presence of massive satellite and aerial measurements, a proper manual categorization has become a tedious job. In this study, we implement three different modest machine learning classifiers along with three statistical feature selectors to classify different cover types from cartographic variables. Our results showed that, among the chosen classifiers, the standard Random Forest Classifier together with Principal Components performs exceptionally well, not only in overall assessment but across all seven categories. Our results are found to be significantly better than existing studies involving more complex Deep Learning models.

Vegetation Cover Type Classification Using Cartographic Data for Prediction of Wildfire Behaviour

“…For classification of the C1 and C2 cover types, the same authors reported an accuracy of 93.7% and 96.7%, respectively, while our method using the XGB model achieved 96.9% and 97.8%, respectively. It is notable that using our proposed approach with the RF model used in [41] resulted in an overall accuracy of 96.4%, which is a significant improvement over the results reported in their study.…”

“…This is slightly higher than the accuracy achieved by CatB (0.967), SC3(0.967), and XT (0.968). Even though the optimised RF achieves an accuracy of "only" 0.964, this still surpasses other similar research results, including [41,43]. Despite its higher overall accuracy, XGB exhibits lower precision compared to XT and SC2 and a lower F1 score compared to XT.…”

“…Nonetheless, it is important to note that the proposed approach with all four models achieves higher accuracy for the undersampled classes than those reported by previous researchers. For example, while our proposed approach with XGB reaches 88.1%, 88.4%, and 93.9% for classes 4, 5, and 6, respectively, Sjöqvist et al [41] reported respective accuracy of 77.9%, 77%, and 86%. This comprises a significant improvement.…”

“…The overall accuracy obtained by the proposed approach using the XGB model on the UCI dataset is 97.1%, which surpasses those reported in other studies. For example, Sjöqvist et al [41] reported an overall accuracy of 94.7%, Al Sameer et al [42] reported 93%, and Macmichael and Si [39] achieved an accuracy of 97.01% (the latter using deep learning models).…”

“…They concluded that even though the proposed method outperforms other models (including basic CatB), this comes at a high computational cost. In a recent study, Sjöqvist et al [41] implemented three classifiers, namely RF, Naïve Bayes (NB), and Support Vector Machine (SVM), integrated with PCA for the classification of different cover types from cartographic variables using the University of California Irvine (UCI) cover type dataset. The combination of RF with PCA achieved an overall accuracy of 94.7%, with class-wise accuracy of 93.7%, 96.7%, 95%, 77.9%, 77%, 86%, and 94.5% for Class 1 through Class 7, respectively.…”