A data-mining-based approach for aeolian desertification susceptibility assessment: A case-study from Northern China

Abstract

Desertification is a grave threat to the environment and livelihoods. Desertification susceptibility assessment (DSA) plays a critical role in reasonable desertification prevention planning by mapping the extent, intensity, and classification of desertification. Numerous desertification maps have been produced using various DSA methods. However, the method of rapid desertification mapping by objectively discovering valuable DSA knowledge from experienced experts stored in such maps has rarely been explored. We propose a data-mining-based approach to mapping aeolian desertification that applies the decision tree (DT) C5.0 (C5) algorithm as a knowledge discovery tool to the reference map and corresponding environmental variables. The results of our case-study in Northern China show that the overall accuracy of aeolian desertification classification based on C5 is 86.69%, and the predicted map is highly consistent with the reference map. The DT algorithm outperforms the artificial neural network and naive Bayes approaches. Our results highlight the importance of selecting more representative training samples across where interleaved distributions of multiple aeolian desertification land exist when applying the DT algorithm. The findings of the present study are valuable for highlighting the significance of the data mining approach in DSA, with the growth of desertification maps. Given that aeolian desertification is a complex process coupling natural and human factors, and there are significant regional and scale differences in Northern China, further studies at a fine-scale regarding human factors deserve more attention.