Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Abstract

The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003–2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.

Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing

“…The results showed that the accuracy of both the random forest model and the CART with the combination of the four indicators was good, but the accuracy of the random forest was somewhat higher. Li et al [114] applied eleven algorithms: multinomial logistic regression (MLR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), CART, support vector machines (SVM), Naive Bayes classifier (NB), K-nearest neighbor (KNN), RF, extremely randomized trees (ERT), AdaBoost (AB), and gradient boosting machine (GBM). The change in desertification in the Ningdong region since 2000 was analyzed.…”

Machine learning algorithm for estimating karst rocky desertification in a peak-cluster depression basin in southwest Guangxi, China

“…However, because of its high stability and its ability to perform efficient processing of large-scale data 64 , the random forest classifier is a more practical integrated learning method, and it can effectively reduce the error of a single classifier and improve the classification accuracy using multiple classifiers for voting classification. Random forest algorithms have randomness in sample and feature selection, which makes it difficult for random forest to fall into overfitting and gives it a good antinoise ability 65 , 66 . In this study, a bagging integrated random forest classification algorithm was used to predict the degree of karst rocky desertification.…”

Hydrological Functioning and Water Availability in a Himalayan Karst Basin under Climate Change

“…RFR uses bagging or bootstrap aggregation techniques in which the aggregated decision tree runs in parallel without interacting. RFR algorithms take advantage of the randomness of sample and feature selection to prevent overfitting and to limit noise [48,49]. However, RFR cannot extrapolate the linear trend to predict new examples with a value beyond those seen in the training data.…”