Deep learning can extract complex and high-dimensional characteristic information with its deep structure, effectively exploring the complex relationship between landslides and their numerous influencing factors, and ultimately, more accurately predict future landslide disasters. This study builds a landslide susceptibility mapping (LSM) method based on deep learning, compares the frequency ratio (FR) sampling method with a buffer random sampling method, and performs resampling operations of landslide and non-landslide samples to explore the applicability of deep learning in LSM. In addition, six indices, precision, accuracy, recall, ROC, and the harmonic mean F1 of accuracy and recall were selected for quantitative comparison. The results show that both the resampling method proposed in this paper and the non-landslide sample selection method based on FR can significantly improve the accuracy of the model, with the area under curve (AUC) increasing by 1.34–8.82% and 3.98–7.20%, respectively, and the AUC value can be improved by 5.32–9.66% by combining the FR selection and resampling methods. Furthermore, all the deep learning models constructed in this study can obtain accurate and reliable landslide susceptibility analysis results compared to traditional models.

Efficient and accurate vegetation type extraction from remote sensing images can provide decision makers with basic forest cover and land use information, and provides a reliable basis for long-term monitoring. With the development of deep learning, the convolutional neural network (CNN) has been used successfully to classify tree species in many studies, but CNN models have rarely been applied in the classification of vegetation types on larger scales. To evaluate the performance of CNN models in the classification of vegetation types, this paper compared the classification accuracy of nine dominant land cover types in Baishuijiang National Nature Reserve with four models: 3D-CNN, 2D-CNN, JSSAN (joint spatial–spectral attention network) and Resnet18, using sentinel-2A data. Comparing the difference in classification accuracy between the direct use of raw sentinel images and fused feature indices sentinel images, the results showed that adding feature indices can improve the overall accuracy of the model. After fusing the characteristic bands, the accuracy of the four models was improved significantly, by 5.46–19.33%. The best performing 3D-CNN model achieved the highest classification accuracy with an overall accuracy of 95.82% and a kappa coefficient of 95.07%. In comparison, 2D-CNN achieved an overall accuracy of 79.07% and a kappa coefficient of 75.44%, JSSAN achieved an overall accuracy of 81.67% and a kappa coefficient of 78.56%, and Resnet18 achieved an overall accuracy of 93.61% and a kappa coefficient of 92.45%. The results showed that the 3D-CNN model can effectively capture vegetation type cover changes from broad-leaved forests at lower elevation, to shrublands and grasslands at higher elevation, across a range spanning 542–4007 m. In experiments using a small amount of sample data, 3D-CNN can better incorporate spatial–spectral information and is more effective in distinguishing the performance of spectrally similar vegetation types, providing an efficient and novel approach to classifying vegetation types in nature reserves with complex conditions.

Mountainous vegetation type classification plays a fundamental role in resource investigation in forested areas, making it necessary to accurately identify mountain vegetation types. However, Mountainous vegetation growth is readily affected by terrain and climate, which often makes interpretation difficult. This study utilizes Sentinel-2A images and object-oriented machine learning methods to map vegetation types in the complex mountainous region of Jiuzhaigou County, China, incorporating multiple auxiliary features. The results showed that the inclusion of different features improved the accuracy of mountain vegetation type classification, with terrain features, vegetation indices, and spectral features providing significant benefits. After feature selection, the accuracy of mountain vegetation type classification was further improved. The random forest recursive feature elimination (RF_RFE) algorithm outperformed the RliefF algorithm in recognizing mountain vegetation types. Extreme learning machine (ELM), random forest (RF), rotation forest (ROF), and ROF_ELM algorithms all achieved good classification performance, with an overall accuracy greater than 84.62%. Comparing the mountain vegetation type distribution maps obtained using different classifiers, we found that classification algorithms with the same base classifier ensemble exhibited similar performance. Overall, the ROF algorithm performed the best, achieving an overall accuracy of 89.68%, an average accuracy of 88.48%, and a Kappa coefficient of 0.879.

The scarcity of landslide samples poses a critical challenge, impeding the broad application of machine learning techniques in landslide susceptibility assessment (LSA). To address this issue, this study introduces a novel approach leveraging a deep convolutional generative adversarial network (DCGAN) for data augmentation aimed at enhancing the efficacy of various machine learning methods in LSA, including support vector machines (SVMs), convolutional neural networks (CNNs), and residual neural networks (ResNets). Experimental results present substantial enhancements across all three models, with accuracy improved by 2.18%, 2.57%, and 5.28%, respectively. In-depth validation based on large landslide image data demonstrates the superiority of the DCGAN-ResNet, achieving a remarkable landslide prediction accuracy of 91.31%. Consequently, the generation of supplementary samples via the DCGAN is an effective strategy for enhancing the performance of machine learning models in LSA, underscoring the promise of this methodology in advancing early landslide warning systems in western Sichuan.

Timely and accurate acquisition of crop planting areas and spatial distribution are deemed essential for grasping food configurations and guiding agricultural production. Despite the increasing research on crop mapping and changes with the development of remote sensing technology, most studies have focused on large-scale regions, with limited research being conducted in fragmented and ecologically vulnerable valley areas. To this end, this study utilized Landsat ETM+/OLI images as the data source to extract additional features, including vegetation index, terrain, and texture. We employed the Random Forest Recursive Feature Elimination (RF_RFE) algorithm for feature selection and evaluated the effectiveness of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Rotation Forest (ROF)—for crop extraction. Then, based on the optimal classifiers, the main crops in the Huangshui basin for the years of 2002, 2014, and 2022 were extracted. Finally, the transfer matrix, the gravity center model, and the Standard Deviation Ellipse (SDE) model were used to analyze the spatio—temporal changes of crops over the past 20 years in the Huangshui basin. The results showed that the spectral, vegetation index, and terrain features played a crucial role in crop extraction. Comparing the performance of the classifiers, the ROF algorithm displayed superior effectiveness in crop identification. The overall accuracy of crop extraction was above 86.97%, and the kappa coefficient was above 0.824. Notably, between 2002 and 2022, significant shifts in crop distribution within the Huangshui basin were observed. The highland barley experienced a net increase in planting area at a rate of 8.34 km²/year, while the spring wheat and oilseed rape demonstrated net decreases at rates of 16.02 km²/year and 14.28 km²/year, respectively. Furthermore, the study revealed that highland barley exhibited the most substantial movement, primarily expanding towards the southeast direction.