Jinghua Zhang

Ecological vulnerability assessment serves as a prerequisite for ecological governance, yet evaluating large-scale ecological vulnerability remains challenging. To address this challenge, this study integrates geological elements into ecological vulnerability assessment, taking Ruyuan Area in the Northern Guangdong Mountains, China, as a case study. The area faces ecological hazards such as land desertification and soil erosion, indicating severe governance challenges. This study selected 14 ecological vulnerability factors and constructed assessment models based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). A total of 800 ecological vulnerability sampling points were obtained by combining field survey data with remote sensing imagery. The models were trained using binary vulnerability labels. The resulting continuous probability outputs were then classified into five vulnerability levels using the natural breaks method to generate the final ecological vulnerability map. It should be noted that the multi-level vulnerability map represents graded probability-based differentiation rather than supervised multi-class prediction. Model performance was validated using three metrics: Area Under Receiver Operating Characteristic Curve (AUC–ROC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The CNN (AUC = 0.916) model outperformed the DNN model (AUC = 0.895). According to the CNN-based classification results, non-vulnerable, slightly vulnerable, mildly vulnerable, moderately vulnerable, and highly vulnerable areas accounted for 36.19%, 22.85%, 14.24%, 12.31%, and 14.41% of the total area, respectively. High ecological vulnerability zones were concentrated in Daqiao, Luoyang, Dabu, and parts of Rucheng towns, with soil parent material and vegetation coverage identified as the main contributing factors, among which parent material was the most important. This finding underscores the notable impact of geological factors on local ecological vulnerability. Based on these results, nine ecological–geological subareas were delineated, and targeted ecological protection and restoration recommendations were proposed. This study, employing machine learning techniques, constructed an ecological vulnerability assessment model incorporating geological elements, thereby providing scientific support for targeted ecological governance in the study area.

This study investigates four rock–soil profiles developed from Proterozoic intermediate–acid rocks in the Maoniushan area of Xichang, Sichuan Province. Through systematic geochemical analysis of major and trace elements and X-ray diffraction analysis of clay minerals, we aim to clarify the dominant controlling factors and environmental response mechanisms of chemical weathering under similar lithological and soil-forming age conditions. The results indicate the following: (1) Major element geochemistry shows that the Chemical Index of Alteration (CIA) of all profiles ranges from 61 to 74, while Na/K ratios and A-CN-K diagrams collectively reveal that the profiles are in a transitional stage from weak weathering (Ca and Na depletion) to moderate weathering (K depletion), with the weathering intensity ranking in the order TP1711 > TP1709 > TP1714 ≈ TP2801. (2) Trace elements exhibit significant differences among profiles: Cu, Zn, and Pb are significantly leached relative to Al₂O₃ in the TP1711 profile, whereas most trace elements are enriched in the TP1714, TP1709, and TP2801 profiles. Variations in ∑LREE/∑HREE ratios further support differences in the weathering stages of the profiles. (3) The clay mineral assemblages are dominated by illite, chlorite, and vermiculite. The TP1714 profile lacks vermiculite and has the highest illite content (54–60%), reflecting a relatively cold and dry local microclimate. In contrast, the other profiles show widespread vermiculite development, accompanied by minor kaolinite, indicating moderate weathering intensity under warm and humid climatic conditions. This study confirms that under similar lithological and soil-forming age conditions, the microclimatic differentiation induced by altitude variation is the key external controlling factor leading to spatial differences in the chemical weathering intensity of granite–soil profiles in the Maoniushan area.