Farmland abandonment monitoring is one of the key aspects of land use and land cover research, as well as being an important prerequisite for ecological environmental protection and food security. A Normalized Difference Vegetation Index (NDVI) time series analysis is a common method used for farmland abandonment data extraction; however, extracting this information using high-resolution data is still difficult due to the limitations caused by cloud influence and data of low temporal resolution. To address this problem, this study used STARFM for GF-6 and Landsat 8 data fusion to enhance the continuity of high-resolution and cloudless images. A dataset was constructed by combining the phenological cycle of crops in the study area and then extracting abandoned farmland data based on an NDVI time series analysis. The overall accuracy of the results based on the NDVI time series analysis using the STARFM-fused dataset was 93.42%, which was 15.5% higher than the accuracy of the results obtained using only GF-6 data and 28.52% higher than those obtained using only Landsat data. Improvements in accuracy were also achieved when using SVM for time series analysis based on the fused dataset, indicating that the method used in this study can effectively improve the accuracy of the results. Then, we analyzed the spatial distribution pattern of abandoned farmland by extracting the results and concluded that the abandonment rate increased with the increase in the road network density and decreased with the increase in the distance to residential areas. This study can provide decision-making guidance and scientific and technological support for the monitoring of farmland abandonment and can facilitate the analysis of abandonment mechanisms in the study area, which is conducive to the sustainable development of farmland.

With the improvements in sensor accuracy, the spectral features of high-resolution remote sensing images become more complex. As a result, the classification accuracy for land cover classification decreases. Remote sensing image enhancements can improve the visual effect and the intra-class consistency and enhance the characteristics of ground objects. These enhancements are important for both image interpretation and improving image segmentation accuracy. In this study, we propose a pseudo-tasseled cap transformation (pseudo-TCT) through an orthogonal linear transformation of Gaofen-2 (GF-2) images using the untransposed tasseled cap transformation (TCT) coefficients, and further, enhance the visual effect and the separability among ground objects by linear stretching and percentage truncation stretching. To examine the separability among ground objects in the pseudo-TCT image, we used K-Means clustering, ISODATA clustering and 3D visualization of the spectral features of typical ground objects. The results show that the separability of buildings and roads from background objects is better than in the original image and the TCT image, and typical ground objects are effectively distinguished. Additionally, we visualized intra-class consistency by calculating the mean Euclidean distance between the pixel values of each point and the pixel values of its eight neighboring points and calculated the standard deviation of the intra-class consistency images. The results indicate that the secondary textures of the objects were weakened, and edges were made clearer, enhancing intra-class consistency. The pseudo-TCT is effective, at least in our work, and could be a candidate for image enhancement under certain applications.

Mineral resources are an important material foundation for economic and social development. The mineral resources evaluation and prediction will provide scientific basis for the development, utilization, and protection of mineral resources. The existing traditional artificial mineral resource comprehensive evaluation methods are costly, time-consuming, and have limited data processing and analysis capabilities. However, computer-based comprehensive evaluation methods often have fixed patterns and cannot incorporate as much expert knowledge as possible into the algorithms. Additionally, the utilization rate of some multi-source heterogeneous data, especially text data, is low. Given these challenges, this study transforms expert knowledge and artificial analysis methods into priori rules and proposes a novel approach for mineral resource evaluation and prediction - the Generative Prior Transformation Model, abbreviated as MineralGPT. The MineralGPT framework is driven by the description, storage and analysis of prior knowledge to support various model algorithms such as data processing and analysis, metallogenic information extraction and prospecting prediction, content generation and optimization. Taking the optimization of the gold polymetallic mine prospecting target area in the Xiaoshan-Xiongershan area as an example, experiments on the optimization model of the prospecting target area based on term weighting in MineralGPT show that MineralGPT supported by a small amount of data are almost consistent with the expert evaluation. Compared with the large-scale language model (such as ChatGPT) that requires massive data and computing power, it has the advantages of low cost, fewer limitations and high customization. MineralGPT, which introduced the rule-based description, storage and analysis of prior knowledge, provides a new method for mineral resources evaluation and prediction, and also provides a new idea for the development of a new generation of artificial intelligence technology combining rules and learning.