Search

Alluvial fans have been proven to have great utilisation potential in QTP, but to what extent they are suitable for agricultural development is unknown. Therefore, using the alluvial fan in the Lhasa River Basin (LRB) as a case study, this paper established an evaluation system of land suitability for agriculture (LSA). Principal component analysis (PCA) and the exhaustive method (EM) were used to define the minimum dataset (MDS) and then the LSA of the alluvial fan in the LRB was evaluated using a comprehensive index of LSA. Two scientific approaches were implemented to improve the LSA using a scenario simulation. The results showed that the MDS assessed by the EA was more representative compared to the PCA. Alluvial fans with suitable grades are mainly distributed in the Lhasa River’s middle and lower reaches. Developing facility agriculture and repairing roads accessing the alluvial fans are effective approaches to improve the LSA, which can increase the suitable-grade area from 58.62% to 97.82% and 63.85%, respectively. Therefore, most alluvial fans in the LRB are suitable for developing agriculture, and under the influence of human activities, there will be more alluvial fans suitable for developing agriculture. Our research provides scientific methods for the sustainable development of land in alpine regions.

The prevention and control of soil erosion through soil and water conservation measures is crucial. It is imperative to accurately and quickly extract information on these measures in order to understand how their configuration affects the runoff and sediment yield process. In this investigation, intelligent interpretation algorithms and deep learning semantic segmentation models pertinent to remote sensing imagery were examined and scrutinized. Our objective was to enhance interpretation accuracy and automation by employing an advanced deep learning-based semantic segmentation model for the astute interpretation of high-resolution remote sensing images. Subsequently, an intelligent interpretation algorithm model tailored was developed for terracing measures in high-resolution remote sensing imagery. Focusing on Fenxi County in Shanxi Province as the experimental target, in this research we conducted a comparative analysis between our proposed model and alternative models. The outcomes demonstrated that our refined algorithm model exhibited superior precision. Additionally, in this research we assessed the model’s generalization capability by utilizing Wafangdian City in Liaoning Province as another experimental target and performed a comparative analysis with human interpretation. The findings revealed that our model possesses enhanced generalization ability and can substantially augment interpretation efficiency.