The application of visible light remote sensing technology in unmanned aerial vehicles (UAVs) has become extensively utilized across a variety of sectors. However, the research on its development and hotspot is very limited. Based on the literature of visible remote sensing of UAVs collected in Web of Science (WOS) database from 1998 to 2022, the literature knowledge map was drawn by visualization analysis software CiteSpace to reveal the research progress and trend in this field. The results show that: (1) The research has cross-application in multiple fields and multi-source data fusion, which main focus is on expanding research perspectives, objects and methods; (2) Research topics mainly focus on monitoring, photogrammetry and agriculture; (3) UAV image, model building and application, vegetation index, classification, etc., are research hotspots, and the main research directions in the future are machine learning, point cloud, virtual reality, etc. The results can provide reference for researchers to carry out further research in this field.
To address the issue of inaccurate migration of rocky desertification evaluation results from a large scale to the micro scale of parcels, this study introduces a series of methods. Firstly, it employs deep learning techniques to assess the level of rocky desertification in arable land units and to accurately identify the boundaries of these arable lands. Subsequently, the study conducts a time-series reconstruction of indexes used to discriminate rocky desertification, all while adhering to the constraints of these boundaries. Finally, it calculates the degree of rocky desertification within each parcel unit using an entropy weighting method. This approach is well-suited for the complex arable land scenarios typically found in mountainous areas. In the study area, a total of 49,961 cultivated lands have been identified. Notably, rocky desertified cultivated land accounts for 58% of the total, with a predominant distribution of mildly rocky desertified cultivated land.
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