## 3 Data and Methods
### Research Methods
The flood season in the Xinqiman-Kelelik area is from June to August, and the dry season is from November to March of the following year. In the process of downloading and sorting Landsat series satellite image data, this paper found that the image data for this river section is limited, and the satellite image data of the same period in different years cannot be obtained. In different periods of each year, the flow of the Tarim River varies greatly, and the water body area in the river channel varies greatly. It can be seen from Figure 3 that the water area and the distribution of vegetation along the river in different periods are quite different. The interpretation of satellite images is highly dependent on the comprehensive analysis of image tone (water depth indication), shape (river bend characteristics) and shadow. The existing automatic method based on the Landsat spectrum is difficult to extract stably and accurately. Similarly, the river shoreline is in the riparian vegetation coverage area or shoal area. When extracting the river shoreline, the machine learning algorithms in ENVI, ArcGIS and NDVI, NDWI technology often become confused by wet beach areas, and the boundary accuracy cannot easily meet the requirements of morphological change analysis. Automatic methods (especially low-resolution images such as Landsat) often face difficulty achieving the accuracy of manual interpretation in the location determination of river shorelines and sandbar edges, especially in curved river sections, bifurcated river sections, and riparian vegetation coverage areas. Visual interpretation can use all the interpretation elements such as hue, texture, shape, shadow, position and correlation to make a comprehensive judgment. For the analysis of long time series (32 years) satellite images, it is very important to ensure the consistency of interpretation standards in different periods. Visual interpretation can flexibly and uniformly apply a set of rules according to the specific characteristics of the study area and the overall performance of all images. However, the parameters of the automatic algorithm may be difficult to maintain stable when the image quality varies greatly in different periods, resulting in systematic deviations in the results. There are interference factors such as clouds, seasonal vegetation cover changes, and turbid water bodies in the study area. Visual interpretation can better identify and  
Figure 3: Satellite images of different periods: (**a**) Satellite image of Paman reservoir reach on 22 August 1994; (**b**) Satellite image of Paman reservoir reach on 5 May 1995.Two satellite images are synthesized by band. The black lines in the figure represent the study area. The red part is the vegetation on both sides. eliminate these interferences, and accurately capture the morphological information of the river itself. It has advantages in distinguishing water shadows and actual land boundaries, and distinguishing wet areas on beaches and permanent water bodies. Therefore, when analyzing the river boundary of the Xinjqiman-Kelelik section in the flood season, considering the characteristics of wide river width and river water area, changes to wet beach areas have a great influence on the river boundary. Taking the riverside vegetation boundary as the boundary of river width, the riverside vegetation is relatively stable, and can remain relatively unchanged in the fluctuation of water level in the flood season, which can eliminate the influence of water level change on the measurement of river width and improve the accuracy of measurement results. This study investigates the multi-scale drivers of sedimentation and erosion in the Tarim River and their effects on ecological and social systems. It provides a scientific basis for managing arid-area river systems through both natural restoration and artificial regulation. It also has crucial practical value for ensuring food security and ecological stability in arid regions [47].