## 1 Introduction  
The Xizang region is an important ecological security zone in China, with natural protected areas accounting for one-third of the total area. The Lhasa-Nyingchi Motorway in the Xizang region not only benefits local economic development and the development and utilization of natural resources but also contributes to border stability and the common prosperity of all ethnic groups. However, on one hand, transportation construction will destroy the permafrost environment that has existed for many years. For example, the heat absorption of asphalt on the road and the exhaust emissions of vehicles along the corridor will increase the surface temperature of the area, thereby accelerating the melting of permafrost [1; 2; 3]. On the other hand, it will damage the surface vegetation and topsoil, accelerating soil erosion [4]. Due to the limited carrying capacity of resources and the environment in the Xizang region, it is difficult to restore the ecological environment once it is damaged [5]. Therefore, coordinating the construction of transportation infrastructure with the quality protection of the local ecological environment is a necessary path for the development of the Xizang region's economy. Timely evaluation of changes in the quality of the ecological environment in the area where linear engineering is located can help improve the negative impact of human activities on the environment [6]. In 2006, the Chinese Ministry of Environmental Protection proposed the ecological environment index (EI) for ecological environment assessment, but this index also has many issues in its application process, such as subjective weighting and lack of visualizable evaluation results [7]. Remote sensing technology has become an indispensable means of ecological monitoring and assessment [8]. Taking advantage of the spatial and temporal coverage of satellite data, [PERSON] [7] proposed an improved method called the remote sensing ecological index (RSEI) [7] based on the EI index. The evaluation indicators in this method mainly come from remote sensing indices. This method has been widely used in ecological environmental quality assessment research and has a high level of credibility [9; 10; 11]. For example, RSEI [7] has been applied in the evaluation of the surface water ecological environment in Freetown [11], the assessment of ecological environmental quality in the Samara region of Russia [12], and the evaluation of ecological conditions during the rainy and dry seasons in Kota Semarang [13]. In addition, researchers have also improved the evaluation algorithms and indicators based on the RSEI [7] from different perspectives. In terms of algorithm models, the RSEI initially mainly used the first principal component (PC1) of the principal component analysis (PCA) to construct the ecological index. However, the variance contribution rate of PC1 extracted in different studies varies greatly, which cannot guarantee a high contribution rate. In other words, the interpretation of the evaluation indicators obtained after dimensionality reduction is more unstable compared to the original indicators. Therefore, some scholars have made improvements to the model, such as using a modified remote sensing ecological index (MRSEI) to evaluate the ecological environment of the Xilingol League Grassland in China [14]. Machine learning has also been introduced, and the RSEI [7] time series data for Beijing, China, has been calculated using PCA combined with the random forest algorithm [15]. In terms of indicator systems, [PERSON] [16] used the land surface ecological status composition index (LESCI) based on the vegetation-impervious-soil triangle model to assess the surface ecological conditions in Iran, as well as some cities in Europe and North America [16]. [PERSON] et al. used net primary productivity, vegetation index, and light index to construct an enhanced remote sensing ecological index for an ecological environment assessment of Hainan Island, China, based on local characteristics [17].