## 1 Introduction  
The Lancang-Mekong region in Southeast Asia, which is adjacent to China, comprises five countries: Myanmar, Laos, Thailand, Cambodia and Vietnam. This region is characterized by the flow of the Mekong River, which provides abundant water and forest resources. Existing research in the Lancang-Mekong region primarily focuses on evaluating the impacts of climate and human activities on processes [1, 2, 3, 4, 5], as well as assessing the risk of frequent natural disasters [6]. However, forest ecosystems play an irreplaceable role in regulating global carbon balance, mitigating the rise of greenhouse gas concentrations, and maintaining the stability of the global climate [7, 8, 9, 10]. Unfortunately, rapid economic growth in the Lancang-Mekong region has led to significant deforestation due to industrial development, agricultural expansion, and urbanization acceleration [1, 2, 11]. Especially after a severe drought in Lancang-Mekong region caused by the abnormal climate in 2010 [6], the risks for the forest ecological environment have posed significant challenges to the sustainable development of the Lancang-Mekong region. Therefore, how to quantitatively assess the status of the forest ecosystem and its variations in the Lancang-Mekong region from 2010 to 2020 is an important issue that needs to be addressed. Remote sensing technology has emerged as the most effective means of obtaining large-scale information and has found widespread application in monitoring and managing forest ecological environments [8, 12]. Initially, the normalized difference vegetation index (NDVI) gained popularity as it is sensitive to the greenness of vegetation, allowing for the monitoring of dynamic changes in vegetation on both regional and global scales [13, 14]. For forest types, forest coverage is indeed a critical indicator that reflects the abundance of forest resources and the overall state of the ecological balance [15]. As research progressed, additional biophysical variables, such as leaf area index (LAI) [16], fraction of green vegetation cover (FVC) [12; 17], and gross primary productivity (GPP) [3], were incorporated to assess the status of the vegetation ecosystem and its long-term temporal variations at a regional and global level. In this research, LAI and FVC consider canopy structures and landscape distributions based on remote sensing observations, while GPP demonstrates carbon desorption capacities. These parameters offer an insight into the vegetation's health, productivity, and ecological functions compared to solely relying on the greenness of the NDVI parameter. The analysis of vegetation changes in an ecosystem has evolved from single-parameter approaches to multi-parameter and multi-resolution monitoring. To better understand the complex vegetation ecosystem, a comprehensive ecological quality index based on FVC and NPP parameters for vegetation is used to quantitatively assess the spatiotemporal changes in ecosystem quality over China [18]. Recently, the Ministry of Ecology and the Environment of China issued an ecosystem quality assessment standard, introducing an ecosystem quality index (EQI). This index is calculated based on LAI, FVC, and GPP, and it is designed to offer a comprehensive reflection of the overall condition of vegetation and ecosystems [19; 20; 21]. It enables a comprehensive assessment of various types of vegetation, including forests, shrubland, grassland, and farmland [20]. The linear regression analysis method is often used to monitor vegetation dynamics; this calculates the slope of a linear relationship between the number of years and a vegetation parameter (NDVI, enhanced vegetation index (EVI), FVC, etc.) for each pixel, assuming that time and vegetation parameter are independent [17; 22; 23]. Nonlinear regression assumes that the vegetation trends are nonlinear and varied, allowing for it to capture internal abrupt changes and trend shifts [14; 24].