# Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region
## 1 Introduction
constructed a remote sensing ecological index with principal component analysis for the rapid environmental quality assessment of the Ebinur Lake Wetland National Nature Reserve [22]. [PERSON] et al. developed an eco-functional quality index using biotic data from three Italian coastal Lagoons for the evaluation of environmental quality [23]. [PERSON] et al. proposed an ecosystem quality ratio by integrating the status of landscape structure, ecosystem services, ecosystem stability and human disturbance relative to their reference conditions and assessed the EQR changes in China's counties from 1990 to 2015 [17]. However, previous studies have mainly focused on small-scale ecosystem quality assessment at the county, province and nation levels based on low-resolution remote sensing data. Ecosystem quality assessments and studies on spatiotemporal changes over the entire B&R area are still rare. Furthermore, a technical specification for the investigation and assessment of ecosystem quality using vegetation parameters based on remote sensing data was released by the Ministry of Ecology and Environment of China on May 2021 [13]. However, the weighting of its indicators adopted average weighting which ignores the different importances of each indicator. Usually, multiple evaluation indicators are involved when evaluating the quality of regional ecosystems and the weights of each indicator largely influence the assessment result of the ecosystem's quality [24]. Therefore, it is important to determine reasonable weights in assessing an ecosystem's quality. Current weighting approaches include two groups: subjective and objective methods. Subjective methods require opinions from various experts to score indicators, such as analytic hierarchy process (AHP) methods, fuzzy methods, grey methods and artificial neural network methods. However, weights from these methods are directly affected by the subjective judgements of the experts. Objective weighting methods determine indicator weights based on the intrinsic structure of the data. Principle component analysis (PCA) is capable of projecting multivariate indicators into independent directions, which has been adopted in multiple ecosystem quality assessments and proven to be a robust technique in reducing the dimensions and extracting relationships among selected indicators [25]. Therefore, this study explores PCA methods to determine the weights of the indicators for an ecosystem quality assessment. In order to make the ecosystem quality status and changes after the five years of the BRI, and especially the implementation of the VAP, explicit, this study proposes an objective and automatic remote sensing-derived method of ecosystem quality assessment and analyzes the spatiotemporal changes in the B&R region during 2016-2020. First, the ecosystem quality index (EQI) regarding vegetation status was obtained from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Second, an assessment method was established to integrate the processes of objective indicator weighting, EQI calculating and categorizing. Third, the spatiotemporal ecosystem quality changes over the B&R region from 2016 to 2020 were analyzed and regions with significant changes were identified, which has seldomly been performed over the B&R region in the existing research.