Tourism Ecological Environment Quality Assessment Using Principal Component Analysis and Remote Sensing Data

Current methods for assessing tourism ecological environment quality suffer from insufficient analysis of complex environmental impact factors and low assessment accuracy. In response to this situation, this study proposes a method for assessing tourism ecological environment quality that takes into account principal component analysis and remote sensing data. The study first combined remote sensing data and principal component analysis to establish an evaluation index system, and then introduced the grey-scale correlation method to rank and assign weights to key factors affecting ecological environment quality. Based on the results of principal component analysis and the grey-scale correlation method, the study constructed a comprehensive assessment model. Subsequently, dynamic simulation and prediction were achieved by combining the meta-cellular automata model and the Markov model. The experimental results indicated that in the ecological assessment of different seasonal environments, the Kappa coefficient and overall accuracy of the proposed method reached 0.93 and 0.93, respectively. Furthermore, the coefficient of determination and mean absolute percentage error were 0.91 and 0.08, respectively, and the assessment accuracy reached 93.88%. The tourism ecological environment quality assessment method proposed in this study can effectively address challenges in different environments. It maintains high-precision assessments and dynamic predictions, providing a reliable basis for decision-making in ecological environment protection.