Daily Monitoring of Ecological Conservation Red Line Based on Intelligent Recognition of Soundscape and Analysis of Intrusion Factors

Abstract

Ecological conservation red line is an important initiative for ecological civilization construction in China. Studies and works have been conducted to delineate the ecological conservation red line using remote sensing and manual survey methods. However, the traditional ecological assessment methods ignore the impact of dynamic elements on the ecological environment, and a permanent monitoring system for ecological conservation red line is still necessary. Therefore, this paper aimed to build a daily monitoring system of the ecological conservation red line from the perspective of ecological soundscape, combining deep learning and traditional Geographic Information System (GIS) methods. We analyzed the main factors affecting the ecological soundscape to provide a benchmark for selecting sites for permanent soundscape monitoring. Initially, this study developed the first classification standard of soundscape elements for daily monitoring of ecological conservation red line concerning the "ecological-production-living spaces" concept and existing literature on soundscape and built a deep learning model for training, with training and validation accuracies of 89.88% and 72.41%, respectively. The identification results of this model confirmed that the solution of intelligent monitoring through soundscape is reliable in daily monitoring. Then, the deep learning model was employed to classify and predict the soundscape collected from the case sites and effectively identify the areas with severe ecological encroachment by calculating the proportion of ecological soundscape elements, performing the simulation of daily monitoring, which is important guidance for the actual ecological conservation red line monitoring. Finally, a regression model was used to analyze the spatial characteristics of ecologically vulnerable areas, with an R 2 of 0.472. The regression model showed that nighttime light intensity had the greatest influence on ecological soundscape, followed by the distance from residential areas, spatial centrality, and road distance. These factors should be considered when implementing permanent soundscape monitoring sites. In addition, Normalized Difference Vegetation Index (NDVI) and topography were not significant in the regression model, which confirmed the shortcomings of considering only the ecological background and ignoring the dynamic biological elements. Daily monitoring should capture dynamic encroachments so that major ecological damage can be detected before it occurs. Therefore, the innovative integration of soundscape elements into the monitoring system meets the basic needs of monitoring ecological conservation red lines daily.

Keywords

• ecological conservation red line
• soundscape
• intelligent recognition
• deep learning
• daily monitoring system
• gongcheng reservoir conservation area