Monitoring Ecological Conditions by Remote Sensing and Social Media Data—Sanya City (China) as Case Study

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 12; p. 2824
• Main Authors: Yang, Tengfei, Xie, Jibo, Song, Peilin, Li, Guoqing, Mou, Naixia, Gao, Xinyue, Zhao, Jing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2022
• Subjects: Algorithms; Case studies; Cellular telephones; Climate change; Decision making; Digital media; Ecological conditions; Ecological effects; ecological environment monitoring; Ecological monitoring; ecological sensitivity analysis; Environmental impact; Environmental monitoring; Environmental protection; Human settlements; Land use; Machine learning; Monitoring methods; Natural language processing; Population density; Precipitation; Remote monitoring; remote sending; Remote sensing; Semantics; Sensitivity analysis; Sentiment analysis; social media; Social networks; Social organization; social semantic network; Vegetation; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14122824

The ecological environment is the basis of human survival and development. Effective methods to monitor the ecological environment are essential for the healthy development of human settlements. At present, methods based on remote sensing images and other basic data have played key roles in ecological environment monitoring, providing support for decision-making on local ecological environment protection. However, these data and methods have obvious limitations. On the one hand, they cannot reflect the feelings of human beings about the ecological environment in which they live. On the other hand, it is difficult to capture more detailed information about the ecological environment. Non-professional observation data represented by social media describe the ecological environment from the perspective of the public, which can be a powerful supplement to traditional data. However, these different data sources have their own characteristics and forms, and it is difficult to achieve efficient integration. Therefore, in this paper, we proposed a framework that comprehensively considers social media, remote sensing, and other data to monitor the ecological environment of a study area. First, the framework extracted the ecological environment-related information contained in social media data, including public sentiment information and topic keyword information, by integrating algorithms such as natural language processing and machine learning. Then, we constructed a social semantic network related to the ecological environment based on the extracted information. We used a remote sensing image and other basic data to analyze the ecological sensitivity in the study area. Finally, based on the keyword with spatial location attribute contained in the social semantic network, we established the link between the constructed network and the results of ecological sensitivity analysis to comprehensively analyze the ecological environment in the study area. The comprehensive analysis results not only reflect the distribution of ecological vulnerability in the study area, but also help identify specific areas worthy of attention and the ecological problems faced by these areas. We used the city of Sanya in China as a case study to verify the effectiveness of the method in this paper.