Measuring, monitoring, and managing biodiversity across agricultural regions depends on methods that can combine high-resolution mapping of landscape patterns with local biodiversity observations. This study explores the potential to monitor biodiversity in agricultural landscapes by linking high-resolution remote sensing with passive acoustic monitoring.

Residents of rural areas live and depend on the land; hence, rural land plays a central role in the human–land relationship. The environment has the greatest direct impact on farmers’ lives and productivity. In recent years, the Chinese government carried out vigorous rural construction under a socialist framework and implemented a rural revitalization strategy. This study was performed in a rural area of Huanjiang County, Guangxi Province, China. We designed a survey to measure rural households’ perceptions of three types of rural spaces: ecological, living, and production spaces.

Social media continues to grow, permanently capturing our digital footprint in the form of texts, photographs, and videos, thereby reflecting our daily lives. Therefore, recent studies are increasingly recognising passively crowdsourced geotagged photographs retrieved from location-based social media as suitable data for quantitative mapping and assessment of cultural ecosystem service (CES) flow. In this study, we attempt to improve CES mapping from geotagged photographs by combining natural language processing, i.e., topic modelling and automated machine learning classification.